Or even just a working cat bot.
@ylecun
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World Models and Planning: A Long-Standing AI Research Call
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Thanks.
My call for world models and planning goes back a long time.
A good example is my NeurIPS 2016 keynote (slide 31 on). https://
drive.google.com/file/d/0BxKBnD
5y2M8NREZod0tVdW5FLTQ
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AI Safety: Engineering Challenge Like Turbojet Safety
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I believe there is as much to worry about AI safety as there is to worry about turbojet safety.
Making turbojets safe and reliable is a very hard engineering problem that is in the capable hands of specialists.
I'm only dismissive of the craziest claims, which should not be taken -
Meta-FAIR and DeepMind Employ Neuroscientists Unlike OpenAI
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Both Meta-FAIR and DeepMind have neuroscientitst and cognitive scientists.
OpenAI doesn't, as far as I know. -
Top AI Labs Race to Add Planning Reasoning Capabilities
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No.
Everyone in top AI research labs is working on giving dialog systems the ability to plan & reason.
There are projects along those lines at Meta-FAIR, DeepMind, and OpenAI, with early results (if you follow the literature).
Q* is just one such project among many. The planning -
AI Systems Learning from Sensory Inputs and Visual Data
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That has been one of my main arguments for years: AI systems need to learn how the world works from sensory inputs (e.g. visual inputs).
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LLMs Struggle With Logical Equivalence in Prompts
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That statement is easily retrived.
But tests have shown that when faced with a situation in which the answer requires to know that B is the same as A, when the prompt clearly says that A is the same as B, the best LLMs often don't answer correctly. -
Human Genome Storage Capacity vs LLM Requirements Analysis
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I'm not.
There just isn't enough capacity in the genome.
Your entire genome fits in 800MB (uncompressed).
The difference between the human and chimp genomes is 1% of that, or 8MB.
Not enough to encode a significant structure.
For comparison, a small 7B LLM requires 14GB. -
Dystopian AI Takeover Narrative Remains Timeless Cliché
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The dystopian fantasy of machines taking over humanity is so old that it's a cliché.
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LLMs lack basic logical reasoning despite massive training data
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Current LLMs are trained on text data that would take 20,000 years for a human to read.
And still, they haven't learned that if A is the same as B, then B is the same as A.
Humans get a lot smarter than that with comparatively little training data.
Even corvids, parrots, dogs,