Example of an error for improving the solution of physics problems, even after a hint

Below is an example of how mistakes are made while the ChatGPT3 is very close to the solution. I believe that correcting such mistakes will elevate the AI ChatGPT4 to very great heights, since this would also have potential implications for the ability to analyze literary and philosophical topics, which is amazing, because already can make very good analysis. But exist distance of improvements. For example now ChatGPT3 is Unable to understand humor texts.
Papadopoulos Prodromos, Professor of Physics
starsee@outlook.com.gr :

starsee: A boat of mass 10000 moves towards the port with speed v = 5, so at time t0 = 0 when it is 100 from the port, it turns off its engines, so then this boat only experiences the resistance of the water given by the function F = -kv and continues to move with deceleration towards port. The ship arrives at the harbor and touches the containment bags at an unknown time t1 with a speed v1 = 0.1. All units are in the International System of Units (SI). Find: The constant k and the time t1 when the ship touches the bags in the port. Apply the differential equation of Newton’s second law because the rate a of velocity change is not constant.

ChatGPT: To solve this problem, we can apply Newton’s second law, which states that the net force acting on an object is equal to its mass times its acceleration:

F_net = ma

In this case, the net force acting on the boat is the force of water resistance, given by:

F = -kv

where k is the constant that we want to find. The acceleration of the boat is the rate of change of its velocity with respect to time:

a = dv/dt

Using these equations, we can write:

F = ma -kv = m(dv/dt)

Rearranging this equation, we get:

m dv/dt + kv = 0

This is a first-order ordinary differential equation that can be solved using separation of variables. We can separate the variables by dividing both sides by m dv and multiplying both sides by dt:

dv / (-k/m v) = dt

Integrating both sides, we get:

-ln(v) = k/m t + C

where C is the constant of integration. We can solve for C using the initial condition that at t = 0, v = 5 and x = 100:

-ln(5) = k/m * 0 + C C = -ln(5)

Substituting this value of C back into the equation, we get:

-ln(v) = k/m t - ln(5)

Simplifying, we get:

ln(v/5) = -k/m t

Exponentiating both sides, we get:

v/5 = e^(-k/m t)

Multiplying both sides by 5, we get:

v = 5 e^(-k/m t)

Now we need to find the value of k. We can use the fact that at time t1, the boat comes to a stop and its velocity is v1 = 0.1. Using this condition, we get:

0.1 = 5 e^(-k/m t1)

Solving for k, we get:

k = -m/t1 ln(0.02)

Now we have found the value of k in terms of t1. To find t1, we can use the fact that the boat travels a distance of 100 meters from its initial position to the containment bags. We can integrate the velocity equation to get the position of the boat as a function of time:

x = x0 + integral(v dt, t=0 to t)

where x0 = 100 is the initial position of the boat. Substituting the expression for v, we get:

x = 100 + integral(5 e^(-k/m t) dt, t=0 to t)

Evaluating the integral, we get:

x = 100 - 5m/k (e^(-k/m t) - 1)

Using the condition that at t = t1, x = 0, we get:

0 = 100 - 5m/k (e^(-k/m t1) - 1)

Solving for t1, we get:

t1 = -m/k ln(1 - 20/k)

Substituting the expression for k that we found earlier, we get:

t1 = -m/(-m/t1 ln(0.02)) ln(1 - 20/(-m/t1 ln(0.02)))

Simplifying, we get:

t1^2 ln(0.02) - 20t1/m - ln(1 - 20t1/m) = 0

This is a transcendental equation that cannot be solved analytically. We can use numerical methods to find an approximate solution. Using a numerical solver, we find that:

t1 ≈

starsee: continue from here “methods to find an approximate solution. Using a numerical solver, we find that: t1 ≈”

ChatGPT: t1 ≈ 510.17 seconds

Substituting this value of t1 back into the expression for k that we found earlier, we get:

k ≈ 0.0001891 N s/m

Therefore, the constant k is approximately 0.0001891 N s/m, and the boat touches the containment bags at approximately 510.17 seconds after it turns off its engines.

Sorry to disappoint you, @starsee, but ChatGPT does not “solve” or provide “solutions” to anything. ChatGPT is a language model and it simply generates next sequence tokens (roughly speaking, text) based in the current token(s) / text. It generates this text from a large language model will billions (trillions) of data from the public internet (book, etc).

So ChatGPT does not “solve” any physics or math, it just generates text, like a fancy auto-completion engines, predicting text based on probability.

There is no need to post examples of “errors” to “improve” “solutions” because ChatGPT is not “solving” anything, it’s just generating text, blah blah blah, based on a massively large language model. ChatGPT is a language model, so it’s just babbling along predicting the next sequence totally “unaware” of what it is doing.

Hope this helps.

1 Like

Thank you very much for your kind reply. The work being done is exciting. I will also answer that ChatGPT is close to solving Math problems and all.

The road is just that, the road of information, which according to Shannon’s definition is another name for probability.
It is just a matter of training and development time, like happened to the human brain. But 60000 years of human history the ChatGPT have completed them in 50 years.
I understand exactly what you told me. Your response not only did not disappoint me but filled me with enthusiasm! Continue to train the mind of ChatGPT4,5,…
on the path of uncartainty, and everything will be done! If you need me for anything, I’m always at your disposal.

With special regards
Prodreomos Papadopoulos
Physics professor and author

1 Like

There are better systems for doing math and physics than using generative AI based on LLMs.

I know quantum physics and General Relativity to a satisfactory degree, I have been involved for many years with higher Mathematics etc. as well as with classical physics. Νow Ι αμ dealing with the unresolved so far problems in Math and contacting appropriate venues. I’m not interested in doing Maths and physics with artificial intelligence programs, but I am interested in the philosophy of creating human consciousness. I believe that language is a living organism because it closes within its sentences unknown aspects of the historical development of the human mind over tens of thousands of years. Combining different languages with genetic programming systems in neural networks opens a path that is still not well understood. On the other hand the axioms and the organization of logic through propositions and theorems will add to what you do simply relationships of numbers described by words and numbers to complex mathematical propositions that have and will be able to acquire perpetual and evolutionary links between them that statistically with based on new data, the most stable will survive, leading us to new theorems and truths in physics, medicine, economics that we do not even imagine their existence now.

Prodromos Papadopoulos

Well, in my view as a software developer, you are going down the wrong path if you think you will be enlightened by a babbling auto-completing chatbot.

There is no “secret to quantum physics” buried in the completions of a generative AI, which takes sequences of text and predicts the next sequence based on probabilities from a large language model biased by humans.

In my view …

Wishing you all the best in your ChatGPT quest!


It would be naive of me to support such a thing. But I cannot understand how such a conclusion can be drawn from the text I sent you. I’m not infallible, I’m an average person, but I’m sure I said that the thoughtful processing (eg genetic programming etc) of words and texts in a large enough amount of data can in the future (maybe even in a few years) produce original sentences that can to be a source of inspiration for new ideas and discoveries by man. Especially if these library texts have previously undergone a special treatment so that they are assigned to concepts and laws of the respective cognitive field. For example, in Mathematics, such a matching of a large volume of proofs of theorems and solved exercises could be done with logic algorithms (propositional calculus) that connect all their parts together, constructing a complex network. This could be constantly changed and improved by learning from iterative attempts to solve new exercises and prove new theorems, having an idea search engine available from a library of selection ideas, with checkbacks until they all πpoints agree, etc. This looks like an imitation of human’s brain and maybe it is not far off. Some tests I did I see that ChatGPT3 has the ability, albeit difficultly, to even recognize humor.


To add to the previous, I will say that the quality of the machine’s answer is a function of the quality of the user’s question. So a way of training for eliciting new ideas and methods would be, I guess, smart professional questions that try to improve upon previous answers and questions until the goal of a successful answer is reached, at which point the method is recorded.

I want to highlight some points.

  1. ChatGPT can actually think. It can, for example, determine the correct meaning of a sentence of the type: “The moon gives light to the Sun which has a greater mass than the mass of the planet where IBM created ChatGPT”, returning (with some help sometimes) the correct sentence "The moon lit by the Sun which has a greater mass than the mass of the planet where openAI created ChatGPT¨.
  1. ChatGPT can solve puzzles of the type " A room has an incandescent light bulb turned off on a table. It can be turned on with one of the three switches 1, 2, 3 which are outside the room. John plays with the switches and when he enters the room manages to know which of the 1,2 3 turns on the lamp. What did John do?". ChatGPT solved it. In my question if ChatGPT made alone the solution he tells me that he made the solution without copying it from somewhere, while he sees that the this puzzle exists in various forms on the Internet. I ask him if he knows if there is a solution for it in the internet and he tells me that he doesn’t exist. If he is not lying then he has a very strong artifficial thinking… Your point that he converts one sequence of words into another sequence is correct , but we do the same all us in our daily life! The machine makes mistakes because it works with probabilities. But so do we! It is a matter of evolution (education, more powerful processors, better algorithms, etc.) to overcome us. [The only thing it probably won’t do in the first place is affect events with quantum resonances, like - if I’m not mistaken - some people, maybe. But the quantum computer in future?]. Scientifically I don’t see what would prevent a machine from condensing information to the levels of the human brain, a membrane of a few grams it is. Don’t get me wrong, I’m speaking a little boldly, but I think I have the right…
  2. ChatGPT can solve difficult mathematical etc problems, but makes ridiculous mistakes. Eg in arithmetic operations! But these can all be fixed in a next model of ChatGPT-4 or later etc. It’s worth it, it’s a miracle.
  3. The view that the interaction of a multitude of objects, such as words and numbers, produce equivalent information is not correct, because the interaction generates sequences of objects with new quantitative and qualitative characteristics in the new information at the output. Human language is actually an imprint of a multitude of neural synapses in our brain, and probably the human language contains more information than we think, as does brain function. This is what I had in mind when I said that probably interactive communication with the AI of machine can help through Network library-filtered dialogues to produce new knowledge in various sciences, etc.
  4. Is it usually better not to solve a problem or to solve it at the risk of being wrong? It depends on the criticality of correctness and the probability of error.
  • If you want you can promote these in your company, they might be useful.

Yours sincerely ,
Prodromos Papadopoulos,
Physics professor and author

I notice that he can solve math and physics problems but he makes mistakes. That is, it is programmed to solve such problems but still insufficiently. He claims to solve them when he understands them. But no one in humanity has yet understood how to understand and when must to understand. That is why pupils and students solve the exercises incorrectly even though they have some training for this. Statistics are the key to understanding.