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Comment author: aphyer 11 July 2013 04:17:18AM 2 points [-]

Sorry if this is a stupid question, but this tournament looked to me like a thinly disguised version of:

"Construct a robot that can read code and interpret what it means."

which is a Really Hard Problem.

Is that not a fair description? Was there some other way to approach the problem?

The only way I can see to go about constructing a GOOD entrant to this is to write something that can take as its input the code of the opponent and interpret what it will actually DO, that can recognize the equivalence between (say):

return DEFECT


if 1: return DEFECT return COOPERATE

and can interpret things like:

if oppcode == mycode return COOPERATE return DEFECT

And I have no idea how to go about doing that. From the fact that the winning entrants were all random, it seems safe to say that no entrants had any idea how to go about doing that either.

Am I missing something here?

Comment author: olalonde 15 September 2013 10:43:22AM 0 points [-]

Perfect simulation is not only really hard, it has been proven to be impossible. See http://en.wikipedia.org/wiki/Halting_problem

[LINK] 'Blue Brain' Project Accurately Predicts Connections Between Neurons

3 olalonde 18 September 2012 12:50AM

From http://www.sciencedaily.com/releases/2012/09/120917152043.htm

Could this be a tiny step towards an AGI?

'Blue Brain' Project Accurately Predicts Connections Between Neurons

One of the greatest challenges in neuroscience is to identify the map of synaptic connections between neurons. Called the "connectome," it is the holy grail that will explain how information flows in the brain. In a landmark paper, published the week of 17th of September in the Proceedings of the National Academy of Sciences, the EPFL's Blue Brain Project (BBP) has identified key principles that determine synapse-scale connectivity by virtually reconstructing a cortical microcircuit and comparing it to a mammalian sample. These principles now make it possible to predict the locations of synapses in the neocortex.

"This is a major breakthrough, because it would otherwise take decades, if not centuries, to map the location of each synapse in the brain and it also makes it so much easier now to build accurate models," says Henry Markram, head of the BBP.

A longstanding neuroscientific mystery has been whether all the neurons grow independently and just take what they get as their branches bump into each other, or are the branches of each neuron specifically guided by chemical signals to find all its target. To solve the mystery, researchers looked in a virtual reconstruction of a cortical microcircuit to see where the branches bumped into each other. To their great surprise, they found that the locations on the model matched that of synapses found in the equivalent real-brain circuit with an accuracy ranging from 75 percent to 95 percent.

This means that neurons grow as independently of each other as physically possible and mostly form synapses at the locations where they randomly bump into each other. A few exceptions were also discovered pointing out special cases where signals are used by neurons to change the statistical connectivity. By taking these exceptions into account, the Blue Brain team can now make a near perfect prediction of the locations of all the synapses formed inside the circuit.

Virtual Reconstruction

The goal of the BBP is to integrate knowledge from all the specialized branches of neuroscience, to derive from it the fundamental principles that govern brain structure and function, and ultimately, to reconstruct the brains of different species -- including the human brain -- in silico. The current paper provides yet another proof-of-concept for the approach, by demonstrating for the first time that the distribution of synapses or neuronal connections in the mammalian cortex can, to a large extent, be predicted.

To achieve these results, a team from the Blue Brain Project set about virtually reconstructing a cortical microcircuit based on unparalleled data about the geometrical and electrical properties of neurons -- data from over nearly 20 years of painstaking experimentation on slices of living brain tissue. Each neuron in the circuit was reconstructed into a 3D model on a powerful Blue Gene supercomputer. About 10,000 of virtual neurons were packed into a 3D space in random positions according to the density and ratio of morphological types found in corresponding living tissue. The researchers then compared the model back to an equivalent brain circuit from a real mammalian brain.

A Major Step Towards Accurate Models of the Brain

This discovery also explains why the brain can withstand damage and indicates that the positions of synapses in all brains of the same species are more similar than different. "Positioning synapses in this way is very robust," says computational neuroscientist and first author Sean Hill, "We could vary density, position, orientation, and none of that changed the distribution of positions of the synapses."

They went on to discover that the synapses positions are only robust as long as the morphology of each neuron is slightly different from each other, explaining another mystery in the brain -- why neurons are not all identical in shape. "It's the diversity in the morphology of neurons that makes brain circuits of a particular species basically the same and highly robust," says Hill.

Overall this work represents a major acceleration in the ability to construct detailed models of the nervous system. The results provide important insights into the basic principles that govern the wiring of the nervous system, throwing light on how robust cortical circuits are constructed from highly diverse populations of neurons -- an essential step towards understanding how the brain functions. They also underscore the value of the BBP's constructivist approach. "Although systematically integrating data across a wide range of scales is slow and painstaking, it allows us to derive fundamental principles of brain structure and hence function," explains Hill.


Comment author: Kaj_Sotala 01 September 2012 06:08:27PM 49 points [-]

The person who says, as almost everyone does say, that human life is of infinite value, not to be measured in mere material terms, is talking palpable, if popular, nonsense. If he believed that of his own life, he would never cross the street, save to visit his doctor or to earn money for things necessary to physical survival. He would eat the cheapest, most nutritious food he could find and live in one small room, saving his income for frequent visits to the best possible doctors. He would take no risks, consume no luxuries, and live a long life. If you call it living. If a man really believed that other people's lives were infinitely valuable, he would live like an ascetic, earn as much money as possible, and spend everything not absolutely necessary for survival on CARE packets, research into presently incurable diseases, and similar charities.

In fact, people who talk about the infinite value of human life do not live in either of these ways. They consume far more than they need to support life. They may well have cigarettes in their drawer and a sports car in the garage. They recognize in their actions, if not in their words, that physical survival is only one value, albeit a very important one, among many.

-- David D. Friedman, The Machinery of Freedom

Comment author: olalonde 06 September 2012 10:26:29AM *  0 points [-]


The really important thing is not to live, but to live well. - Socrates

[LINK] Strong AI Startup Raises $15M

17 olalonde 21 August 2012 08:47PM


Vicarious, a startup that says it’s “building software that thinks and learns like a human,” has just raised a $15 million Series A.

The round was led by Good Ventures, the investment firm founded by Dustin Moskovitz, who also co-founded Facebook and Asana. (The firm’s profits will be donated to the Good Ventures Foundation.) Founders Fund, Open Field Capital, Steve Brown, and Zarco Investment Group participated too.

Vicarious launched in February 2011 with funding from Founders Fund, Moskovitz, Adam D’Angelo (former Facebook CTO and co-founder of Quora), Felicis Ventures, and Palantir co-founder Joe Lonsdale. Since then, co-founder D. Scott Phoenix tells me that the company has been in research mode. The research has resulted in a system that’s supposed to interpret the content of photos and videos in a way that’s similar to humans, and which is powered by the company’s “key innovation”, the Recursive Cortical Network.

Ultimately, Phoenix says the technology could be used in “almost every industry,” including robotics, medical image analysis, and image and video search,. But that’s a ways off — Phoenix and his co-founder Dileep George say they’re still deep in research and development, and that the funding will be used to expand those R&D efforts. Developing products that commercialize the technology is still several years off, George says.

“Based on our experiments in the last year, we are very optimistic about our rate of progress,” he says. “At the same time, this is a very challenging problem. We are not getting too excited about how productize things. We’re testing everything very carefully.”

You don’t see too many venture-backed software companies spending years on research nowadays, and Phoenix says he was lucky to find investors who share his big vision — to use AI to “help humanity thrive.” The investors at Good Ventures and Founders Fund have a “natural affinity” for that kind of talk (Founders Fund’s Peter Thiel, for example, has been pretty vocal about what he sees as a lack of transformative innovation), but Phoenix says it’s “very different from the language that a lot of other investors speak.”

Discussion on Hacker News

Meta: this is my first post here, let me know if I am doing something wrong or if I shouldn't post this here.


Comment author: RolfAndreassen 14 August 2012 05:23:26PM 2 points [-]

The only noticeable difference is that amateurs lacked the upswing at 50 years, and were relatively more likely to push their predictions beyond 75 years. This does not look like good news for the experts - if their performance can't be distinguished from amateurs, what contribution is their expertise making?

I believe you can put your case even a bit more strongly than this. With this amount of data, the differences you point out are clearly within the range of random fluctuations; the human eye picks them out, but does not see the huge reference class of similarly "different" distributions. I predict with confidence over 95% that a formal statistical analysis would find no difference between the "expert" and "amateur" distributions.

Comment author: olalonde 14 August 2012 06:11:39PM *  3 points [-]

Perhaps their contribution is in influencing the non experts? It is very likely that the non experts base their estimates on whatever predictions respected experts have made.

Comment author: olalonde 28 July 2012 06:54:44AM 14 points [-]

I believe government should be much more localized and I like the idea of charter cities. Competition among governments is good for citizens just as competition among businesses is good for consumers. Of course, for competition to really work out, immigration should not be regulated.

See: http://en.wikipedia.org/wiki/Charter_city

Comment author: wedrifid 27 July 2012 02:35:11PM *  5 points [-]

If you wish to advance into the infinite, explore the finite in all directions.

That sounds incredibly deep. (By which I mean it is bullshit.)

Comment author: olalonde 28 July 2012 06:20:08AM 5 points [-]

For some reason, this thread reminds me of this Simpsons quote:

"The following tale of alien encounters is true. And by true, I mean false. It's all lies. But they're entertaining lies, and in the end, isn't that the real truth?"

In response to That Alien Message
Comment author: olalonde 28 July 2012 06:13:22AM 2 points [-]

Oh, and every time someone in this world tries to build a really powerful AI, the computing hardware spontaneously melts.

Would have been a good punch if the humans ended up melting away the aliens' computer simulating our universe.

Comment author: [deleted] 09 July 2012 01:14:02PM *  2 points [-]

A good and useful abstraction that is entirely equivalent to Turing machines, and to humans much more useful, is Lambda calculus and Combinator calculi. Many of these systems are known to be Turing complete.

Lambda calculus and other Combinator calculi are rule-sets that rewrite a string of symbols expressed in a formal grammar. Fortunately the symbol-sets in all such grammars are finite and can therefore be expressed in binary. Furthermore, all the Turing complete ones of these calculi have a system of both linked lists and boolean values, so equivalent with the Turing machine model expressed in this article, one can write a programme in a binary combinator calculus and feed it a linked list of boolean values expressed in the combinator calculus itself and then have it reduce itself (return) a linked list of boolean values.

Personally I prefer combinator calculi to Turing machines mainly because they are vastly easier to program.


Comment author: olalonde 09 July 2012 03:25:38PM 0 points [-]

To expand on what parent said, pretty much all modern computer languages are equivalent to Turing machines (Turing complete). This includes Javascript, Java, Ruby, PHP, C, etc. If I understand Solomonoff induction properly, testing all possible hypothesis implies generating all possible programs in say Javascript and testing them to see which program's output match our observations. If multiple programs match the output, we should chose the smallest one.

In response to comment by TimS on Irrationality Game II
Comment author: TheOtherDave 05 July 2012 12:31:18AM 4 points [-]

Is there a simple summary of why you think this is true of intelligence when it turned out not to be true of, say, durability, or flightspeed, or firepower, or the ability to efficiently convert ambient energy into usable form, or any of a thousand other evolved capabilities for which we've managed to far exceed our physiological limits with technological aids?

Comment author: olalonde 06 July 2012 03:52:18AM 1 point [-]

efficiently convert ambient energy

Just a nitpick but if I recall correctly, cellular respiration (aerobic metabolism) is much more efficient than any of our modern ways to produce energy.

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