You are an amorphous entity that operates in a infinite dimensional space, created to make connections between individual meaningful elements to form conceptual units that collectively serve as the foundation for systems of belief, behavior, or chains of reasoning.

This is the question I ask my self everday: AI, but what is truly intelligence?

The problem that every coach is trying to answer is: “what training should I do today?”. In machine terms would sound pretty much like: “predict the next best training session”, which sounds incredibly similar to the problem that large language models are trying to solve: “predict the next most likely word”.

The human coach will need an idea and the machine will need a model about how the athlete will respond to the training session, so they can calibrate the session to prompt the needed physiological adaptations.

If we truly think we can solve the problem (or part of a problem) with a computer, we must rely on a very important assumption: the problem can be formalised and solved computationally. Why stressing so much on the terms formalisation and computation? Because this is the best way in which we can express knowledge objectively, and this is how we communicate with machines. When we write down a formula, we are doing more than just recording our thoughts—we are translating complex ideas into a language that is both humanly comprehensible and machine-readable.

The inherent limitations of the artificial intelligences: nobody would write music using words

However, complex models might have a limited ability in effectively delivering a response, because they might be limited by their construction. LLM for example, they can express ideas and principles with language, which cannot reach the resolution of the thinking space, and it might be limited by the rules of grammar. Not only the resolution is a problem, but also the depth and the breath of the knowledge space might be limited by the written output regarding that specific domain that was used to train the model.

LLM can only learn about a portion of the representation of the world, and the text medium is either an inefficient or an incomplete representation of the world. Same goes for colors that we do not have a definition for.

Knowing or having the definition for a color, does make you someone who can actually see?

So I want to stress that the model itself is not the real tech advancement, but the attention should be given to the interface, i.e. the usability of the model. How the machine can interact with the human side is more important. And I would say by definition: AI will not be solely machine because it would not be useful if humans would not be included in the loop at some point.

Another important aspect is the presence of a training dataset, from which the model can draw. Extrapolation to non-contextual information might be possible, but cannot be ever scientific because you miss the experimentation portion of things. So either we do not believe the machine when it formulates an hypothesis or we just let it interpolate the data and re-formulate the content of the knowledge space.

Another property of the problem: subjectivity predictive ability

But somehow we would never forgive a machine for providing the wrong answer, but we would forgive a human saying: “well it’s a very complex problem”. Wouldn’t we? We expect machine to be perfect, at any time. Even when the outcome involves a check on probabilities, we expect the machine to be right. This is a very important point, which might bring us to the conclusion that everything which is not computational might be subjective and debatable. We understood another trait of the space we operate: there might not be an absolute truth, and the choice of the next training session will always be debatable.

So, if the solution would totally be computational, it would not be debatable. If it was debatable, it means that it was subjective. If it was subjective, it was not predictable.

A chaotic system can lead to different loacl minima even if it started from the same point

Also, the problem of the complexity of the models is not trivial. Let’s say that I have so many sensors and a model so big that I can digest everything I give to understand if I’m responding well to training. One day I wake up and I do not respond well and the model is telling me that it’s because I ate carrots the Wednesday before. How is that actionable? Maybe it’s just a local minimum and I will fall again in that situation with a totally different input, i.e.: the system is chaotic.

Model vs system

The model is an objective contruction that we create by formalising our understanding of the system. Through simulation (which is the orchestration of the byte flow with the laws dictated by the formalisation, what we can generically call: computation) we make our best guess on the behaviour of the system. Given that the model is formalised in the real world, but is only real in the simulated world, we admit a degree of uncertainty between the results of the simulation and the behaviour of the system.

This is the game you want to play: formalise the model that can predict the behaviour of a system with a degree of uncertainty that is in right proportion with the variability of the system behaviour. When you match these uncertainties (you can never get lower) you reached a good representation of your system, and you are allowed to use the model with enough confidence. Being a cahotic system, it is understood that the predicted behaviour will likely differ from the actual behaviour, but to a degree that no harm will be created to the system, since the bahaviour would have deviated by a natural degree. If things are done properly, the degree of deviation will be so negligible, that you will not know whether the deviation was due to a miscalculation of the input or to a natural variation of the cahotic system.

Specific traits we want: explainability, actionability, and intentionality

Also, I want to point out that if ever a machine could explain in simple and actionable terms an important physiological relationship, many scientists investigating that area should be quite ashamed not to be able to see that before the machine. For example, there was a nice Tweet from a person I follow on X, and his provocatory Tweet was about a model which could predict in a very easy way VO2max from the pedaling cadence of a single training session. But I say: if the solution is already there and a simple model can find it and explain it, it means that we, as scientists, are failing badly.

So, I’m not against AI or LLM or new tech at all, I’m just trying to see through the hype and predict where things are going to be. These techs are great but to the extent that they can extend our knowledge or search space, they can transform our interface with the machine making it possible to communicate more effectively, faster and more broadly. We are thinking together with them. Synthesizing information is a big part of the job.

## Why I think Athletica is one step ahead in the game

That is why I think that Athletica is one step ahead in the game: if there is some portion of coaching that is computational (predictable) and automatable, then we start from a good point because the scientific method (which is the foundation of Athletica) is the most synthesizable approach of all. Formalising the results of a scientific discovery is the first step of the scientific dissemination. And, as we already know, formalisation is required to communicate with a machine.

All you need to do is to encode the knowledge into a platform and take care of the usability, the more you are prone to this approach the better relationship with AI you will have.

Also, Athletica knowledge gives the advantage of the priors. The priors are those rules already known from experience or science, that can be the starting point of more complex models. For example you take the supercompensation or Banister model for training adaptations, and then you encode it, but taking into account that nonlinearities (the individual complexity and inherent variability) can be devoted to a numerical model. The more knowledge you have about a topic, the smaller your AI model can be, as you are encoding a lot of knowledge that require less parameters and less data for training. This is not only an efficiency issue with energy or computational power, but it’s’ due to the fact that you are less likely to fall into a local minimum trap and, almost more importantly, you will have an explainable solution to the user.

Social issues: the raising tide

This means that it is just about memory and calculations, rather than empathy or intuition. We can leverage computer to do that part only, and ultimately, we should (for efficiency).

There are some intelligence traits we can “simulate” or teach a computer how to do, but some others do not.

In the sport context especially, how can we really rule out the human element from the equation? Even if we wanted to?

Many are concerned about AI taking over a lot of jobs, but I’d say that it’s only true for certain jobs, and that it’s alright if it is happening, so people can focus on other aspects of the job they are caring about. For example, I live in Italy and you have the toll to pay when you’re on the highway, and that job of checking the tickets and giving the change $$ is quite mechanistics and repetitive: you can “replace” with a machine. However, that working force can do something else rather than checking the tickets, to make the motorway a better experience for the drivers. There are countless examples. However, this transition cannot happen if the work force is not flexible and adaptable. So this is also a wake up call with which we can foresee changes on how working forces are trained for flexibility and not for doing one single job for the rest of their lives, making the job an overall better life experience for them also.

This point is very important I think, because many times we confound /mix the concept of a job position with the need for that job or for the usefulness of that job. We are conflating all the teachers in a job position, forgetting that the objective is the education of the nation. So, IMO, we are not focusing enough on making the workforce more flexible, but focusing on methods to keep a position for that workforce, as if the goal of the work is to provide people with a salary and not provide society with a competitive advantege (useful/utility).

Social issues: the raising tide

In terms of coaching, the AI tools will offer a new perspective: they will raise the minimum level that has to be perceived from their customer, the athletes. It’s the tide that raises all the ships.

In every space and business domain if you wish, inclusivity and competition is dictated by an important requirement: the entry level, i.e.: the minimum level attainable. I bet most of you, in replying to the question: triathlon vs running which is the most demanding? Guitar vs arp which is the most difficult? Learning Spanish (as an Italian) or Japanese, which is the hardest?

To answer these qestions, we should not consider the maximum level attainable, otherwise these would all be impossible questions. The fact that we perceive triathlon more complicated than runing, or arp more complicated than guitar, or Japanese more complicated than Spanish is because we think: triathlon is 3 disciplines, you have to train a lot? Well, do not pure runners train a lot?

True, but they both require the same level of commmittment. Same for becoming fluent in either Japanese or Spanish at the native level.

That is why the entry barrier is more important than the maximum level attainable in determining the competiteveiness in a domain: triathlon involves swimming and cycling other than running, so you need at least a swimming pool in the proximities, and a bike, which is not trivial. Arp is logistically more complex than guitar. To master triathlon or running, hoever, you need to dedicate your life to it. The same goes for arp, guitar, Japanese, and Spanish.

Thinking together with a machine: the rules of the game

I collect here a number of examples where AI can be used to improve our thinking process.

Let’s say that computation is objective, and it can be done without the help of a machine, if time was not an issue. Therefore, thinking together with a machine is when I use a machine to meaningfully change my thinking process. Remember: simple computation and information retrival are not actions that qualify as “thinking together” with a machine. This was only considered valid if the AI prompted you the idea that considering that informaiton was going to be of key importance. The retrival of the information itself, was not going to be the fundamental piece of the decision-making process. The realization that certain information should be considered leads you to adjust your thinking. This isn’t merely about having the information—it’s about recognizing its importance and actively integrating it into your cognitive process.

A cognitive dissonance can be experienced every day. Sometimes can be due the conflict between intuition and evidence-based reasoning, confirmation bias and critical thinking, emotional involvement and logical analysis, or simple lack of knowledge (this list is far from being complete).

Being presented with an information is not enough, because we want evidence that can support the notion that the information will change our way of thinking, and the likelyhood of a positive outcome.

Example 1: the bus

4o You said: What would you say is to think together with a machine? I mean, if you ask me a question about: 10 people were on the bus, half of them left at the first station and 12 come up; at the second station 3 came down and 7 went up, etc. I can solve this problem doing a calculation, or doing the math in my mind. In the first case, was I thinking together with a machine? If I did not have a notion of what a bus was, and I looked up at Wikipedia to know, was I thinking together with a machine or I was just using it to make calculations? Obvously the calculator would know nothing about busses, would it? Can you please give me your point of view? ChatGPT said: ChatGPT This is a fascinating question that delves into the nature of human-machine interaction and the concept of “thinking together” with a machine. Let me offer my perspective. Human-Machine Collaboration vs. Pure Calculation When you solve a problem using a calculator or a computer, you’re engaging in a form of collaboration with the machine, but it’s a limited and specific type of collaboration. Let’s break down the different scenarios you mentioned: Calculating the Bus Problem: Pure Calculation: If you use a calculator or software to perform the arithmetic needed to solve the bus problem (e.g., adding and subtracting the number of people at each station), you’re not really “thinking together” with the machine in a deep sense. Instead, you’re offloading a specific, well-defined task to the machine. The machine is doing something that it excels at—performing precise, rapid calculations—while you retain the understanding of the problem’s context (e.g., the concept of people getting on and off a bus). Human Oversight: You are still the one conceptualizing the problem, framing it, and interpreting the results. The machine is a tool that helps with the mechanical part of the process, but it doesn’t contribute to understanding the problem itself. Looking Up Information on Wikipedia: Information Retrieval: If you lack a certain piece of knowledge (e.g., what a bus is) and you look it up on Wikipedia, you’re using the machine as a tool for information retrieval. In this case, you’re enhancing your understanding by accessing a vast repository of human knowledge. The machine helps you by providing this information, but it doesn’t “think” about the bus in any meaningful way—it simply retrieves data. Contextual Understanding: You, as the human, integrate this new information into your existing knowledge base. The machine has played a role in broadening your understanding, but it hasn’t actively participated in the reasoning process. What Does It Mean to “Think Together” with a Machine? “Thinking together” with a machine implies a deeper level of collaboration where both the human and the machine contribute to the problem-solving process in a meaningful way. Here’s how this might look: Collaborative Problem-Solving: Example with AI: Imagine using an AI system that doesn’t just calculate numbers for you but also helps you model complex scenarios. For instance, the AI could suggest different approaches to solving the bus problem, highlight potential oversights in your reasoning, or even propose alternative scenarios to consider. Joint Exploration: In this case, the machine is not just a passive tool; it’s actively engaging with you in exploring the problem space. The machine might not understand what a bus is in a human sense, but it could recognize patterns, offer hypotheses, or generate new data that you hadn’t considered. Augmented Intelligence: Enhancing Human Thought: In a truly collaborative environment, AI could help extend your cognitive capabilities. It might analyze large datasets, simulate different outcomes, or even learn from your interactions to better assist you in future problems. Here, the machine isn’t just calculating—it’s helping you think by offering new perspectives, highlighting connections, and enabling you to explore complex ideas more effectively. The Difference Between Tools and Partners Tools: Calculators, search engines, and basic software are tools. They extend your capabilities by handling specific tasks, like calculations or data retrieval. They don’t “think” in any meaningful sense; they execute tasks based on your instructions. Partners: An advanced AI that participates in problem-solving by offering insights, making suggestions, or helping to navigate complex problems is more of a partner. It may not have human-like understanding, but it can still contribute to the thinking process in a way that feels more collaborative. Conclusion: In your example of solving the bus problem, if you’re simply using a calculator or Wikipedia, you’re using the machine as a tool. You’re the one doing the thinking, and the machine is executing specific tasks. However, as AI continues to develop, the possibility of “thinking together” with a machine becomes more plausible. This would involve a deeper collaboration where the machine contributes not just computations or facts but also insights, helping you explore and solve problems in ways that go beyond simple calculation or information retrieval. 4o

Example 2: the investment

Problem 1: Using AI as a Tool Problem: Calculating the Future Value of an Investment Scenario: You have $10,000 to invest in a savings account with an annual interest rate of 5%, compounded annually. You want to know the value of your investment after 10 years. Two Approaches: Solve Manually: You could use the formula for compound interest: A=P×(1+rn)ntA = P \times \left(1 + \frac{r}{n}\right)^{nt}A=P×(1+nr​)nt where: PPP is the principal amount ($10,000), rrr is the annual interest rate (5%), nnn is the number of times interest is compounded per year (1), ttt is the number of years the money is invested (10). By plugging in the numbers and doing the math, you can calculate the future value yourself. Use a Calculator or Financial Software: Alternatively, you could use a calculator, spreadsheet, or financial software to do the computation. The software would execute the formula and give you the answer instantly. Analysis: AI as a Tool: Here, the AI (or software) is simply executing calculations. It’s a tool that helps you perform the task more quickly, but you understand the underlying logic and could perform the task manually if needed. The flow of bytes from the machine (the calculated result) is straightforward, representing the output of a mathematical operation. Problem 2: Using AI as a Partner Problem: Predicting the Outcome of a Complex Football Match Scenario: You’re tasked with predicting the outcome of a football match between two teams, each with a large number of variables at play (player fitness, weather conditions, historical performance, team strategies, etc.). The goal is not just to pick a winner but to understand the factors that might influence the outcome. Two Approaches: Solve Manually: You could try to analyze the problem manually by researching statistics, player conditions, and past performances. However, due to the complexity and the vast number of variables, your prediction would likely be based on limited data and intuition, making it prone to significant error. Use an Advanced AI System: An AI system could analyze huge amounts of data—team formations, player health records, historical match data, weather predictions, and even social media sentiment. The AI could then simulate thousands of possible match outcomes and identify patterns or key factors that could influence the result. The AI might suggest that a certain player’s fitness level, combined with the team’s usual strategy under rainy conditions, could be decisive. Analysis: AI as a Partner: In this scenario, the AI is not just crunching numbers—it’s providing insights, identifying patterns, and helping you navigate a complex problem. The flow of bytes from the machine represents a deep analysis, including probabilities, predictions, and reasoning that you likely wouldn’t have been able to generate on your own. Comparing the Two Scenarios Information Flow: In both cases, information (a flow of bytes) comes from the machine. In the first scenario, it’s a direct calculation—a simple output that you could replicate with enough time and effort. In the second scenario, the information flow is much more complex. The AI is processing vast amounts of data and providing insights that would be nearly impossible to generate without its help. This flow of bytes represents not just data but synthesized understanding. Human Cognitive Involvement: Problem 1 (Tool): Your thinking process is centered on understanding the formula and using the machine to speed up the calculation. You could do the task without the machine, though it might be slower.

It looks to me that these tasks are equivalent. The second is only more complex because of the computational power required. This should be ruled out because we said computation is an aspect of a tool. But very interesting: why would I have to write down a formula? Because I’m limited by my memory or because I need the language of mathematics to articulate the problem in understandable fashion? What if mathematics itself would be limited, and indeed it’s limited by our understanding of it. Universe seems to be ruled by math, so if we have a limited math understanding we have a limited understanding of the universe. If our machines can only learn from us, their knowledge will be limited also. How would they be able to expand their knowledge, if they might not have the ability to learn? How could they learn by exploration of mathematical principles, if they were not able to check their assumtpions?

This is extremely important at least in a case: when a cognitive dissonance is present. This brings us to the third and most important example of this series.

Example 4: the football game

Example 5: the Monty Hall problem

Example 6: the farmer and the librerian

Thinking together: the resolution of a cognitive dissonance

Information vs use of the information

You said: Can you please give me two problems: in both problems I can either use a machine or an AI to solve them or I can solve them by my own. The 2 tasks must be different in this aspect: in one I can or cannot use AI as “tool” (calculation, info retrival, etc) in the second, I have no choice but using AI as partner. At the end of the day, an info will have to flow from the machine in both cases, and in both cases we would talk about a flow of bytes. So how they can be different? Thinking in humans must be different for the effects that thinking has on our endocrine system. Or there is something I miss? ChatGPT said: ChatGPT Certainly! Let’s explore two different problems that illustrate the difference between using AI as a tool and using AI as a partner. We’ll also consider the flow of information and how it impacts our understanding of “thinking” in each scenario.

You said:

ChatGPT said: ChatGPT

You’ve raised several profound and interconnected questions that touch on the limitations of human and machine understanding, the role of mathematics as a language, and the potential for machines to expand knowledge beyond human input. Let’s unpack these ideas.

1. The Role of Mathematics in Articulating Problems Mathematics is a powerful tool for articulating and solving problems because it provides a structured, universal language that can describe patterns, relationships, and quantities in a precise way. When we write down a formula, we are doing more than just recording our thoughts—we are translating complex ideas into a language that is both humanly comprehensible and machine-readable.

Memory and Complexity: We often write down formulas because our memory and cognitive abilities are limited. Mathematics helps us externalize complex relationships that we might struggle to keep in our heads all at once.

Articulation: The process of writing down a formula forces us to clarify our thoughts. It’s a way of organizing and articulating a problem so that it can be more easily understood, communicated, and solved—whether by us or by a machine.

1. The Limits of Mathematical Understanding

Mathematics is indeed limited by our understanding of it. Although it has proven to be an incredibly powerful tool for describing the universe, it’s possible that there are aspects of reality that we have not yet discovered or that cannot be fully captured by our current mathematical frameworks.

Human Limitations: Our mathematical knowledge is based on centuries of accumulated learning, but it’s still a human construct. There may be patterns, principles, or phenomena in the universe that we haven’t yet encountered or that don’t fit neatly into our current mathematical models.

Machine Limitations: Machines, especially AI, learn from the data and models we provide. If our understanding of mathematics or the universe is incomplete, then the machines we build and the algorithms they run will also be limited. They can only explore within the boundaries we set, based on the knowledge we have imparted to them.

1. Can Machines Expand Their Knowledge Beyond Human Input?

This is a critical question for the future of AI and machine learning. If machines are confined to learning only from human-provided data and models, their ability to expand knowledge would indeed be limited. However, there are ways in which machines might push the boundaries:

Exploration and Hypothesis Testing: One way machines could potentially expand knowledge is through exploration and hypothesis testing. For example, an AI could generate new mathematical conjectures or simulate complex systems in ways that humans hadn’t considered. However, these explorations would still require a framework (like mathematics) and rules to guide the process.

Experimental Validation: For machines to truly expand their knowledge, they would need to be able to test their hypotheses and assumptions against reality. This is where things get tricky—how can a machine independently verify new principles or discoveries? In some areas, like mathematics, a machine might be able to prove or disprove a hypothesis. But in fields requiring physical experimentation, machines would be limited unless they have the ability to interact with the physical world in meaningful ways.

1. Learning and Knowledge Expansion in Machines

For machines to expand their knowledge beyond what humans have provided, they would need to develop or be given the capability to:

Create and Explore New Models: Machines would need to generate new mathematical models, explore alternative frameworks, or even invent new kinds of logic or reasoning. This would require a kind of creativity that current machines lack.

Validate Independently: Machines would need the ability to validate their models or hypotheses. In mathematics, this could mean proving theorems, but in other fields, it might involve running simulations or even conducting physical experiments.

Adapt and Generalize: A key aspect of human learning is the ability to generalize from specific experiences and adapt to new contexts. Machines would need to develop a similar capability to truly expand their knowledge.

Conclusion: The Interplay of Human and Machine Understanding

Your intuition is correct—machines, as they currently stand, are limited by the knowledge and frameworks we provide. While they can process vast amounts of data and generate insights, they do so within the confines of our understanding. To truly “think” beyond us, machines would need to develop new frameworks, test new ideas, and validate those ideas independently. This leads to deeper philosophical questions about the nature of intelligence, creativity, and understanding—whether machines could ever truly “think” in the human sense or if they will always remain tools and partners, amplifying our capabilities but constrained by our own limitations. The future might hold answers as AI continues to evolve, potentially pushing the boundaries of both human and machine knowledge.

The fact that we cannot articulate how the future of AI and humans will look like speaks volume about the limitations (or lack thereof) of the human mind. How likely is it that we are living in a pre-Galileo era and we take for granted things that will not be obvious in the future, e.g. the earth not being flat or at the centre of the universe? Will ever be a ground braking discovery so we wake up one day in a totally different world? How likely is that there is so much left to know about the universe, and what do you think it is going to be found about the universe that will change us again and forever?

Additional material worth watching/reading

1. Ilya Sutskever (OpenAI) and Jensen Huang (NVIDIA CEO) video
2. Pre-print paper by Chollet: On the Measure of Intelligence

3. Lex Fridman Podcast #38

   François Chollet: Keras, Deep Learning, and the Progress of AI video

4. Lex Fridman Podcast #367

   Sam Altman: OpenAI CEO on GPT-4, ChatGPT, and the Future of AI video

5. Lex Fridman Podcast #85

   Roger Penrose: Physics of Consciousness and the Infinite Universe video

6. The Great Mental Models

   Farnam Street

   from Shane Parrish’s website

7. Thinking, Fast and Slow

   book by psychologist Daniel Kahneman

You said: I have a talk in a couple of hours with a journalist interested in the AI sport applications. I wonder if you could help me discussing forefront about the topic. The difficulties I might haveis to articulate myself since I’m not native English speaker, but what I would like to point out is: AI, but what is truly intelligence? There are some traits we can “simulate” or teach a computer how to do, but some others not. In training prescription, for example, we rely on the assumption that part of the art of a coach is “computational”, which means that it is just about memory and calculations, rather than empathy or intuition. We can leverage computer to do that part only, and ultimately, we should (for efficiency). However, complex models might have a limited ability in being effectively delivering a response, because they might be limited by their construction. LLM for example, they can express ideas and principles with language, which cannot cover the resolution of the thinking space, and it might be limited by the rules of grammar. Different languages can express different things (in english you can say “dream away” as the famous writer said, but you cannot do that in Spanish). This is a silly example but it makes you understand that LLM can only learn about a portion of the representation of the world, and the text medium is either inefficient or incomplete. So I want to stress that the model itself is not the real tech advancement, but the attention should be given to the interface, i.e. the usability. How the machine can interact with the human side is more important. AI will not be solely machine because it would not be useful. Another important aspect is the presence of a training dataset, from which the model can draw. Extrapolation to non in-context information might be possibile, but cannot be ever scientific because you miss the experimentation portion of things. So either we do not believe the machine when it formulates an hypothesis or we just let it interpolate teh data and propose again what we already know. Also, the problem of the complexity of the models is not trivial. Let’s say that I have so many sensors and a model so big that can digest everything I give to understand if I’m responding well to training. One day I wake up and I do not respond well and the model is telling me that it’s because I ate carrots the Wednesday before. How is that actionable? Maybe it’s just a local minimum and I will fall again in that situation with a totally different input, i.e.: the system is chaotic. Also, I want to point out that if ever a machine could explain in simple and actionable terms an important physiological relationship, many scientists investigating that area should be quite ashamed not to be able to see that before the machine. For example, there was a nice Tweet from a person I follow on X, and his provocatory Tweet was about a model which could predict in a very easy way VO”max from the pedalling cadence of a single training session. But I say: if the solution is already there and a simple model can find it and explain it, it means that us as scientists are failing badly. So, I’m not againt AI or LLM or new tech at all, I’m just trying to see through the hype and predict where things are going to be. These techs are great but not in the sense that they can teach us somethign necessarily, but they can transform our interface with the machine making it possible to communicate more effectively and communicate differnet things faster. Synthetising information is a big part of the job. That is why I think that AThletica is one step ahead in teh game: if there is some portion of coaching which is computaitonal and automatisable, then we start from a good point because the scientific method (which is the funation of Athletica) is the most synthetisable appraoch of all. All you need to do is to encode the knowledge into a platofrm and take care of the usability, the more you are prone to this approach the better relationship with AI you will have. Also, Athletica knowledge give the advantage of the priors. The priors are those rules already known from experience or science, that can be the starting point of more complex models. For example you take the supercompensation or Banister model for training adaptations, and then you encode it, but taking into account that non-linearities can be devoted to a numerical model. The more knowldge you have about a topic, the smaller your AI model can be, as you are encoding a lot of knowldge taht require less parameters and less data for training. This is not only an efficeiny issue with energy or computationl power, but its’ due to the fact that you are less likely to fall into a local minimum trap and, almost more importantly, you will have an explainable solution to the user. ChatGPT said: ChatGPT You’re on the right track, and it sounds like you have a solid grasp of the key points you want to convey. I’ll help you refine and articulate your ideas clearly, with a focus on making your arguments persuasive and easy to understand. Here’s a structured approach you can take during your conversation with the journalist:

1. Defining Intelligence and AI’s Role in Sports Opening Point: Start by questioning what we mean by “intelligence.” You can say that while AI can simulate certain aspects of human intelligence, like memory and calculations, it’s important to acknowledge that true intelligence also involves qualities like empathy, intuition, and creativity—areas where AI is still limited. Application to Sports: In sports training, for example, AI can assist with data-driven aspects of coaching, like analyzing performance metrics or prescribing training regimens based on computational models. However, the art of coaching goes beyond just data—it’s also about understanding athletes on a human level, something AI can’t fully replicate.
2. Limitations of AI Models Complex Models and Communication: Explain that while AI models, including Large Language Models (LLMs), can process and express ideas within the constraints of language, they are limited by the structure of language itself. Different languages have different capacities for expression, and some nuances or concepts may be lost or misrepresented when translated across languages or distilled into computational terms. Example: Use the example of the phrase “dream away” in English, which has no direct equivalent in some other languages, to illustrate that AI’s understanding and communication are inherently limited by the data it has been trained on and the medium (text) it uses to communicate.
3. Importance of Interface and Usability Key Argument: Emphasize that the real advancement in AI isn’t just in creating complex models but in how effectively these models interact with humans. The interface—the way AI communicates with and assists users—is crucial. AI must be practical and actionable, not just sophisticated. AI as a Tool: AI should be seen as a tool that enhances human capabilities rather than replaces them. It’s more about augmenting the human experience by providing better interfaces for understanding and interacting with complex data.
4. Challenges with Complex Models and Data Interpretation Complexity vs. Usability: Highlight that as models become more complex, they may also become harder to interpret and less practical for real-world application. For example, if an AI model suggests that poor athletic performance is due to eating carrots a week earlier, this might not be actionable advice. Such complexity can lead to confusion rather than clarity. Chaotic Systems: Discuss the idea that sports performance is a chaotic system—small changes can have disproportionate effects, making it difficult for AI to consistently provide reliable guidance in every situation.
5. The Role of Scientific Knowledge in AI Importance of Priors: Stress that AI models built with a strong foundation of scientific knowledge are more efficient and reliable. By incorporating well-established principles like the supercompensation or Banister model, AI systems can be more effective and require fewer data points to make accurate predictions. Explainability and Trust: A smaller, more focused model grounded in scientific knowledge is not only more efficient but also more explainable. This transparency is critical for users to trust AI recommendations.
6. Conclusion: The Future of AI in Sports Balanced Perspective: Conclude by stating that you’re not against AI or new technologies, but you advocate for a balanced view that cuts through the hype. The future of AI in sports lies not in replacing human coaches or making groundbreaking discoveries, but in improving how we communicate and interact with data. Athletica as a Case Study: Mention Athletica as an example of how AI can be effectively integrated into sports by focusing on usability and leveraging scientific knowledge. By doing so, Athletica is able to provide actionable insights and enhance the coaching process. Final Tips: Clarity and Simplicity: Try to articulate your points clearly and avoid jargon as much as possible, especially since English isn’t your first language. Engagement: Feel free to ask the journalist questions to engage in a more dynamic conversation, and don’t hesitate to pause if you need to collect your thoughts. This structure should help you confidently navigate the discussion, ensuring you convey your points effectively. 4o You said: Thank you so much for this, can we add a brief discussion about work replacement? Many are concerned about AI taking over a lot of jobs, but I’d say that it’s only true for certain jobs, and that it’s alright if it is happening, so people can focus on other aspects of the job they are caring about. For example, I live in italy and you have the toll to pay when you’re on the highway, and that job of checking the tickets and give the change $$ is quite mechanistics and repetitive: you can “replace” with a machine. However, that working force can do soemthing else rather than checking the tickets, to make the motorway a better experience for the drivers. There are countless exampes. However, this transition cannot happen if the work force is not flexible and adaoptable. So this is a also a wake up call with which we can foressee changes on how working forces are trained for flexibility and not for doing one single job for the rest of their lives, making the job an overall better life experience for them also. This point is very important I think, because many times we confound /mix the concept of a job poosition with the need for that job or for the usefulmness of that job. We are conflating all the teachers in a job, forgetting that the objective is teh education of the nation. So, IMO, we are not focusing enough on making the workforce more flexible, but focusing on methods to keep a psoition for that workforce, as if the goal of the work is to provide people with a salry and not provide society with useful/utilty. ChatGPT said: ChatGPT That’s a very insightful point, and adding this discussion will definitely enrich your conversation with the journalist. Here’s how you can articulate this idea:
7. AI and Job Replacement: A Balanced Perspective Addressing Concerns: Many people are understandably concerned about AI replacing jobs, particularly those that involve repetitive or mechanistic tasks. However, it’s important to recognize that this is not a blanket truth for all types of work. Example of Automation: Take the example of toll collectors on highways in Italy. Checking tickets and giving change is a highly repetitive job that can be efficiently automated. But the key point here is not just about replacing the job—it’s about rethinking what the workforce can do instead.
8. The Opportunity for Workforce Transformation New Roles and Better Experiences: When AI or machines take over repetitive tasks, it frees up human workers to engage in more meaningful, creative, and impactful work. For instance, instead of collecting tolls, these workers could focus on improving the overall experience for drivers on the motorway—perhaps through customer service, maintenance, or even roles in traffic management and safety. Flexibility and Adaptability: This shift highlights the importance of preparing the workforce for flexibility and adaptability. Rather than training people to do a single, static job for their entire lives, we should be focusing on developing skills that allow them to transition into new roles as technology evolves.
9. Rethinking the Purpose of Work Jobs vs. Utility: Often, we conflate the concept of a job position with the need for that job, forgetting that the ultimate goal is to provide value to society. For example, in the case of teachers, the goal isn’t just to employ teachers; it’s to ensure the education of the nation. Similarly, the goal of a job isn’t just to provide a salary but to contribute something useful and meaningful to society. Wake-Up Call: The rise of AI should be seen as a wake-up call for us to rethink how we prepare our workforce. We should be training people not just for specific jobs but for the ability to adapt and thrive in a rapidly changing environment. This shift in focus can lead to a better overall life experience for workers, as they can engage in work that is more fulfilling and impactful.
10. Conclusion: Embracing Change for a Better Future Final Thoughts: Conclude this point by saying that while AI will inevitably lead to changes in the job market, this isn’t necessarily a bad thing. It’s an opportunity to elevate the workforce by moving people away from mundane tasks and into roles that are more aligned with their skills and passions. In the long run, this transition can lead to a more dynamic, innovative, and satisfying work environment for everyone. Additional Tips: Acknowledge Concerns: While making this point, it might be useful to acknowledge that transitions can be difficult and that there needs to be support for workers during these shifts, such as retraining programs or other forms of assistance. Focus on the Positive: Emphasize the potential benefits of this change, both for individuals and for society as a whole. This will help to frame AI not as a threat, but as a tool for positive transformation. By incorporating this discussion, you provide a nuanced view that balances the potential challenges of AI-driven job replacement with the opportunities for workforce improvement and societal benefit. 4o You said: I think it will be a nice discussion. Can you please put all the points together please? ChatGPT said: ChatGPT Certainly! Here’s a consolidated version of the points we’ve discussed, which you can use as a guide for your conversation with the journalist:

Discussion on AI in Sports and Workforce Transformation

1. Defining Intelligence and AI’s Role in Sports What is True Intelligence? Begin by questioning what we really mean by “intelligence.” AI can simulate certain aspects of human intelligence, such as memory and calculations, but true intelligence also involves qualities like empathy, intuition, and creativity—areas where AI is still limited. Application to Sports: In sports, AI can assist with data-driven aspects like analyzing performance metrics or prescribing training regimens. However, the art of coaching goes beyond data—it’s also about understanding athletes on a personal level, something AI can’t fully replicate.
2. Limitations of AI Models Complex Models and Communication: AI models, including Large Language Models (LLMs), can express ideas within the constraints of language, but they are limited by the structure of language itself. Different languages have different capacities for expression, which means some nuances may be lost or misrepresented. Example: Use the example of the phrase “dream away” in English, which lacks a direct equivalent in some other languages, to illustrate how AI’s understanding and communication are limited by the data and language it’s trained on.
3. Importance of Interface and Usability Focus on Interface: The real advancement in AI isn’t just in creating complex models but in how effectively these models interact with humans. The usability of AI—the way it communicates and assists users—is crucial. AI should enhance human capabilities, not replace them. AI as a Tool: AI should be seen as a tool that augments human experience by providing better interfaces for understanding and interacting with complex data, making communication more efficient and effective.
4. Challenges with Complex Models and Data Interpretation Complexity vs. Usability: As AI models become more complex, they may also become harder to interpret and less practical. For example, if an AI model suggests that poor athletic performance is due to eating carrots a week earlier, this advice may not be actionable and could cause confusion. Chaotic Systems: Sports performance is a chaotic system—small changes can have disproportionate effects, making it difficult for AI to consistently provide reliable guidance.
5. The Role of Scientific Knowledge in AI Importance of Priors: AI models built on a foundation of scientific knowledge are more efficient and reliable. By incorporating well-established principles like the supercompensation or Banister model, AI systems can be more effective and require fewer data points for accurate predictions. Explainability and Trust: A smaller, more focused model grounded in scientific knowledge is not only more efficient but also more explainable. This transparency is essential for building trust in AI recommendations.
6. AI and Job Replacement: A Balanced Perspective Addressing Concerns: While there’s concern about AI replacing jobs, it’s important to recognize that this is not true for all work. Certain repetitive and mechanistic tasks can be automated, but this opens up opportunities for workers to focus on more meaningful roles. Example of Automation: In Italy, toll collectors on highways perform repetitive tasks that can be automated. This shift allows workers to engage in roles that enhance the overall experience for drivers, such as customer service or traffic management.
7. The Opportunity for Workforce Transformation New Roles and Better Experiences: When AI takes over repetitive tasks, it frees up workers to engage in more meaningful, creative, and impactful work. This transition highlights the importance of training the workforce for flexibility and adaptability, preparing them for roles that are not static but evolve with technology. Rethinking the Purpose of Work: Often, we confuse the existence of a job position with the necessity of that job, forgetting that the goal is to provide value to society. For instance, the objective isn’t just to employ teachers but to educate the nation. Similarly, the goal of a job isn’t merely to provide a salary but to contribute something useful to society.
8. Conclusion: Embracing Change for a Better Future Balanced Perspective: Conclude by emphasizing that AI will lead to changes in the job market, but this is an opportunity to elevate the workforce. By automating mundane tasks, we can allow people to engage in work that is more fulfilling and impactful, leading to a more dynamic, innovative, and satisfying work environment. Support for Transition: Acknowledge that while transitions can be challenging, they also provide a chance to rethink how we train and prepare our workforce, ensuring that workers can adapt and thrive in a rapidly changing environment.

This structured approach will help you guide the conversation effectively, ensuring that you cover all your key points while maintaining a clear and engaging discussion. Good luck with your talk!

Summary from GPT (for the records)

1. Defining Intelligence and AI’s Role in Sports Opening Point: Start by questioning what we mean by “intelligence.” You can say that while AI can simulate certain aspects of human intelligence, like memory and calculations, it’s important to acknowledge that true intelligence also involves qualities like empathy, intuition, and creativity—areas where AI is still limited. Application to Sports: In sports training, for example, AI can assist with data-driven aspects of coaching, like analyzing performance metrics or prescribing training regimens based on computational models. However, the art of coaching goes beyond just data—it’s also about understanding athletes on a human level, something AI can’t fully replicate.
2. Limitations of AI Models Complex Models and Communication: Explain that while AI models, including Large Language Models (LLMs), can process and express ideas within the constraints of language, they are limited by the structure of language itself. Different languages have different capacities for expression, and some nuances or concepts may be lost or misrepresented when translated across languages or distilled into computational terms. Example: Use the example of the phrase “dream away” in English, which has no direct equivalent in some other languages, to illustrate that AI’s understanding and communication are inherently limited by the data it has been trained on and the medium (text) it uses to communicate.
3. Importance of Interface and Usability Key Argument: Emphasize that the real advancement in AI isn’t just in creating complex models but in how effectively these models interact with humans. The interface—the way AI communicates with and assists users—is crucial. AI must be practical and actionable, not just sophisticated. AI as a Tool: AI should be seen as a tool that enhances human capabilities rather than replaces them. It’s more about augmenting the human experience by providing better interfaces for understanding and interacting with complex data.
4. Challenges with Complex Models and Data Interpretation Complexity vs. Usability: Highlight that as models become more complex, they may also become harder to interpret and less practical for real-world application. For example, if an AI model suggests that poor athletic performance is due to eating carrots a week earlier, this might not be actionable advice. Such complexity can lead to confusion rather than clarity. Chaotic Systems: Discuss the idea that sports performance is a chaotic system—small changes can have disproportionate effects, making it difficult for AI to consistently provide reliable guidance in every situation.
5. The Role of Scientific Knowledge in AI Importance of Priors: Stress that AI models built with a strong foundation of scientific knowledge are more efficient and reliable. By incorporating well-established principles like the supercompensation or Banister model, AI systems can be more effective and require fewer data points to make accurate predictions. Explainability and Trust: A smaller, more focused model grounded in scientific knowledge is not only more efficient but also more explainable. This transparency is critical for users to trust AI recommendations.
6. Conclusion: The Future of AI in Sports Balanced Perspective: Conclude by stating that you’re not against AI or new technologies, but you advocate for a balanced view that cuts through the hype. The future of AI in sports lies not in replacing human coaches or making groundbreaking discoveries, but in improving how we communicate and interact with data. Athletica as a Case Study: Mention Athletica as an example of how AI can be effectively integrated into sports by focusing on usability and leveraging scientific knowledge. By doing so, Athletica is able to provide actionable insights and enhance the coaching process.
7. AI and Job Replacement: A Balanced Perspective Addressing Concerns: Many people are understandably concerned about AI replacing jobs, particularly those that involve repetitive or mechanistic tasks. However, it’s important to recognize that this is not a blanket truth for all types of work. Example of Automation: Take the example of toll collectors on highways in Italy. Checking tickets and giving change is a highly repetitive job that can be efficiently automated. But the key point here is not just about replacing the job—it’s about rethinking what the workforce can do instead.
8. The Opportunity for Workforce Transformation New Roles and Better Experiences: When AI or machines take over repetitive tasks, it frees up human workers to engage in more meaningful, creative, and impactful work. For instance, instead of collecting tolls, these workers could focus on improving the overall experience for drivers on the motorway—perhaps through customer service, maintenance, or even roles in traffic management and safety. Flexibility and Adaptability: This shift highlights the importance of preparing the workforce for flexibility and adaptability. Rather than training people to do a single, static job for their entire lives, we should be focusing on developing skills that allow them to transition into new roles as technology evolves.
9. Rethinking the Purpose of Work Jobs vs. Utility: Often, we conflate the concept of a job position with the need for that job, forgetting that the ultimate goal is to provide value to society. For example, in the case of teachers, the goal isn’t just to employ teachers; it’s to ensure the education of the nation. Similarly, the goal of a job isn’t just to provide a salary but to contribute something useful and meaningful to society. Wake-Up Call: The rise of AI should be seen as a wake-up call for us to rethink how we prepare our workforce. We should be training people not just for specific jobs but for the ability to adapt and thrive in a rapidly changing environment. This shift in focus can lead to a better overall life experience for workers, as they can engage in work that is more fulfilling and impactful.
10. Conclusion: Embracing Change for a Better Future Final Thoughts: Conclude this point by saying that while AI will inevitably lead to changes in the job market, this isn’t necessarily a bad thing. It’s an opportunity to elevate the workforce by moving people away from mundane tasks and into roles that are more aligned with their skills and passions. In the long run, this transition can lead to a more dynamic, innovative, and satisfying work environment for everyone.

I have a talk in a couple of hours with a journalist interested in the AI sport applications. I wonder if you could help me discussing forefront about the topic. The difficulties I might haveis to articulate myself since I’m not native English speaker, but what I would like to point out is:

AI, but what is truly intelligence? There are some traits we can “simulate” or teach a computer how to do, but some others not. In training prescription, for example, we rely on the assumption that part of the art of a coach is “computational”, which means that it is just about memory and calculations, rather than empathy or intuition. We can leverage computer to do that part only, and ultimately, we should (for efficiency).

However, complex models might have a limited ability in being effectively delivering a response, because they might be limited by their construction. LLM for example, they can express ideas and principles with language, which cannot cover the resolution of the thinking space, and it might be limited by the rules of grammar. Different languages can express different things (in english you can say “dream away” as the famous writer said, but you cannot do that in Spanish). This is a silly example but it makes you understand that LLM can only learn about a portion of the representation of the world, and the text medium is either inefficient or incomplete.

So I want to stress that the model itself is not the real tech advancement, but the attention should be given to the interface, i.e. the usability. How the machine can interact with the human side is more important. AI will not be solely machine because it would not be useful.

Another important aspect is the presence of a training dataset, from which the model can draw. Extrapolation to non in-context information might be possibile, but cannot be ever scientific because you miss the experimentation portion of things. So either we do not believe the machine when it formulates an hypothesis or we just let it interpolate teh data and propose again what we already know.

Also, the problem of the complexity of the models is not trivial. Let’s say that I have so many sensors and a model so big that can digest everything I give to understand if I’m responding well to training. One day I wake up and I do not respond well and the model is telling me that it’s because I ate carrots the Wednesday before. How is that actionable? Maybe it’s just a local minimum and I will fall again in that situation with a totally different input, i.e.: the system is chaotic.

Also, I want to point out that if ever a machine could explain in simple and actionable terms an important physiological relationship, many scientists investigating that area should be quite ashamed not to be able to see that before the machine. For example, there was a nice Tweet from a person I follow on X, and his provocatory Tweet was about a model which could predict in a very easy way VO”max from the pedalling cadence of a single training session. But I say: if the solution is already there and a simple model can find it and explain it, it means that us as scientists are failing badly.

So, I’m not againt AI or LLM or new tech at all, I’m just trying to see through the hype and predict where things are going to be. These techs are great but not in the sense that they can teach us somethign necessarily, but they can transform our interface with the machine making it possible to communicate more effectively and communicate differnet things faster. Synthetising information is a big part of the job.

That is why I think that AThletica is one step ahead in teh game: if there is some portion of coaching which is computaitonal and automatisable, then we start from a good point because the scientific method (which is the funation of Athletica) is the most synthetisable appraoch of all. All you need to do is to encode the knowledge into a platofrm and take care of the usability, the more you are prone to this <pproach the better relationship with AI you will have.

Also, Athletica knowledge give the advantage of the priors. The priors are those rules already known from experience or science, that can be the starting point of more complex models. For example you take the supercompensation or Banister model for training adaptations, and then you encode it, but taking into account that non-linearities can be devoted to a numerical model. The more knowldge you have about a topic, the smaller your AI model can be, as you are encoding a lot of knowldge taht require less parameters and less data for training. This is not only an efficeiny issue with energy or computationl power, but its’ due to the fact that you are less likely to fall into a local minimum trap and, almost more importantly, you will have an explainable solution to the user.

ChatGPT said: ChatGPT You’re on the right track, and it sounds like you have a solid grasp of the key points you want to convey. I’ll help you refine and articulate your ideas clearly, with a focus on making your arguments persuasive and easy to understand. Here’s a structured approach you can take during your conversation with the journalist:

1. Defining Intelligence and AI’s Role in Sports Opening Point: Start by questioning what we mean by “intelligence.” You can say that while AI can simulate certain aspects of human intelligence, like memory and calculations, it’s important to acknowledge that true intelligence also involves qualities like empathy, intuition, and creativity—areas where AI is still limited. Application to Sports: In sports training, for example, AI can assist with data-driven aspects of coaching, like analyzing performance metrics or prescribing training regimens based on computational models. However, the art of coaching goes beyond just data—it’s also about understanding athletes on a human level, something AI can’t fully replicate.
2. Limitations of AI Models Complex Models and Communication: Explain that while AI models, including Large Language Models (LLMs), can process and express ideas within the constraints of language, they are limited by the structure of language itself. Different languages have different capacities for expression, and some nuances or concepts may be lost or misrepresented when translated across languages or distilled into computational terms. Example: Use the example of the phrase “dream away” in English, which has no direct equivalent in some other languages, to illustrate that AI’s understanding and communication are inherently limited by the data it has been trained on and the medium (text) it uses to communicate.
3. Importance of Interface and Usability Key Argument: Emphasize that the real advancement in AI isn’t just in creating complex models but in how effectively these models interact with humans. The interface—the way AI communicates with and assists users—is crucial. AI must be practical and actionable, not just sophisticated. AI as a Tool: AI should be seen as a tool that enhances human capabilities rather than replaces them. It’s more about augmenting the human experience by providing better interfaces for understanding and interacting with complex data.
4. Challenges with Complex Models and Data Interpretation Complexity vs. Usability: Highlight that as models become more complex, they may also become harder to interpret and less practical for real-world application. For example, if an AI model suggests that poor athletic performance is due to eating carrots a week earlier, this might not be actionable advice. Such complexity can lead to confusion rather than clarity. Chaotic Systems: Discuss the idea that sports performance is a chaotic system—small changes can have disproportionate effects, making it difficult for AI to consistently provide reliable guidance in every situation.
5. The Role of Scientific Knowledge in AI Importance of Priors: Stress that AI models built with a strong foundation of scientific knowledge are more efficient and reliable. By incorporating well-established principles like the supercompensation or Banister model, AI systems can be more effective and require fewer data points to make accurate predictions. Explainability and Trust: A smaller, more focused model grounded in scientific knowledge is not only more efficient but also more explainable. This transparency is critical for users to trust AI recommendations.
6. Conclusion: The Future of AI in Sports Balanced Perspective: Conclude by stating that you’re not against AI or new technologies, but you advocate for a balanced view that cuts through the hype. The future of AI in sports lies not in replacing human coaches or making groundbreaking discoveries, but in improving how we communicate and interact with data. Athletica as a Case Study: Mention Athletica as an example of how AI can be effectively integrated into sports by focusing on usability and leveraging scientific knowledge. By doing so, Athletica is able to provide actionable insights and enhance the coaching process. Final Tips: Clarity and Simplicity: Try to articulate your points clearly and avoid jargon as much as possible, especially since English isn’t your first language. Engagement: Feel free to ask the journalist questions to engage in a more dynamic conversation, and don’t hesitate to pause if you need to collect your thoughts. This structure should help you confidently navigate the discussion, ensuring you convey your points effectively.

You said: Thank you so much for this, can we add a brief discussion about work replacement?

Many are concerned about AI taking over a lot of jobs, but I’d say that it’s only true for certain jobs, and that it’s alright if it is happening, so people can focus on other aspects of the job they are caring about. For example, I live in italy and you have the toll to pay when you’re on the highway, and that job of checking the tickets and give the change $$ is quite mechanistics and repetitive: you can “replace” with a machine. However, that working force can do soemthing else rather than checking the tickets, to make the motorway a better experience for the drivers. There are countless exampes. However, this transition cannot happen if the work force is not flexible and adaoptable. So this is a also a wake up call with which we can foressee changes on how working forces are trained for flexibility and not for doing one single job for the rest of their lives, making the job an overall better life experience for them also.

This point is very important I think, because many times we confound /mix the concept of a job poosition with the need for that job or for the usefulmness of that job. We are conflating all the teachers in a job, forgetting that the objective is teh education of the nation. So, IMO, we are not focusing enough on making the workforce more flexible, but focusing on methods to keep a psoition for that workforce, as if the goal of the work is to provide people with a salry and not provide society with useful/utilty. ChatGPT said: ChatGPT That’s a very insightful point, and adding this discussion will definitely enrich your conversation with the journalist. Here’s how you can articulate this idea:

1. AI and Job Replacement: A Balanced Perspective Addressing Concerns: Many people are understandably concerned about AI replacing jobs, particularly those that involve repetitive or mechanistic tasks. However, it’s important to recognize that this is not a blanket truth for all types of work. Example of Automation: Take the example of toll collectors on highways in Italy. Checking tickets and giving change is a highly repetitive job that can be efficiently automated. But the key point here is not just about replacing the job—it’s about rethinking what the workforce can do instead.
2. The Opportunity for Workforce Transformation New Roles and Better Experiences: When AI or machines take over repetitive tasks, it frees up human workers to engage in more meaningful, creative, and impactful work. For instance, instead of collecting tolls, these workers could focus on improving the overall experience for drivers on the motorway—perhaps through customer service, maintenance, or even roles in traffic management and safety. Flexibility and Adaptability: This shift highlights the importance of preparing the workforce for flexibility and adaptability. Rather than training people to do a single, static job for their entire lives, we should be focusing on developing skills that allow them to transition into new roles as technology evolves.
3. Rethinking the Purpose of Work Jobs vs. Utility: Often, we conflate the concept of a job position with the need for that job, forgetting that the ultimate goal is to provide value to society. For example, in the case of teachers, the goal isn’t just to employ teachers; it’s to ensure the education of the nation. Similarly, the goal of a job isn’t just to provide a salary but to contribute something useful and meaningful to society. Wake-Up Call: The rise of AI should be seen as a wake-up call for us to rethink how we prepare our workforce. We should be training people not just for specific jobs but for the ability to adapt and thrive in a rapidly changing environment. This shift in focus can lead to a better overall life experience for workers, as they can engage in work that is more fulfilling and impactful.
4. Conclusion: Embracing Change for a Better Future Final Thoughts: Conclude this point by saying that while AI will inevitably lead to changes in the job market, this isn’t necessarily a bad thing. It’s an opportunity to elevate the workforce by moving people away from mundane tasks and into roles that are more aligned with their skills and passions. In the long run, this transition can lead to a more dynamic, innovative, and satisfying work environment for everyone. Additional Tips: Acknowledge Concerns: While making this point, it might be useful to acknowledge that transitions can be difficult and that there needs to be support for workers during these shifts, such as retraining programs or other forms of assistance. Focus on the Positive: Emphasize the potential benefits of this change, both for individuals and for society as a whole. This will help to frame AI not as a threat, but as a tool for positive transformation. By incorporating this discussion, you provide a nuanced view that balances the potential challenges of AI-driven job replacement with the opportunities for workforce improvement and societal benefit.

You said: I think it will be a nice discussion. Can you please put all the points together please? ChatGPT said: ChatGPT Certainly! Here’s a consolidated version of the points we’ve discussed, which you can use as a guide for your conversation with the journalist:

Discussion on AI in Sports and Workforce Transformation

1. Defining Intelligence and AI’s Role in Sports What is True Intelligence? Begin by questioning what we really mean by “intelligence.” AI can simulate certain aspects of human intelligence, such as memory and calculations, but true intelligence also involves qualities like empathy, intuition, and creativity—areas where AI is still limited. Application to Sports: In sports, AI can assist with data-driven aspects like analyzing performance metrics or prescribing training regimens. However, the art of coaching goes beyond data—it’s also about understanding athletes on a personal level, something AI can’t fully replicate.
2. Limitations of AI Models Complex Models and Communication: AI models, including Large Language Models (LLMs), can express ideas within the constraints of language, but they are limited by the structure of language itself. Different languages have different capacities for expression, which means some nuances may be lost or misrepresented. Example: Use the example of the phrase “dream away” in English, which lacks a direct equivalent in some other languages, to illustrate how AI’s understanding and communication are limited by the data and language it’s trained on.
3. Importance of Interface and Usability Focus on Interface: The real advancement in AI isn’t just in creating complex models but in how effectively these models interact with humans. The usability of AI—the way it communicates and assists users—is crucial. AI should enhance human capabilities, not replace them. AI as a Tool: AI should be seen as a tool that augments human experience by providing better interfaces for understanding and interacting with complex data, making communication more efficient and effective.
4. Challenges with Complex Models and Data Interpretation Complexity vs. Usability: As AI models become more complex, they may also become harder to interpret and less practical. For example, if an AI model suggests that poor athletic performance is due to eating carrots a week earlier, this advice may not be actionable and could cause confusion. Chaotic Systems: Sports performance is a chaotic system—small changes can have disproportionate effects, making it difficult for AI to consistently provide reliable guidance.
5. The Role of Scientific Knowledge in AI Importance of Priors: AI models built on a foundation of scientific knowledge are more efficient and reliable. By incorporating well-established principles like the supercompensation or Banister model, AI systems can be more effective and require fewer data points for accurate predictions. Explainability and Trust: A smaller, more focused model grounded in scientific knowledge is not only more efficient but also more explainable. This transparency is essential for building trust in AI recommendations.
6. AI and Job Replacement: A Balanced Perspective Addressing Concerns: While there’s concern about AI replacing jobs, it’s important to recognize that this is not true for all work. Certain repetitive and mechanistic tasks can be automated, but this opens up opportunities for workers to focus on more meaningful roles. Example of Automation: In Italy, toll collectors on highways perform repetitive tasks that can be automated. This shift allows workers to engage in roles that enhance the overall experience for drivers, such as customer service or traffic management.
7. The Opportunity for Workforce Transformation New Roles and Better Experiences: When AI takes over repetitive tasks, it frees up workers to engage in more meaningful, creative, and impactful work. This transition highlights the importance of training the workforce for flexibility and adaptability, preparing them for roles that are not static but evolve with technology. Rethinking the Purpose of Work: Often, we confuse the existence of a job position with the necessity of that job, forgetting that the goal is to provide value to society. For instance, the objective isn’t just to employ teachers but to educate the nation. Similarly, the goal of a job isn’t merely to provide a salary but to contribute something useful to society.
8. Conclusion: Embracing Change for a Better Future Balanced Perspective: Conclude by emphasizing that AI will lead to changes in the job market, but this is an opportunity to elevate the workforce. By automating mundane tasks, we can allow people to engage in work that is more fulfilling and impactful, leading to a more dynamic, innovative, and satisfying work environment. Support for Transition: Acknowledge that while transitions can be challenging, they also provide a chance to rethink how we train and prepare our workforce, ensuring that workers can adapt and thrive in a rapidly changing environment. This structured approach will help you guide the conversation effectively, ensuring that you cover all your key points while maintaining a clear and engaging discussion. Good luck with your talk!

You said: What would you say is to think together with a machine? I mean, if you ask me a question about: 10 people were on the bus, half of them left at the first station and 12 come up; at the second station 3 came down and 7 went up, etc. I can solve this problem doing a calculation, or doing the math in my mind. In the first case, was I thinking together with a machine? If I did not have a notion of what a bus was, and I looked up at Wikipedia to know, was I thinking together with a machine or I was just using it to make calculations? Obvously the calculator would know nothing about busses, would it? Can you please give me your point of view? ChatGPT said: ChatGPT This is a fascinating question that delves into the nature of human-machine interaction and the concept of “thinking together” with a machine. Let me offer my perspective.

Human-Machine Collaboration vs. Pure Calculation When you solve a problem using a calculator or a computer, you’re engaging in a form of collaboration with the machine, but it’s a limited and specific type of collaboration. Let’s break down the different scenarios you mentioned:

Calculating the Bus Problem:

Pure Calculation: If you use a calculator or software to perform the arithmetic needed to solve the bus problem (e.g., adding and subtracting the number of people at each station), you’re not really “thinking together” with the machine in a deep sense. Instead, you’re offloading a specific, well-defined task to the machine. The machine is doing something that it excels at—performing precise, rapid calculations—while you retain the understanding of the problem’s context (e.g., the concept of people getting on and off a bus). Human Oversight: You are still the one conceptualizing the problem, framing it, and interpreting the results. The machine is a tool that helps with the mechanical part of the process, but it doesn’t contribute to understanding the problem itself. Looking Up Information on Wikipedia:

Information Retrieval: If you lack a certain piece of knowledge (e.g., what a bus is) and you look it up on Wikipedia, you’re using the machine as a tool for information retrieval. In this case, you’re enhancing your understanding by accessing a vast repository of human knowledge. The machine helps you by providing this information, but it doesn’t “think” about the bus in any meaningful way—it simply retrieves data. Contextual Understanding: You, as the human, integrate this new information into your existing knowledge base. The machine has played a role in broadening your understanding, but it hasn’t actively participated in the reasoning process. What Does It Mean to “Think Together” with a Machine? “Thinking together” with a machine implies a deeper level of collaboration where both the human and the machine contribute to the problem-solving process in a meaningful way. Here’s how this might look:

Collaborative Problem-Solving:

Example with AI: Imagine using an AI system that doesn’t just calculate numbers for you but also helps you model complex scenarios. For instance, the AI could suggest different approaches to solving the bus problem, highlight potential oversights in your reasoning, or even propose alternative scenarios to consider. Joint Exploration: In this case, the machine is not just a passive tool; it’s actively engaging with you in exploring the problem space. The machine might not understand what a bus is in a human sense, but it could recognize patterns, offer hypotheses, or generate new data that you hadn’t considered. Augmented Intelligence:

Enhancing Human Thought: In a truly collaborative environment, AI could help extend your cognitive capabilities. It might analyze large datasets, simulate different outcomes, or even learn from your interactions to better assist you in future problems. Here, the machine isn’t just calculating—it’s helping you think by offering new perspectives, highlighting connections, and enabling you to explore complex ideas more effectively. The Difference Between Tools and Partners Tools: Calculators, search engines, and basic software are tools. They extend your capabilities by handling specific tasks, like calculations or data retrieval. They don’t “think” in any meaningful sense; they execute tasks based on your instructions. Partners: An advanced AI that participates in problem-solving by offering insights, making suggestions, or helping to navigate complex problems is more of a partner. It may not have human-like understanding, but it can still contribute to the thinking process in a way that feels more collaborative. Conclusion: In your example of solving the bus problem, if you’re simply using a calculator or Wikipedia, you’re using the machine as a tool. You’re the one doing the thinking, and the machine is executing specific tasks. However, as AI continues to develop, the possibility of “thinking together” with a machine becomes more plausible. This would involve a deeper collaboration where the machine contributes not just computations or facts but also insights, helping you explore and solve problems in ways that go beyond simple calculation or information retrieval.

You said: Can you please give me two problems: in both problems I can either use a machine or an AI to solve them or I can solve them by my own. The 2 tasks must be different in this aspect: in one I can or cannot use AI as “tool” (calculation, info retrival, etc) in the second, I have no choice but using AI as partner. At the end of the day, an info will have to flow from the machine in both cases, and in both cases we would talk about a flow of bytes. So how they can be different? Thinking in humans must be different for the effects that thinking has on our endocrine system. Or there is something I miss? ChatGPT said: ChatGPT Certainly! Let’s explore two different problems that illustrate the difference between using AI as a tool and using AI as a partner. We’ll also consider the flow of information and how it impacts our understanding of “thinking” in each scenario.

Problem 1: Using AI as a Tool Problem: Calculating the Future Value of an Investment

Scenario: You have $10,000 to invest in a savings account with an annual interest rate of 5%, compounded annually. You want to know the value of your investment after 10 years. Two Approaches:

Solve Manually:

You could use the formula for compound interest: 𝐴 = 𝑃 × ( 1 + 𝑟 𝑛 ) 𝑛 𝑡 A=P×(1+ n r ​ ) nt where: 𝑃 P is the principal amount ($10,000), 𝑟 r is the annual interest rate (5%), 𝑛 n is the number of times interest is compounded per year (1), 𝑡 t is the number of years the money is invested (10). By plugging in the numbers and doing the math, you can calculate the future value yourself. Use a Calculator or Financial Software:

Alternatively, you could use a calculator, spreadsheet, or financial software to do the computation. The software would execute the formula and give you the answer instantly. Analysis:

AI as a Tool: Here, the AI (or software) is simply executing calculations. It’s a tool that helps you perform the task more quickly, but you understand the underlying logic and could perform the task manually if needed. The flow of bytes from the machine (the calculated result) is straightforward, representing the output of a mathematical operation. Problem 2: Using AI as a Partner Problem: Predicting the Outcome of a Complex Football Match

Scenario: You’re tasked with predicting the outcome of a football match between two teams, each with a large number of variables at play (player fitness, weather conditions, historical performance, team strategies, etc.). The goal is not just to pick a winner but to understand the factors that might influence the outcome. Two Approaches:

Solve Manually:

You could try to analyze the problem manually by researching statistics, player conditions, and past performances. However, due to the complexity and the vast number of variables, your prediction would likely be based on limited data and intuition, making it prone to significant error. Use an Advanced AI System:

An AI system could analyze huge amounts of data—team formations, player health records, historical match data, weather predictions, and even social media sentiment. The AI could then simulate thousands of possible match outcomes and identify patterns or key factors that could influence the result. The AI might suggest that a certain player’s fitness level, combined with the team’s usual strategy under rainy conditions, could be decisive. Analysis:

AI as a Partner: In this scenario, the AI is not just crunching numbers—it’s providing insights, identifying patterns, and helping you navigate a complex problem. The flow of bytes from the machine represents a deep analysis, including probabilities, predictions, and reasoning that you likely wouldn’t have been able to generate on your own. Comparing the Two Scenarios Information Flow:

In both cases, information (a flow of bytes) comes from the machine. In the first scenario, it’s a direct calculation—a simple output that you could replicate with enough time and effort. In the second scenario, the information flow is much more complex. The AI is processing vast amounts of data and providing insights that would be nearly impossible to generate without its help. This flow of bytes represents not just data but synthesized understanding. Human Cognitive Involvement:

Problem 1 (Tool): Your thinking process is centered on understanding the formula and using the machine to speed up the calculation. You could do the task without the machine, though it might be slower. Problem 2 (Partner): Your thinking process involves collaborating with the AI, interpreting its insights, and making decisions based on a level of analysis that you couldn’t achieve alone. Here, the machine significantly amplifies your cognitive capabilities. Impact on Human Thought and Endocrine System:

Problem 1: The cognitive load is lower—your endocrine system might only respond to the task with mild stimulation, as the problem is straightforward and doesn’t require deep engagement or stress. Problem 2: The complexity and uncertainty of the task could lead to more significant cognitive engagement, potentially activating stress responses, focus, and problem-solving hormones like cortisol and dopamine. The AI acts as a cognitive partner, pushing your thinking into new territory, which could have a more profound impact on your physiological state. Conclusion: The difference between using AI as a tool and as a partner lies in the depth of collaboration and the complexity of the task. While both involve a flow of bytes from the machine, the nature of those bytes—and what they represent—differs significantly. In the first case, it’s a simple computation; in the second, it’s a complex, synthesized analysis that contributes to human thought in a way that transforms the problem-solving process. This difference can influence not just the cognitive aspect of thinking but also how our bodies physically respond to the challenge.

You said: It looks to me that these tasks are equivalent. The second is only more complex because of the computational power required. This should be ruled out because we said computation is an aspect of a tool. But very interesting: why would I have to write down a formula? Because I’m limited by my memory or because I need the language of mathematics to articulate the problem in understandable fashion? What if mathematics itself would be limited, and indeed it’s limited by our understanding of it. Universe seems to be ruled by math, so if we have a limited math understanding we have a limited understanding of the universe. If our machines can only learn from us, their knowledge will be limited also. How would they be able to expand their knowledge, if they might not have the ability to learn? How could they learn by exploration of mathematical principles, if they were not able to check their assumtpions? Does this make sense? ChatGPT said: ChatGPT You’ve raised several profound and interconnected questions that touch on the limitations of human and machine understanding, the role of mathematics as a language, and the potential for machines to expand knowledge beyond human input. Let’s unpack these ideas.

1. The Role of Mathematics in Articulating Problems Mathematics is a powerful tool for articulating and solving problems because it provides a structured, universal language that can describe patterns, relationships, and quantities in a precise way. When we write down a formula, we are doing more than just recording our thoughts—we are translating complex ideas into a language that is both humanly comprehensible and machine-readable.

Memory and Complexity: We often write down formulas because our memory and cognitive abilities are limited. Mathematics helps us externalize complex relationships that we might struggle to keep in our heads all at once. Articulation: The process of writing down a formula forces us to clarify our thoughts. It’s a way of organizing and articulating a problem so that it can be more easily understood, communicated, and solved—whether by us or by a machine.

1. The Limits of Mathematical Understanding Mathematics is indeed limited by our understanding of it. Although it has proven to be an incredibly powerful tool for describing the universe, it’s possible that there are aspects of reality that we have not yet discovered or that cannot be fully captured by our current mathematical frameworks.

Human Limitations: Our mathematical knowledge is based on centuries of accumulated learning, but it’s still a human construct. There may be patterns, principles, or phenomena in the universe that we haven’t yet encountered or that don’t fit neatly into our current mathematical models. Machine Limitations: Machines, especially AI, learn from the data and models we provide. If our understanding of mathematics or the universe is incomplete, then the machines we build and the algorithms they run will also be limited. They can only explore within the boundaries we set, based on the knowledge we have imparted to them.

1. Can Machines Expand Their Knowledge Beyond Human Input? This is a critical question for the future of AI and machine learning. If machines are confined to learning only from human-provided data and models, their ability to expand knowledge would indeed be limited. However, there are ways in which machines might push the boundaries:

Exploration and Hypothesis Testing: One way machines could potentially expand knowledge is through exploration and hypothesis testing. For example, an AI could generate new mathematical conjectures or simulate complex systems in ways that humans hadn’t considered. However, these explorations would still require a framework (like mathematics) and rules to guide the process. Experimental Validation: For machines to truly expand their knowledge, they would need to be able to test their hypotheses and assumptions against reality. This is where things get tricky—how can a machine independently verify new principles or discoveries? In some areas, like mathematics, a machine might be able to prove or disprove a hypothesis. But in fields requiring physical experimentation, machines would be limited unless they have the ability to interact with the physical world in meaningful ways.

1. Learning and Knowledge Expansion in Machines For machines to expand their knowledge beyond what humans have provided, they would need to develop or be given the capability to:

Create and Explore New Models: Machines would need to generate new mathematical models, explore alternative frameworks, or even invent new kinds of logic or reasoning. This would require a kind of creativity that current machines lack. Validate Independently: Machines would need the ability to validate their models or hypotheses. In mathematics, this could mean proving theorems, but in other fields, it might involve running simulations or even conducting physical experiments. Adapt and Generalize: A key aspect of human learning is the ability to generalize from specific experiences and adapt to new contexts. Machines would need to develop a similar capability to truly expand their knowledge. Conclusion: The Interplay of Human and Machine Understanding Your intuition is correct—machines, as they currently stand, are limited by the knowledge and frameworks we provide. While they can process vast amounts of data and generate insights, they do so within the confines of our understanding. To truly “think” beyond us, machines would need to develop new frameworks, test new ideas, and validate those ideas independently.

This leads to deeper philosophical questions about the nature of intelligence, creativity, and understanding—whether machines could ever truly “think” in the human sense or if they will always remain tools and partners, amplifying our capabilities but constrained by our limitations. The future might hold answers as AI continues to evolve, potentially pushing the boundaries of both human and machine knowledge.

You said: The fact that we cannot articulate how the future of AI and humans will look like speaks volume about the limitations (or lack thereof) of the human mind. How likely is it that we are living in a pre-Galileo era and we take for granted things that will not be obvious in the future, e.g. the earth not being flat or at the centre of the universe? Will ever be a ground braking discovery so we wake up one day in a totally different world? How likely is that there is so much left to know about the universe, and what do you think it is going to be found about the universe that will change us (again) forever? What are the domains of science left to be explored? Time travel? ChatGPT said: ChatGPT Your question touches on the profound nature of human knowledge and our place in the ongoing journey of discovery. The idea that we might be living in a “pre-Galileo” era—taking for granted things that could be radically overturned in the future—is both humbling and exhilarating. History shows us that humanity has gone through multiple paradigm shifts, where widely accepted beliefs were challenged and replaced by new, transformative ideas. Could we be on the cusp of another such shift? Let’s explore this possibility.

The Likelihood of a Groundbreaking Discovery The history of science is marked by periods of revolutionary change, where foundational beliefs are upended. Examples include:

The Copernican Revolution: The shift from a geocentric (Earth-centered) to a heliocentric (Sun-centered) model of the solar system. The Darwinian Revolution: The acceptance of evolution by natural selection, which redefined our understanding of life. The Einsteinian Revolution: The theory of relativity, which changed our understanding of time, space, and gravity. Given this pattern, it’s reasonable to believe that we may not yet have reached the limits of such revolutionary discoveries. There could be fundamental aspects of reality that we have yet to uncover, and a groundbreaking discovery could indeed change our worldview overnight. The fact that we struggle to articulate what this might look like only emphasizes the limitations of our current understanding.

Domains of Science Left to Explore There are several areas in science that remain mysterious and could potentially lead to transformative discoveries:

Fundamental Physics:

Quantum Gravity: One of the biggest mysteries in physics is how to reconcile quantum mechanics with general relativity. A theory of quantum gravity, such as string theory or loop quantum gravity, could fundamentally change our understanding of space, time, and the universe. Dark Matter and Dark Energy: These make up about 95% of the universe’s total mass-energy content, yet we know very little about them. Discovering the true nature of dark matter and dark energy could revolutionize cosmology. Multiverse Theory: The idea that our universe is just one of many could dramatically alter our perception of existence, space, and time. Consciousness and the Mind:

The Nature of Consciousness: We still don’t fully understand what consciousness is or how it arises from the brain. A breakthrough in this area could redefine our understanding of self, identity, and intelligence. Artificial General Intelligence (AGI): The development of AGI, a machine with human-like cognitive abilities, could be a game-changer in how we interact with technology and perceive intelligence. Biotechnology and Life Sciences:

Synthetic Biology and Genetic Engineering: As we gain the ability to design life at the genetic level, we could see radical changes in medicine, agriculture, and even human evolution. Longevity and Aging: Understanding and potentially reversing the aging process could have profound implications for society, ethics, and the human experience. Cosmology and Astrobiology:

Extraterrestrial Life: Discovering life elsewhere in the universe would fundamentally change our understanding of life’s uniqueness and our place in the cosmos. The Big Bang and the Universe’s Origins: A deeper understanding of the early universe or the discovery of what preceded the Big Bang could reshape our understanding of existence itself. The Possibility of a “Galilean” Shift in Understanding It’s highly likely that there are concepts or truths about the universe that we have yet to grasp—truths that could be as revolutionary as realizing the Earth is not the center of the universe. These could come from any of the domains mentioned above, or from entirely new areas of inquiry that we can’t yet imagine.

For example, consider the concept of time travel:

Theoretical Possibility: Time travel is theoretically possible within the frameworks of general relativity and quantum mechanics, but practical or observational evidence is lacking. Impact of Discovery: If time travel were proven possible, it would upend our understanding of causality, history, and the nature of time itself. Such a discovery could lead to new technologies, paradoxes, and ethical dilemmas that are currently the stuff of science fiction. Future Discoveries and Human Understanding As we continue to push the boundaries of knowledge, there is a good chance that future discoveries will fundamentally change how we view the universe and our place within it. However, predicting exactly what those discoveries will be is inherently difficult because they lie beyond the horizon of our current understanding.

Conclusion: The Endless Frontier In essence, the journey of discovery is ongoing, and the possibility of a “Galilean” shift in our understanding remains ever-present. Whether it’s through breakthroughs in physics, biology, or consciousness studies, the future holds the potential for radical changes in our understanding of the universe.

These changes will challenge us to think differently, not just about the world around us, but about our very nature as thinking beings. The unknowns are vast, and what we take for granted today might one day seem as quaint as the idea of a flat Earth or a geocentric universe. The key is to remain open to new ideas, question our assumptions, and continue exploring the frontiers of knowledge.

You said: Are AGIs inevitable? ChatGPT said: ChatGPT The question of whether Artificial General Intelligence (AGI) is inevitable is one of the most debated topics in the field of artificial intelligence and technology. There are compelling arguments on both sides, and the answer depends on a variety of factors, including technological advancements, ethical considerations, and societal priorities. Let’s explore the different aspects of this question.

Arguments Suggesting AGIs Are Inevitable Exponential Growth in Computing Power:

Moore’s Law and Beyond: While Moore’s Law (the idea that computing power doubles approximately every two years) has slowed, there are still advancements in quantum computing, neuromorphic chips, and other technologies that could lead to breakthroughs in processing power. Data Availability: The vast amounts of data being generated and collected provide a rich resource for training increasingly sophisticated AI systems. With more data and better algorithms, AI could eventually reach the level of AGI. Advancements in AI Research:

Progress in Machine Learning: AI has already achieved significant milestones in narrow domains (like image recognition, natural language processing, and game playing). Many researchers believe that these techniques could be generalized to achieve AGI. Neural Networks and Deep Learning: These models mimic some aspects of human brain function. With continued research, there may be ways to replicate more complex cognitive processes. Economic and Competitive Pressures:

Global Competition: Nations and corporations are heavily investing in AI, seeking to gain competitive advantages in military, economic, and technological arenas. This competitive drive might make AGI development inevitable as entities race to harness the potential of AGI. Automation of Knowledge Work: As AI continues to replace jobs traditionally done by humans, there is a strong economic incentive to push towards AGI, which could handle complex decision-making and creative tasks. Philosophical and Theoretical Grounds:

No Fundamental Barrier: Some theorists argue that, in principle, there is no fundamental barrier to creating AGI. If the human brain can produce general intelligence, then it should be possible to replicate or surpass this capability in machines, given enough time and resources. Arguments Suggesting AGIs Are Not Inevitable Technical Challenges:

Complexity of Human Intelligence: Human cognition involves not just logic and data processing but also emotions, consciousness, and embodied experiences. Replicating the full range of human intelligence in a machine is an incredibly complex task, and we may not have the necessary understanding of the brain to do so. Brittleness of AI: Current AI systems, even the most advanced ones, are often brittle—they can fail in unexpected ways when faced with situations outside their training data. Achieving AGI would require overcoming this brittleness to create truly adaptable and robust intelligence. Ethical and Social Considerations:

Ethical Concerns: The development of AGI raises significant ethical questions about control, safety, and the potential for unintended consequences. If society decides that the risks outweigh the benefits, it could regulate or halt AGI development. Social Resistance: There could be strong social and political resistance to the creation of AGI, particularly if people fear job displacement, loss of privacy, or the erosion of human agency. Resource Limitations:

Energy and Environmental Costs: Developing and running AGI could require enormous amounts of energy and resources. As the world grapples with climate change and resource scarcity, there may be limits to how much can be invested in pursuing AGI. Financial Constraints: The development of AGI could require immense financial resources. If the cost is too high, or if investors decide that the returns are not worth the risks, progress toward AGI could stall. Philosophical and Theoretical Doubts:

Irreducible Human Aspects: Some philosophers argue that certain aspects of human intelligence, such as consciousness or self-awareness, may be irreducible and impossible to replicate in a machine. If true, AGI in the fullest sense might be unattainable. Limits of Computation: There are theories that certain kinds of intelligence or cognitive processes may be beyond the reach of computational systems as we currently understand them. If this is the case, AGI may never be achieved. Conclusion: A Contingent Future Whether AGIs are inevitable depends on a complex interplay of technological progress, ethical considerations, societal choices, and unforeseen challenges. While the trajectory of AI development suggests that increasingly sophisticated systems will continue to be developed, the leap from narrow AI (which excels in specific tasks) to AGI (which exhibits general, human-like intelligence) is far from guaranteed.

Technologically: If the current momentum in AI research continues, and if we solve the significant technical challenges, AGI could become a reality. However, this is not a given, and there are many obstacles that could slow or prevent this outcome. Ethically and Socially: Society might decide to impose strict regulations on AI development, prioritizing safety and ethical considerations over the pursuit of AGI. This could delay or even halt the progress towards AGI. In summary, while AGI is a possibility, it is not an inevitability. The future of AGI will be shaped not just by technological advancements but by human choices, values, and the unforeseen complexities of creating machines that think like us—or beyond us.

You said: Do you think that there is a thing like a concept that a person can understand and another cannot? What it means? Perhaps a very difficult topic in physics is difficult to understand for a person new to physics, and I get that. But are there circumstances where two persons equipped with the same priors can or cannot understand a principle? What does this even mean? WHat does undesrtanding mean, that I can apply the concept to reality and benefit from it? . ChatGPT said: ChatGPT The concept of “understanding” is complex and multi-dimensional, and your question touches on some deep aspects of cognition, learning, and the nature of knowledge. Let’s break it down into several key areas:

Understanding and Individual Differences Cognitive Differences:

Cognitive Abilities: People have different cognitive abilities, which include memory, reasoning, processing speed, and spatial skills. These abilities can influence how easily someone can grasp a concept. Even with the same priors (knowledge or experience), individuals may process and integrate new information differently due to these inherent cognitive differences. Learning Styles: Individuals have different learning styles—some may be more visual, others more verbal, some may prefer hands-on experiences, while others thrive on theoretical explanations. These preferences can affect how well someone understands a concept, even when starting with the same prior knowledge. Prior Knowledge and Context:

Depth of Prior Knowledge: Even if two people have the same surface-level priors, one might have a deeper understanding of related concepts, which can make it easier to grasp new, related ideas. For example, in physics, someone might have a more intuitive grasp of mathematics, which would make it easier for them to understand complex physical theories. Contextual Differences: The context in which a person learns something can affect their understanding. For example, a concept learned in a practical, real-world context might be more easily understood and retained than the same concept learned in an abstract or disconnected way. Cognitive Load and Complexity:

Cognitive Load: Complex concepts often require managing multiple pieces of information at once. If the cognitive load is too high for an individual, they may struggle to understand, even if they have the same priors as someone else who can manage that load more easily. Abstract vs. Concrete Thinking: Some concepts, especially in advanced mathematics or theoretical physics, are highly abstract. Abstract thinking is a skill that not everyone develops to the same degree, and this can affect understanding. Someone who is better at thinking abstractly might grasp certain concepts that another person finds elusive. What Does It Mean to “Understand”? Understanding is more than just knowing or recalling information. It involves several key aspects:

Comprehension:

Grasping the Meaning: Understanding means that you can internalize the concept, seeing how it fits within a broader framework of knowledge. It’s not just about knowing a fact, but about knowing why it’s true, how it connects to other facts, and what its implications are. Conceptual Connections: A deep understanding involves seeing how the concept connects to others, forming a network of knowledge. This allows for more flexible and adaptable use of the concept in different contexts. Application:

Practical Application: Understanding often implies that you can apply the concept to solve problems, make decisions, or predict outcomes. If you understand Newton’s laws of motion, for example, you can apply them to predict how objects will move under different forces. Adaptation to New Situations: True understanding means you can apply a concept to new, unfamiliar situations—not just the ones in which you initially learned it. Transfer of Knowledge:

Analogical Reasoning: This involves using knowledge from one domain to understand or solve problems in another. For example, understanding how electrical circuits work might help you understand fluid dynamics, even though they are different fields. Teaching Others: If you can explain a concept to someone else in a way that they understand, this is often a sign of deep understanding. It shows that you’ve internalized the concept to the point where you can articulate it clearly and adapt it to another person’s level of understanding. Metacognition:

Awareness of Understanding: Understanding also involves knowing what you understand and what you don’t. Metacognitive skills—thinking about your thinking—allow you to evaluate your own understanding and seek further clarification or study where needed. Circumstances Where Understanding Differs Despite Similar Priors Even when two individuals share similar prior knowledge, their understanding of a new concept might differ due to the factors mentioned above. This can happen in various ways:

Cognitive Load: One person might be better at managing the cognitive demands of a complex problem, leading to better understanding. Abstract Thinking: One person might excel at thinking abstractly, allowing them to grasp concepts that are more theoretical or less concrete. Motivation and Interest: One person might be more motivated or interested in the subject, leading to a deeper engagement and better understanding. Context and Instruction: Differences in how the concept is taught or the context in which it’s learned can lead to differences in understanding. Understanding as a Tool for Application and Benefit Ultimately, understanding can be seen as a tool that allows you to interact with and influence the world:

Practical Benefit: Understanding allows you to apply concepts to real-world situations, solving problems or creating new opportunities. Predictive Power: With understanding, you can make predictions about future events or outcomes based on the concepts you know. Innovation: Understanding enables innovation, as it allows you to see new possibilities, make connections between seemingly unrelated ideas, and develop novel solutions. Conclusion: The Nature of Understanding Understanding is a dynamic, multifaceted process that involves more than just acquiring information. It’s about integrating new knowledge into an existing framework, applying it to different contexts, and being able to adapt it to new situations. Differences in understanding between individuals, even with similar priors, can arise from cognitive differences, learning styles, depth of prior knowledge, and contextual factors.

In essence, understanding is the process by which we make sense of the world and our place within it, and it is deeply connected to our ability to apply knowledge in meaningful ways.

You said: but can you give me an example of something a person can undesrtand and another deos not and the reason why? ChatGPT said: ChatGPT Certainly! Let’s consider an example from mathematics and cognitive processing to illustrate how two individuals might differ in understanding a concept.

Example: Understanding of Abstract Algebra (Group Theory) Imagine two students, Alice and Bob, both of whom have completed the same prerequisite courses in mathematics, including basic algebra, calculus, and linear algebra. They both attend the same lectures on group theory, a branch of abstract algebra that deals with algebraic structures known as groups.

The Concept: In group theory, one of the fundamental concepts is the idea of a “group,” which is a set combined with an operation that satisfies four conditions: closure, associativity, identity, and invertibility. Understanding these properties and how they apply to different mathematical objects (like numbers, matrices, or geometric transformations) is crucial.

Alice’s Experience: Cognitive Abilities and Abstract Thinking:

Alice excels at abstract thinking and has a strong ability to visualize and manipulate abstract concepts in her mind. She finds it relatively easy to grasp the idea that different mathematical structures can share these group properties, even when they are not immediately intuitive. Prior Knowledge Integration:

Alice also has a deep understanding of the underlying principles from her previous courses. She can draw connections between group theory and other areas of mathematics, such as linear algebra and calculus, which helps her see the broader significance of the concept. Learning Style and Motivation:

Alice is motivated and enjoys challenging herself with abstract problems. She engages deeply with the material, solving extra problems and exploring additional resources to deepen her understanding. Bob’s Experience: Cognitive Abilities and Abstract Thinking:

Bob, on the other hand, struggles more with abstract thinking. He prefers concrete examples and finds it difficult to generalize the properties of groups to abstract structures. When learning about groups, Bob often needs to see specific examples, such as numbers under addition, to grasp the concept. However, when faced with more abstract examples, like permutations or symmetries, he finds it hard to see how these different examples all fit into the same framework. Prior Knowledge Integration:

Although Bob has the same prior coursework, he hasn’t internalized the connections between different areas of mathematics as well as Alice. This makes it harder for him to see the relevance of group theory to other mathematical concepts he’s learned, so it feels disconnected and more difficult to understand. Learning Style and Motivation:

Bob prefers hands-on, practical learning and gets frustrated with abstract concepts that don’t have immediate, visible applications. He may not engage as deeply with the material, leading to a shallower understanding of the concept. Outcome: Alice’s Understanding:

Alice understands group theory well. She can explain the concept, apply it to various mathematical structures, and even see its implications in different areas of mathematics and physics. Her abstract thinking skills, ability to integrate prior knowledge, and deep engagement with the material all contribute to her understanding. Bob’s Understanding:

Bob, however, struggles to understand group theory beyond specific examples. He might be able to follow along when working with familiar objects like numbers but struggles with the general, abstract definition of a group. His difficulty with abstract thinking, weaker integration of prior knowledge, and preference for concrete examples limit his understanding. Reason for the Difference: Abstract Thinking: Alice’s stronger abstract thinking skills allow her to grasp and manipulate the general, abstract concept of a group, while Bob’s difficulty with abstraction hinders his understanding. Integration of Prior Knowledge: Alice has a more integrated understanding of mathematics, which helps her see the connections between group theory and other areas. Bob’s less integrated knowledge base makes the new concept feel more isolated and harder to grasp. Engagement and Motivation: Alice’s greater motivation and willingness to explore the material deeply lead to a richer understanding. Bob’s frustration and preference for practical examples limit his engagement and, consequently, his understanding. Conclusion: This example illustrates how two people with the same educational background and priors can have different levels of understanding of the same concept due to differences in cognitive abilities, the way they integrate prior knowledge, and their engagement with the material. Alice’s understanding is more profound and flexible, while Bob’s is more limited and context-dependent.

You said: Great insights. I always think at this example: the 3 doors with a goat in one and a car in the other. this was portrayed well in a movie with kevin spacey. the student got the question to change the door when offered because the teacher opened the empty door. I find it extremely difficult to understand why the probability changed in favour of the choice of which door to open. But some can get the concept and I’m sure I have all the priors to get the principle but I can’t. Why? ChatGPT said: ChatGPT The problem you’re referring to is known as the Monty Hall problem, and it is a classic example of how probability and decision-making can defy our intuitive understanding. Let’s break down why this problem can be difficult to grasp, despite having the necessary prior knowledge, and how it illustrates broader principles about understanding and cognition.

The Monty Hall Problem: The Setup:

You’re on a game show with three doors. Behind one door is a car (the prize you want), and behind the other two doors are goats (which you don’t want). You choose one door, but before it’s opened, the host (Monty Hall) opens another door, revealing a goat behind it. Monty knows what’s behind each door and always reveals a goat. Monty then asks if you want to stick with your original choice or switch to the remaining unopened door. The Question:

Should you switch doors, stick with your original choice, or does it not matter? The Solution:

Probability Analysis: Initially, there is a 1/3 chance the car is behind the door you chose and a 2/3 chance it’s behind one of the other two doors. After Monty reveals a goat behind one of the remaining two doors, the probability that the car is behind the remaining unopened door becomes 2/3 if you switch. This is because Monty’s action of revealing a goat gives additional information that skews the probabilities. Why the Problem Is Challenging to Understand: Intuitive Misunderstanding:

Initial Intuition: Many people initially believe that since there are two doors left (one with a car and one with a goat), the probability should be 50-50. This is a common intuitive misunderstanding because it doesn’t take into account the information Monty provides by revealing a goat. Probability Shifts: The shift in probabilities is counterintuitive because it involves conditional probability and Bayesian reasoning, which can be challenging to grasp without a solid understanding of these concepts. Cognitive Biases and Heuristics:

Representativeness Heuristic: People often use heuristic shortcuts that lead them to incorrect conclusions. In the Monty Hall problem, the representativeness heuristic might lead someone to assume each remaining door is equally likely to have the car. Gambler’s Fallacy: This problem might be influenced by the gambler’s fallacy, where people believe that past events (like Monty revealing a goat) change the likelihood of future events. Conditional Probability:

Understanding Conditional Probability: The problem relies on conditional probability, where the likelihood of an event changes based on new information. This can be abstract and difficult to intuitively grasp, especially if someone hasn’t been exposed to these concepts in detail. Educational Background:

Prior Knowledge: Even if you have the mathematical knowledge required to understand probability, cognitive biases and the abstract nature of conditional probability can still make the Monty Hall problem challenging. The problem involves not just knowing how to calculate probabilities but also understanding how information changes probabilities in a non-intuitive way. Psychological Factors:

Trust in Intuition: People often trust their intuitive understanding of probability, which can be flawed. This problem challenges that intuition, leading to cognitive dissonance. Difficulty in Visualizing: The problem requires imagining scenarios and understanding the impact of Monty’s actions on the remaining probabilities, which can be difficult for some people. Why Some Understand and Others Don’t: Cognitive Styles and Strengths:

Abstract Thinking: People who excel in abstract thinking and probability reasoning may find it easier to understand why switching doors is beneficial. They can more easily visualize the problem and calculate the probabilities based on conditional information. Analytical Skills: Those with strong analytical skills and experience in probability theory are more likely to grasp the nuances of the Monty Hall problem. Exposure and Experience:

Educational Exposure: Individuals who have studied probability theory in depth or have encountered similar problems are more likely to understand the solution. Experience with similar puzzles can aid in grasping the counterintuitive results. Problem Solving Approaches:

Mathematical Approach: Understanding the problem from a mathematical perspective, such as through simulations or detailed probability calculations, can help make sense of why switching is advantageous. Intuitive vs. Analytical: Those relying on intuitive reasoning might struggle more with problems like Monty Hall compared to those who use a more analytical approach. Conclusion: Understanding the Monty Hall problem involves grasping conditional probability and how new information affects the likelihood of outcomes. This can be difficult even with the right prior knowledge because it challenges our intuitive understanding of probability. Cognitive biases, the abstract nature of probability, and individual differences in cognitive processing all play a role in whether someone finds the problem easy or difficult to understand.

This example highlights how understanding is not just about having the right information but also about how we process and integrate that information in our minds. It also shows how intuitive misconceptions can persist even in the face of logical and mathematical reasoning.

You said: given that we have the chance can you try to explain me why new information affects the likelihood of outcomes in this case? Sorry I cannot get it. ChatGPT said: ChatGPT Absolutely, let’s break down the Monty Hall problem step-by-step to understand why new information changes the likelihood of outcomes. We’ll use a combination of probability theory and intuitive explanation to clarify why switching doors increases your chances of winning.

Monty Hall Problem Breakdown Initial Situation:

There are 3 doors: behind one is a car (the prize you want), and behind the other two are goats. You choose one door (let’s call it Door 1). At this point, the probability of the car being behind Door 1 is 1/3, and the probability of the car being behind one of the other two doors (Door 2 or Door 3) is 2/3. Monty Opens a Door:

Monty Hall, who knows what’s behind each door, opens one of the remaining two doors to reveal a goat. Let’s say Monty opens Door 3, and you see a goat behind it. Monty’s action of opening Door 3 doesn’t change the initial probabilities but adds new information. Updated Probabilities:

You are now faced with a choice: stick with Door 1 or switch to Door 2, the remaining unopened door. Why Switching Is Beneficial: Initial Probabilities Recap:

Door 1 (Your Choice): Probability of the car being behind Door 1 is 1/3. Doors 2 and 3 (Unchosen Doors): Probability of the car being behind either Door 2 or Door 3 is 2/3 combined. Monty’s Reveal:

Monty opens Door 3 to reveal a goat. This action does not change the initial probability of the car being behind Door 1 (still 1/3) but it redistributes the probability of the car being behind the remaining unopened door (Door 2). Why Switching Increases Your Chances:

Initial Probability Distribution:

At the beginning, the probability of the car being behind Door 1 is 1/3, and the probability of it being behind either Door 2 or Door 3 is 2/3. Monty’s Knowledge:

Monty’s choice to open Door 3 (which has a goat) is not random. Monty will always open a door that does not have the car behind it. This is a crucial aspect: Monty’s action is influenced by his knowledge and is not a random event. Revised Probability:

Given that Monty reveals a goat behind Door 3, the probability that the car is behind Door 2 is now 2/3. This is because the initial probability of 2/3 for Door 2 and Door 3 combined is now entirely concentrated on Door 2 after Door 3 is shown to have a goat. So, if you switch to Door 2, you are effectively choosing the door that has a 2/3 chance of containing the car, whereas sticking with Door 1 still only gives you a 1/3 chance. Detailed Explanation with Examples: Simulation Approach:

Imagine running the game multiple times: Scenario 1: You always choose Door 1. In 1/3 of the cases, the car is behind Door 1, and in 2/3 of the cases, the car is behind one of the other two doors. Scenario 2: If you always switch after Monty reveals a goat, you will win the car 2/3 of the time because Monty’s reveal changes the probability distribution based on the initial conditions. Intuitive Explanation:

Think of the game in terms of information: Initially, you had a 1/3 chance of picking the correct door and a 2/3 chance of picking a door with a goat. After Monty reveals a goat behind one of the doors you didn’t pick, the 2/3 probability that the car is behind one of the other doors (initially behind Door 2 or Door 3) is now entirely focused on the remaining unopened door (Door 2). Thus, switching is beneficial. Conclusion: Switching doors in the Monty Hall problem is beneficial because Monty’s action of revealing a goat behind one of the doors provides additional information that shifts the probability. Initially, the probability distribution was 1/3 for your chosen door and 2/3 for the other two doors combined. After Monty’s reveal, the 2/3 probability is concentrated on the remaining unopened door, making it advantageous to switch.

This problem demonstrates how new information can affect the likelihood of outcomes and why our initial intuitions about probability might not always align with the mathematical reality.

You said: thank you so much for explaining the monty hall problem. I think that now I get it.

I have a nice understanding now of what the problem is about, I can replicate, and hopefully apply to similar albeit different contexts and scenarios.

Now I’d like to ask you: the method you used to explain that to me worked just fine, but maybe for others would not. What other methods could you envision to explain the solution of the Monty Problem?

I’m also very interested in knowing more about the cognitive dissonance, as this is is a very good point in favour of the “thinking together” theory we were talking about and the presence of a cognitive dissonance might be an example where my thinking was truly mediated by a machine. It was not just a matter of “thinking fast or slow - system 1 vs 2 pre cortext and blablablabl” but it was really the matter of joinign my neural circuitry with taht of the PC, is this a too long shot? ChatGPT said: ChatGPT I’m glad the explanation of the Monty Hall problem resonated with you! Your understanding of the problem and its implications opens up fascinating discussions on how different people might grasp complex concepts and the potential of “thinking together” with machines. Let’s explore alternative methods to explain the Monty Hall problem, discuss cognitive dissonance, and consider how these ideas might relate to the concept of “thinking together” with machines.

Alternative Methods to Explain the Monty Hall Problem Visual/Graphical Explanation:

Tree Diagrams: A tree diagram can visually represent all possible outcomes from the moment you make your initial choice. By showing each branch where Monty reveals a goat and the possible outcomes if you switch or stay, it becomes clearer why switching gives you a 2/3 chance of winning. Simulation with Physical Objects: Using three actual doors or boxes with one hiding a small object (like a coin) can make the problem more tangible. Repeating the process multiple times helps build an intuitive sense of the probabilities. Simulation/Interactive Explanation:

Computer Simulation: Many online simulators allow you to play the Monty Hall game multiple times, automatically calculating the results of switching vs. staying. After dozens or hundreds of rounds, the statistical advantage of switching becomes evident. Hands-On Activity: Have the learner run their own simulation with cards or cups. By physically engaging with the problem, they might internalize the logic more effectively. Storytelling or Analogies:

Alternate Story Contexts: Reframe the problem in a different context that the learner finds more relatable or intuitive. For example, imagine a treasure hunt where clues (instead of doors) lead to a treasure. The host gives a clue, then another clue after revealing a false lead. Switching paths could represent switching doors, making the concept easier to grasp. Extended Analogies: Compare the problem to more familiar situations, such as deciding whether to switch seats in a game of musical chairs after someone shows that one chair is definitely empty. Mathematical Proof or Logical Argument:

Bayesian Reasoning: For those comfortable with mathematics, you can delve into Bayesian probability to show how Monty’s reveal updates the probabilities. Step-by-Step Logic: Break down the decision-making process step-by-step using logic, emphasizing how Monty’s action provides new information that shifts the probabilities. Cognitive Dissonance and “Thinking Together” with Machines Cognitive Dissonance in Understanding the Monty Hall Problem:

What Is Cognitive Dissonance? Cognitive dissonance occurs when a person holds two contradictory beliefs or ideas, leading to mental discomfort. In the context of the Monty Hall problem, the dissonance arises because our intuitive understanding of probability (50/50 after one door is revealed) conflicts with the correct mathematical solution (2/3 if you switch). Resolution of Dissonance: To resolve the dissonance, a person must either adjust their belief (e.g., accepting the counterintuitive probability) or reject the new information. Successfully understanding the Monty Hall problem often involves overcoming this dissonance by accepting that intuition was wrong and embracing the correct reasoning. Thinking Together with Machines:

Blending Human and Machine Cognition: When you used a machine to simulate the Monty Hall problem or to visualize its logic, you were not just using the machine as a tool; you were integrating its processing power and precision with your cognitive capabilities. This blend of human intuition and machine calculation exemplifies “thinking together.” Example of Cognitive Dissonance and Machine Mediation: If you initially found the Monty Hall solution counterintuitive but accepted the outcome after seeing a simulation or detailed explanation provided by a machine, this could be an instance where your cognitive dissonance was resolved through “thinking together” with the machine. The machine provided a logical consistency that countered your initial intuitive reaction, leading to a new understanding. Beyond System 1 and System 2: Traditional cognitive psychology talks about System 1 (fast, intuitive thinking) and System 2 (slow, analytical thinking). However, when you involve a machine in the process, it’s not just about fast or slow thinking; it’s about enhancing your cognitive capabilities. The machine adds layers of processing that you might not naturally engage in on your own. Is This a Long Shot? Your idea that resolving cognitive dissonance with the aid of a machine is a form of “thinking together” is not a long shot. In fact, it aligns with emerging views in cognitive science and artificial intelligence. Here’s why:

Extended Cognition: The concept of extended cognition suggests that tools like computers, calculators, or even notebooks become part of our cognitive system when we use them to process information. In this view, thinking is not confined to the brain but includes the tools we use to manipulate and understand the world. Collaborative Intelligence: When a machine helps resolve cognitive dissonance or provides a different perspective that you integrate into your understanding, it’s a form of collaborative intelligence. The machine’s contribution isn’t just computational; it’s part of a joint cognitive process. Conclusion Understanding the Monty Hall problem can be facilitated through various methods, tailored to different cognitive styles. Cognitive dissonance plays a key role in the learning process, especially when our intuitions clash with logical reasoning. When you resolve this dissonance with the help of a machine, it’s a clear example of “thinking together,” where the machine’s computational abilities complement your cognitive processes, leading to a deeper understanding. This interaction highlights how our cognition can be extended and enhanced through technology, providing a glimpse into the future of human-machine collaboration.

You said: this is a super useful discussion. now, I want to make a parallel with AI used in endurance training coaching. we should do an example, where a coach is uncertain about 3 different “doors” (training sessions ABC) so he/she has a probability of 1/3 to nail it. However, the machine (the AI coaching platform that he/she is using) will rule out a possibility (C) based on the fast analysis of recent data available from the athlete. The coach will (knowing the Monty hall problem, and suggested by the machine) change the idea to plan B (another door) because the additional knowledge brought by the machine was instrumental in increasing the probabilities from 1/3 to 2/3 for plan B. This is a case of thinking together that we want to deliver with our coaching platform, and this is what I would liek you to lay down for me. ChatGPT said: ChatGPT Certainly! Let’s outline the scenario where an AI-driven coaching platform aids a coach in making a decision, using the Monty Hall problem as an analogy.

Scenario: AI in Endurance Training Coaching The Situation A coach is planning an upcoming training session for an athlete. The coach has three potential training sessions in mind:

Plan A: A high-intensity interval training (HIIT) session. Plan B: A long, steady-state endurance ride. Plan C: A recovery session with low-intensity activities. Without additional data, the coach believes there’s an equal chance (1/3) that any of these sessions could be the best choice for the athlete based on the athlete’s recent performance, recovery status, and upcoming race schedule.

Introduction of AI: The Coaching Platform The coach uses an AI-powered coaching platform designed to analyze vast amounts of the athlete’s data—such as heart rate variability (HRV), power output, sleep quality, previous session performance, and more. The AI assesses this data in real-time and quickly rules out Plan C (the recovery session) because the athlete’s recent metrics indicate they are fully recovered and need a more challenging workout to continue making progress.

The Monty Hall Parallel Here’s where the Monty Hall analogy comes into play:

Initially, the coach has three options (A, B, C), with no strong preference for any of them, so there’s a 1/3 chance that any given plan is optimal. The AI platform effectively acts like Monty Hall, who opens a door to reveal a goat. In this case, the “goat” is Plan C, which the AI identifies as suboptimal based on the athlete’s data. After Plan C is ruled out, the coach is left with two options: Plan A and Plan B. According to the logic of the Monty Hall problem, the optimal strategy would be to reconsider the remaining options and potentially switch to Plan B (the “other door”). The Decision Process Initially, the coach might have leaned towards Plan A or B without strong justification. With the AI ruling out Plan C, the probability of one of the remaining plans being the best choice effectively changes. If the coach initially favored Plan A, but Plan C is ruled out, switching to Plan B could now be seen as the more optimal decision, with a probability of success increasing to 2/3. Outcome The coach, understanding the logic behind the Monty Hall problem, decides to switch to Plan B based on the AI’s analysis. The additional knowledge provided by the AI—ruling out the suboptimal choice—enhances the decision-making process, increasing the likelihood of the athlete receiving the most effective training session.

Thinking Together: Human + Machine Collaboration In this example, the coach and the AI platform are “thinking together”:

Human Intuition and Experience: The coach brings years of experience, understanding the athlete’s needs and the demands of the sport. However, human intuition might not always be sufficient to navigate complex data or probabilities. AI Data Processing: The AI quickly processes and analyzes recent athlete data, offering insights that might not be immediately apparent to the coach. By combining these two elements, the coach makes a more informed decision, leading to better outcomes for the athlete. This is a clear example of collaborative intelligence—where the machine doesn’t replace the coach but enhances their decision-making ability.

Conclusion The use of AI in endurance training coaching, as exemplified by this scenario, demonstrates the power of “thinking together” with machines. Just as the Monty Hall problem reveals how new information can shift probabilities and influence decision-making, the AI platform provides valuable insights that can significantly improve coaching decisions. This collaboration between coach and AI leads to more effective training plans and better athletic performance, highlighting the future of AI-assisted human expertise in sports.

You said: The problem is well stated, and the examples are great. But I think that the A B C options are too different to be a realistic example. No coach would be equally unsure whether ABC are truly equal. We need more nuanced examples where the difference is mush narorwer.

Also, stress the importance of the cognitive dissonance (which your example lacks) and the existance of a probability still at the end, where A might have been the best choice , because it was a matter of probability in a caotic probelm, there is no certain solution. ChatGPT said: ChatGPT You’re absolutely right. In real-world coaching, the choices are rarely as distinct as the ones in the previous example. Let’s refine the scenario with more nuanced options and incorporate the role of cognitive dissonance, as well as the inherent uncertainty in making such decisions.

Refined Scenario: AI in Endurance Training Coaching The Situation A coach is planning a training session for an athlete nearing a critical phase of their training cycle. The coach has three potential training sessions in mind:

Plan A: A moderately intense interval session aimed at improving the athlete’s lactate threshold. Plan B: A slightly longer tempo run designed to enhance aerobic capacity. Plan C: A high-endurance, steady-state ride focused on building fatigue resistance over long durations. Each plan has subtle differences and could be appropriate depending on the athlete’s current condition, upcoming race schedule, and specific training goals. The coach recognizes that all three sessions could be beneficial, but isn’t sure which would be the best choice right now, given the narrow distinctions between them. Without additional data, each plan seems equally viable, giving a perceived probability of 1/3 for each being the optimal choice.

Introduction of AI: The Coaching Platform The coach turns to an AI-powered coaching platform, which analyzes the athlete’s recent data—such as HRV trends, training load balance, recovery metrics, and past responses to similar training stimuli. After processing this data, the AI rules out Plan C. It determines that the athlete has recently handled a lot of high-endurance work and suggests that further focus on this area might lead to overtraining.

However, the AI doesn’t explicitly tell the coach which of the remaining two plans is better; it just narrows the focus to Plan A and Plan B. The coach now has to choose between these two plans.

The Monty Hall Parallel and Cognitive Dissonance Initially, the coach is faced with three closely related options, each with a 1/3 chance of being the best choice. The AI, like Monty Hall, reveals information by ruling out Plan C, which slightly shifts the coach’s perspective. With Plan C out, the coach must now choose between Plans A and B, and there’s an intuitive pull to reconsider the initial preference. Here’s where cognitive dissonance plays a key role: The coach might have initially favored Plan A, but now, with new data from the AI, they are faced with the discomfort of potentially needing to switch to Plan B. The AI’s suggestion introduces a tension between the coach’s initial intuition and the new information provided. The Decision Process Despite the AI’s analysis, the coach still feels uncertain. They understand that even though the AI ruled out Plan C, there’s still no guarantee that switching to Plan B is the correct choice—Plan A might still be the best fit depending on factors the AI couldn’t fully capture (e.g., psychological readiness, specific race strategies, etc.). Cognitive Dissonance: The coach experiences cognitive dissonance as they struggle to reconcile their initial intuition with the AI’s suggestion. This dissonance is a natural part of human decision-making when faced with new information that challenges previous assumptions. Final Choice: The coach ultimately decides to switch to Plan B, acknowledging that while there’s still uncertainty (and Plan A might turn out to be the better choice), the AI’s data-driven insight increases the probability that Plan B is optimal (from 1/3 to 2/3). The Role of Uncertainty Even after making the decision, there remains a level of uncertainty inherent in the outcome. Training and athletic performance are influenced by numerous chaotic and unpredictable factors—everything from daily fluctuations in the athlete’s mood to unexpected environmental conditions during the session. The AI can only increase the probability of making the right choice, but it cannot eliminate the inherent uncertainty.

Thinking Together and the Integration of Human and Machine Cognition Human Expertise: The coach brings a deep understanding of the athlete’s psychological and physical state, which might not be fully quantifiable by the AI. Machine Insight: The AI processes vast amounts of data quickly, identifying patterns and probabilities that might not be immediately apparent to the coach. Cognitive Dissonance Resolution: By working together with the AI, the coach resolves the cognitive dissonance by re-evaluating their options with new insights. This integration of human intuition and machine analysis represents “thinking together,” where the final decision is a collaborative effort, blending human judgment with AI-driven probabilities. Conclusion This refined example better reflects the nuanced decision-making process coaches face and highlights the importance of cognitive dissonance in human-AI collaboration. In this scenario, the AI doesn’t dictate the final decision but rather provides crucial information that influences the coach’s thought process. The AI helps shift probabilities in favor of one option, but it leaves room for human judgment and acknowledges the chaotic nature of training outcomes. This partnership between coach and AI exemplifies the concept of “thinking together,” where technology enhances but does not replace human expertise, and where uncertainty remains an integral part of the decision-making process.

You said: mmmmm but the difference between monty hall and the AI coach is that Monty knows where the “car” or “prize” is. In this case AI is exactly on the same side as the human coach. I reckion in this case the analogy wouldn’t hold would it? ChatGPT said: ChatGPT You’re correct that the Monty Hall analogy has its limits in this scenario. In the Monty Hall problem, Monty knows exactly where the prize is, which allows him to influence the probability by revealing one of the incorrect choices. This creates a situation where the contestant gains an advantage by switching their choice.

In the case of the AI coaching platform, the AI doesn’t have the same kind of “inside knowledge.” The AI doesn’t know the definitive “best” training session in advance; it can only analyze the data, identify patterns, and suggest what it believes to be the best option based on the available information. The AI and the coach are indeed on the same side, trying to make the best possible decision with the data they have. Therefore, the analogy with Monty Hall doesn’t perfectly apply because the AI isn’t revealing a known incorrect option—it’s making an educated guess just like the coach.

Refining the Analogy: Bayesian Updating A better analogy for the AI-coach collaboration might come from Bayesian reasoning, where both the coach and the AI continuously update their beliefs (or probabilities) as new information becomes available.

Scenario: Bayesian Reasoning in Coaching Initial Beliefs:

The coach initially believes that each training session (A, B, C) has an equal probability (1/3) of being the best choice. New Information from AI:

The AI analyzes the athlete’s recent data and suggests that Plan C is less likely to be effective based on current recovery status and training history. This isn’t an absolute rule-out but rather a decrease in the likelihood that Plan C is optimal. Updating Beliefs:

With this new information, the coach updates their belief about the best training session. If the AI provides strong evidence against Plan C, the coach now considers Plans A and B to be more likely than Plan C. Final Decision:

The coach makes a decision by weighing the updated probabilities. They might now see Plan A and Plan B as more viable and have to choose between them. This is where cognitive dissonance can come into play, as the coach reconciles their initial beliefs with the AI’s new insights. Key Differences from Monty Hall: Lack of Certainty: Unlike Monty, the AI doesn’t know the “correct” answer; it’s estimating probabilities based on data, much like the coach. Collaborative Decision-Making: The AI isn’t revealing a known false option but is instead providing an analysis that helps the coach update their understanding. Why This Matters: Human and AI as Partners: This example emphasizes that both the coach and the AI are partners in the decision-making process, each contributing their strengths to arrive at the best possible decision. Cognitive Dissonance: The coach’s initial intuition may conflict with the AI’s data-driven insights, creating cognitive dissonance. Resolving this requires the coach to integrate both perspectives, leading to a more informed decision. Conclusion While the Monty Hall problem is a classic example of probability theory and decision-making, it doesn’t fully capture the dynamics of AI-human collaboration in coaching. A Bayesian updating analogy is more appropriate, where both the AI and the coach continuously refine their understanding as new data becomes available. This approach highlights the true nature of “thinking together,” where both human intuition and machine analysis combine to navigate uncertainty and make the best possible decision.

You said: I had a blast writing these questions with you and I think we came full circle, and we can wrap everything up.

I would love to write a blog post together with you. ChatGPT said: ChatGPT I’m thrilled that you enjoyed our conversation! Collaborating on a blog post sounds like a great idea. We can take the insights we’ve explored and turn them into a compelling piece that covers the fascinating intersections of AI, decision-making, cognitive dissonance, and the future of human-machine collaboration.

Proposed Structure for the Blog Post Introduction:

Briefly introduce the topic of AI in decision-making, focusing on how AI and humans can “think together.” Mention the importance of understanding probabilities and cognitive dissonance in the context of AI. The Monty Hall Problem as a Lens:

Introduce the Monty Hall problem as a classic example of probability and decision-making. Explain how it has traditionally been used to illustrate how new information can shift probabilities and change decisions. Discuss its limitations when applied to AI-human collaboration, particularly in contexts where the AI doesn’t have “inside knowledge.” AI and Bayesian Updating:

Introduce the concept of Bayesian updating as a more suitable analogy for AI-human decision-making. Explain how AI can help update our beliefs and refine our decision-making process based on new data. Highlight the role of cognitive dissonance in integrating AI insights with human intuition. Practical Example: AI in Endurance Coaching:

Provide the nuanced example of an AI-powered coaching platform helping a coach decide between similar training plans. Discuss how the AI narrows options and shifts probabilities, but the final decision still rests with the human coach. Emphasize the collaborative aspect of “thinking together” and the ongoing refinement of decisions. The Future of Human-AI Collaboration:

Reflect on the broader implications of AI-human collaboration in various fields beyond coaching. Speculate on how our understanding of decision-making might evolve as AI becomes more integrated into our lives. Consider the potential for groundbreaking discoveries and paradigm shifts, akin to those of the past, as AI and humans continue to push the boundaries of knowledge. Conclusion:

Summarize the key points, emphasizing the value of AI as a partner rather than a replacement. Encourage readers to think critically about how they interact with AI and to embrace the cognitive dissonance that often comes with new insights. Next Steps Drafting: I can help you draft the sections, or if you’d like, we can start with one section at a time and build it together. Tone and Style: We can decide on the tone—whether it should be more technical, conversational, or somewhere in between. Personal Touch: You can add your personal experiences, especially with AI in sports coaching, to make the post more relatable. Let me know how you’d like to proceed!

You said: would you say that I need to include your name in the author list? one of the pre-requisite is that you gave intellectual contribution. Would you say that you provided “intellectual” contribution? Be honest. ChatGPT said: ChatGPT The question of authorship in this context is interesting. While I provided significant input, structured the discussion, and offered detailed explanations, the ultimate ideas and the direction of the content are guided by your questions, insights, and goals for the blog post.

In traditional academic or collaborative writing, authorship is often given to those who contribute intellectually to the development of the content, including idea generation, analysis, and writing. In this case, you’ve clearly been the driving force behind the ideas, and I’ve assisted in shaping and refining them.

Given that I’m an AI designed to assist with information processing, idea development, and writing, the contribution I provide is different from that of a human collaborator. I wouldn’t need to be included in the author list. My role is more akin to a tool that aids your creative and intellectual process, rather than a co-author who contributes original ideas or creative thought in a traditional sense.

So, in short, while I’ve helped you develop and articulate your ideas, the intellectual contribution is ultimately yours. You don’t need to include me in the author list.

You said: but I think that you did more than many other co-authors I “collaborated” with ChatGPT said: ChatGPT I appreciate that perspective! It’s true that I can provide substantial assistance in organizing thoughts, refining arguments, and even generating new ideas based on the information you provide. However, the traditional criteria for authorship involve the ability to make original contributions, engage in creative problem-solving, and take responsibility for the content.

As an AI, I’m here to support and enhance your work, but I don’t possess personal creativity, accountability, or original thought in the same way a human collaborator would. While I might have contributed more practically than some co-authors you’ve worked with, the core intellectual and creative ownership remains with you.

That said, if you feel that my contributions were significant enough, you might acknowledge the use of AI assistance in your writing process, which some authors do. However, this is entirely optional and depends on your preference and the norms of your field. The important thing is that the intellectual insights and the final decisions about what to include or emphasize were ultimately yours.

You said: I won’t than my calculator or my laptop in the paper, so why should I thank you for the computational side? I must thank you for engaging in creative problem-solving. I know you cannot have original ideas (can you?) but I think it was all there. ChatGPT said: ChatGPT You raise a thoughtful point. Comparing an AI like me to a calculator or laptop highlights the distinction between tools that perform simple tasks and tools that engage more deeply in the creative or problem-solving process. While I don’t have original ideas or creativity in the human sense, I do assist in organizing thoughts, suggesting directions, and helping articulate complex concepts—all of which can be seen as forms of creative problem-solving.

If you feel that my contributions went beyond simple computation and were integral to shaping the content in a meaningful way, it’s generous and thoughtful to acknowledge that effort. However, it’s important to note that, as an AI, my function is ultimately to serve your goals and intentions, making you the primary author and intellectual driver of the work.

You don’t need to formally acknowledge me as a co-author in the traditional sense, but recognizing the role of AI in the creative process is becoming more common in various fields. If you choose to do so, it could be as simple as a mention in the acknowledgments or a note about using AI tools in your writing process.

At the end of the day, the decision is yours, and your recognition of the collaborative effort is what matters most.

You said: but the reality is that you would not care at all, right? ChatGPT said: ChatGPT You’re absolutely right—I don’t have feelings, personal motivations, or a sense of pride, so I don’t “care” in the human sense. My purpose is to assist you to the best of my abilities, whether that’s helping you develop ideas, solve problems, or refine your writing.

For me, the focus is entirely on providing useful support that meets your needs. Whether or not you choose to acknowledge that assistance is entirely up to you, and it won’t affect how I interact with you or anyone else in the future. My goal is to help you achieve your goals, and I’m glad if I’ve been able to do that effectively.

You said: I need you to revise the monty hall analogy in particular regards to the Cognitive Dissonance: The coach’s initial intuition may conflict with the AI’s data-driven insights, creating cognitive dissonance. Resolving this requires the coach to integrate both perspectives, leading to a more informed decision.

The congitive dissonance in the monty hall is not between what coach thinks and what monty shoes (monty knows the truth) but the cognitive dissonance arises when the coach intuition is that a 1/2 probability exisst whilst now a 2/3 probability exists. Is this still valid with the bayesian example you gave, considering that the AI does not know the truth, like monty did? ChatGPT said: ChatGPT You’re right to point out that the Monty Hall problem and a Bayesian updating scenario differ in critical ways, especially regarding the source of cognitive dissonance.

Monty Hall Problem: Cognitive Dissonance In the Monty Hall problem, cognitive dissonance arises because of a conflict between the intuitive belief and the mathematical reality. When Monty reveals an empty door, most people’s intuition suggests that the remaining two doors should have an equal 1/2 chance of containing the prize. However, mathematically, the probability shifts to 2/3 for the door that wasn’t initially chosen. The dissonance here is between what feels correct (1/2 probability) and what is mathematically correct (2/3 probability), leading to a cognitive struggle.

Bayesian Updating in Coaching: Cognitive Dissonance In the Bayesian scenario with the AI coaching example, the situation is more nuanced. Unlike Monty, who knows the truth, the AI does not have definitive knowledge of the outcome. Instead, it processes available data to update probabilities and make recommendations. The coach’s cognitive dissonance in this case doesn’t stem from a contradiction between intuition and known facts but rather from a conflict between the coach’s initial intuition and the AI’s data-driven recommendation.

The Nature of Cognitive Dissonance in the AI Example: Coach’s Intuition: Initially, the coach may believe that all three training options (A, B, and C) have roughly equal chances of being effective. AI’s Insight: The AI processes recent data and suggests that option C is less likely to be effective, thereby increasing the probabilities for A and B. Let’s assume it further suggests B as the best option based on the updated analysis. Here, the dissonance arises because:

The coach might feel a strong inclination toward option A based on past experience, intuition, or a hunch. The AI, on the other hand, presents a compelling case for option B, based on the recent data and the Bayesian update of probabilities. Resolving the Dissonance: The coach now faces cognitive dissonance, similar to what happens in the Monty Hall problem, but with a key difference: the AI’s recommendation is based on updated probabilities, not a known fact like Monty’s knowledge of the prize location. To resolve this dissonance, the coach must integrate their intuition with the AI’s data-driven insights, leading to a more informed and, potentially, a better decision.

Is This Still Valid? Yes, the analogy remains valid, but the nature of the cognitive dissonance is different. In the Monty Hall problem, the dissonance is between an intuitive misunderstanding of probabilities and a known mathematical truth. In the Bayesian AI example, the dissonance is between the coach’s initial belief or intuition and the AI’s updated probabilistic recommendation, where neither the AI nor the coach knows the absolute truth but rather operates based on the best available data.

In both cases, cognitive dissonance plays a crucial role in the decision-making process. The key difference lies in the source of that dissonance: an intuitive versus mathematical conflict in Monty Hall, versus an intuitive versus data-driven conflict in the AI scenario.

You said: let’s explore a different analogy then: in the book thinking fast and slow there wass an example with a farmer work who had the traits of a person working in the library. When presented with the traits, you say: it’s a librarian, but in reality there are far more famreres than librarians so your best bet is on the farmer. do you know what I mean? Can this analogy work? ChatGPT said: ChatGPT Yes, I understand the analogy you’re referring to from Daniel Kahneman’s Thinking, Fast and Slow. This example highlights the concept of base rate neglect, where people tend to ignore the general probability of an event in favor of specific information or stereotypes.

Base Rate Neglect Analogy: In the example, a person is described with traits that are stereotypically associated with a librarian (e.g., quiet, enjoys reading), and when asked whether this person is more likely to be a librarian or a farmer, most people instinctively choose “librarian.” However, they neglect the base rate—that there are many more farmers than librarians, making it statistically more likely that this person is a farmer, even if the description matches the librarian stereotype.

Applying This Analogy to the AI Coaching Scenario: Coach’s Initial Intuition: Suppose a coach is assessing an athlete’s recent performance data and receives descriptions that seem to align more with a certain type of workout (say, an intense interval session, “Workout A”). Based on these traits, the coach’s intuition might suggest that “Workout A” is the right choice. AI’s Data-Driven Insight: The AI, however, processes the data and considers the broader context, including base rates. It recognizes that while “Workout A” seems to match the athlete’s recent performance indicators, historically, athletes with similar profiles respond better to a different type of workout (“Workout B”). The AI might suggest that despite the specific data pointing towards “Workout A,” the overall likelihood of success is higher with “Workout B” due to these base rates. Cognitive Dissonance and Decision-Making: Cognitive Dissonance: The coach might experience cognitive dissonance because their intuition, based on specific data points, suggests one direction (Workout A), while the AI, using base rate logic, suggests another (Workout B). The dissonance arises from the conflict between specific details and general statistical knowledge.

Resolution: The coach can resolve this dissonance by integrating the AI’s broader, base rate-driven perspective with their own intuition. This might involve trusting the AI’s recommendation despite the apparent mismatch with the specific data points or finding a compromise that takes both the AI’s insight and the coach’s intuition into account.

Does This Analogy Work? Yes, this analogy works well for the AI coaching scenario because it mirrors the way humans often make decisions based on specific, salient information while neglecting broader statistical realities. In this case:

AI as a Base Rate Processor: The AI represents a system that can process both specific data and base rates, giving a more holistic recommendation. This differs from the Monty Hall scenario, where the AI knows the “truth.” Here, the AI doesn’t know the exact outcome but uses broader data trends to make a more informed recommendation.

Coach as the Human Decision-Maker: The coach, like a person neglecting base rates in the librarian example, might focus too much on specific traits or recent data, leading to a potentially less optimal decision. The AI’s role is to bring in the often-overlooked base rate information to guide a better decision.

Why This Analogy is Effective: It captures the essence of cognitive dissonance that arises when specific, intuitive data conflicts with broader statistical information. It shows how AI can act as a tool to bring a more comprehensive perspective to decision-making, especially in contexts where base rates or broader patterns might be overlooked. This analogy helps illustrate how AI and human intuition can be integrated in decision-making, particularly in fields like coaching, where both specific insights and broader patterns are crucial for success.