[zach]

Looking at the trend here, you can see why many business forecasters and economists have predicted that advances in artificial intelligence will create huge new returns to capital. That future is worth reflecting on because it suggests a fundamental change of labor-capital dymamics.

Take startups. Right now, many startups can compete on the same basis to hire talent as huge companies. But if companies with huge capital reserves can put their cash directly to work to train AI models, startups will be hard-pressed to compete with "smarter" products. Specialization will not even be much help.

Looking at Beating the Averages (http://www.paulgraham.com/avg.html), PG enthused that, since established companies are so behind the curve on software development technology, there is always a chance for higher-productivity techniques like more productive languages to give smaller teams a real chance at a huge market. Of course, that this was in the era when Google was not creating new programming languages and there were no Facebook to widely deploy OCaml and Haskell. And now, AI looks to make the averages even harder to beat.

Even today, if you round up the smartest members of a CS grad class, it is going to be quite difficult to directly compete with a machine learning model with access to huge amounts of data and computing resources. Looking further forwards, if machine learning is able to provide "good enough" alternatives to most human-created software, the software startup narrative — that a few talented and determined people can beat billions in resources — may not even be so relevant anymore.

[ddtaylor]

It's worth noting that some prominent figures in AI/ML are saying we are due for another "AI winter" since it's being oversold again. I don't know if I agree with that, since we are seeing some interesting things, but technically Google is kind of saying they can tentatively pass the Turing Test with phones and meanwhile even a car decked out with extra sensors and 360 LIDAR cannot detect a simple stop sign with mud on it.

[aglionby]

> Google is kind of saying they can tentatively pass the Turing Test with phones

This is quite a bold claim, and one I'm not sure they're making. Their promo material suggests that it's limited to quite well-defined domains where conversations aren't really that open-ended, and we haven't seen how it'll perform in the real world.

Relatedly, I don't think headlines like "Google Duplex beat the Turing test: Are we doomed?" [0] are helpful at all. It's disappointingly low-effort clickbait where instead there's plenty of interesting discussion to be had (should machines have to identify themselves as such? What about their use of pauses and fillers?).

[0] https://www.zdnet.com/article/google-duplex-beat-the-turing-...

[computerex]

Right. I personally think the coolest thing about duplex is the end-to-end synthesis of natural speech. The actual call isn't as impressive to me because that's just handed coded stuff. IBM Watson has already had success in this regard.

[ddtaylor]

They aren't explicitly making the claim, but it seems the premise of their demo was "hey look humans think it's another humans which is somewhat like the Turing Test.

[acdha]

> Google is kind of saying they can tentatively pass the Turing Test with phones

Is Google really saying that or just the more breathless commenters? I thought they were pretty good at making it clear that Duplex took a lot of work to do well in very constrained conversational situations.

[ddtaylor]

Well, during the original AI Winter many were open and honest about the capabilities of early ML and it's limits, but what caused the winter itself was it's perception by a large audience as a magic bullet and their disappointment when it didn't work.

[nl]

Sorry if this sounds harsh, but this is a bad comment.

some prominent figures in AI/ML are saying we are due for another "AI winter" since it's being oversold again.

"Some say...". Name one.

We may have a Gartner style "trough of disillusionment", but a 1990's style AI Winter is unlikely. It works too well in too many valuable areas for the money to go away.

technically Google is kind of saying they can tentatively pass the Turing Test with phones

Could you show us where they claim that? That goes well beyond any statement I've heard Google make, and into the kinds of breathless claims click-bait blogs have tried to make.

car decked out with extra sensors and 360 LIDAR cannot detect a simple stop sign with mud on it

Do you have a specific example of that? I did Google, and I couldn't fine anything.

Most examples I've seen handle occulted road signs pretty well. There are of course adversarial examples which are an interesting case, but mud causing a failure like this is surprising to me.

[romaniv]

>I don't know if I agree with that, since we are seeing some interesting things

There were plenty of interesting results in AI research before the last two AI winters.

[goatlover]

> if machine learning is able to provide "good enough" alternatives to most human-created software, the software startup narrative — that a few talented and determined people can beat billions in resources — may not even be so relevant anymore.

Are there any examples where current ML has replaced human-created software, the demand for startups or software engineers?

Seems to me that ML so far has expanded our toolbox of what can be done with software, not replaced programmers, designers, engineers, or really much of anybody yet. All this worry about future automation is imagining that things are going to be different this time, because of recent success with ML in limited domains.

[zach]

Seems trivial, but I have met someone whose ardent hobby was programming go-playing programs. With the creation of AlphaGo Zero, one could say all the clever code ever written by humans for the purpose of playing go is obsolete.

More relevantly, I would be surprised if the shift to AI techniques in fraud detection at places like PayPal is not already having an impact on the career paths of the engineers that were tasked with maintaining and tuning their pre-ML fraud system. At one point the top engineers of the original heuristic system could have been considered their most valuable non-management employees at the company. I'm sure they're not out on the streets or anything, but I also assume the next person to take their job will not be nearly as valued.

Also, ML will impact programmer demand in subtle ways. A lot of programming is refactoring, and there is reason to believe we can refactor code, especially in certain languages, automatically to make it more aesthetic. Realistically, that seems likely to decrease demand for programmer hours. Or an ML system that can run over someone's GitHub account or repo may be the new resume screen, and if one scores badly on it that may limit the demand for them personally.

Finally, I have to think that the overall march of software towards more complex integrated systems is already a major cause of the dearth of entry-level programming positions, and ML will accelerate that trend.

[allenleee]

I believe we still have the chance. The opportunity exists in the business world because when companies become successful is when collective leadership becomes most focused on maintaining success.

Doing well reduces the incentive to explore other ideas, especially when those ideas conflict with your proven business model.