neurosciencestuff:

A More Human Artificial Brain

Staying on task

Its full name is the Semantic Pointer Architecture Unified Network, but Spaun sounds way more epic. It’s the latest version of a techno brain, the creation of a Canadian research team at the University of Waterloo.

So what makes Spaun different from a mindboggingly smart artificial brain like IBM’s Watson? Put simply, Watson is designed to work like a supremely powerful search engine, digging through an enormous amount of data at breakneck speed and using complex algorithms to derive an answer. It doesn’t really care about how the process works; it’s mainly about mastering information retrieval.

But Spaun tries to actually mimic the human brain’s behavior and does so by performing a series of tasks, all different from each other. It’s a computer model that can not only recognize numbers with its virtual eye and remember them, but also can manipulate a robotic arm to write them down.

Spaun’s “brain” is divided into two parts, loosely based on our cerebral cortex and basil ganglia and its simulated 2.5 million neurons–our brains have 100 billion–are designed to mimic how researchers think those two parts of the brain interact.

Say, for instance, that its “eye” sees a series of numbers. The artificial neurons take that visual data and route it into the cortex where Spaun uses it to perform a number of different tasks, such as counting, copying the figures, or solving number puzzles.

Soon it will be forgetting birthdays

But there’s been an interesting twist to Spaun’s behavior. As Francie Diep wrote in Tech News Daily, it became more human than its creators expected.

Ask it a question and it doesn’t answer immediately. No, it pauses slightly, about as long as a human might. And if you give Spaun a long list of numbers to remember, it has an easier time recalling the ones it received first and last, but struggles a bit to remember the ones in the middle.

“There are some fairly subtle details of human behavior that the model does capture,” says Chris Eliasmith, Spaun’s chief inventor. “It’s definitely not on the same scale. But it gives a flavor of a lot of different things brains can do.”

Brain drains

The fact that Spaun can move from one task to another brings us one step closer to being able to understand how our brains are able to shift so effortlessly from reading a note to memorizing a phone number to telling our hand to open a door.

And that could help scientists equip robots with the ability to be more flexible thinkers, to adjust on the fly. Also, because Spaun operates more like a human brain, researchers could use it to run health experiments that they couldn’t do on humans.

Recently, for instance, Eliasmith ran a test in which he killed off the neurons in a brain model at the same rate that neurons die in people as they age. He wanted to see how the loss of neurons affected the model’s performance on an intelligence test.

One thing Eliasmith hasn’t been able to do is to get Spaun to recognize if it’s doing a good or a bad job. He’s working on it.

(via ikenbot)

ikenbot:

Artificial Brain Mimics Human Abilities and Flaws

Side Note: I recommend this fascinating article for anyone who’s been as interested in developments of the brain in the past couple of weeks or in general and the refreshing data about how our pattern recognition works and how it can lead to not only a better understanding of our own minds but also a better understanding into building more accurate artificial intelligence in robots. The accuracy and how natural the intelligence comes off is important if we are to have robots that work for and aid us, if we are to have extensions of what our technology can do with what we know about the human body and brain I think robotics is one way to go about it. It’s like using technology as a canvas and expressing our own biological makeup through it. In this article LS gets into a new software model that accurately replicates certain human-like mistakes with a very limited amount of virtual pattern recognizers. Excuse me for leaving the whole bit of the article I just found it too interesting to leave anything out.

Spaun, a new software model of a human brain, is able to play simple pattern games, draw what it sees and do a little mental arithmetic. It powers everything it does with 2.5 million virtual neurons, compared with a human brain’s 100 billion. But its mistakes, not its abilities, are what surprised its makers the most, said Chris Eliasmith, an engineer and neuroscientist at the University of Waterloo in Canada.

Ask Spaun a question, and it hesitates a moment before answering, pausing for about as long as humans do. Give Spaun a list of numbers to memorize, and it falters when the list gets too long. And Spaun is better at remembering the numbers at the beginning and end of a list than at recalling numbers in the middle, just like people are.

“There are some fairly subtle details of human behavior that the model does capture,” said Eliasmith, who led the development of Spaun, or the Semantic Pointer Architecture Unified Network. “It’s definitely not on the same scale [as a human brain],” he told TechNewsdaily. “It gives a flavor of a lot of different things brains can do.”

Eliasmith and his team of Waterloo neuroscientists say Spaun is the first model of a biological brain that performs tasks and has behaviors. Because it is able to do such a variety of things, Spaun could help scientists understand how humans do the same, Eliasmith said. In addition, other scientists could run simplified simulations of certain brain disorders or psychiatric drugs using Spaun, he said.

A brain with thought and action

Researchers have made several brain models that are more powerful than Spaun. The Blue Brain model at the Ecole Polytechnique Fédérale de Lausanne in France has 1 million neurons. IBM’s SyNAPSE project has 1 billion neurons. Those models aren’t built to perform a variety of tasks, however, Eliasmith said.

Spaun is programmed to respond to eight types of requests, including copying what it sees, recognizing numbers written with different handwriting, answering questions about a series of numbers and finishing a pattern after seeing examples.

Spaun’s myriad skills could shed light on the flexible, variable human brain, which is able to use the same equipment to control typing, biking, driving, flying airplanes and countless other tasks, Eliasmith said. That knowledge, in turn, could help scientists add flexibility to robots or artificial intelligence, he said. Artificial intelligence now usually specializes in doing only one thing, such as tagging photos or playing chess. “It can’t figure out to switch between those things,” he said.

In addition, artificial intelligence isn’t built to mimic the cellular structure of human brains as closely as Spaun and other brain models do. Because Spaun runs more like a human brain, other researchers could use it to run health experiments that would be unethical in human study volunteers, Eliasmith said. He recently ran a test in which he killed off the neurons in a brain model at the same rate that neurons die in people as they age, to see how the dying off affected the model’s performance on an intelligence test.

Such tests would have to be just first steps in a longer experiment, Eliasmith said. The human brain is so much more complex than models that there’s a limit to how much models are able to tell researchers. As scientists continue to improve brain models, the models will become better proxies for health studies, he said.

Next up: a brain in real time

There’s one major way Spaun differs from a human brain. It takes a lot of computingpower to perform its little tasks. Spaun runs on a supercomputer at the University of Waterloo, and it takes the computer two hours to run just one second of a Spaun simulation, Eliasmith said.

So Eliasmith’s next major step for improving Spaun is developing hardware that lets the model work in real time. He’ll cooperate with researchers at the University of Manchester in the U.K. and hopes to have something ready in six months, he said.

In the far future, people may find Spaun’s humanlike flaws deliberately built into robot assistants, Eliasmith said. “Those kinds of features are important in a way because if we’re interacting with an agent and it has a kind of memory that we’re familiar with, it’ll more natural to interact with,” he added.

Eliasmith and his colleagues published their latest paper about Spaun today (Nov. 29) in the journal Science.

ikenbot:

John McCarthy — The Father of Artificial Intelligence

John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist.

He invented the term “artificial intelligence” (AI), developed the Lisp programming language family, significantly influenced the design of the ALGOL programming language, popularized timesharing (the sharing of a computing resource among many users by means of multiprogramming and multi-tasking), and was very influential in the early development of AI.

McCarthy received many accolades and honors, such as the Turing Award for his contributions to the topic of AI, the United States National Medal of Science, and the Kyoto Prize.

neurosciencestuff:

How long before robots can think like us?

Will this summer be remembered as a turning point in the story of man versus machine? On June 23, with little fanfare, a computer program came within a hair’s breadth of passing the Turing test, a kind of parlour game for evaluating machine intelligence devised by mathematician Alan Turing more than 60 years ago.

Turing proposed the test – he called it “the imitation game” – in a 1950 paper titled “Computing machinery and intelligence”. Back then, computers were very simple machines, and the field known as Artificial Intelligence (AI) was in its infancy. But already scientists and philosophers were wondering where the new technology would lead. In particular, could a machine “think”?

wildcat2030:

After decades of trial and error, artificial intelligence applications that aim to understand human language are slowly starting to lose some of their brittleness. Now, a simple mathematical model developed by two psychologists at Stanford University could lead to further improvements, helping transform computers that display the mere veneer of intelligence into machines that truly understand what we are saying. (via Research at Stanford may lead to computers that understand humans)

ikenbot:

Artificial Intelligence Could Be on Brink of Passing Turing Test

One hundred years after Alan Turing was born, his eponymous test remains an elusive benchmark for artificial intelligence. Now, for the first time in decades, it’s possible to imagine a machine making the grade.

Turing was one of the 20th century’s great mathematicians, a conceptual architect of modern computing whose codebreaking played a decisive part in World War II. His test, described in a seminal dawn-of-the-computer-age paper, was deceptively simple: If a machine could pass for human in conversation, the machine could be considered intelligent.

Artificial intelligences are now ubiquitous, from GPS navigation systems and Google algorithms to automated customer service and Apple’s Siri, to say nothing of Deep Blue and Watson — but no machine has met Turing’s standard. The quest to do so, however, and the lines of research inspired by the general challenge of modeling human thought, have profoundly influenced both computer and cognitive science.

There is reason to believe that code kernels for the first Turing-intelligent machine have already been written.

“Two revolutionary advances in information technology may bring the Turing test out of retirement,” wrote Robert French, a cognitive scientist at the French National Center for Scientific Research, in an Apr. 12 Science essay. “The first is the ready availability of vast amounts of raw data — from video feeds to complete sound environments, and from casual conversations to technical documents on every conceivable subject. The second is the advent of sophisticated techniques for collecting, organizing, and processing this rich collection of data.”

Read on..