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LeCun points to four essential characteristics of human intelligence that current AI systems, including LLMs, can’t replicate: reasoning, planning, persistent memory, and understanding the physical world. He stresses that LLMs’ reliance on textual data severely limits their understanding of reality: “We’re easily fooled into thinking they are intel... See more
Azeem Azhar • 🧠 AI’s $100bn question: The scaling ceiling
LLMs struggle with challenges like the compositionality gap (Measuring and Narrowing the Compositionality Gap in Language Models by Ofir Press and colleagues; 2023). This means LLMs cannot connect inferences or adapt responses to new situations. Overcoming these obstacles requires augmenting LLMs with techniques that add true comprehension.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
The chief challenge, as Lee sees it, is how to make AI smarter without just throwing more data and computing power at it. His hope rests on the iterative tweaking of algorithms that improve the performance of AI at a “geometric pace.”
noemamag.com • Will AI Bring Plentitude or Further Imperil the Planet? | NOEMA
that a lot of what you need to know to interpret the diagram is not explicit, and machines don’t know how to deal with what isn’t made explicit.