Maybe in the near future humans can just focus on the reviewing and testing part.

And it's just regurgitating the answers someone else wrote?

As I imagine it's a very high chance given how much uni lecturers recycle exam questions.

When I was at uni you could just get the last 5 years worth of questions from the library for almost any subject and guess what the questions were probably going to be. Often they just changed a few numbers.

Teaching undergrads is like a sausage factory, the actual intellectual value for undergrads is in the seminars, the practical value in the labs. The rest is showing you can regurgitate what you've been told.

Which ChatGPT excels at.

The exact questions aren’t in the top results, but the answers are.

I remember Yann LeCun gave an interview and he came up with some random question like "If I'm holding a peace of paper with both of my hands above the desk and I release one what would happen". His point was that since the LLM doesn't have a world model it wouldn't be able to answer these trivial intuitive questions unless it saw something similar in the training set. And then the interviewer tried it and it failed. That was 3.5. I've tried many variation of that class of problem with 4 and it seems to generalize basic physics concepts quite well. So maybe 4 learned basic physics ? Why couldn't it learn QM theory as well ?

> To the best of my knowledge—and I double-checked—this exam has never before been posted on the public Internet, and could not have appeared in GPT-4’s training data.

For a language model, test results are the end. They are supposed to measure what the model is capable of. If you need better performance, you must train a better model.

The only question is whether, on average, AI-augmented code suggestions reduce overall bug rates. Human devs are already dozing off with crashes and whoopsies at high rates so it doesn't matter too much if AI output has a bug or whoopsie so long as they occur at rates lower than pure human generated code.

If we create a culture of tolerating fatal AI hallucinations as long as they happen at a rate that's below the human caused fatality baseline, we create an opportunity for bad actors to use the "nobody is to blame" window as a plausible deniability weapon (so long as they don't use it too often).

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 scan still has to be reviewed manually by a doctor first, then by the AI as a fallback.

In theory there's some insurance scheme that could overcome this, but in practice when you have software reviewing millions of scans a day you're opening yourself up to class action lawsuits in a way no competent human doctor would.

I find it hard to believe human doctors miss malignant tumors in less than 1 out of every 10 million cases.

As the parent poster put it, it's only a problem if the average doc won't detect it. If it's truly a 1 in 10-million thing, an extreme edge or corner case, malpractice courts may not have a problem with you missing it -- as they say "if you hear hooves, do you think of horses or zebras?". 99% of the time a different diagnosis is the right one, and even at five-nines you're letting someone through eventually.

Whenever someone says "as long as it's better than a human", that's where my mind goes. We shouldn't be satisfied with just being better than a human. We shouldn't be satisfied with five nines! I don't really care about what courts have a problem with — my point is just that our goal should be zero preventable deaths, not just moving from humans to AI once the latter can be better on average than the former.

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.

Medical billing has also been offshored to India+Pakistan btw

In general, a lot of back office Dental+Medical functions were outsourced in the 2000s+2010s.

Eg. Paper about this from 2006 - https://ipc.mit.edu/sites/default/files/2019-01/06-005.pdf

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?

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.

https://worldpopulationreview.com/country-rankings/cancer-su...

I don't think results are anywhere close to that in the field. If hospitals could do without radiologists, they would do so immediately. Currently, we are seeing very little technical progress due to applied statistics in the field, and the real cause of that is that tech people don't understand what we do and why we're still very much needed. The problem lies more in information retrieval capabilities than acting on the data itself.

I think people probably overestimate (maybe vastly) how good at differential diagnosis most doctors are.

Those probabilities are way off given biology, but anyway ...

The interesting cases of AI in radiology would be being able to catch stuff that a human has no hope of catching.

For example, a woman with lobular (instead of ductal) breast cancer generally doesn't present until mid-to-late Stage 3 (which limits treatment options) because those cancers don't form lumps.

You can stare at mammograms and ultrasounds all day and won't see anything because the "lumps" are unresolvable. You're trying to find a sleet particle in a blizzard. Sure, it's totally obvious on an MRI scan, but you don't want to do those without reason (picking up totally benign growths, gadolinium bioaccumulation, infections from IVs, etc.)

An AI, however, could correlate subtle, but broad changes that humans are really bad at catching. Your last 5 mammograms looked like this but there is just something a little off about this one--go get an MRI this time.

And if you really dive deep you see 4 really hasn't any deep understanding and makes obvious mistakes. I think they've also explicitly trained it for tests.

The 95% only problem is an issue for cars cuz that last 5% means you die horribly in a head-on collision, or maybe only get a mild concussion but are stuck in a ditch.

But if I can get 95% of my router configs done, 95% of my documentation written, and 95% of a website whipped up I can hand that off to a Sr Engineer/Admin and have them take care of the last bits. As long as the hours, phone number, and location are good a website just needs to be "directionally accurate" and otherwise fairly basic.

The military, for all its funding and all its training and all its planning, has lost multiple nuclear weapons, on American soil.

We are imperfect machines who aspire to build more perfect versions of ourselves, through children and now through AI. By many measures, we’ve succeeded. The progress will almost certainly continue. The question is, when will it be good enough for you to embrace it despite its imperfections?

Look at what wave done to soil, the oceans, we’re likely on the wrong track. Maybe it’s even unrecoverable.

I think we can have a much more advanced society , but we need to slow down a lot and do things more in harmony with the natural world and out our egos aside. We’d probably have a much better quality of life for doing so.

Obviously the scope of what's possible is different but _any_ craftsman using _any_ tool will only be as good as what they verify themselves.

These tools aren't replacements for knowledge and experience (which is the narrative that is constantly pushed), they bionic enhancements.

It reminds me of anti seatbelt rhetoric or the same stuff skateboarders say about helmets.

The same is true of manufacturers and others in the chain of commerce of goods (see, e.g., the general rules on defective product liability), even where they aren’t individual humans. There’s nothing about AI which makes it particularly special in this regard.

Solve the liability problem and I would 100% take a machine that performs 30% better than a human or helps a human perform 30% better every time because it means fewer humans die on the road.

How is it valid? It's currently a solved problem. The driver of the car is still held liable because they are still, ultimately, driving the car. Tesla's with autopilot seem to make drivers safer, and acting like we don't know who is liable in the event of a crash is just a red herring.

It's surprisingly hard to establish those parameters though, since (i) the more meaningful indicators of good performance (driver error fatalities) happen only every million or so miles, even less frequently once you've narrowed your pool down to errors made by sober drivers that weren't racing or attempting to driver in conditions unsuited to electronic assistance, so that's a lot of real world road use required to establish statistically significant evidence a machine is a 30% or so safe than a human driver (ii) complex software doesn't improve monotonically, so really you need that amount of testing per update to be confident that the next minor version of something "30% safer" hasn't introduced regression bugs which mean that it is now a bit worse than the average driver and (iii) performance in different road conditions is likely highly variable such that it might be both 30% better overall and 30x as likely to cause an accident if not disengaged in that particular circumstance.

To make a valid assessment of the overall safety impact, you'd also have to factor in that (iv) the worst drivers who skew the stats are the generally the ones least likely to buy it and (v) if it's fully autonomous driving, road use would increase substantially and whilst that may have other benefits the likely outcome from substantial increase in miles driven using tech that's only marginally better than a human is more humans dying on the road

The fact that overall road use is so high that a sufficiently bad regression bug in sufficiently widely-deployed software could rack up a massive body count within hours obviously doesn't make the case for introducing something believed to only be a marginal improvement any stronger.

This simply isn't true. By the mile they have a worse safety record than other cars in their class (mitigating factors: where they're driven and who drives them). You might be referring to Tesla's marketing statistic that there are fewer accidents per mile involving autopilot - typically engaged in ideal driving circumstances - than when it's switched off, or across all other drivers. But that's not very meaningful data. Some would argue that citing marketing figures from a company with a track record for obfuscation and dishonesty as "the data" (and really, there isn't much better data available to the average person) is an indication we're not being careful.

https://medium.com/@MidwesternHedgi/teslas-driver-fatality-r...

how often is the “have to check output” more work than just doing the task at hand without the LLM in the first place?

These AI have "Male Answer Syndrome" to the nth degree. They will shamelessly give you an answer even if they have to completely make one up.

is that like mansplaining?

is botsplaining the new mansplaining?

It needs interaction and calibration from a human to keep it in check. Even we as humans are using all kinds of different feedback mechanisms to validate our thought process.

Though this might be a different way of hooking GPT up to reality.