nacc

I don't get it. Where is the spec? What's the aperture size, focal length of this beast? What about abberations? How does it work in extreme conditions that a normal lens can't take a good image?

So many questions, but the author decided to use it to take portraits in day light ...

Being someone who is still struggling with these concepts, I like this tutorial because at least it doesn't use the common list/maybe/state to illustrate the concepts. Somehow I feel these concepts are so abstract - unless one is well versed in category theory, maybe only a data approach can prevent people from overfitting these concepts to specific examples.

I would really hope to see a tutorial that have a diverse set of examples and just fmap each example with a light explanation of say, what is a monad in this code and what is not, and because it's a monad we can do this.

Essentially the tutorial can just train a classifier in one's head, and with a nice set of examples maybe the brain can learn a general representation of concepts for the classifier ...

I am still pretty new learning haskell, and find this very helpful. Not necessarily agree whether monadic solution is prefered, but I like how it shows how general monad is: it's hard to grasp the generalization from only a couple of simple examples normally found in any haskell book. I hope there are more articles like this showing how these abstract concepts can be applied in somewhat unexpected places.

Just to clarify, these MRI images looks at white matter, which are mostly bundles of long-range myelinated axons. This is more likely a map of major highways in the brain (Or really the map of bundles of major highways in the brain, as MRI only has millimeter resolution).

The connectome at the cellular level is massive. I think part of the mouse visual cortical connectome has been mapped out, and the data is on the order of tens of terabytes.

I think it really depends on the task. We are just hijacking the brain machinery to do jobs it is not evolved to deal with. If we can find some highly evolved / optimized brain function which reflects the structure of the new job, the brain can process it much more efficiently.

Text might be good for programming because, most of the programming are sequential, text is sequential, and text processing is highly optimized in the brain because language. But in fact, we also use a bit of visual programming (indentation, paragraphs) in text to reflect part of the program structure that are not sequential.

If the program is completely non-sequential, visual tools which reflects the structure of the program are going to be much better than text. For example, if you are designing a electronic circuit, you draw a circuit diagram. Describing a electronic circuit purely in text is not going to be very helpful.

I have thought about the same thing when playing with deep learning at home, until I put electricity bills into the equation. A beast like what the author has built, with full power, will cost $0.1/hour in electricity bill alone at where I live ... which almost equal to the cost of a p2.xlarge spot instance on Amazon.

So if someone else is paying for the electricity, build your own gig, otherwise, Amazon is pretty hard to beat.

I spent almost equal amount of my life in China / North America now, it's interesting to see and compare what one side think about the other.

In north America, what I get is China is controlled by dictators. Life sucks over there because there is no freedom, a lot of corruption, no respect to rules, and is generally a chaotic mess. While in north America, democracy and freedom is wonderful, and everyone has the power to make changes of society, and in the end the government has to care about people to stay in power.

In China, what I get is the US (Canada is usually less discussed) is controlled by a corrupt and impotent government. Life sucks over there because the majority of the people, who actually has the power, believes the doublespeak of the government and are easily manipulated by the wealth. While in China, people make jokes about the official media, and if the government decides to do something people don't like, people simply ignore it and with enough people (there is always enough people in China) doing this, the government has to care about people's opinion to do anything.

I'm a little worried about the situation of both sides now. They seem to go towards what the opposite believes: on one hand in China, the government starts to get better at propaganda and manipulating public opinion; on the other hand in the States ... there is enough happening in the past year.

One question to ask is then: what method is useful to figure out the function of an unknown microprocessor? The engineers sometimes reverse engineer chips with electron microscope, with a perfect understanding of electronic principle, a rudimentary idea of how the microprocessor works, what the high-level function of the chip is, etc. And even equipped with this knowledge cracking a chip still takes a lot of effort, and may not succeed at all.

The tools of a neuroscientist would be laughable in comparison, yet neuroscientists have cracked the auditory code (and you can have an artificial cochlea now), the lower visual system is close to be cracked, we have a pretty good understanding of hippocampus and how space is represented in the brain, and many other accomplishment. These are all results from the laughable tools we have for investigating brain.

Having a full connectome of the brain, and running the brain in a simulator will be a huge step of understanding it. We don't know how to use the data now simply because we don't have the data yet, and therefore no effort is putting on interpreting the data. However the usefulness can be glimpsed from neural structures where the connections are clear: the peripheral system, spinal cord, midbrain, and lower sensory and motor regions in the brain. We understand them far better than regions we don't even know where it connects: claustrum comes into mind.

A simulator of the brain, I imagine, will be similar to the human genome project: nobody will understand the whole thing quickly, but it will hugely prop neuroscience forward, sometimes in ways we cannot imagine now.