The post "Questioning Representational Optimism in Deep Learning" challenges the prevailing belief that deep learning's success stems from its ability to learn optimal representations of data. It argues that current empirical evidence doesn't definitively support this claim and suggests focusing instead on the inductive biases inherent in deep learning architectures. These biases, such as the hierarchical structure of convolutional networks or the attention mechanism in transformers, might be more crucial for generalization performance than the specific learned representations. The post proposes shifting research emphasis towards understanding and manipulating these biases, potentially leading to more robust and interpretable deep learning models.
Upgrading a large language model (LLM) doesn't always lead to straightforward improvements. Variance experienced this firsthand when replacing their older GPT-3 model with a newer one, expecting better performance. While the new model generated more desirable outputs in terms of alignment with their instructions, it unexpectedly suppressed the confidence signals they used to identify potentially problematic generations. Specifically, the logprobs, which indicated the model's certainty in its output, became consistently high regardless of the actual quality or correctness, rendering them useless for flagging hallucinations or errors. This highlighted the hidden costs of model upgrades and the need for careful monitoring and recalibration of evaluation methods when switching to a new model.
HN commenters generally agree with the article's premise that relying solely on model confidence scores can be misleading, particularly after upgrades. Several users share anecdotes of similar experiences where improved model accuracy masked underlying issues or distribution shifts, making debugging harder. Some suggest incorporating additional metrics like calibration and out-of-distribution detection to compensate for the limitations of confidence scores. Others highlight the importance of human evaluation and domain expertise in validating model performance, emphasizing that blind trust in any single metric can be detrimental. A few discuss the trade-off between accuracy and explainability, noting that more complex, accurate models might be harder to interpret and debug.
The blog post explores the potential downsides of using polynomial features in machine learning, particularly focusing on their instability in high dimensions. While polynomial expansion can improve model fit by capturing non-linear relationships, it can also lead to extreme sensitivity to input changes, causing wild oscillations and poor generalization. The author demonstrates this issue with visualizations of simple polynomials raised to high powers and illustrates how even small perturbations in the input can drastically alter the output. They suggest Bernstein polynomials as a more stable alternative, highlighting their properties like non-negativity and partition of unity, which contribute to smoother behavior and better extrapolation. The post concludes that while polynomial features can be beneficial, their inherent instability requires careful consideration and potentially exploration of alternative basis functions like Bernstein polynomials.
HN users discuss potential downsides of polynomial features, particularly in the context of overfitting and interpretability issues. Some argue against their broad categorization as "evil," suggesting they can be valuable when applied judiciously and with proper regularization techniques. One commenter points out their usefulness in approximating non-linear functions and highlights the importance of understanding the underlying data and model behavior. Others discuss alternatives like splines, which offer more local control and flexibility, and the role of feature scaling in mitigating potential problems with polynomial features. The trade-off between complexity and interpretability is a recurring theme, with commenters emphasizing the importance of selecting the right tool for the specific problem and dataset.
"Understanding Machine Learning: From Theory to Algorithms" provides a comprehensive overview of machine learning, bridging the gap between theoretical principles and practical applications. The book covers a wide range of topics, from basic concepts like supervised and unsupervised learning to advanced techniques like Support Vector Machines, boosting, and dimensionality reduction. It emphasizes the theoretical foundations, including statistical learning theory and PAC learning, to provide a deep understanding of why and when different algorithms work. Practical aspects are also addressed through the presentation of efficient algorithms and their implementation considerations. The book aims to equip readers with the necessary tools to both analyze existing learning algorithms and design new ones.
HN users largely praised Shai Shalev-Shwartz and Shai Ben-David's "Understanding Machine Learning" as a highly accessible and comprehensive introduction to the field. Commenters highlighted the book's clear explanations of fundamental concepts, its rigorous yet approachable mathematical treatment, and the helpful inclusion of exercises. Several pointed out its value for both beginners and those with prior ML experience seeking a deeper theoretical understanding. Some compared it favorably to other popular ML resources, noting its superior balance between theory and practice. A few commenters also shared specific chapters or sections they found particularly insightful, such as the treatment of PAC learning and the VC dimension. There was a brief discussion on the book's coverage (or lack thereof) of certain advanced topics like deep learning, but the overall sentiment remained strongly positive.
The article argues that integrating Large Language Models (LLMs) directly into software development workflows, aiming for autonomous code generation, faces significant hurdles. While LLMs excel at generating superficially correct code, they struggle with complex logic, debugging, and maintaining consistency. Fundamentally, LLMs lack the deep understanding of software architecture and system design that human developers possess, making them unsuitable for building and maintaining robust, production-ready applications. The author suggests that focusing on augmenting developer capabilities, rather than replacing them, is a more promising direction for LLM application in software development. This includes tasks like code completion, documentation generation, and test case creation, where LLMs can boost productivity without needing a complete grasp of the underlying system.
Hacker News commenters largely disagreed with the article's premise. Several argued that LLMs are already proving useful for tasks like code generation, refactoring, and documentation. Some pointed out that the article focuses too narrowly on LLMs fully automating software development, ignoring their potential as powerful tools to augment developers. Others highlighted the rapid pace of LLM advancement, suggesting it's too early to dismiss their future potential. A few commenters agreed with the article's skepticism, citing issues like hallucination, debugging difficulties, and the importance of understanding underlying principles, but they represented a minority view. A common thread was the belief that LLMs will change software development, but the specifics of that change are still unfolding.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=44038549
Hacker News users discussed the linked GitHub repository, which explores "representational optimism" in deep learning. Several commenters questioned the core premise, arguing that the examples presented didn't convincingly demonstrate a flaw in deep learning itself, but rather potential issues with specific model architectures or training data. Some suggested that the observed phenomena might be explained by simpler mechanisms, such as memorization or reliance on superficial features. Others pointed out the limitations of using synthetic datasets to draw conclusions about real-world performance. A few commenters appreciated the author's effort to investigate potential biases in deep learning, but ultimately felt the presented evidence was inconclusive. There was also a short discussion on the challenges of interpreting the internal representations learned by deep learning models.
The Hacker News post titled "Questioning Representational Optimism in Deep Learning" (linking to a GitHub repository discussing the phenomenon) sparked a brief but insightful discussion with a few key comments.
One commenter questioned the novelty of the observation, pointing out that the tendency of deep learning models to latch onto superficial features (like textures over shapes) has been known for some time. They referred to "shortcut learning" as the established term for this phenomenon, highlighting prior research and discussions around this topic. This comment essentially challenges the framing of the linked GitHub repository as presenting a new discovery.
Another commenter delved into the practical implications, suggesting that this reliance on superficial cues contributes to the brittleness of deep learning models. They argued that this explains why these models often fail to generalize well to out-of-distribution data or slight perturbations in input. This comment connects the "representational optimism" discussed in the repository to the real-world challenges of deploying deep learning models reliably.
A third comment provided a concise summary of the core issue, stating that deep learning models often prioritize easily learnable features even when they are not robust or semantically meaningful. This comment reinforces the main point of the repository in simpler terms.
The discussion also briefly touched upon the potential role of data augmentation techniques in mitigating this problem. One commenter suggested that augmentations could help models learn more robust features by exposing them to a wider range of variations in the training data.
While the discussion is relatively short, these comments offer valuable perspectives on the limitations of deep learning and the ongoing challenges in making these models more robust and reliable. They highlight the known issue of shortcut learning and its practical consequences, raising questions about the long-term viability of current deep learning approaches if these issues are not addressed.