Problems the AI industry is not addressing adequately • Hacker News Board

Re: Problems the AI industry is not addressing adequately

#241

Post by drillsteps5 » Mon, Jul 07, 2025, 6:10 PM UTC

potamic wrote: post #214

drillsteps5 wrote: post #180

Earlier quoted context omitted.

Hammer is not a perfect analogy because of how simple it is, but sure let's go with it. Imagine that occasionally when getting in contact with the nail it shatters to bits, or goes through the nail as it were liquid, or blows up, or does something else completely unexpected. Wouldn't you want to fix it? And sure, it might require deep understanding of the nature of the materials and forces involved. That's what I'd d…

A better analogy might be something like medicine. There are many drugs prescribed that are known to help with certain conditions, but their mechanism of action is not known. While there may be research trying to uncover those mechanisms, that doesn't stop or slow down rolling out of the medicine for use. Research goes at its own pace, and very often cannot be sped up by throwing money at it, while the market dictate…

That analogy I agree with. Medicine, even modern medicine, has so many caveats for every medication/procedure/approach that at times it borders on scam (snake oil). Doesn't mean we must accept the same level of accuracy in other areas...

Re: Problems the AI industry is not addressing adequately

#242

Post by dummydummy1234 » Tue, Jul 08, 2025, 3:48 AM UTC

vrighter wrote: post #238

dummydummy1234 wrote: post #231

Earlier quoted context omitted.

Ok, so take the example of RF communications: You need to send data, but only 50% at random comes through. You could move things closer together, send at higher power, improve the signal modulation to give you higher snr. All of these things are done, this is improving the base system. But at the end of the day rf is in the real world, and the real world sucks. Random shit happens to make your signal not come through…

Except that a corrupt packet can easily be detected when compared to a valid packet (is the checksum valid?). There is an algorithm to execute that can tell you, with high confidence, whether a given packet is corrupt or not. In an LLM a token is a token. There are no semantics to anything in there. In order to answer the question "is this a good answer or not?" you would need a model that somehow doesn't hallucinate…

But, my point is that, in the 1800s it was not understood, yet we still had telegraphs. They came up with inefficient hodge-podge engineered solutions that mostly worked, and sometimes did not.

We are in the equivalent time period for llms and ML in general, we can hack things together that kinda work.

We only understand a sliver of fundamentals of how these things behave as complex systems. But that does not mean we should or need to wait 20 years for the research and science to keep up.

These are useful today, hallucinations and all, and building things that get the right hallucinations for your use case, even if not 100% reliable is possible.

Re: Problems the AI industry is not addressing adequately

#243

Post by Ashkee » Fri, Jul 18, 2025, 10:25 AM UTC

grafmax wrote: post #222

Redoubts wrote: post #199

Earlier quoted context omitted.

Incredibly bizarre take. You can build more capacity without frying the planet. Many ai companies are directly investing in nuclear plants for this reason, for example.

Several companies investing in AI Have made commitments to renewable and clean energy. However at most half of this increased energy demand is expected to come from renewables through 2030 and fossil fuels will continue to be heavily utilized for the massive data center build outs occurring beyond that, according to the International Energy Agency’s April 2025 report. Nuclear energy has long build outs of 10+ years.…

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