Tag Archives: Quanta

What Is Distributed Computing?

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Quanta Magazine

Our computers can get a lot more done when they share the load with other machines.

No device is an island: Your daily computational needs depend on more than just the microprocessors inside your computer or phone. Our modern world relies on “distributed computing,” which shares the computational load among multiple different machines. The technique passes data back and forth in an elaborate choreography of digital bits — a dance that has shaped the internet’s past, present and likely future.

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The Hidden World of Electrostatic Ecology

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QUANTA MAGAZINE

Invisibly to us, insects and other tiny creatures use static electricity to travel, avoid predators, collect pollen and more. New experiments explore how evolution may have influenced this phenomenon.

Imagine, for a moment, that you’re a honeybee. In many ways, your world is small. Your four delicate wings, each less than a centimeter long, transport your half-gram body through looming landscapes full of giant animals and plants. In other ways, your world is expansive, even grand. Your five eyes see colors and patterns that humans can’t, and your multisensory antennae detect odors from distant flowers.

For years, biologists have wondered whether bees have another grand sense that we lack. The static electricity they accumulate by flying — similar to the charge generated when you shuffle across carpet in thick socks — could be potent enough for them to sense and influence surrounding objects through the air.

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With ‘Digital Twins,’ The Doctor Will See You Now

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Quanta Magazine

By creating a digital twin of your circulatory system, Amanda Randles wants to bring unprecedented precision to medical forecasts.

Amanda Randles wants to copy your body. If the computer scientist had her way, she’d have enough data — and processing power — to effectively clone you on her computer, run the clock forward, and see what your coronary arteries or red blood cells might do in a week. Fully personalized medical simulations, or “digital twins,” are still beyond our abilities, but Randles has pioneered computer models of blood flow over long durations that are already helping doctors noninvasively diagnose and treat diseases.

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For AI to Know What Something Is, It Must Know What Something Isn’t

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Quanta Magazine

Today’s language models are more sophisticated than ever, but challenges with negation persist.

Nora Kassner suspected her computer wasn’t as smart as people thought. In October 2018, Google released a language model algorithm called BERT, which Kassner, a researcher in the same field, quickly loaded on her laptop. It was Google’s first language model that was self-taught on a massive volume of online data. Like her peers, Kassner was impressed that BERT could complete users’ sentences and answer simple questions. It seemed as if the large language model (LLM) could read text like a human (or better).

But Kassner, at the time a graduate student at Ludwig Maximilian University of Munich, remained skeptical. She felt LLMs should understand what their answers mean — and what they don’t mean. It’s one thing to know that a bird can fly. “A model should automatically also know that the negated statement — ‘a bird cannot fly’ — is false,” she said. But when she and her adviser, Hinrich Schütze, tested BERT and two other LLMs in 2019, they found that the models behaved as if words like “not” were invisible.

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Machines Learn Better if We Teach Them the Basics

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QUANTA MAGAZINE

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

Imagine that your neighbor calls to ask a favor: Could you please feed their pet rabbit some carrot slices? Easy enough, you’d think. You can imagine their kitchen, even if you’ve never been there — carrots in a fridge, a drawer holding various knives. It’s abstract knowledge: You don’t know what your neighbor’s carrots and knives look like exactly, but you won’t take a spoon to a cucumber.

Artificial intelligence programs can’t compete. What seems to you like an easy task is a huge undertaking for current algorithms.

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The AI Researcher Giving Her Field Its Bitter Medicine

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QUANTA MAGAZINE

Anima Anandkumar wants computer scientists to move beyond the matrix, among other challenges.

Anima Anandkumar, Bren Professor of computing at the California Institute of Technology and senior director of machine learning research at Nvidia, has a bone to pick with the matrix. Her misgivings are not about the sci-fi movies, but about mathematical matrices — grids of numbers or variables used throughout computer science. While researchers typically use matrices to study the relationships and patterns hiding within large sets of data, these tools are best suited for two-way relationships. Complicated processes like social dynamics, on the other hand, involve higher-order interactions.

Luckily, Anandkumar has long savored such challenges. When she recalls Ugadi, a new year’s festival she celebrated as a child in Mysore (now Mysuru), India, two flavors stand out: jaggery, an unrefined sugar representing life’s sweetness, and neem, bitter blossoms representing life’s setbacks and difficulties. “It’s one of the most bitter things you can think about,” she said.

She’d typically load up on the neem, she said. “I want challenges.”

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Machine Learning Gets a Quantum Speedup

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QUANTA MAGAZINE

Two teams have shown how quantum approaches can solve problems faster than classical computers, bringing physics and computer science closer together.

For Valeria Saggio to boot up the computer in her former Vienna lab, she needed a special crystal, only as big as her fingernail. Saggio would place it gently into a small copper box, a tiny electric oven, which would heat the crystal to 77 degrees Fahrenheit. Then she would switch on a laser to bombard the crystal with a beam of photons.

This crystal, at this precise temperature, would split some of those photons into two photons. One of these would go straight to a light detector, its journey finished; the other would travel into a tiny silicon chip — a quantum computing processor. Miniature instruments on the chip could drive the photon down different paths, but ultimately there were only two outcomes: the right way, and the many wrong ways. Based on the result, her processor could choose another path and try again.

The sequence feels more Rube Goldberg than Windows, but the goal was to have a quantum computer teach itself a task: Find the right way out.

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Surprising Limits Discovered in Quest for Optimal Solutions

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Quanta Magazine

Algorithms that zero in on solutions to optimization problems are the beating heart of machine reasoning. New results reveal surprising limits.

Our lives are a succession of optimization problems. They occur when we search for the fastest route home from work or attempt to balance cost and quality on a trip to the store, or even when we decide how to spend limited free time before bed.

These scenarios and many others can be represented as a mathematical optimization problem. Making the best decisions is a matter of finding their optimal solutions. And for a world steeped in optimization, two recent results provide both good and bad news.

In a paper posted in August 2020, Amir Ali Ahmadi of Princeton University and his former student, Jeffrey Zhang, who is now at Carnegie Mellon University, established that for some quadratic optimization problems — in which pairs of variables can interact — it’s computationally infeasible to find even locally optimal solutions in a time-efficient manner.

But then, two days later, Zhang and Ahmadi released a second paper with a positive takeaway. They proved that it’s always possible to quickly identify whether a cubic polynomial — which can feature three-way interactions between variables — has a local minimum, and to find it if it does.

The limits are not what their discoverers expected.

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