Elon Musk has launched a new AI company called xAI with the goal of understanding the true nature of the universe. The team at xAI includes AI researchers who have worked at companies like OpenAI, Google, Microsoft and DeepMind. Little is known about xAI currently except that Musk seeks funding from SpaceX and Tesla to start it. The xAI team plans to host a Twitter Spaces discussion on July 14th to introduce themselves. xAI is separate from Musk’s X Corp but will work closely with his other companies like Tesla and Twitter.
Given that all of the existing “AI” models are in fact not intelligent at all, and are basically glorified predictive text… any insights an AI could come up with about the true nature of the universe would likely be like one of those sayings that initially sounds deep and meaningful, but is in fact completely inane and meaningless. Calling it now: it’ll come out with “if you immediately know the candlelight is fire, then the meal was cooked a long time ago”.
There’s a term for that:
Deepity
Thank you! I love that there’s a actually a term for it!
AI coming up with sayings of that type is something already being done ( https://inspirobot.me/ ). Youtube reaction videos exist referring to that site (like “Ai Generates Hilarious Motivational Posters” by jacksepticeye).
Tried it, the quote was “A hermaphrodite can also be a cop”. I’m truly inspired
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To me, what is surprising is that people refuse to see the similarity between how our brains work and how neural networks work. I mean, it’s in the name. We are fundamentally the same, just on different scales. I belive we work exactly like that, but with way more inputs and outputs and way deeper network, but fundamental principles i think are the same.
The difference is that somehow the nets in our brains are creating emergent behaviour while the nets in code, even with a lot more power aren’t. I feel we are probably missing something pivotal in constructing them.
I’m not so sure we’re missing that much personally, I think it’s more just sheer scale, as well as the complexity of the input and output connections (I guess unlike machine learning networks, living things tend to have a much more ‘fuzzy’ sense of inputs and outputs). And of course sheer computational speed; our virtual networks are basically at a standstill compared to the paralellism of a real brain.
Just my thoughts though!