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Comment by leggomylibro | Hacker News Reader

Everything is always painted in such an adversarial light, it makes you despair sometimes.

I think that The Atlantic's recent article on this topic is a more nuanced insight[1]; human-machine cooperation is probably where the big money will be. Companies that seek to cut people out of the loop will probably run into a lot of problems, as will those that smash the looms. Whereas trying to smooth the interface between AI/ML conclusions and human oversight is probably going to see the most success.

[1]: https://www.theatlantic.com/education/archive/2018/02/employ...

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> human-machine cooperation is probably where the big money will be.

As it has been for as long as machines have existed, really. This reminds me of Douglas Engelbart and his vision for computers. I'll cite the section of his wikipedia page that paraphrases an interview with him from 2002[0][1].

> [Douglas Engelbart] reasoned that because the complexity of the world's problems was increasing, and that any effort to improve the world would require the coordination of groups of people, the most effective way to solve problems was to augment human intelligence and develop ways of building collective intelligence. He believed that the computer, which was at the time thought of only as a tool for automation, would be an essential tool for future knowledge workers to solve such problems.

He was right of course, and his work lead to "The Mother of All Demos"[1].

Machine learning is the next step in using computers as thought enhancement tools. What we still need to figure out is an appropriate interface that is not as "black-boxy" as "we trained a neural net, and now we can put X in and get Y out".

EDIT: Now that I read that quoted section of wikipedia again, it's funny to note that computers were "only seen as tools of automation", and how modern fears of AI are also about automation. Automation of thinking.

[0] https://en.wikipedia.org/wiki/Douglas_Engelbart

[1] https://www.youtube.com/watch?v=VeSgaJt27PM

[2] https://www.youtube.com/watch?v=yJDv-zdhzMY

Ironically enough, Engelbart was often derided by his colleagues at the time who thought hard AI was just around the corner and so all of this intelligence augmentation stuff would be obsolete soon enough. Today we are closer than ever (always just 20 years away!), but still IA rather than AI is very much the way to go.

(if you check an old comment)

Do you have any sources for this? I find "sentiment at the time" especially hard to find, historically speaking.

And I'd be fascinated to read something about this.

It's funny that you bring that up - it does seem like the concept of 'extended cognition' is one of the biggest benefits that we've collectively realized from computers (and other relatively nonvolatile communication mediums like books.)

This is a computer-oriented analogy, but most fields have their own tables and charts and maths that are tedious to keep on the tip of your mind. Still, for example, I don't need to remember the details of every API that I use; I can just remember that there is a 'do X' call available, and refer to the documentation when and if I need to actually use it.

In the same vein, I can quickly get a feel for whether an idea is possible by stringing together a bunch of abstract mental models. "Can I do X?" becomes, "are there good tools available for doing A, B, C, and D?", and that information is only a quick search away. Actually using those tools involves an enormous amount of detail, but it's detail that I can ignore when putting an idea together.

And in most cases, that 'detail' is a library or part that already abstracts a broad range of deeper complexities into something that I don't have to think about.

The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in?

Your example also is related to my experience briefly working in P&G chemicals R & D lab; the ChemE's around me routinely used google to look up reaction kinetics of different compounds (as well as other similar queries) rather than rely on their memory of such. I was attending a local university at the time (mostly for calculus and mathematical modeling using mathematica, and french), but I'd say this experience is largely one that started my questioning the value attending university in general (I dropped out an ivy about two years later, for this reason among others).

I suspect that the concept of 'extended cognition' as it is realized with the use of computers and how people use it day to day to get work done is in conflict with how we all are mostly taught via rote memorization, and then application of information; therefore it should naturally follow that those who are heavily invested/exposed in 'non extended' cognition services have relatively more to lose, as well as any currently realistic answer to this:

>The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in?

will bring cognitive dissonance to those who need the answer most (those with heavy exposure to relatively 'non extended' cognition services).

When you're looking at effects, I think you need to dig down into what exactly is being extended.

Are more data sources being made available? Is data being preprocessed? Is an initial task being automated?

Because the truth of any worker (in less than a ruthlessly specialized huge company) is that they may be an "extended cognition" worker, but still perform many "non-extended cognition" activities as part of their job. Because there was previously no alternative and work needs to get done.

Fast forward that, and you're never going to fully automate a goal. But you will automate sections of the process that are amenable to machines.

Advice? Recognize which type of work you spend most of your time in, and don't get caught being the "non-extended cognition" person...

This is a fantastic point, and I think a lot of AI development that goes in the direction of trying to replace human beings is essentially absurd. We already have humans, why would we want something that can do what humans can already do? Rather, we want something that extends the capabilities of humans into areas where they aren't proficient. For instance, why do we put all this effort into natural language processing when humans are already totally optimized for it? What we need is a solution to scaling, not a solution to NLP itself.

EDIT: To expand; one way to do something like Siri would be to have a system that routed requests to human operators. The human operator would give the correct answer to the request, and then the system would use that as training data. If the system was reasonably confident it already knew the answer to the request from previous training data, it would answer right away, but if it was below a certain confidence it would route to a human. This seems like the smartest way to leverage machine learning in these kinds of scenarios, and I'd be surprised if someone hasn't already tried it or something similar in the past.

Some of the most successful investors, like Warren Buffet, are old school and also operating on a completely different level than most investors since he can force companies to change course, or like after the 2008 crash he was able to come in and offer deals to big banks in return for preferred shares.

It would be difficult to see how to apply narrow AI to this kind of thing. It seems really good for routine tasks, like high frequency trading, but maybe not so great for these big one-off deals which constitute many of the best investments.

Of course, Buffet might still benefit from AI analysis of broad market trends, and the like. If I'm wrong, I'd be interested to know.

Buffet was a small time investor at some point. He just made a lot of good investments over a long time.

He also had a very very good mentor who wrote the original book on securities analysis.

This.

Also check out The Intelligent Investor, which is arguably Ben Graham's most accessible work on the topic.

Absolutely the modern copy I have had some commentary comparing some of Grahams analysis of problems in the 50's and 60's and how some of the problems in the dot.com crash where strikingly similar.

> It seems really good for routine tasks, like high frequency trading, but maybe not so great for these big one-off deals which constitute many of the best investments.

I understand what you mean from an opportunity identification perspective, but you have to keep in mind that even the "big one-off deals" require routine tasks at a lower level to verify the merit of such deals. If you think about tasks like reviewing financial statements, AI could provide faster evaluation and potentially identify trends that would elude a human analyst. In any case, Buffett is known for avoiding investments in companies he doesn't deeply understand and I would bet the same stance holds for employing new technology in his investment process.

I don't see any reason to believe this isn't happening already. As old school as Buffett is (and the man is a legend in my eyes), it just doesn't seem a stretch to think that they're running at least some deep-learning-based analysis over the deals they consider.

Whether or not they give any weight to the output is quite something else, but it would be such a (relatively) trivial cost to get that answer that it seems less likely they'd not do it.

I'm pretty sure Buffett doesn't use that stuff. Although he reads the papers so he may pick up on things other people have done.

Buffett’s investing algorithm hasn’t changed in 65 years. Every day he reads financial reports. If he likes a companies business model, he makes a mental estimate of the companies value, and then looks up their trading price. If there is a big enough discrepancy in his favor he will start buying.

At a certain point humans and AI can also play off one another predictably.

Everyone has played games where the AI can beat you in a straight shot, but you can lead the AI into situations that are predictable where you can gain a predictable advantage, and vice versa with humans.

Example: Buy on the dip strategy and technical strategies, big players could drive down the market and HFT can buy in the dip based on fundamentals. Bad news floods the market and HFT reacts.

Humans can predict what AI would do, then AI will reactively start to predict what humans will do when this happens, but humans always have one step ahead in new techniques, AI will be built to defend against it.

Regarding defending against a buy on the dip strategy, AI can start to learn player specifics and not react, or react differently (preemption) if those players return, however this can also eventually be played.

Humans and AI will be playing a cat and mouse game for eternity, microcosms of this can be seen in gaming AI. I think of it more like a game that will be fun to play, yes sometimes you will lose, other times you will predictably win. Bots will be challenging bots unexpectedly and predictably, but they will almost always originate from human programming originally.

This is sort of tangental.. I think it's curious that in a lot of writing AI is almost being defined as that which replaces human labour. The context of technological unemployment and whatnot.

In that frame, I think its natural that discomfort is linked to autonomy. Autonomous taxis & cruise control may be points on a continuum technically, but economically, no human involvement is different. Autonomy separates the PCs from the Looms. Cooperation where a human is involved the machine is tool use, recognizably. The humans labour gets more efficient with tools. More trinkets per human.

Maybe the Luddites thought of looms as autonomous, with humans in a supporting role.

Anyway, I think it's hard to predict where this goes on a 25y scale.

This kind of my beef with most SciFi movies. They always seem to paint an antagonistic relationship between man and machine when the reality will probably be something in the middle.

That's because science fiction movies are mostly not projecting a predicted future, they are projecting and exploring our fears and aspirations with the future.

I actually find it really funny that a lot of SciFi predicts a future where we are actually talented and intelligent enough to create a human-like, sentient being. The reality seems so distinctly far off from that- though I guess that is the point of fiction.

> human-machine cooperation is probably where the big money will be

The sceptical counterargument to that, which I go back and forth on, is "that's what they said about chess". There was a transitional period when this was true, then the engines disappeared into the middle distance.

I work on the hunch that the middle-ground of tasks where humans improve on, or with, machines is both small and unpredictable; computers will tend towards being either useless or strongly superhuman for each problem.

this is still true in chess. Humans use chess computers to play the game of chess. The tournament money still goes to humans, everybody still cares about the world's best players, and so forth. Even as chess engines vastly have surpassed humans in technical capability, they haven't somehow sidelined humans in all aspects of the domain. Not even literally in competitive games.

It's not like everybody has somehow switched to watching engine games. That is in fact just a niche market of the chess world. We are humans and as such we still enjoy seeing real humans thrive and compete in chess more than we care about machines.

If anything chess is the perfect example that the pessimism is misplaced, chess engines have not killed chess as a human endeavour.

But the purpose of chess is taking part and having fun. The purpose of finance is making money. The comparison is weird. No one will give their money to someone to manage for fun if an AI could make more money.

I don't agree. I feel that chess engines have killed chess as a pastime that people play. I know growing up in the 80s I played it during lunch at school, and schools had chess clubs which really weren't on the order of forming neophyte pros, but just students who liked the game.

But now it's rather futile to play it, because at some point you'll run up against a machine that can outplay you perfectly. I mean, before you could play a grandmaster or something, but the moment you or someone else for you buys a ten-dollar chess program, you realize that despite improvement, the computer will always defeat you.

I don't really see chess as that big any more in the public eye. The kids might play Minecraft, which rewards human creativity and doesn't really force you to compete against an A.I. optimized to beat 99% of all Minecraft players.

As a sport, yes. We do the same with the 100m dash, and there is no-one anywhere who would employ humans based on the business utility of being to run 100 metres fast outside of entertainment. Pro chess is in the entertainment business; the strength of the players isn't really material to that.

You're confusing the economics of recreation and entertainment with the economics of efficiently making things. They both exist, but they're very different.

>it makes you despair sometimes.

On the other hand, if you are optimistic and excited in a world where everyone else is in despair, you have some distinct advantages. :)

That's not entirely comforting

As Alistair Cook said on Black Tuesday back in 29 tomorrow might be a good day to buy some shares.

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