Impressive StarCraft 2 AI More Fair to Fleshy Opponents

There was a discussion in the comments when the Alpha Go results were released. Some commentors were postulating that AI researchers are discounting more fluid games such as the RTS StarCraft.

The comments then devolved into a discussion of what would make the AI fair to consider against a human player. Many times, AI in RTS games win because they have direct access to the variables in the game. Rather than physically looking at the small area of the screen where a unit is located and then moving their eye to take in strategic information like exact location, health, unit level, etc, the AI just knows that it’s at 120x,2000y,76%,lvl5, etc instantly. The AI also has no click lag as it gets direct access to the game’s API, it simply changes the variables and action queue of a unit directly.

So we were interested to see [Matt]’s Star Craft AI that required the computer to actually look at the game board and click. [Matt]’s AI doesn’t see using OpenCV, which in its own way is forcing the computer to look in a way that’s unnatural to it. He instead wrote some code to intercept the behind the scenes calls to the DirectX library.

The computer is then able to make determinations about what it is looking at using the texture information and other pieces sent to the library. Unlike AI’s that get a direct look at the variables, it has to then translate this and keep its own mental picture of the map and the situation. If a building is destroyed, for example, it has to go over and look at that part of the map, test what it’s seeing against a control, and then remove the building from its list.

The AI’s one big advantage are its robot fingers. Even though this AI has to click on the interface, it doesn’t do it with a weak articulated fleshy nub like the rest of us. This allows the AI to get crazy Actions Per Minute (APM) in the range of 500 to 2000.

The AI has only been tested against StarCraft’s built in cheater bots. So far it can win most games against the hard level bots. If you want to see a video of what the AI is looking at, check after the break.

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Swarm of Robot Boats Coming To An Ocean Near You Soon

Planning a hostile takeover of your local swimming pool? This might help: [Dr Anders Lyhne Christensen] sent us a note about his work at the BioMachines Lab of the Institute of Telecommunications in Portugal. They have been building a swarm of robot boats to experiment with autonomous swarms, with some excellent results.

In an autonomous swarm, each robot makes its own decisions and talks to its neighbors, and the combined behavior of the swarm produces an overall behavior, like ants in a nest. They’ve created swarms that can autonomously navigate, patrol an area or monitor the temperature in an area and return to base to report the results. In an excellent video, [Anders] outlines how they used computational evolution to create these behaviors, randomly mutating a neural net to find the best approach, which is then sent to the real boats.

Perhaps coolest of all: the whole project is open source, with the brains of each boat running on a Raspberry Pi, and a CNC milled foam hull with 3D printed component mounts. Each boat costs about 300 Euro (about $340), but you could reduce the cost a bit by salvaging components and once the less-expensive Pi Zero becomes obtainable. This project will no doubt be useful for many an evil genius who is sick of being splashed by the toughs at the local pool: a swarm of killer robots surrounding them would be an excellent way to keep them at bay.

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Marvin Minsky, AI Pioneer, Dies at 88

Marvin Minsky, one of the early pioneers of neural networks, died on Sunday at the age of 88.

The obituary in the Washington Post paints a fantastic picture of his life. Minsky was friends with Richard Feynman, Isaac Asimov, Arthur C. Clarke, and Stanley Kubrick. He studied under Claude Shannon, worked with Alan Turing, had frequent conversations with John Von Neumann, and had lunch with Albert Einstein.

Single_layer_ann
“Single layer ann” by Mcstrother

Minsky’s big ideas were really big. He built one of the first artificial neural networks, but was aiming higher — toward machines that could actually think rather than simply classify data. This was one of the driving forces behind his book, Perceptrons, that showed some of the limitations in the type of neural networks (single-layer, feedforward) that were being used at the time. He wanted something more.

Minsky’s book The Society of Mind is interesting because it reframes the problem of human thought from being a single top-down process to being a collaboration between many different brain regions, the nervous system, and indeed the body as a whole. This “connectionist” theme would become influential both in cognitive science and in robotics.

In short, Minksy was convinced that complex problems often had necessarily complex solutions. In research projects, he was in for the long-term, and encouraged a bottom-up design procedure where many smaller elements combined into a complicated whole. “The secret of what something means lies in how it connects to other things we know. That’s why it’s almost always wrong to seek the “real meaning” of anything. A thing with just one meaning has scarcely any meaning at all.”

useless_machine-shot0005Minsky was a very deep thinker, but he kept grounded by also being a playful inventor. Minsky is credited with inventing the “ultimate machine” which would pop up in modern geek culture and shared numerous times on Hackaday as the “most useless machine”. He inspired Claude Shannon to build one. Arthur C. Clarke said, “There is something unspeakably sinister about a machine that does nothing — absolutely nothing — except switch itself off.”

He also co-designed the Triadex Muse, which was an early synthesizer and sequencer and “automatic composer” that creates fairly complex and original patterns with minimal input. It’s an obvious offshoot of his explorations in artificial intelligence, and on our bucket list of must-play-with electronic instruments.

Minsky’s web site at MIT has a number of his essays, and the full text of “The Society of Mind”, all available for your reading pleasure. It’s worth a bit of your time, not just in memoriam of a great thinker and a wacky inventor, but also because we bet you’ll see the world a little bit differently afterwards. That’s a legacy that lasts.

A Short History of AI, and Why It’s Heading in the Wrong Direction

Sir Winston Churchill often spoke of World War 2 as the “Wizard War”. Both the Allies and Axis powers were in a race to gain the electronic advantage over each other on the battlefield. Many technologies were born during this time – one of them being the ability to decipher coded messages. The devices that were able to achieve this feat were the precursors to the modern computer. In 1946, the US Military developed the ENIAC, or Electronic Numerical Integrator And Computer. Using over 17,000 vacuum tubes, the ENIAC was a few orders of magnitude faster than all previous electro-mechanical computers. The part that excited many scientists, however, was that it was programmable. It was the notion of a programmable computer that would give rise to the ai_05idea of artificial intelligence (AI).

As time marched forward, computers became smaller and faster. The invention of the transistor semiconductor gave rise to the microprocessor, which accelerated the development of computer programming. AI began to pick up steam, and pundits began to make grand claims of how computer intelligence would soon surpass our own. Programs like ELIZA and Blocks World fascinated the public and certainly gave the perception that when computers became faster, as they surely would in the future, they would be able to think like humans do.

But it soon became clear that this would not be the case. While these and many other AI programs were good at what they did, neither they, or their algorithms were adaptable. They were ‘smart’ at their particular task, and could even be considered intelligent judging from their behavior, but they had no understanding of the task, and didn’t hold a candle to the intellectual capabilities of even a typical lab rat, let alone a human.

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The Machine that Japed: Microsoft’s Humor-Emulating AI

Ten years ago, highbrow culture magazine The New Yorker started a contest. Each week, a cartoon with no caption is published in the back of the magazine. Readers are encouraged to submit an apt and hilarious caption that captures the magazine’s infamous wit. Editors select the top three entries to vie for reader votes and the prestige of having captioned a New Yorker cartoon.

The magazine receives about 5,000 submissions each week, which are scrutinized by cartoon editor [Bob Mankoff] and a parade of assistants that burn out after a year or two. But soon, [Mankoff]’s assistants may have their own assistant thanks to Microsoft researcher [Dafna Shahaf].

[Dafna Shahaf] heard [Mankoff] give a speech about the New Yorker cartoon archive a year or so ago, and it got her thinking about the possibilities of the vast collection with regard to artificial intelligence. The intricate nuances of humor and wordplay have long presented a special challenge to creators. [Shahaf] wondered, could computers begin to learn what makes a caption funny, given a big enough canon?

[Shahaf] threw ninety years worth of wry, one-panel humor at the system. Given this knowledge base, she trained it to choose funny captions for cartoons based on the jokes of similar cartoons. But in order to help [Mankoff] and his assistants choose among the entries, the AI must be able to rank the comedic value of jokes. And since computer vision software is made to decipher photos and not drawings, [Shahaf] and her team faced another task: assigning keywords to each cartoon. The team described each one in terms of its contextual anchors and subsequently its situational anomalies. For example, in the image above, the context keywords could be car dealership, car, customer, and salesman. Anomalies might include claws, fangs, and zoomorphic automobile.

The result is about the best that could be hoped for, if one was being realistic. All of the cartoon editors’ chosen winners showed up among the AI’s top 55.8%, which means the AI could ultimately help [Mankoff and Co.] weed out just under half of the truly bad entries. While [Mankoff] sees the study’s results as a positive thing, he’ll continue to hire assistants for the foreseeable future.

Humor-enabled AI may still be in its infancy, but the implications of the advancement are already great. To give personal assistants like Siri and Cortana a funny bone is to make them that much more human. But is that necessarily a good thing?

[via /.]

Inceptionism: Mind Blown by What Neural Nets Think They See

Dr. Robert Hecht-Nielsen, inventor of one of the first neurocomputers, defines a neural network as:

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

These ‘processing elements’ are generally arranged in layers – where you have an input layer, an output layer and a bunch of layers in between. Google has been doing a lot of research with neural networks for image processing. They start with a network 10 to 30 layers thick. One at a time, millions of training images are fed into the network. After a little tweaking, the output layer spits out what they want – an identification of what’s in a picture.

The layers have a hierarchical structure. The input layer will recognize simple line segments. The next layer might recognize basic shapes. The one after that might recognize simple objects, such as a wheel. The final layer will recognize whole structures, like a car for instance. As you climb the hierarchy, you transition from fast changing low level patterns to slow changing high level patterns. If this sounds familiar, we’ve talked out about it before.

Now, none of this is new and exciting. We all know what neural networks are and do. What is going to blow your knightmind, however, is a simple question Google asked, and the resulting answer. To better understand the process, they wanted to know what was going on in the inner layers. They feed the network a picture of a truck, and out comes the word “truck”. But they didn’t know exactly how the network came to its conclusion. To answer this question, they showed the network an image, and then extracted what the network was seeing at different layers in the hierarchy. Sort of like putting a serial.print in your code to see what it’s doing.

They then took the results and had the network enhance what it thought it detected. Lower levels would enhance low level features, such as lines and basic shapes. The higher levels would enhance actual structures, such as faces and trees. ibisThis technique gives them the level of abstraction for different layers in the hierarchy and reveals its primitive understanding of the image. They call this process inceptionism.

 

Be sure to check out the gallery of images produced by the process. Some have called the images dream like, hallucinogenic and even disturbing. Does this process reveal the inner workings of our mind? After all, our brains are indeed neural networks. Has Google unlocked the mind’s creative process?  Or is this just a neat way to make computer generated abstract art.

So here comes the big question: Is it the computer chosing these end-product photos or a google engineer pawing through thousands (or orders of magnitude more) to find the ones we will all drool over?

Hackaday Prize Entry: A Medical Tricorder

We have padds, fusion power plants are less than 50 years away, and we’re working on impulse drives. We’re all working very hard to make the Star Trek galaxy a reality, but there’s one thing missing: medical tricorders. [M. Bindhammer] is working on such a device for his entry for the Hackaday Prize, and he’s doing this in a way that isn’t just a bunch of pulse oximeters and gas sensors. He’s putting intelligence in his medical tricorder to diagnose patients.

In addition to syringes, sensors, and electronics, a lot of [M. Bindhammer]’s work revolves around diagnosing illness according to symptoms. Despite how cool sensors and electronics are, the diagnostic capabilities of the Medical Tricorder is really the most interesting application of technology here. Back in the 60s and 70s, a lot of artificial intelligence work went into expert systems, and the medical applications of this very rudimentary form of AI. There’s a reason ER docs don’t use expert systems to diagnose illness; the computers were too good at it and MDs have egos. Dozens of studies have shown a well-designed expert system is more accurate at making a diagnosis than a doctor.

While the bulk of the diagnostic capabilities rely on math, stats, and other extraordinarily non-visual stuff, he’s also doing a lot of work on hardware. There’s a spectrophotometer and an impeccably well designed micro reaction chamber. This is hardcore stuff, and we can’t wait to see the finished product.

As an aside, see how [M. Bindhammer]’s project has a lot of neat LaTeX equations? You’re welcome.


The 2015 Hackaday Prize is sponsored by: