Anyone who knows anything about the Chinese mobile phone market would not be surprised about something like the Xiaomi phone being labeled "innovative." As much as Chinese companies have a reputation for making cheap knockoffs and cloning anything from baby food to Apple stores, there's a significant and competitive domestic Chinese market for cell phones which does reward innovation. Not necessarily YC-caliber innovation, but cell phones became prevalent and dominant in China faster than in the West, because of the large domestic market, low regulations, and international manufacturers eager to jumpstart the market. For example, Motorola was for a while the largest foreign business in China and was the market leader in mobile devices. Cheap operating costs and a rising affluent middle class = lots of cell phones sold.
You want an anecdote? I remember when I was in China in 2003. (Or 2004. Around that time.) Back here in the US, Motorola RAZRs were sorta popular. Here's an image for comparison of scale:
In China, the popular phones were very different. They were much smaller, they had more varied ringtones, they were more colorful. And they were not all Motorola phones either, Chinese domestic manufacturers were all trying to make their phones as thin, sleek, and minimalist as possible. (Sound familiar?) Since then Motorola has lost a lot of market share to Chinese manufacturers who mostly are not delivering tasteless clones. Part of it is that expensive cell phones become a status symbol, and the people who can afford those phones take them seriously. I couldn't find any pictures by Googling combinations of "china," "cell phone," "2003," etc. So take my word on it or not, but that was the case. (Of course, the Xiaomi device looks like an iPhone. But then everything looks like an Apple product these days.)
What's the moral of the story? Don't be like Motorola, don't throw away your Chinese market share, and don't force Google to acquire you just for your overpriced IP.
A lot of the challenges in making driverless cars has been in visual object recognition, which is probably the "visual post-processing" you mean.
It is important to note that humans, and other mammals, are very hard-wired to process vision and other inputs. For example, the retina is more than just an organic lens; it also encodes information about the motion of objects seen within its field of vision:
So in addition to post-processing, there is probably significant pre-processing done by the sensory extensions of our brain. Similarly, the physicist Georg Zweig studied the cochlea and found how it mechanically separates sound into its frequency distribution. Zweig's research on the cochlea also resulted in the discovery of the continuous wavelet transform, whose discrete version may be familiar through its use in JPEG2000.
But that's a straw man argument. The brain is a massively parallel net of neurons connected by synapses. Saying that it takes "10 steps" to respond to a stimulus is like saying that a GPU only applies a few pixel shaders on a scene per frame. Perhaps, but that's over millions of pixels.
Also, human DNA is unique from that of close mammals on a set of base pairs of size the order of 1-5 percent of our genome. So that means 10-50 MB. That's actually a pretty substantial size: it can even store a small operating system kernel (Linux can be compiled to under 10 MB).
Edit: The 1 GB figure comes from the fact that the human genome size is almost 3 billion base pairs, where each base pair exactly encodes 2 bits. So, (3x10^9)*2/8=750 MB which rounds up.
He didn't say it takes 10 neurons to compute the action, but ten steps. I am sure he is aware that millions or billions of neurons (? sorry, I am lazy) are involved in those 10 steps. His point is that even if we had those millions of neurons or transistors at our disposal, we don't know how to program them in such a way as to get a result in ten steps.
Also, human DNA is unique from that of close mammals on a set of base pairs of size the order of 1-5 percent of our genome. So that means 10-50 MB. That's actually a pretty substantial size: it can even store a small operating system kernel (Linux can be compiled to under 10 MB).
I "passionately love" metaphors and comparisons of this kind, they are so much meaningful. I mean, 50MB of "code" is "a bit" different when your computer is build from logic gates and a bit different when your computer is the universe, or, to be more precise, the laws of physics that determine the final form and function of the proteins that are build from the DNA. In other words, I don't think it is right to use a measure of information like it would be a measure of complexity (which such analogies imply). Also it completely ignores the very complicated process of development of the brain, where information is supplied from the outside all the time and without which the brain isn't too useful.
It's the same with the page linked:
We don’t have any idea how to make a description of such a complex machine that is both dense and flexible.
Just like the fact that we have computers build from gates and not laws of the universe to depend on didn't make the task of writing "dense and flexible descriptions" quite a bit harder. Especially we do it with our brains and we don't have millions of years of trial and error at our disposal like evolution has.
The question is not really what does the computer depend on, but what does _the program_ depend on. The boolean algebra that computers are an implementation of is very simple and can be implemented in a wide variety of ways and regardless which one you choose, programs can be compiled and ran using the new architecture obtained in this way. So you could hypothetically build a biological computer that would realize ANDs, ORs etc., in the end providing the same instruction set as our current PC, and the unchanged Linux source code (example from the parent post) could be compiled an ran on it. In other words, while our computers depend on laws of physics in some way, those laws are not part of the computational model.
This is different with the DNA. There is no middle layer like Boolean algebra to abstract-out the device that does the computation. The protein that will be the result of connecting the amino acids specified by the DNA, it's form and function, is very highly dependent directly on physical laws in a very complicated way - if you simulate protein folding (and consider how hard this is in the first place), you can see how much the outcome will vary when you for example change the value of some physical constant by a small amount. Then all those proteins start interacting with each other in highly complicated ways, also dependent on a wide variety of physical laws and on the outside environment, and if you consider DNA a program, those physical laws are parts of the computational model, of course if the concept of a computational model makes any sense when studying non-man-made artefacts. That's roughly why applying computer science metaphors to the DNA always sounds a bit ridiculous to me.
Of course, it is a different question whether we can find a computational model that would explain to us the working of a healthy, fully-developed human brain, I think it is worth mentioning as it is easy to confuse those two questions.
I just assume poor phrasing. Proteins are much more complex than logic gates, so the universe is doing some "free" computation for you. I.e. you've pushed more computation to the runtime, and out of your source code.
That's true. The brain has billions of neurons, and trillions of synapses. Parallel doesn't even come close to describing the density of connections, or the method of "computation" done by the brain. But it was an easy analogy. Don't look too hard into it :)
Hurd is still using Mach? I don't see the point if it's just going to cheat around the IPC problem like OS X did.
And in the past decade, there have been more promising microkernels, like L4. Unless I missed something and there's still a good reason for Hurd to still be using Mach.
There were attempts to use L4 and several other micro kernels like Coyotos and Viengoos, but as far as I know all of them stalled and the Mach variant has been the only one to be continued to present day.
Related to the idea of a "freedom zone" is that of a university "bubble," where the institution's endowment and funding allows the needs and interests of research to ignore what's beyond its walls. But while scientists may want to publish in Nature so they can get offered good faculty positions, and while the bubble may make it easy for them to exercise freedom in research, I think a lot of them are seeing and believing in the benefits of open publications, if not yet open data. The success of the arxiv and PLoS as well as the NIH's new rule of delayed public paper release are exemplary of the trend toward open academic publications.
It's not my startup :) One of my friends worked with them during the prototyping phase a few years ago. I guess it's good to see the site active. Anyway, you should email the right people w/ your suggestions, I'm just an observer.
Two photons interfering is just two photons interfering; that's not measurement. More precisely, the wavelike photon, which is the "cause" of the interference pattern, hits a component of a photodetector, which generates a voltage and sends an electron current through a cable, that feeds into a computer which is running a program that displays photon counts. On the other hand, if you isolate parts of the setup, for example if the interference pattern of the photon hits just a very thin slice of photosensitive material that would otherwise be part of the photodetector, I'm sure you may very well observe an electron interference pattern, for example (and a non-rigorous one, at that).
In "real life" (meaning in the laboratory), measurement isn't some discrete process that can be isolated at a specific point in an experimental setup. Otherwise, there would be much better explanations for measurement in undergraduate physics curriculums.
In fact, even in top physics curriculums, the problem of measurement is barely treated at all, since the interpretation of measurement in quantum mechanics is a "philosophical" problem that apparently has no place in real physics, which instead must concern itself with mathematics that is phenomenologically accurate.
You want an anecdote? I remember when I was in China in 2003. (Or 2004. Around that time.) Back here in the US, Motorola RAZRs were sorta popular. Here's an image for comparison of scale:
http://farm3.static.flickr.com/2331/2498240940_80a83f70e9.jp...
In China, the popular phones were very different. They were much smaller, they had more varied ringtones, they were more colorful. And they were not all Motorola phones either, Chinese domestic manufacturers were all trying to make their phones as thin, sleek, and minimalist as possible. (Sound familiar?) Since then Motorola has lost a lot of market share to Chinese manufacturers who mostly are not delivering tasteless clones. Part of it is that expensive cell phones become a status symbol, and the people who can afford those phones take them seriously. I couldn't find any pictures by Googling combinations of "china," "cell phone," "2003," etc. So take my word on it or not, but that was the case. (Of course, the Xiaomi device looks like an iPhone. But then everything looks like an Apple product these days.)
What's the moral of the story? Don't be like Motorola, don't throw away your Chinese market share, and don't force Google to acquire you just for your overpriced IP.