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Category: information science – Page 249

It sounds like science fiction: a device that can reconnect a paralyzed person’s brain to his or her body. But that’s exactly what the experimental NeuroLife system does. Developed by Battelle and Ohio State University, NeuroLife uses a brain implant, an algorithm and an electrode sleeve to give paralysis patients back control of their limbs. For Ian Burkhart, NeuroLife’s first test subject, the implications could be life-changing.

Featured in this episode:

Batelle:
https://www.battelle.org/

Ohio State University

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A new method enables researchers to test algorithms for spotting genes that contribute to a complex trait or condition, such as autism.

Researchers often study the genetics of complex traits using genome-wide association studies (GWAS). In these studies, scientists compare the genomes of people who have a condition with those of people without the condition, looking for genetic variants likely to contribute to the condition. These studies often require tens of thousands of people to yield statistically significant results.

GWAS have identified more than 100 genomic regions associated with schizophrenia, for example, and 12 linked to autism. Results are often difficult to interpret, however. Causal variants for a condition may be inherited with nearby sections of DNA that do not play a role.

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The Defense Advanced Research Projects Agency made headlines last fall when it announced that it was pledging $2 billion for a multi-year effort to develop new artificial intelligence technology.

Months later, DARPA’s “AI Next” program is already bearing fruit, said Peter Highnam, the agency’s deputy director.

DARPA — which has for decades fostered some of the Pentagon’s most cutting-edge capabilities — breaks down AI technology development into three distinct waves, he said during a meeting with reporters in Washington, D.C.

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Irina Kareva translates biology into mathematics and vice versa. She writes mathematical models that describe the dynamics of cancer, with the goal of developing new drugs that target tumors. “The power and beauty of mathematical modeling lies in the fact that it makes you formalize, in a very rigorous way, what we think we know,” Kareva says. “It can help guide us to where we should keep looking, and where there may be a dead end.” It all comes down to asking the right question and translating it to the right equation, and back.

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Thanks to a $1.5 million grant from the National Science Foundation, a group of Virginia Tech engineers hopes to redefine these search and rescue protocols by teaming up human searchers with unmanned aerial robots, or drones.

In efforts led by Ryan Williams, an assistant professor in the Bradley Department of Electrical and Computer Engineering within the College of Engineering, these drones will use autonomous algorithms and machine learning to complement search and rescue efforts from the air. The drones will also suggest tasks and send updated information to human searchers on the ground.

Using mathematical models based on historical data that reflect what lost people actually do combined with typical searcher behavior, the researchers hope this novel approach of balancing autonomy with human collaboration can make searches more effective. The team has received support from the Virginia Department of Emergency Management and will work closely with the local Black Diamond Search and Rescue Council throughout the project.

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Quantum supremacy sounds like something out of a Marvel movie. But for scientists working at the forefront of quantum computing, the hope—and hype—of this fundamentally different method of processing information is very real. Thanks to the quirky properties of quantum mechanics (here’s a nifty primer), quantum computers have the potential to massively speed up certain types of problems, particularly those that simulate nature.

Scientists are especially enthralled with the idea of marrying the quantum world with machine learning. Despite all their achievements, our silicon learning buddies remain handicapped: machine learning algorithms and traditional CPUs don’t play well, partly because the greedy algorithms tax classical computing hardware.

Add in a dose of quantum computing, however, and machine learning could potentially process complex problems beyond current abilities at a fraction of the time.

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Conscious “free will” is problematic because brain mechanisms causing consciousness are unknown, measurable brain activity correlating with conscious perception apparently occurs too late for real-time conscious response, consciousness thus being considered “epiphenomenal illusion,” and determinism, i.e., our actions and the world around us seem algorithmic and inevitable. The Penrose–Hameroff theory of “orchestrated objective reduction (Orch OR)” identifies discrete conscious moments with quantum computations in microtubules inside brain neurons, e.g., 40/s in concert with gamma synchrony EEG. Microtubules organize neuronal interiors and regulate synapses. In Orch OR, microtubule quantum computations occur in integration phases in dendrites and cell bodies of integrate-and-fire brain neurons connected and synchronized by gap junctions, allowing entanglement of microtubules among many neurons. Quantum computations in entangled microtubules terminate by Penrose “objective reduction (OR),” a proposal for quantum state reduction and conscious moments linked to fundamental spacetime geometry. Each OR reduction selects microtubule states which can trigger axonal firings, and control behavior. The quantum computations are “orchestrated” by synaptic inputs and memory (thus “Orch OR”). If correct, Orch OR can account for conscious causal agency, resolving problem 1. Regarding problem 2, Orch OR can cause temporal non-locality, sending quantum information backward in classical time, enabling conscious control of behavior. Three lines of evidence for brain backward time effects are presented. Regarding problem 3, Penrose OR (and Orch OR) invokes non-computable influences from information embedded in spacetime geometry, potentially avoiding algorithmic determinism. In summary, Orch OR can account for real-time conscious causal agency, avoiding the need for consciousness to be seen as epiphenomenal illusion. Orch OR can rescue conscious free will.

Keywords: microtubules, free will, consciousness, Penrose-Hameroff Orch OR, volition, quantum computing, gap junctions, gamma synchrony.

We have the sense of conscious control of our voluntary behaviors, of free will, of our mental processes exerting causal actions in the physical world. But such control is difficult to scientifically explain for three reasons:

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Our deepfake problem is about to get worse: Samsung engineers have now developed realistic talking heads that can be generated from a single image, so AI can even put words in the mouth of the Mona Lisa.

The new algorithms, developed by a team from the Samsung AI Center and the Skolkovo Institute of Science and Technology, both in Moscow work best with a variety of sample images taken at different angles – but they can be quite effective with just one picture to work from, even a painting.

mona lisa talk 1024 (Egor Zakharov)

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