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Deep Learning Is Hitting a Wall
For real intelligence you also need reasoning, language, and analogy, none of which is nearly so well handled by current technology.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
The third contributor to the AI Chasm is what we call the robustness gap. Time and again we have seen that once people in AI find a solution that works some of the time, they assume that with a little more work (and a little more data) it will work all of the time.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
Start by developing systems that can represent the core frameworks of human knowledge: time, space, causality, basic knowledge of physical objects and their interactions, basic knowledge of humans and their interactions. Embed these in an architecture that can be freely extended to every kind of knowledge, keeping always in mind the central tenets
... See moreErnest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
o, alternative pathways to building Type-2 reasoning-capable AI systems, likely using neurosymbolic approaches, have become much more attractive. People like Gary Marcus have argued for neurosymbolic approaches for decades. Such approaches combine the pattern recognition of neural nets, like LLMs, with symbolic reasoning’s logic and rules. Vinod Kh... See more
Azeem Azhar • 🧠 AI’s $100bn question: The scaling ceiling
good as deep learning at learning from data, and language is too complex to encode everything you would need strictly by hand. Classical AI systems often use templates. For example, the template [PLACE1 is DISTANCE from PLACE2] could be matched against the sentence The moon is 240,000 miles from the earth, and used to identify this as a sentence th
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