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Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

nytimes.com

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Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#81

Post by newsbinator »

skohan wrote: post #58
newsbinator wrote: post #41

Earlier quoted context omitted.

It may not be necessary for a system to be as complex as the human brain to reach the raw intelligence levels of the human brain. You don't need to make a vehicle as complex as a cheetah for it to move as quickly as cheetah. You just strap a rocket to a box and you're there. Granted a rough solution like that is not as good or as agile as a live cheetah in a lot of ways, but it depends on what you're optimizing for.

You can also train a horse to tap it's foot once for "yes" and twice for "no". You can ask it a bunch of questions, and it can seem like you're having a conversation, but it turns out the horse is just paying attention to a few details about your body language and performing the action you gave it carrots for. It might seem like intelligence, even to the trainer, but it's a parlor trick.

Once the horse starts tapping its foot to accurately detect cancer better than humans, then, parlor trick or not, you've got a fine horse there.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#82

Post by username90 »

garmaine wrote: post #72
chii wrote: post #31

Earlier quoted context omitted.

> we start with some priors (random weights) i dont think we start with random priors. We start with a set of priors that are geared for certain things - facial recognition, language etc. The environment then fine tunes them.

There is no way that the genome encodes synaptic layouts to such a detail as to specify an algorithmic prior. At best it specifies, loosely, the generic architecture of how many neurons, how many layers, how tightly folded, and where the inputs (senses) connect. We develop the same algorithms because we all start with the same priors in largely the same gestational and early infant environments.

Right, watch this genius horse learn to walk and navigate a 3d environments just hours after birth: https://www.youtube.com/watch?v=RXKdYThau7c

No, we don't "learn" how to balance, how to identify objects and navigate 3d environments. All of that is highly codified in our DNA from hundreds of millions of years of evolution. Human babies, similar to kangaroos, are just born too early for those systems to have developed.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#83

Post by randcraw »

EGreg wrote: post #79
randcraw wrote: post #66

Earlier quoted context omitted.

That's like saying that causation doesn't matter; correlation will suffice. But the causal directionality of separating subject from object in a relation is essential to forming or sharing an idea usefully. In the synthetic game worlds you mention, causality is decided by play sequence. But at no point does the agent ever have to employ abstraction, or 'think about thinking', which diminishes the needed depth of thou…

Funny that you say that. David Hume said that you can’t meaningfully prove causation, only describe things and speak about about correlation. Also time is a local phenemenon. You can’t simply compare A and B time if A and B are really far apart. When you get to really small scales you would be hard pressed to find which event happened before the other, so you get uncaused events like Virtual Particles in quantum mech…

I won't challenge Hume; I'm referring only to the fact that a causal relation implies necessity: B can't happen unless A acted to cause it. Probability can't provide this insight, it can only encourage or discourage it. The agent must formulate a mental model of the relation in order to hypothecate the presence of necessity / causality between a pair of events. A model that lacks that ability (e.g. probability) can lead to all sorts of nonsense in interpreting events, like proposing causality. Or at least meaning by connecting a baseball pitcher's hat color with the weather on game day.

BTW, precision isn't necessary for relative time to have great value in a huge fraction of possible world events. Obviously all you need is relative temporal sequence to disprove causality - since the batter can't hit until the pitcher throws.

In a great many event pairs, a lot of temporal precision Is unnecessary. Often it's only approximate relative time that matters, since the mental task intuits all the temporal constraints needed for understanding event coupling, like knowing the parent child relation implies the relative ages of each and much more latent info that may or not be relevant to a given interpretive task at hand.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#84

Post by randcraw »

YeGoblynQueenne wrote: post #78
randcraw wrote: post #67

Earlier quoted context omitted.

But its not clear to me that encoding productions in source code adds any value over encoding them in the various other forms used by expert systems of old. Prolog also compels binary constraint satisfaction, recursive descent parsing, backtracking, and depth-first resolution — all undesirable constraints that are easily avoided using other fact representations or resolution engines. Until the startup in question can…

I wouldn't say that backtracking and depth first search (not resolution) are undesirable. Far as I'm concerned they're pragmatic choices that minimise the amount of resources necessary to perform resolution theorem-proving.

I would hate for my logic engine to be required to backtrack every time a candidate interpretation was evaluated. AFAIK, prolog allows no alternative. Nor can it support the many powerful probabilistic extensions for rule-based reasoning that arose 25(?) years ago, nor the many improvements and variations on impasse resolution.

IMHO, the choice of prolog unnecessarily straitjackets a modern production system, making the engineering approach of this startup that much less powerful, flexible, or viable than it should be.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#85

Post by EGreg »

randcraw wrote: post #83
EGreg wrote: post #79

Earlier quoted context omitted.

Funny that you say that. David Hume said that you can’t meaningfully prove causation, only describe things and speak about about correlation. Also time is a local phenemenon. You can’t simply compare A and B time if A and B are really far apart. When you get to really small scales you would be hard pressed to find which event happened before the other, so you get uncaused events like Virtual Particles in quantum mech…

I won't challenge Hume; I'm referring only to the fact that a causal relation implies necessity: B can't happen unless A acted to cause it. Probability can't provide this insight, it can only encourage or discourage it. The agent must formulate a mental model of the relation in order to hypothecate the presence of necessity / causality between a pair of events. A model that lacks that ability (e.g. probability) can l…

And what I am saying is that when you say A implies B you’re making a ton of assumptions. If those assumptions are violated (eg all swans are white, all men are taller than all women) then suddenly you have caveats and hedges.

So what you actually use is not Propositional Logic / Boolean Algebra but Bayes’ Theorem. You only use rules of inference because you make assumptions.

I am saying that may work sometimes but those models are not exact.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#86

Post by garmaine »

username90 wrote: post #82
garmaine wrote: post #72

Earlier quoted context omitted.

There is no way that the genome encodes synaptic layouts to such a detail as to specify an algorithmic prior. At best it specifies, loosely, the generic architecture of how many neurons, how many layers, how tightly folded, and where the inputs (senses) connect. We develop the same algorithms because we all start with the same priors in largely the same gestational and early infant environments.

Right, watch this genius horse learn to walk and navigate a 3d environments just hours after birth: https://www.youtube.com/watch?v=RXKdYThau7c No, we don't "learn" how to balance, how to identify objects and navigate 3d environments. All of that is highly codified in our DNA from hundreds of millions of years of evolution. Human babies, similar to kangaroos, are just born too early for those systems to have develope…

Is this sarcasm? You do know that babies (human or animal) exercise their muscles and gain body awareness during gestation, right?

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#87

Post by garmaine »

randcraw wrote: post #74
garmaine wrote: post #68

Earlier quoted context omitted.

I’m not sure what you are saying. Computational semantics parses sentences to possible interpretations in first order, or higher order logics. It is not some sort of alternative to FOL. And it is still a rapidly developing field.

I'm trying to suggest (perhaps badly) that formal models of logic modeling like LS / CS have an innate achilles heel (from what I've seen): either A) the knowledgebase is precise but tiny, and will remain so because it had to be meticulously hand crafted to comply with a formal semantic model, or B) the KB is representationally sloppy but big and scaleable, because it was populated automatically and informally (proba…

That describes the weaknesses of existing academic attempts not the theory in any way. I could say the same things of machine learning actually.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#88

Post by dhab »

randcraw wrote: post #84
YeGoblynQueenne wrote: post #78

Earlier quoted context omitted.

I wouldn't say that backtracking and depth first search (not resolution) are undesirable. Far as I'm concerned they're pragmatic choices that minimise the amount of resources necessary to perform resolution theorem-proving.

I would hate for my logic engine to be required to backtrack every time a candidate interpretation was evaluated. AFAIK, prolog allows no alternative. Nor can it support the many powerful probabilistic extensions for rule-based reasoning that arose 25(?) years ago, nor the many improvements and variations on impasse resolution. IMHO, the choice of prolog unnecessarily straitjackets a modern production system, making…

what would be a better alternative to Prolog?

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#89

Post by IshKebab »

garmaine wrote: post #71
gaius wrote: post #17

Earlier quoted context omitted.

It doesn't mean "deep and meaningful". It would not be nearly as well funded were it not for this misconception on the part of budget holders, and everyone involved in ML knows it.

I have never once encountered this misconception until now. Every explanation of deep learning I’ve seen begins with an explanation of the origin of the phrase.

A few weeks ago someone submitted an entire article to HN that consisted of someone complaining that deep learning wasn't really deep, in the deep & meaningful sense. They clearly misunderstood. Was pretty funny.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#90

Post by skohan »

newsbinator wrote: post #81
skohan wrote: post #58

Earlier quoted context omitted.

You can also train a horse to tap it's foot once for "yes" and twice for "no". You can ask it a bunch of questions, and it can seem like you're having a conversation, but it turns out the horse is just paying attention to a few details about your body language and performing the action you gave it carrots for. It might seem like intelligence, even to the trainer, but it's a parlor trick.

Once the horse starts tapping its foot to accurately detect cancer better than humans, then, parlor trick or not, you've got a fine horse there.

I don't mean to diminish the power and value provided by deep learning, but it doesn't change the fact that pattern recognition is not equivalent to intelligence.
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