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Category: information science – Page 215

For all its comparisons to the human brain, AI still isn’t much like us. Maybe that’s alright. In the animal kingdom, brains come in all shapes and sizes. So, in a new machine learning approach, engineers did away with the human brain and all its beautiful complexity—turning instead to the brain of a lowly worm for inspiration.

Turns out, simplicity has its benefits. The resulting neural network is efficient, transparent, and here’s the kicker: It’s a lifelong learner.

Whereas most machine learning algorithms can’t hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in “neuroplasticity.” That is, as it goes about its work—say, in the future, maybe driving a car or directing a robot—it can learn from experience and adjust its connections on the fly.

In a world that’s noisy and chaotic, such adaptability is essential. ## **Worm-Brained Driver**

The algorithm’s architecture was inspired by the mere 302 neurons making up the nervous system of *C. elegans*, a tiny nematode (or worm).

In work published last year, the group, which includes researchers from MIT and Austria’s Institute of Science and Technology, said that despite its simplicity, *C. elegans* is capable of surprisingly interesting and varied behavior. So, they developed equations to mathematically model the worm’s neurons and then built them into a neural network.

Their worm-brain algorithm was much simpler than other cutting-edge machine learning algorithms, and yet it was still able to accomplish similar tasks, like keeping a car in its lane.

Continue reading “New ‘Liquid’ AI Learns Continuously From Its Experience of the World” | >

But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”

This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).

The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200–300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.

The human eye can only see so much, and despite embryologists’ efforts to select the “best” embryo, IVF success rates are still relatively low.

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Fast-forwarding quantum calculations skips past the time limits imposed by decoherence, which plagues today’s machines.

A new algorithm that fast forwards simulations could bring greater use ability to current and near-term quantum computers, opening the way for applications to run past strict time limits that hamper many quantum calculations.

“Quantum computers have a limited time to perform calculations before their useful quantum nature, which we call coherence, breaks down,” said Andrew Sornborger of the Computer, Computational, and Statistical Sciences division at Los Alamos National Laboratory, and senior author on a paper announcing the research. “With a new algorithm we have developed and tested, we will be able to fast forward quantum simulations to solve problems that were previously out of reach.”

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Very interesting.

Albert Einstein’s theory of general relativity profoundly changed our thinking about fundamental concepts in physics, such as space and time. But it also left us with some deep mysteries. One was black holes, which were only unequivocally detected over the past few years. Another was “wormholes” – bridges connecting different points in spacetime, in theory providing shortcuts for space travellers.

Wormholes are still in the realm of the imagination. But some scientists think we will soon be able to find them, too. Over the past few months, several new studies have suggested intriguing ways forward.

Black holes and wormholes are special types of solutions to Einstein’s equations, arising when the structure of spacetime is strongly bent by gravity. For example, when matter is extremely dense, the fabric of spacetime can become so curved that not even light can escape. This is a black hole.

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New technology from Stanford scientists finds long-hidden quakes, and possible clues about how earthquakes evolve.

Tiny movements in Earth’s outermost layer may provide a Rosetta Stone for deciphering the physics and warning signs of big quakes. New algorithms that work a little like human vision are now detecting these long-hidden microquakes in the growing mountain of seismic data.

Measures of Earth’s vibrations zigged and zagged across Mostafa Mousavi’s screen one morning in Memphis, Tenn. As part of his PhD studies in geophysics, he sat scanning earthquake signals recorded the night before, verifying that decades-old algorithms had detected true earthquakes rather than tremors generated by ordinary things like crashing waves, passing trucks or stomping football fans.

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I like this idea. I don’t want AI to be a black box, I want to know what’s happening and how its doing it.

The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.

However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.

Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

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Samsung’s memory technology innovates artificial intelligence and Big Data analytics to bring impactful change to the way we live, work, and interact with each other. Through next-generation memory technology that enables faster and more complex tasks in AI and Big Data, Samsung takes part in the revolutionary advancement of technology that is enriching our everyday lives.

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Oneskin — the first skin cream that destroys senescent cells:

Longevity, Health, Long Lifespans, and Halthspans, Psychology, Spirituality — I and Carolina Reis Oliveira talk about all these things in relation to the skin. Find out how you can have very healthy skin with OneSkin!

Visit OneSkin’s website — https://www.oneskin.co/

0:00 — Logo & Title.
0:17 — H! & Intro.
1:40 — Presentation.
2:20 — Presentation | Skin Health — Longevity.
3:57 — Presentation | The Root Cause of Aging.
4:46 — Presentation | Senescent Cells.
5:49 — Presentation | Current solutions.
6:32 — Presentation | OneSkin Approach.
7:47 — Presentation | Let’s Dive Deeper into the Science.
9:51 — Presentation | Replicating Skin Aging.
11:42 — Presentation | Developing an Algorithm to Measure Skin Aging.
12:58 — Presentation | A Drug Discovery Process.
14:23 — Presentation | Senotherapeutic Compounds.
15:00 — Presentation | OS1
15:42 — Presentation | OS1 & UVB Radiation.
17:13 — Presentation | OS1 — Validate effects in 3D models.
19:33 — Presentation | OS1 — Treatment in Skin Biopsies.
20:55 — Presentation | OS1 — Safety.
21:43 — Presentation | OS1 — Clinical Study Results.
23:18 — Presentation | OS1 — Applications Beyond Skin.
26:14 — Presentation | Team.
28:07 — Q&A + the Conversation.
28:25 — Futuristic Psychology & Spirituality.
31:34 — Myths Regarding Immortality.
34:20 — The Collective Rejuvenation.
37:10 — Biologic Hygiene.
41:56 — Cellular Senescence.
46:00 — The Molecular Clock.
48:04 — Morphogenesis of a Scar.
51:15 — Differences Between Skin Types on a Body.
52:35 — Skin Types Regarding Different Races.
54:44 — Skin Conditions.
56:03 — Closing & Ending

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A new algorithm capable of inferring goals and plans could help machines better adapt to the imperfect nature of human planning.

In a classic experiment on human social intelligence by psychologists Felix Warneken and Michael Tomasello (see video below), an 18-month old toddler watches a man carry a stack of books towards an unopened cabinet. When the man reaches the cabinet, he clumsily bangs the books against the door of the cabinet several times, then makes a puzzled noise.

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