A blog post challenges readers to solve a math puzzle involving predicting the output of a hypothetical AI model trained on specific numerical sequences. The AI, named "Predictor," is trained on sequences like 1,2,3,4,5 -> 6 and 2,4,6,8,10 -> 12, seemingly learning to extrapolate the next number in simple arithmetic progressions. However, when given the sequence 1,3,5,7,9, the AI outputs 10 instead of the expected 11. The puzzle asks readers to determine the underlying logic of the AI and predict its output for the sequence 1,2,3,5,8. A symbolic prize (bragging rights) is offered to anyone who can crack the code.
The blog post entitled "Can you solve this AI math puzzle and get a prize? (I couldn't)" presents a challenging mathematical problem involving artificial intelligence and seeks solutions from its readership. The puzzle, originating from a competition hosted by the French company Numalis, tasks participants with determining the exact minimal floating-point representation of a specific numerical output generated by a complex AI model. This model, implemented in PyTorch, involves several layers of computations including matrix multiplications and activation functions. The provided code snippet defines the model's architecture and the specific input values, allowing for replication of the numerical output using standard floating-point arithmetic. However, due to the inherent imprecision of floating-point calculations, obtaining the precise minimal representation proves difficult. The author of the post admits their inability to solve the puzzle and expresses hope that a reader might possess the necessary mathematical acumen and computational tools to determine the correct solution. The ultimate goal is to find the shortest string of decimal digits that, when interpreted as an IEEE 754 floating-point number, precisely matches the theoretical output of the AI model, thereby minimizing the representational error. The post emphasizes the difficulty of the problem stemming from the complexities of floating-point arithmetic and the cascading effects of rounding errors throughout the model’s computations. It highlights the importance of such precision in critical applications of AI, where even minor discrepancies can have significant consequences. The post concludes by inviting readers to share their solutions and insights, offering the potential for recognition and, possibly, a prize associated with the original Numalis competition.
Summary of Comments ( 9 )
https://news.ycombinator.com/item?id=43538986
HN users generally found the AI/Math puzzle unimpressive and easily solvable. Several commenters quickly pointed out the solution involves recognizing the pattern as powers of 2, leading to the answer 2^32. Some criticized the framing as an "AI" puzzle, arguing it's a straightforward math problem solvable with basic pattern recognition. Others debated the value of the $100 prize and whether it justified the effort. A few users noted potential ambiguity in the problem's wording, but these concerns were largely dismissed by others who found the intended pattern clear. There was some discussion about the puzzle's suitability for testing AI, with skepticism expressed about its ability to distinguish genuine intelligence.
The Hacker News post titled "AI/Math Puzzle" linking to an article about an unsolved math problem related to AI generated text has a moderate number of comments, sparking a discussion around the puzzle's difficulty, potential approaches, and the nature of the challenge itself.
Several commenters discuss the ambiguity of the problem, particularly focusing on the interpretation of "random" and its implications for solving the puzzle. One commenter suggests the problem is ill-defined because the concept of "random text generated by a large language model" lacks a precise mathematical definition. They argue that without specifying the underlying distribution of the LLM's output, the problem becomes intractable. This point is echoed by other users who highlight that the inherent complexity and evolving nature of LLMs make it challenging to establish a fixed probabilistic framework for analysis.
Another thread of discussion revolves around the computational feasibility of brute-force approaches. Some commenters suggest that the vast search space makes it impractical to solve the puzzle by simply enumerating all possible strings and checking if they satisfy the given conditions. One user proposes a more targeted approach by focusing on shorter strings, arguing that the probability of finding a solution increases with decreasing string length.
A few commenters also touch upon the philosophical implications of the puzzle, pondering the nature of randomness and its relationship to AI-generated text. One user raises the question of whether LLM output can be considered truly random, given its deterministic nature. Another commenter speculates about the potential connection between this problem and other areas of mathematics, such as Kolmogorov complexity.
Finally, some comments express skepticism about the puzzle's originality and significance. One commenter questions whether the problem is genuinely novel or simply a repackaged version of existing mathematical concepts. Another expresses doubt about the practical value of solving the puzzle, suggesting that it may be more of a recreational challenge than a significant scientific endeavor. Despite some negativity, several users express interest in the problem and share ideas for potential solutions, demonstrating the engaging nature of the puzzle.