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Comment by Barrin92 | Hacker News Reader

I really like that people asking fundamental questions about the way AI can be pushed forward beyond just ML seems to gain a little bit of traction again, at least in the public eye. Even Kissinger opined on the topic just a few days ago

https://www.theatlantic.com/magazine/archive/2018/06/henry-k...

I agree with Pearl that there's something deeply misguided about thinking that intelligence truly can be solved as a data optimization problem. I'd be happy if we'd see more research about AI at a symbolic level again.

The biggest hindrance seems to be funding. ML is really successful in a lot of commercial domains, general AI is a big moonshot. With most researchers moving from long term university positions to the business sector I'm concerned about this sort of research, not just in computer science.

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> I agree with Pearl that there's something deeply misguided about thinking that intelligence truly can be solved as a data optimization problem.

When all you have is a hammer... I think we’re really thinking about the whole thing backwards, as if intelligence only comes from the cortex of the brain. I think human level intelligence is fundamentally an emotional state of being - our core lizard brain values aren’t something to be swept aside - they’re a fundamental part of real intelligence. It’s like trying to build a car starting from the body and electronics rather than the motor.

Well, if a computer scientist only has a bunch of data, is there any conceivable way to reason about that data that doesn't ultimately boil down to a data optimization problem?

I hear that correlation does not imply causation but also that you can't distinguish correlation from causation merely with a stream of data. Is there any way out of this situation with just data?

I think apart from the work Pearl and others did with the do-calculus, all you can do is suggest experiments. You can only ever have a true ai if it can interact and ask questions about the environment.

Well, even if an AI can interact with it's environment, how can those interactions not simply also be correlations between input and output. We have for example, recurrent neural networks which seem to do this already.

It seems like saying "it's not optimization" has to boil down to "it's not generic optimization, it's a special kind of optimization." But maybe there's some formalism I'm missing.

As a thought experiment, imagine an AI is trying to work out if a rooster crowing makes the sun rise or the other way around (because they are always correlated in its experience). It can do the experiment of putting the rooster in a bag and see if the sun doesn't rise. It can refine it's model after doing this experiment (all with optimization). It is more about untangling correlations that come from latent variables that the system doesn't know about yet.

Well, that sounds an awful lot like an optimization problem, and is actually reminescent of a Machine Learning technique called Active Learning: the model suggests data points for which it has low confidence (say images that resembles both a cat and dog according to its own understanding), and a human labels those (or in your case: the AI itself runs an experiment to get the ground truth) to gain the most information.

Yes. I completely agree. I think he (Pearl) is being unfairly provocative and there has been a lot of progress in these areas.

The thing is that "not knowing casualty" along with "lack of understanding" feels very plausible for an explanation of what current AI is missing. And while AI has made lots of progress, it certainly feels like this progress is all of a certain kind (though that could the human reflex to discount any problem solved by robot).

The problem might be that "understanding" isn't something we really have a good working definition of.

It's not quite the same as active learning. Experimentation and active learning are quite different in ways I didn't fully appreciate at the beginning of my career, and they come in the form of (conditional) independence relations, which you can get around with either structural assumptions or randomized intervention.

It's the entire reason why we randomize in experiments. In active learning, the machine doesn't know what happens when the rooster is in a bag, so decides to try it. In a randomized experiment, the machine knows what happens when the rooster is in a bag, but thinks there might be other factors at work, so it decides to try it in a way that it can be sure that all other factors are equal (at least on average).

To be considered a generalized AI, it does not just have to run experiments, it has to create hypotheses and devise experiments that will both test them and reject the null hypotheses, which brings us right back to reasoning about causality.

We can call it a data optimization problem, but that does not tell us anything about how we go about solving it, or even what we are optimizing for.

Alternatively, perhaps what's being optimized is so general that generalized AI is not, and cannot be, 'just' an optimization problem in any useful sense. Something like the biologically-inspired 'maximize lifetime production of descendants' does not get us very far.

"We can call it a data optimization problem, but that does not tell us anything about how we go about solving it, or even what we are optimizing for."

If you have a time-ordered stream of data, the problem of predicting the output given previous input naturally suggests itself. There are other approaches, of course.

"Alternatively, perhaps what's being optimized is so general that generalized AI is not, and cannot be, 'just' an optimization problem in any useful sense."

- Perhaps you mean this as "the problem is so complex that it can't be naively approached only with conventional, local optimization tools". Because a huge optimization problem is still an optimization problem in some meaningful senses, I mean I could consider my life as a "fun" or "flow" or "enlightenment" optimization problem.

In general, it seems like a lot of reactions to particular AI methods go from "this approach isn't enough to get us all the way there" to "real AI is fundamentally, absolutely different from this, this approach is useless", a jump I don't think is justified.

Your definition of data is too narrow. The result of any experiment is just data. All the human brain has to work with is just data. Either there is enough information to distinguish correlation from causation analytically, or humans are actually incapable of distinguishing the two. That doesn't mean current analytic techniques are advanced enough to do it, just that it is possible for machines if it is possible for man.

Either there is enough information to distinguish correlation from causation analytically, or humans are actually incapable of distinguishing the two.

Well, what I'd like to know is how you distinguish correlation from causation with any amount of data. What definition, in terms of data, do you use?

Note, probabilistic and algorithmic theories of causation have a long history and many problems, see:

https://plato.stanford.edu/entries/causation-probabilistic/

One thing that I never see brought into the equation but I believe is fundamental to human "general" intelligence is emotional response. It's hugely instrumental in terms of us deciding what's important to investigate, which input to analyze and how to mold our output. The importance of emotion in guiding and driving our learning is most evident in children, during the first four or five years of life of development.

I think a huge leap forward in the field could be made by modeling an emotional system similar to ours. It also seems like it would be an incredibly difficult and fuzzy problem to solve.

Addendum: After thinking about it for a few minutes more, I bet an implicit understanding of cause and effect could be derived by a system designed to try to optimize it's emotional state

I do think the impact of emotions on our thinking is understated, but I also think that focusing on this aspect is going at it backwards. A value system is necessary for emotions, otherwise happy and sad mean nothing. I think this is what you mean when you talk about "modeling emotions". At the same time, some intelligence seems like a prerequisite as blindly following a value system will lead to massive hedonistic AI orgies. Er, I mean, an override mechanism is required, and intelligence is the best possible solution, so we're back at the fundamental problem. I think there is a lot more intelligence behind even our most primitive urges than we realize. Hope that made sense, typing on mobile.

Or perhaps another angle can be explored - better than human intelligence directly instead of copying humans.

There are certain machines already better than humans(any other life form) in physical tasks — why do we need to have human intelligence as the base scale to measure machine intelligence?

I mean, the early man didn’t create his tools to compete with human nails.

> A value system is necessary for emotion

I think a value system is mostly a post hoc justification of our emotional states. I view emotions as primarily a tight biochemical feedback loop. Think more of the four “f”s than, say, platonic feelings of love.

I think it’s the opposite - they come from innate chemical values (procreation, socialization, base metabolic needs) and emotions are created upwards from reaction to stimuli as it pertains to those values. Maybe we’re saying the same thing, but the starting point is important as it pertains to any artificial intelligence being able to be created.

Isn't this the theory of co sciousness presented in After On? I am curious what people are formally studying emotional theories of co sciousness. I'd love to read more.

You've read After On? How was it? I picked up a copy a couple of weeks ago but haven't gotten around to reading it yet. Is it worth bumping it up the priority queue a bit?

It could start with a value system rather than raw emotions - at some point it might decide that it doesn’t really want to do some work. The idea of controllability in all senses I think complicates the issue, but it’s definitely a necessary safety net.

Not just emotion, but also aversion to certain physical states - such as hunger or pain.

Because of that, I think a "true AI" - at least one without a simulated humanlike body - would have rather different desires. Optimization towards those desires rather than humanlike desires would likely result in something that distinctly does not act human.

I can't help but wonder if we've ignored or disregarded any first steps to a general AI because the resulting building blocks didn't match any of our expectations from real-world models of instinct/intelligence.

More than funding, then issue is data. Untangling correlation from causation usually requires running additional experiments.

It's important for people to understand these limits of current ML, but they are happening for a good reason. You can apply associative reasoning in many environments that don't support causal reasoning, and we are still scratching the surface of the value discovering associations can create.

Nature herself "solved" intelligence as a data optimization problem. We can too.

I don't disagree. However, the next time you find yourself pondering the question "Why did Bob do X? That's so bizarre, and it makes no sense", you should consider what the repercussions may be if Bob were not just your awkward acquaintance but rather an autonomous computer system distributed across the globe that's assigning credit scores to people. Is there really room for 'Well it just felt right at the time' in those situations?

Humans are often quite bright, but we're also known to do the wrong thing for no discernible reason. This is to be expected when there's no fundamental formal system behind behavior, and behavior is instead driven by a black-box neural network.

Sure, I don't concede the point.

But even if it were true, do you want to spend billions of years solving it that way?

There's problems with that attitude. First, it offers no scientific insight. We don't learn anything about the fundamental nature and skeleton of minds by simply replicating evolutionary processes. It does not grant us knowledge.

The equivalent in engineering would be to throw over trees and rocks in the hope of building a bridge. Clearly, that is unsatisfactory, we strive to understand the meaning of systems so that we can reason about them and alter them in predictable and fundamental ways.

Secondly, we don't know how likely it is that evolution produces intelligence. Maybe we're the only intelligent spot in the universe and it's an aberration. It took 4 billion years as well.

That seems to be a fundamentally impoverished way to go about things. We shouldn't forego the ability to understand minds at a deep level just because we have made practical strides in closed domains. That would be to mistake a trojan horse for an actual horse.

>The equivalent in engineering would be to throw over trees and rocks in the hope of building a bridge.

Imagine if aliens dropped a machine learning computer on earth in the 17th century.

Maybe we'd have never bothered to derive the laws of classical mechanics.

I don't follow. Classical mechanics are still very useful, no?

Yes, but we might have never created metal models and theories if we had a magical machine that could predict the outcomes of physical systems.

The machines might have replaced classical mechanics, but the downside is that the machine would be a black box, and we would never really understood how it derived its results.

Fair. That assumes we wouldn't try and understand the black box. I suspect somebody would have, but I have nothing to base that suspicion.

Nature has a huge environment to run its agents in. AI can only access games. Robots IRL are too expensive for optimisation. Solution: better simulators.

What was the data optimization problem? To pass our genes on? Because lots of critters have been doing it far longer than the big brained ones. Bacteria and viruses are more successful than any life forms, and they're not running any neural equivalent of data optimization.

Well, if you consider nature as an agent, we are part of it. And if we do successfully leave the planet, that will be nature successfully leaving a planet. Which will get nature closer to surviving a sun collapse. Something the rest of nature here is not likely to do.

And, in the scale, nature has had to deal with many stars collapsing.

We have an intelligence, while Nature doesn't. We can do better than Nature.

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