The article discusses a viral tweet claiming that large language models (LLMs) consistently choose the number 7 when asked to pick a number between 1 and 10, attributing this to gradient descent. It argues that the underlying mathematics is more nuanced than the tweet suggests, offering a deeper exploration of the phenomenon. The piece appears on Towards Data Science and examines the interplay between LLM behavior and mathematical principles.
The article titled "Ten Is Not a Hundred" discusses how the number ten can mislead hallucination detectors. It highlights a specific instance where this numerical confusion caused errors in automated systems. The piece was originally published on Towards Data Science.
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .
The article describes a small NumPy reproduction of subliminal learning, demonstrating how a neural network can learn to read digits from pure noise. It provides a concise implementation and a brief argument explaining why this phenomenon occurs.
The article reports on a benchmark where the author evaluated TypeSafe AI’s Jev against large language models (LLMs) across 3,080 classification tasks, comparing metrics such as accuracy, latency, calibration, and confidence. It examines whether Jev can serve as a practical decision layer for AI systems, effectively moving AI from merely generating outputs to making informed decisions.
The article examines the distribution of logits from open AI models, both base and instruction-tuned, to determine how many guesses are required to crack an AI’s supposedly random number. By converting the logits into bits, the author quantifies the predictability of the models’ outputs. The analysis highlights the gap between perceived randomness and actual statistical patterns in AI-generated numbers.
The article examines the performance of AI models on a 2,000-question test, finding that the top two models were indistinguishable at the conventional 5% significance level. When the test was reduced to 200 questions, a different model would have claimed first place roughly three times out of ten trials. The piece questions the reliability of declaring a single model as the best based on limited testing.
From one gradient to every gradient The post Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works appeared first on Towards Data Science .
The article examines the practice of routing requests to different large language models (LLMs) during a single session. It reports that switching models mid‑session can negate the expected cost savings from using cheaper inference options. The author conducted tests to illustrate how these switches impact overall expenses.
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
The article explains how LoRA fine‑tuning addressed an under‑labeling issue in the SigLip model. It outlines that whether this approach is suitable depends on three specific questions. The post provides guidance on evaluating the appropriateness of fine‑tuning for your own use case.
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .