# ChatGPT can't multiply, but can AI do math?

TLDRThe video discusses the limitations of AI in performing mathematical tasks, such as multiplication, due to its reliance on statistical predictions rather than understanding. It highlights the use of AI in mathematical research through SAT solvers, which can efficiently solve complex Boolean satisfiability problems, exemplified by the Boolean Pythagorean triples problem. Additionally, the video mentions the application of neural networks by Adam Wagner to find counterexamples in combinatorics, suggesting AI as a valuable tool for mathematicians without replacing them.

### Takeaways

- 🔢 ChatGPT struggles with multiplication due to its predictive nature based on statistical observations rather than true mathematical understanding.
- 📊 The initial and final digits of a multiplication result can be statistically predicted by ChatGPT, but the middle digits are more complex and often incorrect.
- 🧠 AI like ChatGPT doesn't understand the text it generates; it relies on patterns from previously seen data.
- 🔍 AI is currently used in mathematical research, particularly with SAT solvers to determine the Boolean satisfiability of complex sentences.
- 🛠 SAT solvers can handle problems that would take an exponential amount of time to solve by brute force, making them efficient for certain mathematical problems.
- 📚 The Boolean Pythagorean triples problem was resolved using a SAT solver, demonstrating the practical application of AI in mathematical proofs.
- 🤖 While AI tools like SAT solvers are powerful, they require human ingenuity to convert problems into a format they can solve.
- 🔄 Neural networks have been used in pure math research to find counterexamples to conjectures, showing the potential of AI in disproving mathematical hypotheses.
- 📈 The cross entropy method, used with neural networks, can efficiently generate potential counterexamples by training on known data and refining predictions.
- 🧐 AI techniques are not set to replace mathematicians but are likely to become valuable tools that assist in their research, saving time and effort.
- 🚀 The future of AI in mathematics is promising, with the potential to uncover solutions and examples that might be beyond human capacity to find manually.

### Q & A

### Why does ChatGPT fail at multiplication even though computers have been performing this task easily for a long time?

-ChatGPT fails at multiplication because it operates on predictions based on patterns it has seen in text, rather than truly understanding the mathematical operations. It can recognize the start and end digits of a product statistically but struggles with the middle digits, which depend on all input digits and require more complex understanding.

### How does ChatGPT make predictions about the multiplication of two numbers?

-ChatGPT makes predictions by relying on statistical observations from the text it has been trained on. It can predict the likely endings of products based on the last digits of the numbers involved but lacks the ability to accurately determine the middle digits of the product.

### What is a SAT solver and how is it used in mathematical research?

-A SAT solver is a software tool used to solve Boolean satisfiability problems, determining if it's possible to substitute 'true' or 'false' for variables in a sentence to make it evaluate to 'true'. It's used in mathematical research to solve complex problems that can be converted into Boolean sentences, leveraging heuristics and optimizations to handle large numbers of variables efficiently.

### How did a SAT solver contribute to resolving the Boolean Pythagorean triples problem in 2016?

-A SAT solver was used to generate a 68-gigabyte proof that no coloring of positive integers red and blue can prevent all Pythagorean triples from being either all red or all blue. The computation took two days, demonstrating the power of SAT solvers in mathematical proof generation.

### What is the role of AI in the field of pure mathematics research?

-AI, in the form of SAT solvers and neural networks, serves as a tool to assist mathematicians in research. It can help solve specific types of problems, generate counterexamples, and potentially save time by automating the testing of conjectures.

### How can neural networks be used to find counterexamples in combinatorics problems?

-Neural networks can be trained using the cross entropy method to generate graphs or configurations that are likely to be counterexamples to a given conjecture. By iteratively refining the network based on the closest disproving examples, it can eventually find a valid counterexample, if one exists.

### What is the cross entropy method and how does it work in the context of finding counterexamples?

-The cross entropy method is a technique that trains a neural network to predict how to build a graph or configuration for a specific problem. It generates many such configurations, computes which ones are closest to disproving a conjecture, and retrains the network based on those examples, with the aim of gradually finding a counterexample.

### Is AI likely to replace mathematicians in the foreseeable future?

-While AI can be a powerful tool in mathematical research, it is not likely to replace mathematicians during the foreseeable future. The creative and intuitive aspects of mathematics, as well as the ability to formulate and understand complex problems, are still uniquely human.

### What are some limitations of using AI in mathematical research?

-AI has limitations in mathematical research, such as the inability to understand and solve problems that cannot be converted into a form that AI can process, like Boolean sentences or graph configurations. Additionally, AI requires human input to formulate problems and interpret results.

### How can AI techniques complement the work of mathematicians?

-AI techniques can complement the work of mathematicians by automating the testing of conjectures, finding counterexamples, and solving specific types of problems more efficiently. This can save time and allow mathematicians to focus on more creative and conceptual aspects of their research.

### What is the significance of AI in enhancing the efficiency of mathematical problem-solving?

-The significance of AI in enhancing mathematical problem-solving efficiency lies in its ability to handle large datasets, perform complex computations quickly, and potentially identify patterns or solutions that might be overlooked by human researchers.

### Outlines

### 🤖 AI's Limitations in Mathematical Computation

This paragraph discusses the limitations of AI, specifically ChatGPT, in performing multiplication tasks accurately. It explains that while AI can predict the beginning and end digits of a multiplication result based on statistical patterns, it struggles with the middle digits due to the complexity of the calculation. The explanation highlights that AI operates on predictions from text it has seen before rather than a true understanding of the mathematical process.

### Mindmap

### Keywords

### 💡Multiplication

### 💡Statistical Observations

### 💡Language Models

### 💡SAT Solver

### 💡Boolean Satisfiability

### 💡Heuristics

### 💡Combinatorics

### 💡Cross Entropy Method

### 💡Neural Networks

### 💡Counterexamples

### 💡AI in Mathematical Research

### Highlights

ChatGPT struggles with multiplication due to its statistical prediction approach rather than actual understanding.

AI's limitations in understanding the middle digits of multiplication results.

Large language models like ChatGPT make predictions based on patterns in text rather than true comprehension.

AI's current role in assisting mathematicians with research.

Introduction of SAT solvers and their application in solving Boolean satisfiability problems.

Modern SAT solvers' ability to handle sentences with thousands of variables efficiently.

The Boolean Pythagorean triples problem and its resolution using a SAT solver.

The necessity of converting math problems into Boolean sentences for SAT solvers to be effective.

The potential of AI techniques like neural networks in pure math research.

Adam Wagner's use of neural networks to find counterexamples in combinatorics.

The cross entropy method for generating counterexamples to mathematical conjectures.

The process of training a neural net to predict counterexamples and its iterative improvement.

AI's potential to save mathematicians time by disproving false conjectures.

The anticipation of AI becoming another tool for mathematicians, not replacing them.

The exploration of AI techniques in finding examples that might be time-consuming for humans.

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