Lifeboat Foundation

Danish astronaut Andreas Mogensen performed the breath-taking experiment in which he placed a peg into a very tight hole on Monday under the careful control of the European Space Agency.

While orbiting some 400 kilometres (250 miles) above Earth, Mogensen took control of the Interact Centaur rover which has a pair of arms for delicate, high-precision work.

The blue-and-white fibreglass robot, which cost less than 200,000 euros ($224,000) to build, also has a camera on its head which allows the controller to directly see the task it is performing.

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PRIMER-V2 robot rides a bike just like a man. It maintains balance with a built-in gyroscope, through which he holds the balance and steers in a straight line.

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The media is all-abuzz with tales of Artificial Intelligence (AI). The provocative two-letter symbol conjures up images of invading autonomous robot drones and Terminator-like machines wreaking havoc on mankind. Then there’s the pervading presence of deep learning and big data, also referred to as artificial intelligence. This might leave some of us wondering, is artificial intelligence one or all of these things?

In that sense, AI leaves a bit of an ambiguous trail – there does not seem to be a clear definition, even amongst scientists and researchers in the field. There are certainly many different branches of AI. I asked Dr. Roger Schank, Professor Emeritus at Northwestern University, for a more clear definition; he told me that artificial intelligence is not big data and deep learning algorithms, at least not in the pure sense of the definition.

Roger emphasizes that intelligence has everything to do with the intersection of learning and interaction and memory. “I will tell you the number one thing people do, it’s pretty obvious – they talk to each other. Guess how hard that is? That is phenomenally hard, that is the subsection of AI called natural language processing, the part that I worked on my whole life, and I understand how far away we are from that.”

Take a “simple” AI concept, such as how to create a computer that plays chess, to better understand the challenge. There are, more or less, two approaches to creating an intelligent machine that can play chess like a champion. The first approach requires programming the computer to predict thousands of moves ahead of time, while the second approach involves building a computer system that tries to imitate a grand master. In the historical pursuit of how to create an artificially intelligent entity, a vast majority of scientists chose the first option of programming based on prediction.

Continue reading “Finding Artificial Intelligence Through Storytelling — An Interview with Dr. Roger Schank” »

Two experts on robotics and machine learning will reveal breakthrough developments in humanlike robots and machine learning at the annual SXSW conference in Austin next March, in a proposed* panel called “The Holy Grail: Machine Learning + Extreme Robotics.”

Participants will interact with Hanson Robotics’ forthcoming state-of-the-art female Sophia robot as a participant on the panel as she spontaneously tracks human faces, listens to speech, and generates a natural-language response while participating in dialogue about the potential of genius machines.

This conversation on the future of advanced robotics combined with machine learning and cognitive science will feature visionary Hanson Robotics founder/CEO David Hanson and Microsoft executive Jim Kankanias, who heads Program Management for Information Management and Machine Learning in the Cloud + Enterprise Division at Microsoft. The panel will be moderated by Hanson Robotics consultant Eric Shuss.

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One day, all that internet fan fiction could be put to good use.

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No-A is a short student film directed by Liam Murphy. In it, a hulking robot risks everything to save its creator from an army of faceless soldiers. It’s a really neat CGI film with some really outstanding designs in it.

Overall, this feels like a bit of a sliver from a much larger story, and I hope that the creators and production team will add onto the story and continue it in another short film or maybe a longer feature. It seems like there’s a lot more story to uncover.

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Today, Toyota announced that it has hired Gill Pratt to drive its autonomous car research. Pratt is best known in this field for his work at DARPA and MIT, including starting the Robotics Challenge. The company is also investing $50 million in the research over the next five years as well as partnering with MIT and Stanford.

Pratt has spent the past five years with DARPA, and laid out what’s important for Toyota at an event in Palo Alto today: “Our long-term goal is to make a car that is never responsible for a crash.”

Pratt will serve as Toyota’s “Executive Technical Advisor” on the research.

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A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ and IBM IBM, have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years.

The basis for Knowm’s work is a piece of hardware called a memristor, which functions (warning: oversimplification coming) by mimicking synapses in the brain. Rather than committing certain information to a software program and traditional computing memory, memristors are able to “learn” by strengthening the electrical charge between two resistors (the “ristor” part of memristor) much like synapses strengthen connections between commonly used neurons in the brain.

Done correctly—and this is the result that HP and IBM are after—memristors can make computer chips much smarter, but also very energy efficient. That could mean data centers that don’t use as much energy as small towns, as well as more viable robotics, driverless cars, and other autonomous devices. Alex Nugent, Knowm’s founder and CEO, says memristors—especially the ones his company is working on—offer “a massive leap in efficiency” over traditional CPUs, GPUs, and other hardware now used to power artificial intelligence workloads.

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