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Because it's not a revolution if it hasn't massively improved the state of the art. The SotA for robotics is that it can already do non-local power, non-local compute. There are technical reasons as well, but at this late on a Sunday, I don't want to get into them. Ask Chat or something.
Waddle had me nodding along until the reveal was "Instead of using a massive dataset + training, we'll just use something with a massive data that was already trained!!". Talk about missing the trees for the forest. A chance to have an inventive idea squandered by doing what every other AI startup in the world is doing: using someone else's LLM with zero moat.I'd be remiss if I didn't point out the majority of what Waddle is doing was invented/discovered originally in 2022. I don't think that meets the bar of "revolutionary new thing".
When people in the field first started talking about creating an Artificially Intelligent Being, the need to pick apart minutiae of definitions between a "General Intelligence" vs a "Singularity Intelligence" was pointlessly pedantic and not at all relevant. It hasn't become more relevant in the past 30 years either in my opinion. It feels like a pointless wordcell argument to make pointless wordcell definition fights, for pointless wordcell internet debates. The Culture is not a "Singularity Intelligence" either because The Culture is not "AI-GOD", neither are the AIs in Hyperion. Intelligent yes, self aware, with their own agency, absolutely. But considering the whole plot of Hyperion is them getting bamboozled by some time travel + human empathy, with a strong concurrency of actually trying to create an "AI-GOD" to worship, that would point them towards not being an "ASI".
This is always a confusing objection, to the point it feels uncharitable. Spend 5 mins looking in the mirror and think about all the things that you do that are NOT task oriented. Think about all the thoughts, feelings, or other cognitive processes that every human does that have nothing to do with the task their boss gave them. Literally 5 mins thinking about human cognition points to a massive gap between how a programatic/algorithmic intelligence like an AI operates vs how Humans operate. I'll reiterate, AGI is human-level artificially intelligent being. Getting stuck on "general" is some, idk, semantic trip-up.
This is cute, the real number is likely north of 100,000x or above and is non-linear across tasks.
How much training data does a combat medic get when doing field operations on a wounded soldier? A month long course? How much training data would you like to bet it would take a combat medic AI to do that job to the same level of efficiency, with the same level of situational awareness? How much do you think it will cost to collect all of that data? It's easy to give models 10,000x training data when that data is relatively easy to access and buy. Suddenly sample efficiency becomes a massive burden on any real-practical ML model. Making it sound like such a simple thing is annoying. It comes across as handwaving the actual hard problem.
Paying hundreds of millions of dollars to create datasets specifically for training ML models on math + reasoning. Naturally-available data did run out, but money didn't, and the frontier labs were able to prove the business case for spending obscene amounts of money creating new data.
If synthetic data is such a solved problem then how come the Sim2Real gap still exists, is unsolved, and is the target of plenty of research dollars? I feel like you are miscommunicating something here, or misunderstanding what "synthetic data" means in ML terminology.
Huh? It literally does take millions of samples to train a LLM are you arguing elsewise? And it is impractical for areas where millions of samples do not exist. This argument/objection you are making makes no sense. Please elaborate.
Analogical reasoning - Ever heard a sports analogy applied to a non-sports topic. Did that analogy help convey a better intuitive understanding of some facet? Did that analogy allow someone who has never done that non-sports topic a better starting point, or better performance before being given that analogy? Such is the power of analogical reasoning, or the ability to convey how one statistical distribution is similar along a particular latent axis to another statistical distribution, with the purpose of using the already learned statistical distribution for performance improvements on the new unknown distribution.
Yes such sci-fi talk much wow, if only we could solve FTL then we can truly conquer the stars. Obviously FTL is a forgone conclusion, it's so trivially simple to solve, leave it to the shape-rotators. Let's get back to planning galactic expansion or galactic political organizations.... I pointed out the challenge on RSI on a different post below. It's not "simply reduce".
Then why try to create new terminology? It's an good, useful AI model, nothing more is needed, no new terms needed. AGI means what it has always meant, the semantic definition isn't being pushed to the stratosphere so that we can classify Astra as AGI and win our internet arguments and secure more VC dollars.
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