I have to say, I agree 90% with Jon on this. Which is significantly less than I usually agree with him.
I think he could have talked more about the lack of reliability of AI. It's not simply a drop in replacement for people like the invention of the conveyor belt or sewing machine. A better analogy would be the mass outsourcing of call center jobs to South Asia.
A better analogy would be the mass outsourcing of call center jobs to South Asia.
Well that's where it's at now. There's no guarantee it will stay that way. Give Moore's law several more cycles, and maybe we'll have enough computing power to make drop in replacement humans.
I think people are misinformed about the current readiness of AI specifically because Silicon Valley VCs have taken a lot of the R&D funding market share from the DARPA government types.
VC funding decisions are heavily oriented around the prototype product demo. (No grant writing!). This encourages "fake it till you make it": demo a fake product to get the funding to build the real one. This stuff does leak out to the public, and you end up with overstated capabilities.
Give Moore's law several more cycles, and maybe we'll have enough computing power to make drop in replacement humans.
There seems to be a misunderstanding of how LLM's and statistical modelling work. Neither of these can solve their accuracy as they operate based on a probability distribution and only find correlations in ones and zeros. LLM's generate the probability distribution internally, without supervision (a "black box"). They're only as "smart" as the human-generated input data, and will always find false positives and false negatives. This is unavoidable. There simply is no critical thought or intelligence whatsoever — only mimicry.
I'm not saying LLM's won't shakeup employment, find their niche, and make many jobs redundant, or that critical general AI advances won't occur, just that LLM's simply can't replace human decision making or control, and doing so is a disaster waiting to happen — the best they can do is speed up certain tasks, but a human will always be needed to determine if the results make (real world) sense.
Give Moore’s law several more cycles, and maybe we’ll have enough computing power
If it were only a matter of processing power, we’d already be able to demonstrate much more capable AIs. More computing power in more places will facilitate further development, but it’s the “further development” that’s key.
Personally, I’m looking for Moore’s Law to make home AIs more responsive and more similar to today’s cloud-based AIs.
The one I have configured is slow and not very good, but it’s running on a Raspberry Pi, so I could throw more processing at it and probably will at some point.
there was an Apple announcement several weeks ago about optimizing performance on memory-constrained devices, that has me really hopeful for effective home-based devices soon. I don’t know what Apples “neural processors” do but I know my phone has them and maybe they apply here
I mean, he isn't wrong that it will be used to fire people and to decimate labour. In fact I don't think he really said anything "wrong". He just didn't paint as complete a picture as I would have liked.
To be fair, it's not straightforward to explain on a comedy show the nuanced problems inherent in trying to replace people with token prediction engines.
As with every big technological advancement, the powerful rush to consolidate their control over it and prioritize how it can benefit them over how it can benefit society at large.