91–100 of 261 posts
I'm curious how the predictive aspect of LLMs can generate / "solve" equations. Is it purely "these inputs are most likely followed by this output", and thus needs to have actually seen the problem to get it right, or is it able to infer some of the rules underlying the operations?
This is obviously very cool, but at this point — who knows what I’ll say in a year — my concern with these LLMs is that they’re in the uncanny valley. Here’s one passing a very difficult test. Amazing! Now, rely on it to build a nuclear doohickey for a power station or a multi-billion dollar device for CERN or anything really and, well, no. So humans still have to check the output, and now we’re in that situation whe…
>No negativity towards AI here. It’s amazing and it’ll change the future. But we need to be careful on the way. Yeah, I suspect a lot of fields will have a similar trajectory to how AI has impacted radiology. It might catch the tumor in 99.9999% of cases, better than any human doctor. But missing a malignant tumor 0.0001% of the time is unacceptable, because it spikes the hospital's malpractice costs. So every single…
Meanwhile, in every country that isn't America, tumor detection rates go up, cancer outcomes improve, and the cost of delivery falls.
Americans continue to claim their system of funding insurance company profits rather than actual healthcare is best.
>Google's recent quantum supremacy experiment demonstrated the successful use of quantum error-correction.
How "recent" is the experiment in question? Would it have been publicized before GPT-4's training cutoff?
Earlier quoted context omitted.
I don't get why people are so optimistic about machine translation. Computers can get explicit meaning across – that's obvious to anyone who understands linear algebra, information theory, and linguistics. But many aspects of translation (puns, tone, cultural context) aren't just about mapping from one vector space to another. A human, no matter how fluently bilingual, would have to think about the problem, and the c…
Have you actually used GPT-4 for translation? Seriously all this talk about only getting explicit meaning across would be easily dispelled in an afternoon if you only bothered to try.
Its ability to feedback (3) allows it to execute algorithms, but only a certain class of algorithms. Without tailored prompting, it's further restricted to (a weak generalisation of) algorithms spelled out in its corpus. This is very cool, but this is a skill I possess too, so it's rarely useful to me.
Its ability to plagiarise (2) can make it seem like it has capacity that it doesn't possess, but it's usually possible to poke holes in that facade (if not even identify the sources it's plagiarising from!).
It is genuinely capable of explicit translation (1) – though a dedicated setup for translation will work better than ChatGPT-style prompting, even on the same model. A sufficiently-large, sufficiently well-trained model will be genuinely capable of translating idiomatic language (for known idioms), for the same reason it can translate grammatical structures (for known grammar).
It can only perform higher-level, "abstract" translations – like those necessary to translate a Phoenix Wright game – if it's overfit on a corpus where such translations exist. (https://xkcd.com/2048/ last graph) This is not a property you want from a translation model: it gives better results on some inputs, sure, and confident-seeming very wrong results on other inputs. These are two sides of the same coin (2).
When the computer can't translate something, I want to be able to look at the result and go "this doesn't look right; I'll crack out a dictionary". I can't do that with GPT-4, because it doesn't give faithfully-literal translations and it isn't capable of giving complete translations correctly: it's not fit for this purpose.
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I suspect AI went that way in radiology not because of the chances of False Negatives, but because radiologist are entrenched in the system and will not yield an insanely lucrative stream of revenue.
Hospitals would love to fire all radiologists and replace them with software. They've already done it with outsourcing; a large chunk of what used to be done entirely in-house has been contracted out to remote overseas doctors.
From what I understand it isn't even possible generally to see a doctor remotely in a cheaper state, because medical licensing is per-state.
Earlier quoted context omitted.
Machine translation isn't super human yet. But yes, it's probably only a few years away.
I don't get why people are so optimistic about machine translation. Computers can get explicit meaning across – that's obvious to anyone who understands linear algebra, information theory, and linguistics. But many aspects of translation (puns, tone, cultural context) aren't just about mapping from one vector space to another. A human, no matter how fluently bilingual, would have to think about the problem, and the c…
Maybe it's already in the training set, but GPT-4 does give that exact translation.
I've found that GPT-4 is exceptionally good at translating idioms and other big picture translation issues. Where it occasionally makes mistakes is with small grammatical and word order issues that previous tools do tend to get right.
Earlier quoted context omitted.
>No negativity towards AI here. It’s amazing and it’ll change the future. But we need to be careful on the way. Yeah, I suspect a lot of fields will have a similar trajectory to how AI has impacted radiology. It might catch the tumor in 99.9999% of cases, better than any human doctor. But missing a malignant tumor 0.0001% of the time is unacceptable, because it spikes the hospital's malpractice costs. So every single…
But missing a malignant tumor 0.0001% of the time is unacceptable, because it spikes the hospital's malpractice costs. Meanwhile, in every country that isn't America, tumor detection rates go up, cancer outcomes improve, and the cost of delivery falls. Americans continue to claim their system of funding insurance company profits rather than actual healthcare is best.
This is obviously very cool, but at this point — who knows what I’ll say in a year — my concern with these LLMs is that they’re in the uncanny valley. Here’s one passing a very difficult test. Amazing! Now, rely on it to build a nuclear doohickey for a power station or a multi-billion dollar device for CERN or anything really and, well, no. So humans still have to check the output, and now we’re in that situation whe…
>No negativity towards AI here. It’s amazing and it’ll change the future. But we need to be careful on the way. Yeah, I suspect a lot of fields will have a similar trajectory to how AI has impacted radiology. It might catch the tumor in 99.9999% of cases, better than any human doctor. But missing a malignant tumor 0.0001% of the time is unacceptable, because it spikes the hospital's malpractice costs. So every single…
This does seem like an odd outcome though, right? I guess fundamentally people will be legally/economically advantageous in some sense because the amount of insurance that an individual doctor can be expected to hold is much less than a hospital. Is the fate of humans to exist not as a unit of competence, but as a unit of ablative legal armor?
Reading stuff like this, one thing I cannot stop wondering is this: If Ai can be trusted to do all the trivial tasks and if non trivial tasks require a scaffold of trivial practice, where are we going to keep finding the people qualified enough to actually do the non trivial stuff?