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Original author: 
boesing

fge

As a novelist, Daniel Suarez spins dystopian tales of the future. But on the TEDGlobal stage, he talks us through a real-life scenario we all need to know more about: the rise of autonomous robotic weapons of war. Advanced drones, automated weapons and AI-powered intelligence-gathering tools, he suggests, could take the decision to make war out of the hands of humans.

http://www.ted.com/talks/daniel_suarez_the_kill_decision_shouldn_t_belon...

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Original author: 
Soulskill

An anonymous reader writes "We're seeing a new revolution in artificial intelligence known as deep learning: algorithms modeled after the brain have made amazing strides and have been consistently winning both industrial and academic data competitions with minimal effort. 'Basically, it involves building neural networks — networks that mimic the behavior of the human brain. Much like the brain, these multi-layered computer networks can gather information and react to it. They can build up an understanding of what objects look or sound like. In an effort to recreate human vision, for example, you might build a basic layer of artificial neurons that can detect simple things like the edges of a particular shape. The next layer could then piece together these edges to identify the larger shape, and then the shapes could be strung together to understand an object. The key here is that the software does all this on its own — a big advantage over older AI models, which required engineers to massage the visual or auditory data so that it could be digested by the machine-learning algorithm.' Are we ready to blur the line between hardware and wetware?"

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Original author: 
Rob Beschizza

Charlie Warzel: "THIS is what google's self driving car can see. So basically this thing is going to destroy us all." [via Matt Buchanan]    

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Original author: 
Cory Doctorow

A "Snowball" is a poem "in which each line is a single word, and each successive word is one letter longer." Nossidge built an automated Snowball generator that uses Markov Chains, pulling text from Project Gutenberg. It's written in C++, with code on GitHub. The results are rather beautiful poems (these ones are "mostly Dickens"):

o
we
all
have
heard
people
believe
anything

i
am
the
dawn
light
before
anybody
expected
something
disorderly

i
am
the
very
great
change

Snowball (also called a Chaterism) (via Waxy)     

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Original author: 
Cory Doctorow

In Wired, Steven Levy has a long profile of the fascinating field of algorithmic news-story generation. Levy focuses on Narrative Science, and its competitor Automated Insights, and discusses how the companies can turn "data rich" streams into credible news-stories whose style can be presented as anything from sarcastic blogger to dry market analyst. Narrative Science's cofounder, Kristian Hammond, claims that 90 percent of all news will soon be algorithmically generated, but that this won't be due to computers stealing journalists' jobs -- rather, it will be because automation will enable the creation of whole classes of news stories that don't exist today, such as detailed, breezy accounts of every little league game in the country.

Narrative Science’s writing engine requires several steps. First, it must amass high-quality data. That’s why finance and sports are such natural subjects: Both involve the fluctuations of numbers—earnings per share, stock swings, ERAs, RBI. And stats geeks are always creating new data that can enrich a story. Baseball fans, for instance, have created models that calculate the odds of a team’s victory in every situation as the game progresses. So if something happens during one at-bat that suddenly changes the odds of victory from say, 40 percent to 60 percent, the algorithm can be programmed to highlight that pivotal play as the most dramatic moment of the game thus far. Then the algorithms must fit that data into some broader understanding of the subject matter. (For instance, they must know that the team with the highest number of “runs” is declared the winner of a baseball game.) So Narrative Science’s engineers program a set of rules that govern each subject, be it corporate earnings or a sporting event. But how to turn that analysis into prose? The company has hired a team of “meta-writers,” trained journalists who have built a set of templates. They work with the engineers to coach the computers to identify various “angles” from the data. Who won the game? Was it a come-from-behind victory or a blowout? Did one player have a fantastic day at the plate? The algorithm considers context and information from other databases as well: Did a losing streak end?

Then comes the structure. Most news stories, particularly about subjects like sports or finance, hew to a pretty predictable formula, and so it’s a relatively simple matter for the meta-writers to create a framework for the articles. To construct sentences, the algorithms use vocabulary compiled by the meta-writers. (For baseball, the meta-writers seem to have relied heavily on famed early-20th-century sports columnist Ring Lardner. People are always whacking home runs, swiping bags, tallying runs, and stepping up to the dish.) The company calls its finished product “the narrative.”

Both companies claim that they'll be able to make sense of less-quantifiable subjects in the future, and will be able to generate stories about them, too.

Can an Algorithm Write a Better News Story Than a Human Reporter?

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First time accepted submitter Shaterri writes "Which is more likely: that a low-ranked player could play through a high-level tournament at grandmaster level, or that they were getting undetected assistance from a computer? How about when that player is nearly strip-searched with no devices found? How about when their moves correlate too well with independent computer calculations? Ken Regan has a fascinating article on one of the most complex (potential) cheating cases to come along in recent memory."

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Jeremy Kun, a mathematics PhD student at the University of Illinois in Chicago, has posted a wonderful primer on probability theory for programmers on his blog. It's a subject vital to machine learning and data-mining, and it's at the heart of much of the stuff going on with Big Data. His primer is lucid and easy to follow, even for math ignoramuses like me.

For instance, suppose our probability space is \Omega = \left \{ 1, 2, 3, 4, 5, 6 \right \} and f is defined by setting f(x) = 1/6 for all x \in \Omega (here the “experiment” is rolling a single die). Then we are likely interested in more exquisite kinds of outcomes; instead of asking the probability that the outcome is 4, we might ask what is the probability that the outcome is even? This event would be the subset \left \{ 2, 4, 6 \right \}, and if any of these are the outcome of the experiment, the event is said to occur. In this case we would expect the probability of the die roll being even to be 1/2 (but we have not yet formalized why this is the case).

As a quick exercise, the reader should formulate a two-dice experiment in terms of sets. What would the probability space consist of as a set? What would the probability mass function look like? What are some interesting events one might consider (if playing a game of craps)?

Probability Theory — A Primer

(Image: Dice, a Creative Commons Attribution (2.0) image from artbystevejohnson's photostream)

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Trepidity writes "AI systems can (sort of) paint and compose classical music, but can they design games? Slashdot looked at the question a few years ago, and several research groups now have experimental systems that design board games and platformers with varying levels of success. I've put together a survey of the AI game designers I know of, to round up what they can do so far (and what they can't). Are there any others out there? 'Pell's METAGAME is, to my knowledge, the first published game generator. He defines a generative space of games more general than chess, which he calls "symmetric, chess-like games." They're encoded in a representation specific to this genre, which is also symmetric by construction. By symmetric I mean that mechanics are specified only from the perspective of one player, with the starting positions and rules that apply to the other player always being the mirror of the first player's. The rules themselves are represented in a game grammar, and generation is done by stochastically sampling from that grammar, along with some checks for basic game playability, and generative-parameter knobs to tweak some aspects of what's likely to be generated.'"

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New submitter dj_tla writes "A team of Canadian researchers has created a state-of-the-art brain model that can see, remember, think about, and write numbers. The model has just been discussed in a Science article entitled 'A Large-Scale Model of the Functioning Brain.' There have been several popular press articles, and there are videos of the model in action. Nature quotes Eugene Izhikevich, chairman of Brain Corporation, as saying, 'Until now, the race was who could get a human-sized brain simulation running, regardless of what behaviors and functions such simulation exhibits. From now on, the race is more [about] who can get the most biological functions and animal-like behaviors. So far, Spaun is the winner.' (Full disclosure: I am a member of the team that created Spaun.)"

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If the thought of a robot apocalypse is keeping you up at night, you can relax. Scientists at Cambridge University are studying the potential problem. From the article: "A center for 'terminator studies,' where leading academics will study the threat that robots pose to humanity, is set to open at Cambridge University. Its purpose will be to study the four greatest threats to the human species - artificial intelligence, climate change, nuclear war and rogue biotechnology."

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