The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

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 overcome those deficits. "Rebooting AI" https://www.penguinrandomhouse.com/books/603982/rebooting-ai... And a few articles, for audiences both popular and technical: Deep Learning: A Critical Appraisal https://arxiv.org/abs/1801.00631 The Next Decade in AI https://arxiv.org/pdf/2002.06177.pdf How to Build AI We Can Trust https://www.nytimes.com/2019/09/06/opinion/ai-explainability... And a HBR podcast ("Beyond Deep Learning") https://hbr.org/podcast/2019/10/beyond-deep-learning-with-ga...

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 that end as well. https://garymarcus.substack.com/p/seven-replies-to-the-viral... https://substack.com/@cattelainf/note/c-135021342 https://arxiv.org/abs/2002.06177 https://garymarcus.substack.com/p/the-ai-2027-scenario-how-r... https://garymarcus.substack.com/p/25-ai-predictions-for-2025...

> 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 in the world to make scaling maximalism feasible. > 3. Some important tasks might simply not scale. https://arxiv.org/abs/2002.06177 Note: scaling maximalism hypothesis -> "that AGI might arise from larger scale without fundamental new innovation"