Computing & Technology News

Tesla is actually going to make a ‘Tesla Bot’ humanoid robot for general purpose use

Tesla announced that it is actually going to make a humanoid robot, called Tesla Bot, and it will be able to grab your groceries for you and perform other menial tasks.

No, it’s not a joke.

At its “AI Day” today, Tesla released many details about its progress to develop AI technology to power its self-driving system.

But as we expected, there was a “one more thing” moment, and it was robots.

CEO Elon Musk announced that Tesla plans to build a humanoid robot called Tesla Bot.
https://electrek.co/2021/08/19/tesla-bot-humanoid-robot/

http://www.gotai.net/forum/default.aspx?postid=308998#308998

Русские уже имеют Искусственный Интеллект

>>1

Tesla announced that it is actually going to make a humanoid robot, called Tesla Bot, and it will be able to grab your dick

ok cool *grabs dick*

Scientists around the world are developing new hardware for quantum computers, a new type of device that could accelerate drug design, financial modeling, and weather prediction. These computers rely on qubits, bits of matter that can represent some combination of 1 and 0 simultaneously. The problem is that qubits are fickle, degrading into regular bits when interactions with surrounding matter interfere. But new research at MIT suggests a way to protect their states, using a phenomenon called many-body localization (MBL).

MBL is a peculiar phase of matter, proposed decades ago, that is unlike solid or liquid. Typically, matter comes to thermal equilibrium with its environment. That's why soup cools and ice cubes melt. But in MBL, an object consisting of many strongly interacting bodies, such as atoms, never reaches such equilibrium. Heat, like sound, consists of collective atomic vibrations and can travel in waves; an object always has such heat waves internally. But when there's enough disorder and enough interaction in the way its atoms are arranged, the waves can become trapped, thus preventing the object from reaching equilibrium.

MBL had been demonstrated in "optical lattices," arrangements of atoms at very cold temperatures held in place using lasers. But such setups are impractical. MBL had also arguably been shown in solid systems, but only with very slow temporal dynamics, in which the phase's existence is hard to prove because equilibrium might be reached if researchers could wait long enough. The MIT research found a signatures of MBL in a "solid-state" system—one made of semiconductors—that would otherwise have reached equilibrium in the time it was watched.
https://phys.org/news/2021-08-peculiar-state-layers-semiconductors.html

Yi's group created a quantum source in an optical microresonator a ring-shaped, millimeter-sized structure that envelopes the photons and generates a microcobe, a device that efficiently converts photons from single to multiple wavelengths. Light circulates around the ring to build up optical power. This power buildup enhances chances for photons to interact, which produces quantum entanglement between fields of light in the microcomb.

Through multiplexing, Yi's team verified the generation of 40 qumodes from a single microresonator on a chip, proving that multiplexing of quantum modes can work in integrated photonic platforms. This is just the number they are able to measure.

"We estimate that when we optimize the system, we can generate thousands of qumodes from a single device," Yi said.
https://phys.org/news/2021-08-path-quantum-real-world-conditions.html

High Intensity Focused Ultrasound; novel technological breakthough
to treat cancer, blood clots and drug delivery/absorption.
https://www.eurekalert.org/news-releases/926214
“It’s an area that I think is going to take center stage in clinical medicine,” he said. “It doesn’t have the negative side effects of radiation therapy or chemotherapy. There are no side effects other than the effect of heat, which we are working on right now. It also has applications as a new way to break up blood clots and even to administer drugs.”

A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions, an achievement that resulted from the COVID-19 pandemic lockdown. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks.
https://phys.org/news/2021-09-artificial-brain-networks-simulated-quantum.html

Using a groundbreaking new technique at the National Institute of Standards and Technology (NIST), an international collaboration led by NIST researchers has revealed previously unrecognized properties of technologically crucial silicon crystals and uncovered new information about an important subatomic particle and a long-theorized fifth force of nature.
https://www.nist.gov/news-events/news/2021/09/groundbreaking-technique-yields-important-new-details-silicon-subatomic

Researchers have developed artificial cell-like structures using inorganic matter that autonomously ingest, process, and push out material—recreating an essential function of living cells.

Their article, published in Nature, provides a blueprint for creating “cell mimics,” with potential applications ranging from drug delivery to environmental science.
https://www.nyu.edu/about/news-publications/news/2021/september/artificial-cells.html

Researchers realize a spin field-effect transistor at room temperature
https://phys.org/news/2021-09-field-effect-transistor-room-temperature.html
A crucial goal of spintronics research is to coherently manipulate electron spins at room temperature using electrical current. This is particularly valuable as it would enable the development of numerous devices, including spin field-effect transistors.

In experiments using conventional materials, engineers and physicists have so far only observed coherent spin precession in the ballistic regime and at very low temperatures. Two-dimensional (2D materials), however, have unique characteristics that could provide new control knobs to manipulate spin procession.

Researchers at CIC nanoGUNE BRTA in Spain and University of Regensburg in Germany have recently demonstrated spin precession at room temperature in the absence of a magnetic field in bilayer graphene. In their paper, published in Physical Review Letters, they used 2D materials to realize a spin field-effect transistor.

"In our group, there is a long tradition of studying spin transport in multiple materials, such as simple metals, for instance," Josep Ingla-Aynes, Franz Herling, Jaroslav Fabian, Luis E. Hueso and Felix Casanova, the researchers who carried out the study, told Phys.org via email. "Our main goal is to understand how the spin of the electron can carry information and how this degree of freedom can help to create devices with new functionalities."

Graphene is among the materials with the greatest spin relaxation lengths. Nonetheless, manipulating spins as they travel on graphene can be very challenging and has so far only been achieved using external magnetic fields, which is far from ideal for practical applications.

Recently, Ingla-Aynés and his colleagues have been examining how heterostructures based on different 2D materials, also known as van der Waals heterostructures, perform in spintronics. Van der Waals heterostructures, are a class of graphene-based 2D materials with layers that are not chemically bonded.

"We have particularly been exploring structures where a material with weak spin-orbit coupling (such as graphene) is stacked with a material with a strong spin-orbit coupling (such as WSe2) and observing experimentally how this spin-orbit coupling is actually transferred into the graphene by proximity," the researchers explained. "More technically, by achieving a strong interaction between the layers, it is possible to imprint such an efficient spin-orbit coupling on the graphene (that acts as an effective magnetic field) that can reverse the spins without the need for applying a magnetic field and this is what we wanted to do."

Instead of using a single material, Ingla-Aynés and his colleagues used a combination of two materials with different significant properties. The first of these materials is graphene, which has a weak spin-orbit coupling and long spin relaxation length. The second is WSe2, which has a strong and anisotropic spin-orbit coupling.

"We prepared bilayer graphene/WSe2 van der Waals heterostructures using a dry polymer-based stacking technique," the researchers said. "Then, to promote proximity between the layers, we annealed our samples above 400 degrees Celsius. To measure spin transport, we used ferromagnetic electrodes that, combined with magnetic fields, allow us to measure in-plane and out-of-plane spins that travel across the graphene/WSe2 channel."

Ingla-Aynés and his colleagues were able to control the spin transport times in the material they used by applying an in-plane electric field and a backgate voltage to them. This ultimately enabled the electrical control of spin precession at room temperature, without the need to apply an external magnetic field.

"This has been sought by the community for decades and exploring many different materials, yet no one was successful, until now," the researchers said. "This finding has implications for the applicability of spintronics, as our device operates like the long sought-after Datta-Das spin transistor, which has been one of the goals of spintronics since it was first proposed in 1990."

In their paper, the researchers presented the first spin field-effect transistor at room temperature using the spin precession strategy they developed. In the future, their work could pave the way towards the practical implementation of energy efficient spin-based logic.

"Our study also has a fundamental consequence, as it provides valuable information on how spin transport is affected by the spin-orbit interactions in graphene-based van der Waals heterostructures," the researchers said. "In our next studies, we plan to study multiple other combinations of 2D materials which will provide new physical effects related to the spin degree of freedom."
More information: Electrical control of Valley-Zeeman spin-orbit-coupling-induced spin precession at room temperature. Physical Review Letters(2021). DOI: 10.1103/PhysRevLett.127.047202

Researchers at the Institute of Laser Physics at the University of Hamburg have recently realized a time crystal in an open quantum system for the first time. Their paper, published in Physical Review Letters, could have important implications for the study of exotic phases of matter in quantum systems.
https://phys.org/news/2021-09-experimental-dissipative-crystal.html

Researchers report an insulator made of two conductors
New correlated state

"That's a completely new correlated state of electrons and holes which has no overall charge," says Ensslin. "This neutral state can, nevertheless, transmit information or conduct heat. Moreover, what's special about it is that we can completely control it through the twisting angle and the applied voltage." Similar states have been observed in other materials in which electron-hole pairs (also known as excitons) are created through excitation using laser light. In the experiment at ETH, however, the electrons and holes are in their ground state, or state of lowest energy, which means that their lifetime is not limited by spontaneous decay.
https://phys.org/news/2021-09-insulator-conductors.html

Found by Googling "Mentifex":
"Archive of stories about Mentifex"

Sep 17, 2016 Mentifex Codes AI in a Dream
Dec 25, 2016 Mentifex FAQ Updated for AI Thesis or PhD Dissertation
Nov 22, 2019 The Jungle Prince of Delhi
Sep 2, 2019 [LJM&ATM] Night Terror

https://medium.com/tag/mentifex/archive

"What usually happens in a regular, impure semiconductor is that electrons undergo so many collisions with impurities that you basically never know what the electron-electron interactions are actually doing," said Jean Heremans, a professor in the Department of Physics in the College of Science. "But when you remove those impurities, you're left with an ultrapure material, and suddenly those electron-electron interactions become evident. It was a bit of a surprise to us that it was such a big effect—that we could use it to quantify the electron interactions."

However, this wasn't the only surprise that the team encountered. Scientists have recently found that in certain materials and conditions, groups of electrons flow collectively and behave similar to a liquid. Using high-powered computers, project collaborators at Rensselaer Polytechnic Institute in Troy, New York, simulated how the group of electrons flowed. Their images revealed that the electrons flowed into vortices, like whirlpools—a behavior that has yet to be documented in the presence of a magnetic field.

"The whirlpools actually persist even if the interactions between electrons are very weak," said Adbhut Gupta, the lead author of the study and a Ph.D. candidate in Heremans's lab. "At this point, not much is known about this collective behavior in the weak interaction limit. It's a new phenomenon, one that a single particle would not have shown. Ours is the first experiment to hint at this kind of collective behavior."
https://phys.org/news/2021-09-physics-reveals-fresh-complexities-electron.html

The result indicates that with the increase of the spin-wave frequency, the decay length decreases and the evanescent wave are more concentrated at the interface, showing a magnonic skin effect which is similar to the skin effect of electromagnetic waves.

Furthermore, a positive magnonic Goos-Hänchen shift of the reflected waves was also predicted. It can be understood by an effective reflection interface shift induced by the nonzero decay length of the evanescent waves.

In summary, the results show that the efficient manipulation of coherent/incoherent magnons by magnon junctions stems from the inherent chirality of magnons in magnetic materials. These discoveries confirm the physical basis of magnon devices to efficiently manipulate magnon transport, and provides a new development direction and technical route for the development of pure magnon-type storage and logic devices.
https://phys.org/news/2021-09-magnon-blocking-effect-magnonic-skin.html

QLD police will use AI to predict crimes before they happen:
https://phys.org/news/2021-09-qld-police-ai-domestic-violence.html
The approach relies on an algorithm that has been developed from existing QPS administrative data (QPRIME). All statistical algorithms must assess risk based on available data, which in turn means they are only as good as the data underpinning them.

Experts who criticize the use of data-driven risk assessment tools in policing point to the lack of transparency in the specific kinds of data analyzed, as well as how predictions based on these data are acted upon.

Because of how police operate, the key data most consistently captured are information about past situations police have been called to, and criminal history data.

Using this information to train an AI algorithm could reinforce existing biases in the criminal justice system. It could create an endless feedback loop between police and those members of the public who have the most contact with police.

Effect of electrons with negative mass in novel semiconductor nanostructures
https://phys.org/news/2021-09-effect-electrons-negative-mass-semiconductor.html
However, the team observed an astonishing effect. When irradiated with a red laser, the electrons emit not only red light, as expected, but also show a faint blue glimmer. Low-energy red light is therefore converted into blue light of higher energy, an extraordinary effect. By looking closely at the color distribution and brightness of this blue light, i.e. the optical spectrum, it can be concluded that the blue glow arises from electrons with negative mass. This unexpected experimental finding could be substantiated with detailed quantum mechanical calculations of the electronic structure, which were carried out in this form for the first time.

At present, the discovery may still seem like more of a scientific oddity, but the scientists already have a number of possible applications in mind. For example, the concept may aid the development of superfast computers, where electrons move almost without resistance. The transition from positive to negative mass also creates so-called singularities. Such singularities—familiar from trying to divide something by zero on a calculator—are not entirely dissimilar to the black holes of cosmology.

Finally, due to the fact that the electrons in the semiconductor can apparently assume discrete energy states, as in an atom, it should be possible to transfer concepts of atomic quantum optics directly to the semiconductor. This could be used, for example, to develop new electronic components that convert the wavelength of light, store or even amplify light, or function as optical switches.

Scientists develop the next generation of reservoir computing
https://phys.org/news/2021-09-scientists-reservoir.html
A relatively new type of computing that mimics the way the human brain works was already transforming how scientists could tackle some of the most difficult information processing problems.

Now, researchers have found a way to make what is called reservoir computing work between 33 and a million times faster, with significantly fewer computing resources and less data input needed.

In fact, in one test of this next-generation reservoir computing, researchers solved a complex computing problem in less than a second on a desktop computer.

Using the now current state-of-the-art technology, the same problem requires a supercomputer to solve and still takes much longer, said Daniel Gauthier, lead author of the study and professor of physics at The Ohio State University.

"We can perform very complex information processing tasks in a fraction of the time using much less computer resources compared to what reservoir computing can currently do," Gauthier said.

"And reservoir computing was already a significant improvement on what was previously possible."

The study was published today in the journal Nature Communications.

Reservoir computing is a machine learning algorithm developed in the early 2000s and used to solve the "hardest of the hard" computing problems, such as forecasting the evolution of dynamical systems that change over time, Gauthier said.

Dynamical systems, like the weather, are difficult to predict because just one small change in one condition can have massive effects down the line, he said.

One famous example is the "butterfly effect," in which—in one metaphorical illustration—changes created by a butterfly flapping its wings can eventually influence the weather weeks later.

Previous research has shown that reservoir computing is well-suited for learning dynamical systems and can provide accurate forecasts about how they will behave in the future, Gauthier said.

It does that through the use of an artificial neural network, somewhat like a human brain. Scientists feed data on a dynamical network into a "reservoir" of randomly connected artificial neurons in a network. The network produces useful output that the scientists can interpret and feed back into the network, building a more and more accurate forecast of how the system will evolve in the future.

The larger and more complex the system and the more accurate that the scientists want the forecast to be, the bigger the network of artificial neurons has to be and the more computing resources and time that are needed to complete the task.

One issue has been that the reservoir of artificial neurons is a "black box," Gauthier said, and scientists have not known exactly what goes on inside of it—they only know it works.

The artificial neural networks at the heart of reservoir computing are built on mathematics, Gauthier explained.

"We had mathematicians look at these networks and ask, 'To what extent are all these pieces in the machinery really needed?'" he said.

In this study, Gauthier and his colleagues investigated that question and found that the whole reservoir computing system could be greatly simplified, dramatically reducing the need for computing resources and saving significant time.

They tested their concept on a forecasting task involving a weather system developed by Edward Lorenz, whose work led to our understanding of the butterfly effect.

Their next-generation reservoir computing was a clear winner over today's state—of-the-art on this Lorenz forecasting task. In one relatively simple simulation done on a desktop computer, the new system was 33 to 163 times faster than the current model.

But when the aim was for great accuracy in the forecast, the next-generation reservoir computing was about 1 million times faster. And the new-generation computing achieved the same accuracy with the equivalent of just 28 neurons, compared to the 4,000 needed by the current-generation model, Gauthier said.

An important reason for the speed-up is that the "brain" behind this next generation of reservoir computing needs a lot less warmup and training compared to the current generation to produce the same results.

Warmup is training data that needs to be added as input into the reservoir computer to prepare it for its actual task.

"For our next-generation reservoir computing, there is almost no warming time needed," Gauthier said.

"Currently, scientists have to put in 1,000 or 10,000 data points or more to warm it up. And that's all data that is lost, that is not needed for the actual work. We only have to put in one or two or three data points," he said.

And once researchers are ready to train the reservoir computer to make the forecast, again, a lot less data is needed in the next-generation system.

In their test of the Lorenz forecasting task, the researchers could get the same results using 400 data points as the current generation produced using 5,000 data points or more, depending on the accuracy desired.

"What's exciting is that this next generation of reservoir computing takes what was already very good and makes it significantly more efficient," Gauthier said.

He and his colleagues plan to extend this work to tackle even more difficult computing problems, such as forecasting fluid dynamics.

"That's an incredibly challenging problem to solve. We want to see if we can speed up the process of solving that problem using our simplified model of reservoir computing."

Co-authors on the study were Erik Bollt, professor of electrical and computer engineering at Clarkson University; Aaron Griffith, who received his Ph.D. in physics at Ohio State; and Wendson Barbosa, a postdoctoral researcher in physics at Ohio State.

Sex robot? Will it replace women?

What programming language is this?

>>19

Sex robot? Will it replace women?

Just marry little girl children like the Taliban does.

>>21
Freedom isn't free. Taliban fought for it.