Tag Archives: universe

#438886 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
This Chip for AI Works Using Light, Not Electrons
Will Knight | Wired
“As demand for artificial intelligence grows, so does hunger for the computer power needed to keep AI running. Lightmatter, a startup born at MIT, is betting that AI’s voracious hunger will spawn demand for a fundamentally different kind of computer chip—one that uses light to perform key calculations. ‘Either we invent new kinds of computers to continue,’ says Lightmatter CEO Nick Harris, ‘or AI slows down.’i”

BIOTECH
With This CAD for Genomes, You Can Design New Organisms
Eliza Strickland | IEEE Spectrum
“Imagine being able to design a new organism as easily as you can design a new integrated circuit. That’s the ultimate vision behind the computer-aided design (CAD) program being developed by the GP-write consortium. ‘We’re taking the same things we’d do for design automation in electronics, and applying them to biology,’ says Doug Densmore, an associate professor of electrical and computer engineering at Boston University.”

BIOLOGY
Hey, So These Sea Slugs Decapitate Themselves and Grow New Bodies
Matt Simon | Wired
“That’s right: It pulled a Deadpool. Just a few hours after its self-decapitation, the head began dragging itself around to feed. After a day, the neck wound had closed. After a week, it started to regenerate a heart. In less than a month, the whole body had grown back, and the disembodied slug was embodied once more.”

INTERNET
Move Over, Deep Nostalgia, This AI App Can Make Kim Jong-un Sing ‘I Will Survive’
Helen Sullivan | The Guardian
“If you’ve ever wanted to know what it might be like to see Kim Jong-un let loose at karaoke, your wish has been granted, thanks to an app that lets users turn photographs of anyone—or anything remotely resembling a face—into uncanny AI-powered videos of them lip syncing famous songs.”

ENERGY
GM Unveils Plans for Lithium-Metal Batteries That Could Boost EV Range
Steve Dent | Engadget
“GM has released more details about its next-generation Ultium batteries, including plans for lithium-metal (Li-metal) technology to boost performance and energy density. The automaker announced that it has signed an agreement to work with SolidEnergy Systems (SES), an MIT spinoff developing prototype Li-metal batteries with nearly double the capacity of current lithium-ion cells.”

TECHNOLOGY
Xi’s Gambit: China Plans for a World Without American Technology
Paul Mozur and Steven Lee Myers | The New York Times
“China is freeing up tens of billions of dollars for its tech industry to borrow. It is cataloging the sectors where the United States or others could cut off access to crucial technologies. And when its leaders released their most important economic plans last week, they laid out their ambitions to become an innovation superpower beholden to none.”

SCIENCE
Imaginary Numbers May Be Essential for Describing Reality
Charlie Wood | Wired
“…physicists may have just shown for the first time that imaginary numbers are, in a sense, real. A group of quantum theorists designed an experiment whose outcome depends on whether nature has an imaginary side. Provided that quantum mechanics is correct—an assumption few would quibble with—the team’s argument essentially guarantees that complex numbers are an unavoidable part of our description of the physical universe.”

PHILOSOPHY
What Is Life? Its Vast Diversity Defies Easy Definition
Carl Zimmer | Quanta
“i‘It is commonly said,’ the scientists Frances Westall and André Brack wrote in 2018, ‘that there are as many definitions of life as there are people trying to define it.’ …As an observer of science and of scientists, I find this behavior strange. It is as if astronomers kept coming up with new ways to define stars. …With scientists adrift in an ocean of definitions, philosophers rowed out to offer lifelines.”

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Posted in Human Robots

#437992 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
This Chinese Lab Is Aiming for Big AI Breakthroughs
Will Knight | Wired
“China produces as many artificial intelligence researchers as the US, but it lags in key fields like machine learning. The government hopes to make up ground. …It set AI researchers the goal of making ‘fundamental breakthroughs by 2025’ and called for the country to be ‘the world’s primary innovation center by 2030.’ BAAI opened a year later, in Zhongguancun, a neighborhood of Beijing designed to replicate US innovation hubs such as Boston and Silicon Valley.”

ENVIRONMENT
What Elon Musk’s $100 Million Carbon Capture Prize Could Mean
James Temple | MIT Technology Review
“[Elon Musk] announced on Twitter that he plans to give away $100 million of [his $180 billion net worth] as a prize for the ‘best carbon capture technology.’ …Another $100 million could certainly help whatever venture, or ventures, clinch Musk’s prize. But it’s a tiny fraction of his wealth and will also only go so far. …Money aside, however, one thing Musk has a particular knack for is generating attention. And this is a space in need of it.”

HEALTH
Synthetic Cornea Helped a Legally Blind Man Regain His Sight
Steve Dent | Engadget
“While the implant doesn’t contain any electronics, it could help more people than any robotic eye. ‘After years of hard work, seeing a colleague implant the CorNeat KPro with ease and witnessing a fellow human being regain his sight the following day was electrifying and emotionally moving, there were a lot of tears in the room,’ said CorNeat Vision co-founder Dr. Gilad Litvin.”

BIOTECH
MIT Develops Method for Lab-Grown Plants That May Eventually Lead to Alternatives to Forestry and Farming
Darrell Etherington | TechCrunch
“If the work of these researchers can eventually be used to create a way to produce lab-grown wood for use in construction and fabrication in a way that’s scalable and efficient, then there’s tremendous potential in terms of reducing the impact on forestry globally. Eventually, the team even theorizes you could coax the growth of plant-based materials into specific target shapes, so you could also do some of the manufacturing in the lab, by growing a wood table directly for instance.”

AUTOMATION
FAA Approves First Fully Automated Commercial Drone Flights
Andy Pasztor and Katy Stech Ferek | The Wall Street Journal
“US aviation regulators have approved the first fully automated commercial drone flights, granting a small Massachusetts-based company permission to operate drones without hands-on piloting or direct observation by human controllers or observers. …The company’s Scout drones operate under predetermined flight programs and use acoustic technology to detect and avoid drones, birds, and other obstacles.”

SPACE
China’s Surging Private Space Industry Is Out to Challenge the US
Neel V. Patel | MIT Technology Review
“[The Ceres-1] was a commercial rocket—only the second from a Chinese company ever to go into space. And the launch happened less than three years after the company was founded. The achievement is a milestone for China’s fledgling—but rapidly growing—private space industry, an increasingly critical part of the country’s quest to dethrone the US as the world’s preeminent space power.”

CRYPTOCURRENCY
Janet Yellen Will Consider Limiting Use of Cryptocurrency
Timothy B. Lee | Ars Technica
“Cryptocurrencies could come under renewed regulatory scrutiny over the next four years if Janet Yellen, Joe Biden’s pick to lead the Treasury Department, gets her way. During Yellen’s Tuesday confirmation hearing before the Senate Finance Committee, Sen. Maggie Hassan (D-N.H.) asked Yellen about the use of cryptocurrency by terrorists and other criminals. ‘Cryptocurrencies are a particular concern,’ Yellen responded. ‘I think many are used—at least in a transactions sense—mainly for illicit financing.’i”

SCIENCE
Secret Ingredient Found to Power Supernovas
Thomas Lewton | Quanta
“…Only in the last few years, with the growth of supercomputers, have theorists had enough computing power to model massive stars with the complexity needed to achieve explosions. …These new simulations are giving researchers a better understanding of exactly how supernovas have shaped the universe we see today.”

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#437872 AlphaFold Proves That AI Can Crack ...

Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.

AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.

Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.

Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.

As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.

How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.

“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”

DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”

AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.

For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.

The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.

“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”

Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.

Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.

“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”

Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading

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#437345 Moore’s Law Lives: Intel Says Chips ...

If you weren’t already convinced the digital world is taking over, you probably are now.

To keep the economy on life support as people stay home to stem the viral tide, we’ve been forced to digitize interactions at scale (for better and worse). Work, school, events, shopping, food, politics. The companies at the center of the digital universe are now powerhouses of the modern era—worth trillions and nearly impossible to avoid in daily life.

Six decades ago, this world didn’t exist.

A humble microchip in the early 1960s would have boasted a handful of transistors. Now, your laptop or smartphone runs on a chip with billions of transistors. As first described by Moore’s Law, this is possible because the number of transistors on a chip doubled with extreme predictability every two years for decades.

But now progress is faltering as the size of transistors approaches physical limits, and the money and time it takes to squeeze a few more onto a chip are growing. There’ve been many predictions that Moore’s Law is, finally, ending. But, perhaps also predictably, the company whose founder coined Moore’s Law begs to differ.

In a keynote presentation at this year’s Hot Chips conference, Intel’s chief architect, Raja Koduri, laid out a roadmap to increase transistor density—that is, the number of transistors you can fit on a chip—by a factor of 50.

“We firmly believe there is a lot more transistor density to come,” Koduri said. “The vision will play out over time—maybe a decade or more—but it will play out.”

Why the optimism?

Calling the end of Moore’s Law is a bit of a tradition. As Peter Lee, vice president at Microsoft Research, quipped to The Economist a few years ago, “The number of people predicting the death of Moore’s Law doubles every two years.” To date, prophets of doom have been premature, and though the pace is slowing, the industry continues to dodge death with creative engineering.

Koduri believes the trend will continue this decade and outlined the upcoming chip innovations Intel thinks can drive more gains in computing power.

Keeping It Traditional
First, engineers can further shrink today’s transistors. Fin field effect transistors (or FinFET) first hit the scene in the 2010s and have since pushed chip features past 14 and 10 nanometers (or nodes, as such size checkpoints are called). Korduri said FinFET will again triple chip density before it’s exhausted.

The Next Generation
FinFET will hand the torch off to nanowire transistors (also known as gate-all-around transistors).

Here’s how they’ll work. A transistor is made up of three basic components: the source, where current is introduced, the gate and channel, where current selectively flows, and the drain. The gate is like a light switch. It controls how much current flows through the channel. A transistor is “on” when the gate allows current to flow, and it’s off when no current flows. The smaller transistors get, the harder it is to control that current.

FinFET maintained fine control of current by surrounding the channel with a gate on three sides. Nanowire designs kick that up a notch by surrounding the channel with a gate on four sides (hence, gate-all-around). They’ve been in the works for years and are expected around 2025. Koduri said first-generation nanowire transistors will be followed by stacked nanowire transistors, and together, they’ll quadruple transistor density.

Building Up
Growing transistor density won’t only be about shrinking transistors, but also going 3D.

This is akin to how skyscrapers increase a city’s population density by adding more usable space on the same patch of land. Along those lines, Intel recently launched its Foveros chip design. Instead of laying a chip’s various “neighborhoods” next to each other in a 2D silicon sprawl, they’ve stacked them on top of each other like a layer cake. Chip stacking isn’t entirely new, but it’s advancing and being applied to general purpose CPUs, like the chips in your phone and laptop.

Koduri said 3D chip stacking will quadruple transistor density.

A Self-Fulfilling Prophecy
The technologies Koduri outlines are an evolution of the same general technology in use today. That is, we don’t need quantum computing or nanotube transistors to augment or replace silicon chips yet. Rather, as it’s done many times over the years, the chip industry will get creative with the design of its core product to realize gains for another decade.

Last year, veteran chip engineer Jim Keller, who at the time was Intel’s head of silicon engineering but has since left the company, told MIT Technology Review there are over a 100 variables driving Moore’s Law (including 3D architectures and new transistor designs). From the standpoint of pure performance, it’s also about how efficiently software uses all those transistors. Keller suggested that with some clever software tweaks “we could get chips that are a hundred times faster in 10 years.”

But whether Intel’s vision pans out as planned is far from certain.

Intel’s faced challenges recently, taking five years instead of two to move its chips from 14 nanometers to 10 nanometers. After a delay of six months for its 7-nanometer chips, it’s now a year behind schedule and lagging other makers who already offer 7-nanometer chips. This is a key point. Yes, chipmakers continue making progress, but it’s getting harder, more expensive, and timelines are stretching.

The question isn’t if Intel and competitors can cram more transistors onto a chip—which, Intel rival TSMC agrees is clearly possible—it’s how long will it take and at what cost?

That said, demand for more computing power isn’t going anywhere.

Amazon, Microsoft, Alphabet, Apple, and Facebook now make up a whopping 20 percent of the stock market’s total value. By that metric, tech is the most dominant industry in at least 70 years. And new technologies—from artificial intelligence and virtual reality to a proliferation of Internet of Things devices and self-driving cars—will demand better chips.

There’s ample motivation to push computing to its bitter limits and beyond. As is often said, Moore’s Law is a self-fulfilling prophecy, and likely whatever comes after it will be too.

Image credit: Laura Ockel / Unsplash Continue reading

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#437267 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
OpenAI’s New Language Generator GPT-3 Is Shockingly Good—and Completely Mindless
Will Douglas Heaven | MIT Technology Review
“‘Playing with GPT-3 feels like seeing the future,’ Arram Sabeti, a San Francisco–based developer and artist, tweeted last week. That pretty much sums up the response on social media in the last few days to OpenAI’s latest language-generating AI.”

ROBOTICS
The Star of This $70 Million Sci-Fi Film Is a Robot
Sarah Bahr | The New York Times
“Erica was created by Hiroshi Ishiguro, a roboticist at Osaka University in Japan, to be ‘the most beautiful woman in the world’—he modeled her after images of Miss Universe pageant finalists—and the most humanlike robot in existence. But she’s more than just a pretty face: Though ‘b’ is still in preproduction, when she makes her debut, producers believe it will be the first time a film has relied on a fully autonomous artificially intelligent actor.”

VIRTUAL REALITY
My Glitchy, Glorious Day at a Conference for Virtual Beings
Emma Grey Ellis | Wired
“Spectators spent much of the time debating who was real and who was fake. …[Lars Buttler’s] eyes seemed awake and alive in a way that the faces of the other participants in the Zoom call—venture capitalist, a tech founder, and an activist, all of them puppeted by artificial intelligence—were not. ‘Pretty sure Lars is human,’ a (real-person) spectator typed in the in-meeting chat room. ‘I’m starting to think Lars is AI,’ wrote another.”

FUTURE OF FOOD
KFC Is Working With a Russian 3D Bioprinting Firm to Try to Make Lab-Produced Chicken Nuggets
Kim Lyons | The Verge
“The chicken restaurant chain will work with Russian company 3D Bioprinting Solutions to develop bioprinting technology that will ‘print’ chicken meat, using chicken cells and plant material. KFC plans to provide the bioprinting firm with ingredients like breading and spices ‘to achieve the signature KFC taste’ and will seek to replicate the taste and texture of genuine chicken.”

BIOTECH
A CRISPR Cow Is Born. It’s Definitely a Boy
Megan Molteni | Wired
“After nearly five years of research, at least half a million dollars, dozens of failed pregnancies, and countless scientific setbacks, Van Eenennaam’s pioneering attempt to create a line of Crispr’d cattle tailored to the needs of the beef industry all came down to this one calf. Who, as luck seemed sure to have it, was about to enter the world in the middle of a global pandemic.”

GOVERNANCE
Is the Pandemic Finally the Moment for a Universal Basic Income?
Brooks Rainwater and Clay Dillow | Fast Company
“Since February, governments around the globe—including in the US—have intervened in their citizens’ individual financial lives, distributing direct cash payments to backstop workers sidelined by the COVID-19 pandemic. Some are considering keeping such direct assistance in place indefinitely, or at least until the economic shocks subside.”

SCIENCE
How Gödel’s Proof Works
Natalie Wolchover | Wired
“In 1931, the Austrian logician Kurt Gödel pulled off arguably one of the most stunning intellectual achievements in history. Mathematicians of the era sought a solid foundation for mathematics: a set of basic mathematical facts, or axioms, that was both consistent—never leading to contradictions—and complete, serving as the building blocks of all mathematical truths. But Gödel’s shocking incompleteness theorems, published when he was just 25, crushed that dream.”

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