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Category: robotics/AI – Page 1,646

A.I. is translating messages of long-lost languages

There are about 6,500–7,000 languages currently spoken in the world. But that’s less than a quarter of all the languages people spoke over the course of human history. That total number is around 31,000 languages, according to some linguistic estimates. Every time a language is lost, so goes that way of thinking, of relating to the world. The relationships, the poetry of life uniquely described through that language are lost too. But what if you could figure out how to read the dead languages? Researchers from MIT and Google Brain created an AI-based system that can accomplish just that.

While languages change, many of the symbols and how the words and characters are distributed stay relatively constant over time. Because of that, you could attempt to decode a long-lost language if you understood its relationship to a known progenitor language. This insight is what allowed the team which included Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google’s AI lab to use machine learning to decipher the early Greek language Linear B (from 1,400 BC) and a cuneiform Ugaritic (early Hebrew) language that’s also over 3,000 years old.

Elon Musk Thinks Destinus Technology Will Soon End The War Against Russia, Know How

Mikhail Kokorich is the founder of Destinus. This serial entrepreneur has been dubbed Russia’s Elon Musk by his public relations team. The Russian businessman says his business, Destinus, is developing a hydrogen-powered, zero-emissions transcontinental delivery drone that can travel at speeds up to Mach 15.

Destinus plans to combine the technological advancements from a spaceplane with the ordinary and straightforward physics from a glider to create a hyperplane that will meet the many demands of a hyper-connected world.

This hyperplane will use clean hydrogen fuel to transport cargo between Europe and Australia in mere hours. The hyperplane will be fully autonomous; it will take off from ordinary runways, traveling leisurely to the coast before accelerating to supersonic speeds.

Musk reveals plan to scale Tesla to ‘extreme size’

Elon Musk signaled plans to scale Tesla to the “extreme” while teasing the release of Tesla’s “Master Plan Part 3” on Twitter one day before opening the automaker’s first European factory.

On Monday, Musk revealed on Twitter the themes that will dominate the next installment in Tesla’s long-term playbook: artificial intelligence and scaling the automaker’s operations.

“Main Tesla subjects will be scaling to extreme size, which is needed to shift humanity away from fossil fuels, and AI,” Musk tweeted. “But I will also include sections about SpaceX, Tesla and The Boring Company.”

Meta’s Yann LeCun strives for human-level AI

What is the next step toward bridging the gap between natural and artificial intelligence? Scientists and researchers are divided on the answer. Yann LeCun, Chief AI Scientist at Meta and the recipient of the 2018 Turing Award, is betting on self-supervised learning, machine learning models that can be trained without the need for human-labeled examples.

LeCun has been thinking and talking about self-supervised and unsupervised learning for years. But as his research and the fields of AI and neuroscience have progressed, his vision has converged around several promising concepts and trends.

In a recent event held by Meta AI, LeCun discussed possible paths toward human-level AI, challenges that remain and the impact of advances in AI.

An artificial intelligence invents 40,000 chemical weapons in just 6 hours

A.I. is only beginning to show what it can do for modern medicine.

In today’s society, artificial intelligence (A.I.) is mostly used for good. But what if it was not?

Naive thinking “The thought had never previously struck us. We were vaguely aware of security concerns around work with pathogens or toxic chemicals, but that did not relate to us; we primarily operate in a virtual setting. Our work is rooted in building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery,” wrote the researchers in their paper. “We have spent decades using computers and A.I. to improve human health—not to degrade it. We were naive in thinking about the potential misuse of our trade, as our aim had always been to avoid molecular features that could interfere with the many different classes of proteins essential to human life.”

Full Story:

Researchers from Collaborations Pharmaceuticals tweaked artificial intelligence to look for chemical weapons, and impressively enough the machine learning algorithm found 40,000 options in just six hours.

Using electron microscopy and automatic atom-tracking to learn more about grain boundaries in metals during deformation

A team of researchers affiliated with multiple institutions in China and the U.S. has found that it is possible to track the sliding of grain boundaries in some metals at the atomic scale using an electron microscope and an automatic atom tracker. In their paper published in the journal Science, the group describes their study of platinum using their new technique and the discovery they made in doing so.

Scientists have been studying the properties of metals for many years. Learning more about how crystal grains in certain metals interact with one another has led to the development of new kinds of metals and applications for their use. In their recent effort, the researchers took a novel approach to studying the sliding that occurs between grains and in so doing have learned something new.

When crystalline metals are deformed, the grains that they are made of move against one another, and the way they move determines many of their properties, such as malleability. To learn more about what happens between grains in such metals during deformity, the researchers used two types of technologies: and automated atom-tracking.

Gensyn applies a token to distributed computing for AI developers, raises $6.5M

For self-driving cars and other applications developed using AI, you need what’s known as “deep learning”, the core concepts of which emerged in the ’50s. This requires training models based on similar patterns as seen in the human brain. This, in turn, requires a large amount of compute power, as afforded by TPUs (tensor processing units) or GPUs (graphics processing units) running for lengthy periods. However, cost of this compute power is out of reach of most AI developers, who largely rent it from cloud computing platforms such as AWS or Azure. What is to be done?

Well, one approach is that taken by U.K. startup Gensyn. It’s taken the idea of the distributed computing power of older projects such as SETI@home and the COVID-19 focussed Folding@home and applied it in the direction of this desire for deep learning amongst AI developers. The result is a way to get high-performance compute power from a distributed network of computers.

Gensyn has now raised a $6.5 million seed led by Eden Block, a web3 VC. Also participating in the round is Galaxy Digital, Maven 11, Coinfund, Hypersphere, Zee Prime and founders from some blockchain protocols. This adds to a previously unannounced pre-seed investment of $1.1 millionin 2021 — led by 7percent Ventures and Counterview Capital, with participation from Entrepreneur First and id4 Ventures.

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