Comment on Google has sent internet into ‘spiral of decline’, claims DeepMind co-founder
Aceticon@lemmy.world 11 months agoThat’s what’s usually gets said about lots of alternative fusion energy generation methods that later turn out to be impossible to have net-positive energy generation.
And this is just one example. Another example: tons of medical compounds end up dropper at the medical testing stage because of their nasty side effects or it turns out their “positive” effects are indistinguisheable from the placebo effect.
The point being that you can’t actually extrapolative from “neat concept that shows potential” even to merelly “will works”, much less to “will be a great success”.
PS: Equally, one can’t just say it’s not going to be a great success - being a “neat concept that shows potential” has a pretty low informational content when it comes to predicting the future, worse so when there are people monetarilly heavilly invested into it who have a strong interest in making it look like a “neat concept that shows potential” whilst hiding any early stage problem.
Zeth0s@lemmy.world 11 months ago
You are mixing sci-fi level of cutting edge basic research (fusion), with commercial products (chatgpt). They are 2 very different type of proof of concepts.
And both will likely revolutionize human society. Fusion will simply commercially become a thing in 30/50 years. AI has been on the market for years now. Generative models are also few years old. They are simply becoming better and now new products can be built on top of them
Aceticon@lemmy.world 11 months ago
I seem to not have explained myself correctly.
This specific tech you seem to be emotionally invested in is no different from the rest in this sense because it still faces in the real world the very same kind of risks and pitfalls as the rest - there are possible internal pitfalls inherent to every new technology (i.e. a problem we never knew about because we never used it with so many people in the real world before, becomes visible with widespread use) and there are possible external pitfalls inherent to how it fits in the complex world we live in (i.e. it turns out the use cases don’t make quite as much economic sense as was first tought or it indirectly generates more problems than it solves).
Such Process and Fit risks are true for every early stage “revolutionary” tech (i.e. we never did it before, now that we do it, we discover problems we were not at all aware of before) and is why the bean counters rarelly put money in revolutionary and instead go mainly for incremental improvements on proven tech. At times one or more of such “we had no idea this could happen problems” turn out to be surmountable, sometimes they’re not.
In the case of LLMs, the two risky problems from what I’ve heard are in how LLMs being trained in material which includes LLM-generated material actually get worse and the other is the so-called Hallucinations, which are really just the natural side effect of them being Language Models hence all that they do is generate compositions of language tokens that pass for human generated language, with no reasoning involved hence cannot validate through inductive or deductive reasoning said “compositions of language tokens”.
Unless you want to deny decades of History in Tech, you can’t logically extrapolate from an early “looks light it migh be a success” to “it will be a success”, especially the era of overhype we live in.