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Comment I remember when... (Score 4, Informative) 138

The good ol' days, when Intel just announcing the Itanium caused all the other proprietary Unix vendors' stock to crash. Everyone was sure that within one generation, all the SPARC & POWER chips would shrivel up and die. HP rolled over immediately and gave up their line of PA-RISC procs to use Itanium. But Intel crippled their Xeons in fear that the Xeons would eat into their Itanium line, and then AMD walked in and gave people what they really wanted with their Opterons. There were a few years when things were really rocky for Intel, and it was very entertaining to watch, especially since I worked for them at the time :)

Comment Great ! (Score 1) 75

Great research, Microsoft! congrats to Haiyan Zhang. That said it doesn't look like the real Emma in the video has classic Parkinson, which is a lot more than hand tremor (also slurred speech, posture changes, impaired balance, slowed movements, and more). I doubt Microsoft's Emma watch will help all of these. Nonetheless, new thinking, progress!

Comment Re:What if... (Score 1) 149

The point of periodic password changes is to protect against an *UNKNOWN* breach, where the password has been compromised and the user doesn't know. Is there some other method of mitigation for this attack?

Except, many times the new password is easily guessable if you knew the old password. Say the old password was: HelloWorld1, there's a pretty good chance the new password is HelloWorld2. If you use the complete set of NIST recommendations, you'll be in really good shape. MFA, a dictionary of common passwords and sets of known passwords from compromised systems (hackers will test against those before they bother brute forcing), and you'll be in pretty good shape.

Comment Re:Unrealistically limited view (Score 1) 284

A few examples in increasing order of complexity

1- Automated translation is not yet beaten. A lot of progress has been made but not there yet. It's not a matter of building a larger computer...
2- Robots and hooliganism. Once we have a self driving car out in the streets, it will be put on its back like a tortoise so it cannot escape, get stolen and sold for parts to a competitor. Empty self-driving cars will get pushed off the road by human drivers just for fun. How do we solve that problem?
3- The energy problem. Alright, let's see your superintelligent computer solve that one. The new super-AI will need vast amounts of energy too, so it has a stake in it. Where will it get it from? We basically have an existing menu of how to produce energy from solar to nuclear but none is foreseen to solve all of our needs in this current century while simultaneously not destroying the planet, the environment, many other humans and so on. SuperAI will not be able to devise new physics by reading or even understanding our current physics textbooks.
4- Exponential grow. Right. As you know, the myth of exponential growth in silico is pretty much at an end. My 2009 computer is not significantly slower than the 2017 ones. It has 6 cores and many gigabytes of RAM and many terabytes of disk, i.e. not fewer in any measure than newer computers. I plan to use it until It dies of old age, probably in another two years or so or perhaps even longer. So how will SuperAI pull all this amazing exponential growth from? New physics perhaps?
5- Ok we have a super intelligent nasty AI. How does it prevent us from pulling the plug? See question 3.

Basically most of this AI stuff is science fiction at present.

Comment Re:Unrealistically limited view (Score 1) 284

A few things:
1- humans are actually improving, if only relatively slowly. The basic substrate is the same, sure, but the accumulated historical and scientific knowledge does add up. We do not need to solve old problems, only new ones.
2- "I don't know of any fundamental limit" does not mean there isn't one, for example a methodological limit. For me the current direction of mainstream AI research will not give us thinking computers anytime soon. Better statistical machine learning methods, yes, for sure. The dichotomy between supervised and unsupervised learning is not very productive, they do not solve the same problems at all.

Comment Re: Well it's easy to show superhuman AI is a myth (Score 1) 284

Yes, of course reinforcement learning is intrinsically limited. The current state of the art is deep learning, but even this technique is still statistical machine learning. In other words, it works by devising a cost or objective function (in the case of go, winning the game, which is clearly defined), and optimises it by starting from examples. In the case of the game of go, the hundreds of thousands of games already annotated and played by humans. Reinforcement learning artificially creates many new games from old ones, and playing these, which helps finding a better local minimum in the cost function.

Now "strong" AI or whatever you want to call some version of human-like AI have to cope with situations where there is no clearly defined cost function, and very few if any example to start from. The complexity of these problems is way beyond that of Go, which is a very limited system. Note that these problems are every day ones that humans have to cope with every day, like how do I persuade my clients to pay me ? how do I explain to my work partners the value of my work ? they typically include other complex systems that are not rule-based or for which the rules may exist but are unknown. Example: we do not know enough of physics to accurately predict next week's weather. How do we improve the situation?

You will not be able to better predict next week's weather with reinforcement learning.

Submission + - An Infinite Number of Mathematicians Enter A Bar

ergo98 writes: The basis of floating-point numbers remains a mystery to most programmers, with many misunderstanding the benefits and correlating detriments. A new interactive explanation makes the mysterious obvious, revealing the secrets of the magic trick for all. Hopefully this improves the general knowledge about this powerful and common foundation of our work.

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