The "Outside the Box" Box | Hacker News Reader

The "Outside the Box" Box

overcomingbias.com

44 pointsprakash18 comments

you're always in a box. being aware of the box can help you tremendously. it's when you think that you've left the box that's dangerous, because you're still in the box, but now you don't know it.

It's even better to recognize when your competition has sequestered themselves in a box; when your competition is being predictable.

You're always in a box, but it's not necessarily a cubic box, and it usually has more odd nooks and crannies than you'd expect. Most people restrict themselves to a certain well-explored subset of your problem space. The trick is to explore the rest of it.

And this analogy is overextended and cliché. Let's move on and find better analogies.

Yeah, you are always in the box. But, you dont know its boundaries, thats when it gets intersting and challenging

of course exploring the box from the inside can never tell us if the box is bright purple and unusually shaped, because there is no outside to stand in. if aliens ever come along, they might tell us things that are along the lines of "well of course your box is purple how could you not see that?" and of course we'll be able to tell them that their box is lime green.

Well said.

http://www.harveycartel.org/metanet/corporate/letter16072004...

So it's not a box, it's more of a hypercube.

People should stop associating neural networks with backpropagation networks. That is like saying all of AI is based on simplistic rule-based systems. I guess it is mostly people with only casual knowledge of both fields who make suggestions of that sort. Also, there is no reason to get outside the box. Most research and VC money tends to concentrate in one corner of the box. Go forth, be brave, and explore the box first!

I've been taking grad school courses in AI, and I'd say it's all rule based...stochasticity doesn't change that.

By "simplistic rule...", I implied something deterministic. If you define a rule as something that can include stochasticity, then how is a neural network something more than a stochastic graph with rules for communication?

It isn't.

I don't object to "backpropagation" aka gradient descent, which is a bit of simple calculus-based optimization. What I object to is the widespread public perception that saying "neural networks" is a brilliant new paradigm-overthrowing Key to General Intelligence, after over three damn decades.

Biologically inspired stuff? I applaud it to the extent that it works. It's not a magic key to anything, and it doesn't avert the challenge of understanding.

it also seems the surest path to producing an intelligence that acts as if it were a product of natural selection, i.e. one that is selfish, xenophobic, etc. (other sexual competition based heuristics).

Considering that the backpropagation and variants thereof gained significant commercial/industrial adoption while "monkey and banana" type problem solving systems mostly failed, I think his criticism of artificial neural networks is quite unwarranted.

The entire change has been that people aren't trying to go for 'general intelligence'. They approach it purely as a statistics problem. We have some data, what can we do with the data, how accurately can we do that?

You know, our box is made of billions of tiny vibrating geometric figures. The box is weirder than we thought.

not this guy again

/not a fan of this author

Why can't everyone love me? Why, why, why?

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