arXiv:2002.06177cs.AI
Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
Linked by 3 identifiable commenters in 3 comments across 3 threads, from Jun 2020 to Aug 2025. Found in 9 HN submissions from Feb 2020 to Nov 2024. Open submission discussions.
> This week I saw not one but three striking premonitions for how the scaling maximalism hypothesis might end. > 1. There might not be enough data in the world to make scaling maximalism work. https://arxiv.org/pdf/2209.12407.pdf > 2. There might not be enough available compute…
For a rather complete view of what deep learning still does not do, I recommend the work of Gary Marcus and Ernest Davis. While seeming solely critical, I think they make very good points about the limits inherent in deep learning as we know it now, and how it needs to grow to…
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A friend of mine works in AI professionally. He told me months ago that it is basically just all a scam and hype to garner investment money. He said the technology and paradigm itself will never lend toward AGI or anything like that. He sent me all these articles geared toward…
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