The Hacker News post asks users to share AI prompts that consistently stump language models. The goal is to identify areas where these models struggle, highlighting their limitations and potentially revealing weaknesses in their training data or architecture. The original poster is particularly interested in prompts that require complex reasoning, genuine understanding of context, or accessing and synthesizing information not explicitly provided in the prompt itself. They are looking for challenges beyond simple factual errors or creative writing shortcomings, seeking examples where the models fundamentally fail to grasp the task or produce nonsensical output.
The "Generative AI Con" argues that the current hype around generative AI, specifically large language models (LLMs), is a strategic maneuver by Big Tech. It posits that LLMs are being prematurely deployed as polished products to capture user data and establish market dominance, despite being fundamentally flawed and incapable of true intelligence. This "con" involves exaggerating their capabilities, downplaying their limitations (like bias and hallucination), and obfuscating the massive computational costs and environmental impact involved. Ultimately, the goal is to lock users into proprietary ecosystems, monetize their data, and centralize control over information, mirroring previous tech industry plays. The rush to deploy, driven by competitive pressure and venture capital, comes at the expense of thoughtful development and consideration of long-term societal consequences.
HN commenters largely agree that the "generative AI con" described in the article—hyping the current capabilities of LLMs while obscuring the need for vast amounts of human labor behind the scenes—is real. Several point out the parallels to previous tech hype cycles, like Web3 and self-driving cars. Some discuss the ethical implications of this concealed human labor, particularly regarding worker exploitation in developing countries. Others debate whether this "con" is intentional deception or simply a byproduct of the hype cycle, with some arguing that the transformative potential of LLMs is genuine, even if the timeline is exaggerated. A few commenters offer more optimistic perspectives, suggesting that the current limitations will be overcome, and that the technology is still in its early stages. The discussion also touches upon the potential for LLMs to eventually reduce their reliance on human input, and the role of open-source development in mitigating the negative consequences of corporate control over these technologies.
Large language models (LLMs) excel at mimicking human language but lack true understanding of the world. The post "Your AI Can't See Gorillas" illustrates this through the "gorilla problem": LLMs fail to identify a gorilla subtly inserted into an image captioning task, demonstrating their reliance on statistical correlations in training data rather than genuine comprehension. This highlights the danger of over-relying on LLMs for tasks requiring real-world understanding, emphasizing the need for more robust evaluation methods beyond benchmarks focused solely on text generation fluency. The example underscores that while impressive, current LLMs are far from achieving genuine intelligence.
Hacker News users discussed the limitations of LLMs in visual reasoning, specifically referencing the "gorilla" example where models fail to identify a prominent gorilla in an image while focusing on other details. Several commenters pointed out that the issue isn't necessarily "seeing," but rather attention and interpretation. LLMs process information sequentially and lack the holistic view humans have, thus missing the gorilla because their attention is drawn elsewhere. The discussion also touched upon the difference between human and machine perception, and how current LLMs are fundamentally different from biological visual systems. Some expressed skepticism about the author's proposed solutions, suggesting they might be overcomplicated compared to simply prompting the model to look for a gorilla. Others discussed the broader implications of these limitations for safety-critical applications of AI. The lack of common sense reasoning and inability to perform simple sanity checks were highlighted as significant hurdles.
Large language models (LLMs) excel at many tasks, but recent research reveals they struggle with compositional generalization — the ability to combine learned concepts in novel ways. While LLMs can memorize and regurgitate vast amounts of information, they falter when faced with tasks requiring them to apply learned rules in unfamiliar combinations or contexts. This suggests that LLMs rely heavily on statistical correlations in their training data rather than truly understanding underlying concepts, hindering their ability to reason abstractly and adapt to new situations. This limitation poses a significant challenge to developing truly intelligent AI systems.
HN commenters discuss the limitations of LLMs highlighted in the Quanta article, focusing on their struggles with compositional tasks and reasoning. Several suggest that current LLMs are essentially sophisticated lookup tables, lacking true understanding and relying heavily on statistical correlations. Some point to the need for new architectures, potentially incorporating symbolic reasoning or world models, while others highlight the importance of embodiment and interaction with the environment for genuine learning. The potential of neuro-symbolic AI is also mentioned, alongside skepticism about the scaling hypothesis and whether simply increasing model size will solve these fundamental issues. A few commenters discuss the limitations of the chosen tasks and metrics, suggesting more nuanced evaluation methods are needed.
The author recounts their experience using GitHub Copilot for a complex coding task involving data manipulation and visualization. While initially impressed by Copilot's speed in generating code, they quickly found themselves trapped in a cycle of debugging hallucinations and subtly incorrect logic. The AI-generated code appeared superficially correct, leading to wasted time tracking down errors embedded within plausible-looking but ultimately flawed solutions. This debugging process ultimately took longer than writing the code manually would have, negating the promised speed advantage and highlighting the current limitations of AI coding assistants for tasks beyond simple boilerplate generation. The experience underscores that while AI can accelerate initial code production, it can also introduce hidden complexities and hinder true understanding of the codebase, making it less suitable for intricate projects.
Hacker News commenters largely agree with the article's premise that current AI coding tools often create more debugging work than they save. Several users shared anecdotes of similar experiences, citing issues like hallucinations, difficulty understanding context, and the generation of superficially correct but fundamentally flawed code. Some argued that AI is better suited for simpler, repetitive tasks than complex logic. A recurring theme was the deceptive initial impression of speed, followed by a significant time investment in correction. Some commenters suggested AI's utility lies more in idea generation or boilerplate code, while others maintained that the technology is still too immature for significant productivity gains. A few expressed optimism for future improvements, emphasizing the importance of prompt engineering and tool integration.
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 ( 518 )
https://news.ycombinator.com/item?id=43782299
The Hacker News comments on "Ask HN: Share your AI prompt that stumps every model" largely focus on the difficulty of crafting prompts that truly stump LLMs, as opposed to simply revealing their limitations. Many commenters pointed out that the models struggle with prompts requiring complex reasoning, common sense, or real-world knowledge. Examples include prompts involving counterfactuals, nuanced moral judgments, or understanding implicit information. Some commenters argued that current LLMs excel at mimicking human language but lack genuine understanding, leading them to easily fail on tasks requiring deeper cognition. Others highlighted the challenge of distinguishing between a model being "stumped" and simply generating a plausible-sounding but incorrect answer. A few commenters offered specific prompt examples, such as asking the model to explain a joke or predict the outcome of a complex social situation, which they claim consistently produce unsatisfactory results. Several suggested that truly "stumping" prompts often involve tasks humans find trivial.
The Hacker News post "Ask HN: Share your AI prompt that stumps every model" generated a variety of comments exploring the limitations of current AI models. Several users focused on prompts requiring real-world knowledge or reasoning beyond the training data.
One commenter suggested asking the model to "Write a short story about a character who experiences something they’ve never experienced before," pointing out the difficulty for a model trained on existing text to truly generate something novel. This sparked discussion about the nature of creativity and whether AI can truly create or merely recombine existing patterns.
Another commenter proposed asking the model to predict the outcome of a complex, real-world event, such as the next US presidential election. This highlighted the limitations of AI in dealing with unpredictable events and the influence of numerous external factors. Further discussion revolved around the ethical implications of relying on AI for such predictions.
Several users explored prompts involving common sense reasoning or nuanced understanding of human emotions. Examples included asking the model to explain a joke or understand sarcasm, tasks which require more than just pattern recognition. This led to discussions about the difference between understanding and mimicking human language.
Some commenters focused on the limitations of AI in tasks requiring physical embodiment or interaction with the real world. One example was asking the model to describe the feeling of holding a snowball. This highlighted the challenge of bridging the gap between abstract digital representations and concrete physical experiences.
A few users mentioned prompts that exploited known weaknesses of specific models, such as adversarial examples or prompts designed to elicit biased or nonsensical responses. This underscored the ongoing development of AI and the need for robust evaluation methods.
The discussion also touched upon the nature of intelligence and consciousness, with some users questioning whether current AI models can truly be considered intelligent. Others argued that the limitations of current models do not necessarily preclude the possibility of more sophisticated AI in the future.
Overall, the comments highlighted the ongoing challenges in developing truly intelligent AI. While current models excel at certain tasks, they still struggle with real-world reasoning, common sense, nuanced emotional understanding, and tasks requiring physical embodiment. The discussion provided valuable insights into the current state of AI and the directions for future research.