dhammack

Was this summary generated by a LLM?

On the broader point though, there certainly are some companies that have access to unique datasets that they can take advantage of. Meta and Google come to mind as obvious ones, probably Microsoft too. Apple I'm less sure given their privacy stance. Any others?

Had a similar experience in middle school. We started initially doing net-send to specific users and having played around a little bit with batch files/command prompt before I tried net send *.

Well the entire county was on the same network. All computers in the school district received my message. Fortunately the message was just "hi", and I think it was only sent once.

Like the OP, in retrospect I saw that the message shows the sending computer+user. Since we had student-specific logins it didn't take long before I was tracked down and reprimanded. I think they even told my parents about it and were threatening suspension etc. But at a certain point it became kind of obvious that the network/configuration was more at fault than a curious kid. So I got off with a stern warning and narrowly avoided a life of crime.

You can get the real content without the commentary at https://www.volcanodiscovery.com/lapalma/sep2021seismic-cris...

Also see https://en.wikipedia.org/wiki/Cumbre_Vieja_tsunami_hazard for a good summary of the risks.

Thanks for posting. In the early 2000's me, my brothers, and my dad spent several months taking pictures and submitting each sequential minute between 8-9. Looking back probably 20 years later now it is quite amusing to still see the pictures there! It has been a long time since I heard about the human clock.

Looks like only scoring code, not training :(

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Hey I'm not familiar with this way of comparing classical and quantum computation. Can you point me to some more details? I have Nielson's book but don't remember seeing this analogy before!

I see. I still think it's easier to apply deepmind's feature transform on text rather than to try to invert a neural network. Armed with a network trained on Trump, deepmind's feature transform from text->network inputs, you should be able to make him say whatever you want, right?

Text -> features -> TrumpWaveNet -> Trump saying your text

It seems like you're using WaveNet to do speech-to-text when we have better tools for that. To transfer text from Trump to Clinton, first run speech-to-text on Trump speech and then give that to a WaveNet trained on Clinton to generate speech that sounds like her but says the same thing as Trump.

My hypothesis is that all RNNs (and in general complex dynamical systems) need to be reset periodically. If run for too long without resetting, they tend to get stuck in strange states, blow up, or cease activating. You can see this effect by running a generative RNN model for a long time - eventually the output is garbage.

Under this model the next obvious question is why it takes so long to reset the brain's state. Maybe it can be done faster.

It just finished, I got 4th. I used convnets. With the correct initialization and data preprocessing they actually are quite easy to train.

The other thing hedge funds offer is alternative markets to track. So while some may track the U.S. stock market (which you can get by just buying SPY), some funds track other markets like commodities, foreign equities, housing, bonds, etc. Almost anything that you can put a price on you can find a hedge fund willing to trade. For these "nontraditional" investments it can be rational to pay someone even if they cannot beat the benchmark, simply because you couldn't do it yourself.

If you think the true sentiment is symmetric, you can just change the decision threshold so that your algorithm answers positively about half the time. Just say positive when the sentiment is greater than the mean sentiment over your training set.

You also may want to try betting on the spread between male-only companies and ones with at least one woman on the board. If the investment thesis is sound (board gender diversity is beneficial), this would be a good way to test it. It would also bring you into market-neutral territory.

#4 is the only one which I would say does not hold. I'd be very surprised if a firm of manual traders could consistently outperform the market (or beat a Sharpe of 1).

FYI - Hinton is doing an AMA on r/machinelearning on Monday!

Very impressive! Some cool ones it recognized:

milk jug http://cdn-jpg.allyou.com/sites/default/files/image/2014/01/...

["milk", "gallon", "jug", "product", "detergent", "plastic", "water", "soap", "canteen", "white"]

mushroom cloud: http://www.atomicarchive.com/Effects/Images/WE12.jpg

["fire", "bomb", "mushroom", "flame", "letter", "font", "hell", "volcano", "smoke", "burn"]

I think it's rescaling all images to fit the training size. If that is the case, then when your image has very different dimensions it gets distorted and confused. Try something with a height/width ratio like the samples.

Your argument is more philosophical than mathematical. Lines are traditionally defined as the set of all points which satisfy some critera. In this case, a line is precisely made up of points.

Can you imagine the datasets FB has to work with? Statuses, pictures, locations, social graphs. It's incredible. It's hard to blame LeCun/Hinton/Ng for moving towards industry with data like that. I'd bet that the only place with more data than FB/Google is our good friends at the NSA.

I like to think of the vectorized representation as just a nonlinear transformation to a higher dimensional space with a classifier afterwords. If you're familiar with linear algebra, then z = Wx, where W is a matrix of weights and x is a feature vector maps x (which could be something like 5 dimensional) to a new space (which could be like 50 dimensional). z is the representation of x in that new space. After this linear mapping, we apply a nonlinear transform (sigmoid, rectifier, etc). If we didn't have the nonlinear transform, then the entire model would just be linear! This follows from the fact that the composition of linear functions is itself linear.

The final layer is just a standard logistic regression classifier in the new (usually higher dimensional) space.

I've managed to get an intuition for backpropagation (the way gradients are computed for neural networks) using a similar analogy. The basic idea is that it's just a signal moving in the opposite direction in the network - it starts with computing the derivative of the loss, and goes back through each layer, like a breadth-first search.

The test suite is amazing.

https://github.com/ajalt/fuckitpy/blob/master/tests.py

    def test_chaining():
        fuckit(fuckit('fuckit')) 
        
        assert 'false' # Good thing this isn't PHP

It's up in North FL

Ah, that explains why the groupings are so good. That's cool, I hadn't thought about topic models outside of NLP before.

Nice! I assume LDA was run on the lyrics?

Did you roll your own version of t-sne to use in Erstaz? Now that I think about it, I've never seen a 3D visualization with t-sne before (and searching just now didn't find any), it could be that t-sne doesn't work as well in 3D (or has bugs which only appear in >2 dims).

Hey Dave - Your visualization is cool, no doubt, but I'm not seeing as much structure in the 3D cloud as I see in 2D visualizations. In 2D, the words form clusters of similarity which are pretty obvious, but I don't really see that here. What training data did you use for word2vec, and for how many iterations did you run t-sne?

I ran some visualizations of my own a while back, and they're a lot less aesthetically pleasing, but you can see more structure and clustering.

https://raw.github.com/dhammack/Word2VecExample/master/visua... https://raw.github.com/dhammack/Word2VecExample/master/visua...

(It could also just be a WebGL thing, because the third dimension seems to have a lot less variance than the other two)

For 2D visualization, t-sne is an excellent tool. I've used it with word2vec and you can see clusters of similar words:

https://raw.github.com/dhammack/Word2VecExample/master/visua...

https://raw.github.com/dhammack/Word2VecExample/master/visua...

https://raw.github.com/dhammack/Word2VecExample/master/visua...

And more in https://github.com/dhammack/Word2VecExample/tree/master/visu...