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Building high-level features using large scale unsupervised learning

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Re: Building high-level features using large scale unsupervised learning

#191
post #169

Earlier quoted context omitted.

Maybe you didn't realize this, but the science fiction author bit wasn't meant as a slander. Many high quality people, and also Kurtzweil, are science fiction authors. What I was getting at was "you realize they're writing books to make people happy for money, not doing legitimate science on that day, right?" . "words - such as variety and polymorphism - have different context specific meanings." Sure. All handwaving…

1.) Okay. 2.) Singularity as in breakdown not as in single. You purposely muddled the meaning to make your quip work. 3.) Moore's law fueled as in AI gets interest on their intelligence. Recursive as in AI makes smarter AI makes smarter AI... 4.) Science fiction or not i find it unlikely. 5.) Bayesian. Look up prior. 6.) 2131 was tongue in cheek. 7.) Thanks ;) You actually never address my main point though.

"2.) Singularity as in breakdown not as in single. You purposely muddled the meaning to make your quip work."

This is a blatant falsehood. I have solely and exclusively used it as a title for Kurtzweil's concept. It has no meaning; it's a name. I have muddled nothing. It is inappropriate for you to make accusations like this without evidence.

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"3.) Moore's law fueled as in AI gets interest on their intelligence"

Yes, that's what I said at the outset: this whole thing is driven by the false belief that intelligence is a function of CPU time. There is no experimental evidence in history to support this, and there are 65 years of counter-examples.

Repeating it won't make it less wrong.

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"Recursive as in AI makes smarter AI makes smarter AI..."

Oh.

This gets to a different false presumption, namely that the ability to create an intelligence, as well as that the power of the intelligence created, is a linear function of the prior intelligence.

This whole treating everything like it's a score, like it's a number you tweak upwards? It's crap.

You can't make an AI with an IQ of 106 just because you have a 104, and the guy who made the 104 had a 102.

This is numerology, not computer science.

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"4.) Science fiction or not i find it unlikely."

I can't even tell what noun you're attached to, at this point. What do you find unlikely?

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"5.) Bayesian. Look up prior."

What about bayesian, sir? I don't need to look up prior; I used it, correctly, in what I said to you. You're just telling me to look things up to pretend that there is an error there, so that you can take the position of being correct without actually having done the work.

There are zero priors of alien life, sir. That was my point, in bringing up what you're now blandly one-word repeating at me, in your effort to gin up a falsehood where none actually exists.

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"7.) Thanks ;) You actually never address my main point though."

You don't appear to have one.

Maybe you've forgotten that you were replying to someone else, who already said that to you?

Re: Building high-level features using large scale unsupervised learning

#192
post #184

Earlier quoted context omitted.

The train is not labeled, so it is not possible; and they do not mention that the labeled set was split or used in validation -- it is just called "test".

"We followed the experimental protocols specified by (Deng et al., 2010; Sanchez & Perronnin, 2011), in which, the datasets are randomly split into two halves for training and validation. We report the performance on the validation set and compare against state-of-theart baselines in Table 2. Note that the splits are not identical to previous work but validation set performances vary slightly across different splits."

As I understand, this is only about this side experiment with ImageNet data which uses logistic regression on those neurons in some cryptic way; I was trying to comprehend the core work (faces) before that.

Re: Building high-level features using large scale unsupervised learning

#193

Earlier quoted context omitted.

Claude Shannon had demonstrated a chess learning system which taught itself by playing him to defeat him in under two weeks as early as 1949. I call "citation needed", and needed quite badly: Shannon was a much better chess player than any program available in 1949.

I have no interest in your presenting your unwillingness to do basic research as if it was a valid form of skepticism. Whether or not you believe me, everyone else just went ahead and took a quick look, and learned something. Frankly, I would be happier, given your seeming inability to be a part of this conversation in a polite way, yet also your seeming unwillingness to depart this conversation even after it was req…

At one endgame, perhaps, with a winning position to start with, but not a chess program as currently understood.

Re: Building high-level features using large scale unsupervised learning

#194
post #192

Earlier quoted context omitted.

"We followed the experimental protocols specified by (Deng et al., 2010; Sanchez & Perronnin, 2011), in which, the datasets are randomly split into two halves for training and validation. We report the performance on the validation set and compare against state-of-theart baselines in Table 2. Note that the splits are not identical to previous work but validation set performances vary slightly across different splits."

As I understand, this is only about this side experiment with ImageNet data which uses logistic regression on those neurons in some cryptic way; I was trying to comprehend the core work (faces) before that.

Well, that's the record breaking bit.

Re: Building high-level features using large scale unsupervised learning

#195

Earlier quoted context omitted.

Actually, what's significant about this work is that labeled training data was not required: "Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not."

Unsupervised training is not particularly significant, and was the original form of neural network. "our experimental results reveal" No, they don't. We had one of these in the 1980s.

[deleted]

Re: Building high-level features using large scale unsupervised learning

#196
post #50
post #41

I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.

That's what I did a few months ago - quit my job and decided to go to a grad school to study AI (with focus on neural nets and ML).

I'm too dumb for gradschool. It'll have to be self taught.
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