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Google has sent internet into ‘spiral of decline’, claims DeepMind co-founder
(www.telegraph.co.uk)
this post was submitted on 15 Oct 2023
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Perplexity.ai has been my go to for this reason.
It often brings up bad solutions to a problem and checking the sources it references shows it regulary misses the gist of these sources.
There sources it selects are often not the ones I end up using. They are starting point, but not the best starting point.
What it is good for is for finding content when I don't know the terminology of the domain. It is a starting point ready to lead me astray with exquisitely written content.
Find trustworthy sources and use them.
It is more of a proof of concept at the moment, but it shows the potential
That'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 "neat concept that shows potential" medical compounds end up dropped 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 work", 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 as they're activelly poluting the information space around it.
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
(btw I already use chatgpt 4 productively every day for my daily work and it helps me a lot)
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) - business guys might say that revolutionary tech starts with a lot more "unknown unknowns" than incremental improvements on existing tech - 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 insurmountable, sometimes they can be overcomed but the result is not especially great, sometimes they're all overcomed without any nasty side-effects and the thing ends up being a world-changing tech: you can't really tell upfront.
In the case of LLMs, the two risky problems from what I've heard which might stop it from being "world changing" are in how LLMs being trained in material which includes LLM-generated material actually get worse (so as the Internet gets flooded with LLM-generated material passing for human-generated one, LLMs would get worse and 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", so LLMs wouldonly usefull for altering format without touching the information (for example, turn lists of cold hard facts into fluffy longwinded text or do the opposite and summarize lots of fluffy text into just the facts) which would still have a big impact in certain professions but not necessarilly be "world changing" (or, even more interestingly, make over time people value "fluffy text" less and less, which would be "world changing" but not in a way that benefits the makers of LLM).
Unless you want to deny the last 4 decades of History in Tech, you can't logically extrapolate from an early "looks like it might be a success" to "it will be a success", especially in the era of money-driven overhype we live in.