Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 1,867

The human brain operates on roughly 20 watts of power (a third of a 60-watt light bulb) in a space the size of, well, a human head. The biggest machine learning algorithms use closer to a nuclear power plant’s worth of electricity and racks of chips to learn.

That’s not to slander machine learning, but nature may have a tip or two to improve the situation. Luckily, there’s a branch of computer chip design heeding that call. By mimicking the brain, super-efficient neuromorphic chips aim to take AI off the cloud and put it in your pocket.

The latest such chip is smaller than a piece of confetti and has tens of thousands of artificial synapses made out of memristors—chip components that can mimic their natural counterparts in the brain.

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Deepfakes⁠ have struck a nerve with the public and researchers alike. There is something uniquely disturbing about these AI-generated images of people appearing to say or do something they didn’t.

With tools for making deepfakes now widely available and relatively easy to use, many also worry that they will be used to spread dangerous misinformation. Politicians can have other people’s words put into their mouths or made to participate in situations they did not take part in, for example.

That’s the fear, at least. To a human eye, the truth is that deepfakes are still relatively easy to spot. And according to a report from cybersecurity firm DeepTrace Labs in October 2019, still the most comprehensive to date, they have not been used in any disinformation campaign. Yet the same report also found that the number of deepfakes posted online was growing quickly, with around 15,000 appearing in the previous seven months. That number will be far larger now.

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Duke University researchers have developed an AI tool that can turn blurry, unrecognizable pictures of people’s faces into eerily convincing computer-generated portraits, in finer detail than ever before.

Previous methods can scale an image of a face up to eight times its original resolution. But the Duke team has come up with a way to take a handful of pixels and create realistic-looking faces with up to 64 times the resolution, ‘imagining’ features such as fine lines, eyelashes and stubble that weren’t there in the first place.

“Never have super-resolution images been created at this resolution before with this much detail,” said Duke computer scientist Cynthia Rudin, who led the team.

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A memristor1 has been proposed as an artificial synapse for emerging neuromorphic computing applications2,3. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform3. An electrochemical metallization (ECM) memory4,5, typically based on silicon (Si), has demonstrated a good analogue switching capability6,7 owing to the high mobility of metal ions in the Si switching medium8. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.

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Here are a few details that have remained more or less intact: J, when he was alive, had dark brown hair, large eyes, olive skin. He loved Proust and Fitzgerald, Marsalis and Bechet. Said things like “this too shall pass,” wrote them on rogue scraps of paper, and hid them in the bottoms of our backpacks.

Then, one morning, he was gone.

I tried to remember all of the details that constituted him, but my memory would not allow it. Instead, I saw a body vaulting through the sky, a bike clumsily skidding below, and I was furious with myself for not being able to imagine something else or, at the very least, remember more. Me, the keeper of a thousand notebooks. The guardian of a thousand pens. But The Cloud, my trusty companion, seemed to have stored all of him. And when grief took me, it preempted my needs like any good lover would; showed me parts of myself I didn’t know were there and parts of others I had almost completely forgotten—and because the bereaved and the writer in me were rattled in their obsessive need to remember, they both gave themselves over to it entirely.

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Rice University researchers have demonstrated methods for both designing innovative data-centric computing hardware and co-designing hardware with machine-learning algorithms that together can improve energy efficiency by as much as two orders of magnitude.

Advances in machine learning, the form of artificial intelligence behind self-driving cars and many other high-tech applications, have ushered in a new era of computing—the data-centric era—and are forcing engineers to rethink aspects of computing architecture that have gone mostly unchallenged for 75 years.

“The problem is that for large-scale deep neural networks, which are state-of-the-art for machine learning today, more than 90% of the electricity needed to run the entire system is consumed in moving data between the and processor,” said Yingyan Lin, an assistant professor of electrical and .

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