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Comment by axegon_ | Hacker News Reader

I've spent an unhealthy amount of time at presentations, conferences, seminars and meetups. With that, on your first point, I'm more inclined to believe somewhere around 90% have absolutely no "AI" or "ML" for that matter in their products. And a large fraction of those who do have re-used existing open source solutions without digging a lot into them. De omnibus dubitandum est, of course, but this is the impression I get.

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Your suspicion is largely correct, in my experience. I've worked as a consultant on a number of different AI / ML projects for start-ups. Most aren't doing anything all that new or groundbreaking from an ML point of view. Their real innovation is usually more about applying ML to industries / areas where it hasn't been used much before. But that doesn't get the investor dollars flowing in, so the founders try to make it seem like they have some radical new ML breakthrough. And in recent times, it has worked.

> And in recent times, it has worked.

This is what __REALLY__ bugs me. Personally I'd love to see money poured into actual R&D rather than people abusing the "ML" and "AI" acronyms. Investors don't care about your R&D at all and commonly see it as a huge risk factor. Which it is of course. A semi-working prototype has a much better chance of succeeding so they stick to that.

But as a consequence many people(myself included) are not even bothering with pitching anything to anyone and invest their own money, time, resources and savings into it. Blocking? Yes. Painful? Absolutely. Slow? Incredibly. I'm sure the next AI winter is around the corner, if it isn't here already: The virus outbreak might be the catalyst that triggers(or has triggered) it, given the staggering amount of people going full "I have AI which will provide a cure, vaccine and time travel to go back in time and warn the world, just gimme cash". I doubt anyone would deliver on any of those promises(and that's me being optimistic). But the crisis will likely push a lot of investors to pour millions into the empty promises that have a few buzzwords thrown in. I hope I'm wrong.

I share your frustration. If you're trying to be realistic and straight-forward about the limitations of AI/ML, you're getting little interest from investors. Here's an anecdote. I was at an "AI in drug discovery" conference 2 years ago. One of the presenters, the founder of a drug discovery start-up, emphatically made the claim in a talk that "we should deliberately overhype AI in drug discovery, to raise awareness of what we as an industry can accomplish". I was gobsmacked by this. And yet, in the social mixer afterwards, the investors I spoke with LIKED this approach- they said the boastful founder was bold and visionary, and they didn't care that he was exaggerating the capabilities of his company. So, that's why all we hear is hype- founders are just responding to investor incentives.

That's also why I work as a freelance consultant and not as a founder. I think that autonomous driving (rather, the lack of such) is going to be what triggers the next AI winter. Too much money and hype, too little results for too long- the rope is wearing quite thin from what I can see.

I think the autonomous driving, while a factor, will not be as much of a kick as covid-19 cure and/or vaccine. I mean all the best to everyone trying and I hope someone proves me wrong about this by actually discovering a drug/vaccine through AI. But as far as autonomous driving is concerned, at least there are some visible results. Not as advertised and(to my mind at least) but most importantly not that valuable but at least there's SOMETHING on the table....

I've been working in this space for a few years, as an engineer and now founder, and I think a lot of savvy investors are beyond this.

In my experience, everyone who is informed acknowledges that ML is very powerful, but the algorithms are widely accessible.

Instead, it seems that investors are looking for a company that protects itself with a proprietary source of data that allows for results that are unobtainable by competitors. Also, to a lesser extent, domain expertise that allows them to tailor existing ML architectures specifically for the problem at hand.

Yep, just grab any random ML paper off the top of arxiv and have your product development team replicate it. Even if it doesn't end up working properly at all and you have to shelve it for a more common solution, you can still claim the 'revolutionary' technique is in R&D at your company. Most investors can't tell the difference.

> And a large fraction of those who do have re-used existing open source solutions without digging a lot into them.

This is a good thing. Coming up with new ideas is for researchers. Turning them into a product is for entrepreneurs and engineers. One of the reasons I see so much promise in ai startups is because the research has matured to a point where anyone can take it off the shelf and apply it to their domain. Similar to the web, there was a few early companies that did great things with novel engineering, but most of the value generated was CRUD apps built on top of frameworks like Ruby on rails.

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