Lifeboat Foundation

Singapore residents can now ride in a self-driving taxi. http://cnnmon.ie/2bS9lYj

Motobot — motorcycle riding humanoid robot

Look closely.

That’s not a human riding this motorcycle. http://cnnmon.ie/2bzSrh1

“The cities of the future will be huge and super-dense — but will they also be alive?”

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We’re in the midst of a jobs crisis, and rapid advances in AI and other technologies may be one culprit. How can we get better at sharing the wealth that technology creates?

By the year 2030, artificial intelligence (A.I.) will have changed the way we travel to work and to parties, how we take care of our health and how our kids are educated.

That’s the consensus from a panel of academic and technology experts taking part in Stanford University’s One Hundred Year Study on Artificial Intelligence.

Focused on trying to foresee the advances coming to A.I., as well as the ethical challenges they’ll bring, the panel yesterday released its first study.

Continue reading “Scientists look at how A.I. will change our lives by 2030” »

Excellent marathon runner, A.I. pioneer or outstanding scientist: who was Alan Turing? Read more here 👉 http://bit.ly/1USRuOF

The Anthrobotics Cluster seeks to start conversations (and answer questions) regarding some of the biggest topics in AI research. Here, Luis de Miranda, one of the founders, discusses anthrobots and the relationship between humans and machines.

Technology is accelerating at an ever increasing rate. Each year, we develop smaller and smarter systems…systems that allow us to interact with information in ways that previous eras only dreamed about. In fact, given their ability to process, identify, and categorize information—and their uncanny ability to synthesize information and make judgments—many of our systems seem to be developing a true form of intelligence. In this respect, it seems that the dawning age of AI is truly upon us.

But what does this mean?

Continue reading “Anthrobotics: Where The Human Ends and the Robot Begins” »

Friday, Sep 2, 2016 4:35 PM UTC

Robot “employees” coming to select Lowe’s this fall.

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[Figure about depicts a layout, showing two ‘somas’, or circuits that simulate the basic functions of a neuron. The green circles play the role of synapses. From presentation of K.K. Likharev, used with permission.]

One possible layout is shown above. Electronic devices called ‘somas’ play the role of the neuron’s cell body, which is to add up the inputs and fire an output. In neuromorphic hardware, somas may mimic neurons with several different levels of sophistication, depending on what is required for the task at hand. For instance, somas may generate spikes (sequences of pulses) just like neurons in the brain. There is growing evidence that sequences of spikes in the brain carry more information than just the average firing rate alone, which previously had been considered the most important quantity. Spikes are carried through the two types of neural wires, axons and dendrites, which are represented by the red and blue lines in figure 2. The green circles are connections between these wires that play the role of synapses. Each of these ‘latching switches’ must be able to hold a ‘weight’, which is encoded in either a variable capacitance or variable resistance. In principle, memristors would be an ideal component here, if one could be developed that could be mass produced. Crucially, all of the crossnet architecture can be implemented in traditional silicon-based (“CMOS”-like) technology. Each crossnet (as shown in the figure) is designed so they can be stacked, with additional wires connecting somas on different layers. In this way, neuromorphic crossnet technology can achieve component densities that rival the human brain.

Likarev’s design is still theoretical, but there are already several neuromorphic chips in production, such as IBM’s TrueNorth chip, which features spiking neurons, and Qualcomm’s “Zeroeth” project. NVIDIA is currently making major investments in deep learning hardware, and the next generation of NVIDIA devices dedicated for deep learning will likely look closer to neuromorphic chips than traditional GPUs. Another important player is the startup Nervana systems, which was recently acquired by Intel for $400 million. Many governments are are investing large amounts of money into academic research on neuromorphic chips as well. Prominent examples include the EU’s BrainScaleS project, the UK’s SpiNNaker project, and DARPA’s SyNAPSE program.

Continue reading “Neuromorphic Chips: a Path Towards Human-level AI” »