[mojuba]

I think it should be obvious to most of us that intelligence is a combination of statistical learning, tree search, and the kind of generalized knowledge about the physical world the article is talking about. I don't believe any of the three components alone can excel at any meaningful or interesting task. ML can play go, but can't fold clothes, isn't it absurd?

Going back to self-driving, the main challenge on the roads seems to be the fact that anything or anyone can suddenly appear on the road in front of the car. It can be a drunk person, a slow animal (or a fast one), it can be a huge but empty cardboard box, or it can be a fridge in cardboard packaging left on the road for whatever stupid reason. The possibilities are almost literally infinite. A good FSD system should be able to assess, try to make a good prediction of the behavior (it's kind of OK to hit an empty box if I don't want to cause much discomfort to my passengers, but not OK to hit a fridge).

Hence in my opinion ML-based FSD is a dead end, always has been from the beginning. If you asked me 10 years ago I'd have told you the whole effort and billions of investments are going to get us some improved hardware at best but never a true universal self-driving system. The self-confidence of Google's executives, Tesla's and others' who repeatedly made predictions about this technology in the past decade is just astonishing. I've been thinking to myself all this time: how can they not see it? Really, where is this enthusiasm around ML coming from?

[mindvirus]

I think that's an overly pessimistic view.

For one, those are all situations people get wrong all of the time. FSD doesn't have to be perfect, it just has to be better than humans.

Two, once FSD is better than humans in most conditions, we can build infrastructure around it. Things like FSD only lanes to reduce erratic human driving etc.

The way I see it - solving FSD on highways seems like a very solvable and economically valuable thing to solve, even in limited ways (i.e. dedicated lanes).

Time will tell.

[andreyk]

"ML-based FSD is a dead end, always has been from the beginning"

To be fair, Google's (Waymo's) FSD is most definitely not fully ML-based, and afaik neither is Tesla's. Sure some people may speak like that's the case, but the technical approaches these companies take are not so naive.

[mojuba]

Surely with this amount of money and some of the smartest people working on the tech it won't be naive. It is however naive to think that a technology that can play the go game (also a mixed tree search + ML system!) can also navigate in the real world while causing minimal damage to the objects around it.

[jjk166]

> Going back to self-driving, the main challenge on the roads seems to be the fact that anything or anyone can suddenly appear on the road in front of the car. It can be a drunk person, a slow animal (or a fast one), it can be a huge but empty cardboard box, or it can be a fridge in cardboard packaging left on the road for whatever stupid reason. The possibilities are almost literally infinite. A good FSD system should be able to assess, try to make a good prediction of the behavior (it's kind of OK to hit an empty box if I don't want to cause much discomfort to my passengers, but not OK to hit a fridge).

How does a human driver determine if the box they are looking at is empty or has a fridge in it? How many times has there been no way to get to your destination without ramming refrigerator boxes? How often do you even find yourself pondering whether you should ram a box?

A good FSD system need only distinguish between obstacles to the degree that it can predict if the obstacle will remove itself or if it will need to find an alternate route, with the latter being a safe default assumption. Claiming an FSD system needs to do something even a human can't do is just moving the goal posts.

[rcxdude]

> Really, where is this enthusiasm around ML coming from

Because the ML based approaches to exactly the kind of problem you are describing are the most effective which have appeared for a long time. This isn't just hype (though the hype will paper over the still huge difficulties involved in applying this approach), it's genuinely the only approach which has much hope of working (It's also worth pointing out that these systems are almost universally a collection of subsystems each of which employs neural nets or other ML approaches to varying degrees, not a single black box).

[capableweb]

> Really, where is this enthusiasm around ML coming from?

Same as most other "enthusiasm" in this industry: HYPE!

Everything from self-driving to machine learning, TypeScript, React, Containers and everything in between is driven by hype, that is usually supported by huge companies investing in marketing to create said hype. Once the ball is rolling, it'll keep on rolling until people realize they got got, then we all move on to something else to hype. Rinse and repeat.

[mojuba]

Agreed though probably not with everything in your list, but it is true that it's the hype, typically originating from the executives of giant corporations that are capable of buying small startups in bulk. That's the source of some good things and some bullshit too. Then the investors follow suit, fund startups that can be potentially acquired and it doesn't even matter to them whether the thing works or not.

[mdp2021]

Hype to some - but without any hype, ANN are entities that show huge potential, are a still fresh, explored only for a fragment territory after so many decades, and present very intriguing mysteries in the translation between a network snapshot and the encoded knowledge.

So, an area fully deserving interest.

Of course the enthusiasm brings a lot of unduly expectation, unfruitful trial and errors etc. - just normal expectable accompaniments. They are not the most important phenomenon, unless the interest is posed for the wrong reasons (industrial implementations pretending the technology is a universal solution).

By the way: "hype cycles" have occurred many times in the history of AI. All experience for collection.

[mdp2021]

> Really, where is this enthusiasm around ML coming from

Pioneering, squeezing the potentials out of a technology which has immensities to offer - just not necessarily ready for specific industrial applications. That, the article shows pretty well: a problem - "self driving vehicles" - was proposed and the tehcnology is imperfect for that one specifically.