All models can do that. I wonder if they found Fable was significantly better at it.

My favorite is how elegant solutions often look simple in retrospect. So if you noodle on a problem for a while and then come up with a clever solution: once you explain it to someone they'll be like, "yeah, of course."

Meanwhile the guy next to you that overcomplicates the problem ends up getting kudos for building something so difficult :D

I’m trying to understand what people think we should correct for.

In general, or if it isn't the correct answer?

Like: young men pay more for car insurance than young women (today). This is based on statistical models. Should they be outlawed? I think that is a very interesting question (but they aren't, today).

If the LLM was in charge, would it be wrong for it to charge young men more? Should we train that "bias" out? Or should we only train out biases that are wrong? And would that be different than how we train them today?

I don't know the answer. But I think it is less obvious than some people seem to think.

At the risk of stepping into a hornets nest: is that different than "knowledge"?

Or maybe, what would it mean if an LLM had no social biases? (Would we ever agree that was the case?)

Maybe getting more of these big private companies public will bring valuations down a bit.

(Just my impression. No math or financial studies behind it :)

Comments URL: https://news.ycombinator.com/item?id=48356341

Points: 17

# Comments: 15

The only one I remember was to check if two strings were equal (in C). I wrote (maybe buggy) code to iterate both pointers, comparing while looking for the null terminator.

The interviewer stopped me and said, “You should compare their lengths first. If they are different, you can exit early.”

I was pretty young and didn’t know much, but I explained, “But you have to look for the terminator to find length so it’ll take twice as long.”

He snapped, “There are optimized functions for that!”

I assumed he was right. Needless to say, I didn’t get the job.

Maaaany years later, I realized the std library was probably open source. So I checked (one). It was nice to be vindicated :D

But my guess is that the cost of SWEs themselves mean that the more expensive ones will be worth the delta to most companies.

But time will tell.

I'm not sure if this runs counter to your point or not, but: I don't see any future where LLMs aren't a core part of Software Engineering. The horse is out of the barn. There is no going back.

Seems like making the frontier models know Norwegian and their culture is a better (or additional!) way to reach the end they are going for here.

A golf course uses a lot of water. A factory can use a lot of power -- and generate pollution. A chemical factory could have all kinds of externalities (if not properly managed.) Heck, switching to electric heat (over gas) or electric cars over ICE for an area will also drive up power usage.

But we don't freak out when someone builds a golf course or a factory or switch to electric.

We have rules about all those things. Sound is one: you need to be within reasonable limits. Electricity usage is another: power operators always need to manage their load and expand generation (that's why we keep adding solar and wind everywhere.) Air pollution is similarly managed.

I can understand if people are concerned about "infrasound" -- why not pass a law that regulates it -- like other noise limits?

Datacenters may have specific potential issues. But none of them are unique to datacenters. And we've been managing these issues for hundreds of years.

I saw a poll recently that people would rather live near a nuclear power plan than a datacenter. That's... their choice, of course, but doesn't seem logical to me.

I have heard several "concern stories" about them on NPR recently. Maybe there is a political component to it. But I do worry there is some kind of manipulation being done.

Both seem to be influences of War Games.

Maybe I’m reading something wrong. Or maybe there is an anticipated increase in demand?