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Researchers at Oxford University’s Department of Materials, working in collaboration with colleagues from Exeter and Munster, have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.

The new research published in Optica took its inspiration from Nobel Prize laureate Ivan Pavlov’s discovery of classical conditioning. In his experiments, Pavlov found that by providing another stimulus during feeding, such as the sound of a bell or metronome, his dogs began to link the two experiences and would salivate at the sound alone. The repeated associations of two unrelated events paired together could produce a learned response—a conditional reflex.

Co-first author Dr. James Tan You Sian, who did this work as part of his DPhil in the Department of Materials, University of Oxford, said, “Pavlovian associative learning is regarded as a basic form of learning that shapes the behavior of humans and animals—but adoption in AI systems is largely unheard of. Our research on Pavlovian learning in tandem with optical parallel processing demonstrates the exciting potential for a variety of AI tasks.”

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You are on the PRO Robots channel and today we present you with some high-tech news. The first robot with self-awareness, a new breakthrough in the creation of general artificial intelligence, evolving robots, a Japanese home for a space colony, an unexpected turn in the fate of XPENG Robotics and other news from the world of high technology in one issue! Let’s roll!

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Humans are good at looking at images and finding patterns or making comparisons. Look at a collection of dog photos, for example, and you can sort them by color, by ear size, by face shape, and so on. But could you compare them quantitatively? And perhaps more intriguingly, could a machine extract meaningful information from images that humans can’t?

Now a team of Standford University’s Chan Zuckerberg Biohub scientists has developed a machine learning method to quantitatively analyze and compare images—in this case microscopy images of proteins—with no prior knowledge. As reported in Nature Methods, their algorithm, dubbed “cytoself,” provides rich, detailed information on protein location and function within a cell. This capability could quicken research time for cell biologists and eventually be used to accelerate the process of drug discovery and drug screening.

“This is very exciting—we’re applying AI to a new kind of problem and still recovering everything that humans know, plus more,” said Loic Royer, co-corresponding author of the study. “In the future we could do this for different kinds of images. It opens up a lot of possibilities.”

The greatest artistic tool ever built, or a harbinger of doom for entire creative industries? OpenAI’s second-generation DALL-E 2 system is slowly opening up to the public, and its text-based image generation and editing abilities are awe-inspiring.

The pace of progress in the field of AI-powered text-to-image generation is positively frightening. The generative adversarial network, or GAN, first emerged in 2014, putting forth the idea of two AIs in competition with one another, both “trained” by being shown a huge number of real images, labeled to help the algorithms learn what they’re looking at. A “generator” AI then starts to create images, and a “discriminator” AI tries to guess if they’re real images or AI creations.

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Quantum AI is the use of quantum computing for computation of machine learning algorithms. Thanks to computational advantages of quantum computing, quantum AI can help achieve results that are not possible to achieve with classical computers.

Quantum data: Quantum data can be considered as data packets contained in qubits for computerization. However, observing and storing quantum data is challenging because of the features that make it valuable which are superposition and entanglement. In addition, quantum data is noisy, it is necessary to apply a machine learning in the stage of analyzing and interpreting these data correctly.

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In late 2020, Alphabet’s DeepMind division unveiled its novel protein fold prediction algorithm, AlphaFold, and helped solve a scientific quandary that had stumped researchers for half a century. In the year since its beta release, half a million scientists from around the world have accessed the AI system’s results and cited them in their own studies more than 4,000 times. On Thursday, DeepMind announced that it is increasing that access even further by radically expanding its publicly-available AlphaFold Protein Structure Database (AlphaFoldDB) — from 1 million entries to 200 million entries.

Alphabet partnered with EMBL’s European Bioinformatics Institute (EMBL-EBI) for this undertaking, which covers proteins from across the kingdoms of life — animal, plant, fungi, bacteria and others. The results can be viewed on the UniProt, Ensembl, and OpenTargets websites or downloaded individually via GitHub, “for the human proteome and for the proteomes of 47 other key organisms important in research and global health,” per the AlphaFold website.

“AlphaFold is the singular and momentous advance in life science that demonstrates the power of AI,” Eric Topol, Founder and Director of the Scripps Research Translational Institute, siad in a press statement Thursday. “Determining the 3D structure of a protein used to take many months or years, it now takes seconds. AlphaFold has already accelerated and enabled massive discoveries, including cracking the structure of the nuclear pore complex. And with this new addition of structures illuminating nearly the entire protein universe, we can expect more biological mysteries to be solved each day.”

We think of data storage as a modern problem, but even ancient civilizations kept records. While much of the world used stone tablets or other media that didn’t survive the centuries, the Incas used something called quipu which encoded numeric data in strings using knots. Now the ancient system of recording numbers has inspired a new way to encode qubits in a quantum computer.

With quipu, knots in a string represent a number. By analogy, a conventional qubit would be as if you used a string to form a 0 or 1 shape on a tabletop. A breeze or other “noise” would easily disturb your equation. But knots stay tied even if you pick the strings up and move them around. The new qubits are the same, encoding data in the topology of the material.

In practice, Quantinuum’s H1 processor uses 10 ytterbium ions trapped by lasers pulsing in a Fibonacci sequence. If you consider a conventional qubit to be a one-dimensional affair — the qubit’s state — this new system acts like a two-dimensional system, where the second dimension is time. This is easier to construct than conventional 2D quantum structures but offers at least some of the same inherent error resilience.

Explains why we can meet aliens soon. He is on to something. Elon Musk disagrees with the research that argues that there are not aliens,. Elon Musk explains why drake equation is important and why Fermi paradox is wrong.

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The field of Artificial Intelligence was founded in the mid 1950s with the aim of constructing “thinking machines” — that is to say, computer systems with human-like general intelligence. Think of humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than that of human beings. But in the intervening years, the field has drifted far from its ambitious old-fashioned roots.

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