And this can't be a one-time deal. In my field anyway (neuroscience & psychology) there is a legitimate need for revisions every few years as the science is progressing quickly.

If the states are serious about this, they should pay good salaries to teams of professors, editors, graphic designers, etc... to create high quality material that they then provide by open access. The current open access textbooks available in my field (neuroscience & psychology) are horrible. This would save the students significant money, but would cost the states more. Otherwise, this is just a combination of talk and trying to get people to do work for free.

When on the drugs the Lempel-Ziv (LZ) complexity score was higher, but the paper also states:
"Since the value of LZc (also LZs) for a binary sequence of fixed length is maximal if the sequence is entirely random, the normalized values indicate the level of signal diversity on a scale from 0 to 1."
In other words, higher LZ means more random. However, "true" complexity does not increase monotonically with randomness. In fact, they have an inverted U relationship, where past a certain point, complexity _decreases_ with increasing randomness. For example, see Figure 2 on this page:
http://www.cs.brandeis.edu/~pa...
I would argue that consciousness if far more likely to relate to this type of "true" complexity rather than the uncompressability expressed by LZ.

The comparison of "deep learning that needs tons of examples" vs "Bayesian programming that can learn from a few examples" is a false dichotomy. It all depends on how much structure you assume a priori versus how much structure you learn from the data.

Typical neural net (deep learning) examples start with no structure and thus require lots of examples. Typical Bayesian net examples start with a lot of structure and thus require only a few examples.

On the other hand, if you start with a highly pretrained net like Inception-v3, then your deep learning cat expert may not need as many examples to generalize. And if your Bayesian programming model starts out with very general, very simple "building blocks" then it may need a lot of examples to extract the predictable structure.

A main difference is that if you want to start with a lot of structure built in, you will probably have to pretrain for the neural net, whereas you can "hand code" the knowledge in your Bayes net. And the structure in the Bayes net may be a lot more transparent and easily interpretable than in the neural net. On the other hand, you had better hope you picked the right structure to begin with or else you will be reasoning over possibilities that are all very wrong! Knowing that an image is 50 times more likely to be a cat than a dog is not very helpful if it is actually a penguin.

To understand Trump, you must appreciate that he is not a liar, he is a bullshitter:
"Bullshit can be neither true nor false; hence, the bullshitter is someone whose principal aim—when uttering or publishing bullshit—is to impress the listener and the reader with words that communicate an impression that something is being or has been done, words that are neither true nor false, and so obscure the facts of the matter being discussed. In contrast, the liar must know the truth of the matter under discussion, in order to better conceal it from the listener or the reader being deceived with a lie; while the bullshitter’s sole concern is personal advancement and advantage to their own agenda." (https://en.wikipedia.org/wiki/On_Bullshit)
The truth value of Trump's statements are irrelevant to Trump and, apparently, his supporters. Once you accept this, you realize that debating about Trump on the basis of facts is pointless. This is the challenge of dealing with Trump.

This study is the latest in a long line of studies which show that we have poor insight into the causes of our choices. We think we know why we have decided the way we have, but experiment after experiment has shown that this is often not entirely true. This study is showing that even events which occur _after_ the moment when we think we have decided can influence our decisions without our being aware of this fact.

Our explanations of why we have decided the way we have are stories that we construct. Whether these stories are constructed before or after the decision is made, they are still stories that do not fully account for the actual causes of the choices.

Our sense of free will is grounded in the idea that we know why we make the choices we do. This experiment contributes to a large body of literature which demonstrates that this is simply not true.

Sorry, this article is not an intelligent take on artificial intelligence.

First, the Chinese Room argument applies equally to whatever new algorithm Jeff Hawkins comes up with as it does to the English speaking man plus book algorithm. The Chinese Room argument applies equally to ANY algorithmic approach to intelligence. Thus, his call for a new definition of intelligence will do nothing to disarm the Chinese Room argument.

Second, the Chinese Room argument has already been thoroughly addressed by Daniel Dennett and others. If you really care, go read Searle's The Mystery of Consciousness and Dennett's Consciousness Explained, and then go from there.

Third, in the mean time, researchers will continue on their merry way toward developing AI. Guess what? It is harder than Minsky and others ever imagined, but none the less, we are making progress. And guess what else? When we have "truly" intelligent systems, there won't be any magic in there. Just boring, dumb algorithms. Our robot overlords will be Chinese Rooms.

We all pose, because the "self" is nothing but a pose. To think that there is a way to be a living human and not pose is naive, and to berate others for posing is just silly.

You can't possibly be serious. You are, indeed, an anonymous coward.

Wow.

Actually, what Apple does is to create computing _appliances_ with intuitive interfaces. Hype not withstanding, the reason why Apples devices are successful is because they ARE easier and simpler to use. All of the control that Apple exerts over the ecosystems for their devices has one primary aim: simplicity and consistency. This has been going on since the elaborate "Macintosh Human Interface Guidelines" for the original MacOS. This is why their mice have one button. This is why they resist third-party clones. This is why they have a single iTunes store. Etc, etc... By standardizing and making many choices for the user, they allow the user to focus on their non-technical goals: writing a document, listening to music, etc... For those of us who LIKE to have complete control over the technical details of our devices, and like to spend time making choices about technical matters, this can be exceedingly frustrating. I wish I could modify the OS on my wrist watch as well. I'm a freak. So are you. We aren't Apple's target market. Get over it.

Any chance they have fixed quoted-line prefixing for replies and added "Paste as Quotation" to Outlook? That alone would be worth an upgrade. Yes, I know about the "Prefix each line of the original message" option. And no, I don't care whether you like inline replies or not, just make it reasonably easy to use them, and give me the choice. Is this really so hard?

I'm not surprised at all. Having diligently tried to use IE8 for months, I can confidently say it is a horrible experience. Much worse than IE6 ever was. It hangs on a regular basis - not just one tab, but the whole progam. New tabs can take a long time to come up. It slowly eats more and more memory. I've experienced bizarre bugs, for example I load a page that renders incorrectly, I click through to another page, and then hit the 'back' button, and the first page now renders correctly. Etc... Microsoft is driving down their own market share by providing a shitty product. We're not talking bells and whistles here, just basic functionality.

How insightful, or is that inciteful?

Anyway, you're missing the point. The savings are realized by individual employers and consumers at the critical point of decision making about how to spend their money. The costs are realized indirectly and in a distributed fashion across society by higher government expenses leading to higher taxes. As a result, the employers and consumers are making decisions that are NOT in their own best interests, because the meager savings are obvious and immediate while the high costs are indirect and delayed. I'm not arguing that this situation is rational, in fact, I'm arguing the opposite, people need to wake up to the true costs and act accordingly.

I repeatedly see people discussing the costs illegal immigrant incur on society, particularly since they typically don't have car insurance or medical insurance. What I don't see is a discussion of the demand driving them to be here:
1. Employers, who are US citizens, seek them out because they are cheap labor and can be abused without repercussions, since they are afraid to report the abuse to authorities.
2. Consumers, who are US citizens, choose to purchase the goods and services created by their labor, because they are cheaper than the competing products.

Did you buy a newly built home in the last 10 years? Did you visit the construction site? Can you honestly say you were not aware that many of the day laborers working on the site where probably illegal? Did you do anything about it?

Have you bought berries on sale at the grocery store brought in from California? Can you honestly say you weren't aware that they were probably picked by illegal immigrants?

If we really want to quell the flow of illegal immigrants across the border, we need to address the demand for their services. Take an active role in NOT buying goods and services provided by them, and the demand will go down. Yes, its going to cost you money. Lots of money. That's the reality - we will all pay more for the goods and services currently provided by illegal immigrants if they are instead provided by legal immigrants and US citizens. Let's talk about that for a while.