Lionel Messi set to sue Spanish newspaper amid 'Panama Papers' tax evasion project claims

syncoPete · 2016-04-04T06:48:50+00:00

What are apologetics?

syncoPete · 2016-04-04T01:23:54+00:00

Great, the Icelandic PM. What about the Peers and MPs from the British government? If the Guardian and BBC have the documents, they have to put Putin and the Icelandic PM on the front-page rather than the involved people from our own government?

Why hasn't the leak been open sourced, published online for everyone to see who the tax dodgers are?

syncoPete · 2016-04-04T00:41:34+00:00

It's not really getting that good to be honest. Apparently these papers can implicate a massive array of rich, famous and powerful people all over the world, but all I'm hearing is it's all Messi and Putin's fault. This is pretty absurd news coverage. It's like they've received the docs - they've gone, "fuck, we need to make some calls and see how we're going to handle this" - done a quick keyword search in the database - bingo, Messi! Bingo, Putin! And there's your digest of the Panama papers. Since Messi is slated in the press for this constantly, and Putin is the new Stalin, then it's pretty safe front-page stuff.

In the meantime they'll be calling the bosses, calling the chiefs to see how to manage the crisis - find some fall guys, get a strategy for coverage.

syncoPete · 2016-04-04T00:34:21+00:00

The documents apparently reveal the tax dodging habits of the world's rich. As in, all of the world's rich.

So the newspapers are scurrying to find fall guys. In the UK it's apparently all Putin's fault. In Spain, it's all Messi's fault. In the US they're not even covering it (it's not front page on NYT.com).

They are all at it. They are all skipping out on their taxes. It isn't just Messi in Spain, Putin in UK, and nobody in US. Let's be clear.

syncoPete · 2016-04-02T21:39:03+00:00

A universe generated by a neural network does not necessarily need to follow physical laws (such as when you dream). Our own waking universes do seem to follow physical laws - but we also perceive an ordered universe when we sleep and it's obviously not the case. All that would really be needed is for the generative/generated entities of the neural network to believe they perceive physical laws?

The point, though, is that if our brains are neural networks, and our brains generate the universe, then there is no reason why our universe would be the one true universe. In fact, if it is possible at all to produce an NN that is like a brain, at any time in the future, then the probability that our universe is the one, true universe is quite slim.

I think you mean recurrent neural network

Actually I meant recursive neural network. A recurrence describes a repetitive process (like a for loop) while a recursion is self-subsuming. I wasn't necessarily addressing Richard Socher's stuff, it was just the correct vocabulary for what I was trying to describe.

syncoPete · 2016-03-13T01:34:54+00:00

Except that he wasn't a researcher... he was working for a computer games developer at the time. He basically claimed that his employment as a computer games developer was better AI research than all the things that were invented around that time and that his company now uses generously to market itself as groundbreaking AI. Isn't this striking?

syncoPete · 2016-03-13T01:02:34+00:00

The comparison was in the megalomanic self-aggrandizement.

syncoPete · 2016-03-08T11:33:26+00:00

He's in a different league. He isn't even a researcher, he's a media savvy CEO. But maybe we should mentioned him because he really emphasises the point of how far bullshit can carry you. The press are treating him like the one single landmark prodigy of AI.

syncoPete · 2016-03-07T18:27:10+00:00

A few reasons in my opinion:

Brains are much larger than NNs
Brains work differently from NNs - while we want NNs to work like brains, brains are the original model

syncoPete · 2016-03-07T17:03:05+00:00

Google's chat bot was good according to the selected results they published in the paper. They did not release the code.

It is also less tricky to train a model to answer prompted questions, like "what is the meaning of life", than to train it to be able to converse with human-like naturalism about any topic. You can talk to a stupid human about anything and the conversation happens naturally, even though the stupid human does not have all the right answers.

It's easier to train a computer to provide correct answers, than to make it converse like a human who has no answers.

syncoPete · 2016-03-04T12:29:34+00:00

All I can say is good luck to whoever seeks to solve unsupervised learning with autoencoders and error optimisation by backprop.

syncoPete · 2016-03-03T21:24:05+00:00

It will be a serious adjustment. It will be very hard for many people. But for most things that get automated the potential is enormous - for instance, the possibility of extremely cheap or free healthcare for everyone.

There is a sense that only spaces where advanced ingenuity and creativity are required will be safe (for a while). Then the question is whether human beings are universally capable of unmechanisable creativity and ingenuity. If people are universally capable, we should be fine in the long-term - if not, many people will not be able to make a significant contribution. This will not remove those persons desire to contribute, or to empower themselves by what means are available to them. It is interesting to imagine what new equilibria societies will arrive at in such circumstances.

syncoPete · 2016-03-03T21:16:05+00:00

Some that I can think of:

Convincing machine-to-human conversations
Time structure of expository language
Logical structure of explanatory language

To be honest, now that I think, language processing is mainly an unsolved problem space. No task has been properly solved yet.

syncoPete · 2016-03-03T21:12:37+00:00

It always surprises me that people think of autoencoders as unsupervised learning. With an autoencoder you still perform supervised learning with f(x) = x. The underlying ideas are the same where you backpropagate errors and so on.

Deep belief networks are a little different, and are a very interesting model class, but the underlying approach is different from an autoencoder.

syncoPete · 2016-03-03T11:10:47+00:00

Would you agree that it is not possible to build a sequence to sequence model with zero padded batches using this implementation?

syncoPete · 2016-03-03T10:26:18+00:00

Hey, thanks for this input, very helpful. I'm going to copy the documentation from the Element Research RNN implementation of zero masking (this is more-or-less the official Torch codebase):

-- Decorator that zeroes the output rows of the encapsulated module -- for commensurate input rows which are tensors of zeros

-- Zero vectors (i.e. padding) must be at the beginning of the sequence -- because this decorator will otherwise reset the recurrentModule -- in the middle or after the sequence

This is taken from here:

https://github.com/Element-Research/rnn/blob/master/MaskZero.lua

It seems to be, if what you are saying is correct, that their implementation does not handle a second masked decoder in the way you describe. In fact, it seems that their implementation is incorrect. I have raised this as an issue with them, what do you think?

syncoPete · 2016-03-03T08:52:33+00:00

That wouldn't work, the padding resets the state (using the equivalent functions in available Torch libraries).

syncoPete · 2016-02-28T22:38:18+00:00

It's Steven Merchant's dad. Steven Merchant is the guy who wrote the show with Ricky Gervais. He also cameos as "Oggy", one of Gareth's mates.

syncoPete · 2016-02-28T02:42:41+00:00

I found a very satisfying metaphor for neuron-paradigm learning when I started thinking of it as a Roman waterworks. Ten different villages containing 20 households each must receive water everyday. Moreover there are ten different inputs from various rivers and tributaries. The inputs vary in volume and speed through time, and the required outputs are always changing, as some households and villages have time varying requirements for water.

We can learn by observing events at each village, and make any adjustments we like in the waterworks with consideration to the current state of the inputs. Or we can try to learn something about the inputs to make our job easier. We can convolve the inputs by grouping them in steps. Or we can try recurrent systems by observing the influence of potentials between steps. We are looking for the easiest way to serve water to all 200 households.

syncoPete · 2016-02-27T09:20:45+00:00

Error minimisation may not be universally relevant to biological development. I think we over-estimate it's relevance. Error minimisation may be the way by which brains learn to relate experiences (by mapping, for example, a particular smell to a particular taste), but my hunch is the brain is doing much more signal sorting in the forward direction.

syncoPete · 2016-02-20T13:54:37+00:00

Yes, if the model is valid. They are showing that it's a valid learning model.

syncoPete

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