fortune--2020-11-30--Know when to fold ’em: How a company best known for playing games used A.I. to solve one of biology’

Know when to fold ’em: How a company best known for playing games used A.I. to solve one of biology’s greatest mysteries

It is March 13, 2016. Two men, dressed in winter coats and woolen hats to defend against the frigid night air, walk side by side through the crowded streets of downtown Seoul. Locked in animated conversation, they seem oblivious to the pulsating neon enticements of the surrounding dumpling houses and barbecue joints. They are visitors, having come to South Korea on a mission, the culmination of years of effort—and they have just succeeded.
This is a celebratory stroll. What they have achieved will cement their places in the annals of computer science: They have built a piece of artificial intelligence software able to play the ancient strategy game Go so expertly that it handily defeated the world’s top player, Lee Sedol. Now the two men are discussing their next goal, their conversation captured by a documentary film crew shadowing them.
“I’m telling you, we can solve protein folding,” Demis Hassabis says to his walking companion, David Silver. “That’s like, I mean, it’s just huge. I am sure we can do that now. I thought we could do that before, but now we definitely can do it.” Hassabis is the cofounder and chief executive officer of DeepMind, the London-based A.I. company that built AlphaGo. Silver is the DeepMind computer scientist who led the AlphaGo team.
Four years later, DeepMind has just accomplished what Hassabis broached in that nocturnal amble: It has created an A.I. system that can predict the complex shapes of proteins down to an atom’s-width accuracy from the genetic sequences that encode them. With this achievement, DeepMind has completed an almost 50-year-old scientific quest. In 1972, in his Nobel Prize acceptance speech, chemist Christian Anfinsen postulated that DNA alone should fully determine the final structure a protein takes. It was a remarkable conjecture. At the time, not a single genome had been sequenced yet. But Anfinsen’s theory launched an entire subfield of computational biology with the goal of using complex mathematics, instead of empirical experiments, to model proteins.
DeepMind’s achievement with Go was important—but it had little concrete impact outside the relatively cliquish worlds of Go and computer science. Solving protein folding is different: It could prove transformative for much of humanity. Proteins are the basic building blocks of life and the mechanism behind most biological processes. Being able to predict their structure could revolutionize our understanding of disease and lead to new, more targeted pharmaceuticals for disorders from cancer to Alzheimer’s disease. It will likely accelerate the time it takes to bring new medicines to market, potentially shaving years and hundreds of millions of dollars in costs from drug development, and potentially saving lives as a result.
Demis Hassabis, DeepMind’s cofounder and CEO. His early-in-life fascination with chess and video game design helped spur his interest in creating artificial intelligence systems. Courtesy of DeepMind
The new method pioneered by DeepMind is already yielding results in the fight against SARS-CoV-2, the virus that causes COVID-19. What follows is the story of how a company best known for playing games came to unlock one of biology’s greatest secrets.
Building blocks with elusive shapes
“Proteins are the main machines of the cell,” Ian Holmes, a professor of bioengineering at the University of California at Berkeley, says. “And the structure and shape of them is crucial to how they operate.” Small “pockets” within the lattice of molecules that make up the protein are where various chemical reactions take place. If you can find a chemical that will bind to one of these pockets, then that substance can be used as a drug—to either disable or accelerate a biological process. Bioengineers can also create entirely new proteins never before seen in nature with unique therapeutic properties. “If we could tap into the power of proteins and rationally engineer them to any purpose, then we could build these remarkable self-assembling machines that could do things for us,” Holmes says.
But to be sure the protein will do what you want, it’s important to know its shape.
Proteins consist of chains of amino acids, often compared to beads on a string. The recipe for which beads to string in what order is encoded in DNA. But the complex physical shape the completed chain will take is extremely difficult to predict from those simple genetic instructions. Amino acid chains collapse—or fold—into a structure based on electrochemical rules of attraction and repulsion between molecules. The resulting shapes frequently resemble abstract sculptures formed from tangles of cord and ribbon: pleated banderoles joined to Möbius strip–like curlicues and looping helixes. In the 1960s, Cyrus Levinthal, a physicist and molecular biologist, determined that there were so many plausible shapes a protein might assume that it would take longer than the known age of the universe to arrive at the correct structure by randomly trying combinations—and yet, the protein folds itself in milliseconds. This observation has become known as Levinthal’s Paradox.
Until now, the only way to know a protein’s structure with near certainty was through a method known as X-ray crystallography. As the name implies, this involves turning solutions of millions of proteins into crystals, a chemical process that is itself tricky. X-rays are then fired at these crystals, allowing a scientist to work backward from the diffraction patterns they make to build up a picture of the protein itself. Oh, and not just any X-rays: For many proteins, the X-rays need to be produced by a massive, stadium-size circular particle accelerator called a synchrotron.
The process is expensive and time-consuming: It takes about 12 months and approximately $120,000 to determine a single protein’s structure with X-ray crystallography, according to one estimate from researchers at the University of Toronto. There are over 200 million known proteins, with about 30 million more being discovered every year, and yet fewer than 200,000 of these have had their structures mapped with X-ray crystallography or other experimental methods. “Our level of ignorance is growing rapidly,” says John Jumper, a computational physicist who is now a senior researcher at DeepMind and leads its protein-folding team.
Over the past 50 years, ever since Christian Anfinsen’s famous speech, scientists have tried speed up the analysis of protein structure by using complex mathematical models run on high-powered computers. “What you do is essentially try to create a digital twin of the protein in your computer, and then try to manipulate it,” says John Moult, a professor of cell biology and molecular genetics at the University of Maryland and a pioneer in using mathematical algorithms to predict protein structures from their DNA sequences. The problem is, these predicted folding patterns were frequently wrong, failing to match the structures scientists found through X-ray crystallography. In fact, until about 10 years ago, few models were able to accurately predict more than about a third of a large protein’s shape.
Some protein-folding simulations also take up tremendous amounts of computing power. In the year 2000, researchers created a “citizens science” project called Fold@home in which people could donate the idle processing capacity of their personal computers and game consoles to run a protein-folding simulation. All those devices, chained together through the Internet, created one of the world’s most powerful virtual supercomputers. The hope was that this would allow researchers to escape Levinthal’s Paradox—to speed up the time it would take to hit upon the accurate protein structures through random trial and error. The project, which is still running, has provided data for more than 225 scientific papers on proteins implicated in a number of diseases.
But despite having access to so much processing power, Fold@home is still mired in Levinthal’s Paradox: It is trying to find a protein structure by searching through all possible permutations. The holy grail of protein folding is to skip this laborious search and to instead discover elusive patterns that link a protein’s DNA sequence to its structure—allowing a computer to take a radical shortcut, leaping directly from genetics to the correct shape.
Games with a serious purpose
Demis Hassabis’s interest in protein folding began, as many of Hassabis’s passions do, with a game. Hassabis is a former chess prodigy, a master by the time he was 13 and at one time ranked second in the world for his age. His love of chess fed a fascination with two things: game design and the inner mechanisms of his own mind. He began working for a video games company while still in high school and, after studying computer science at the University of Cambridge, founded his own computer games startup, Elixir Studios, in 1998.
Despite producing two award-winning games, Elixir eventually sold off its intellectual property and shut down, and Hassabis went on to get a Ph.D. in cognitive neuroscience from University College London. By then, he had already embarked on the crusade that would lead him to cofound DeepMind in 2010: the creation of artificial general intelligence—software capable of learning to perform many disparate tasks as well or better than people. DeepMind’s lofty goal, Hassabis once said, was “to solve intelligence, and then use it to solve everything else.” Hassabis already had an inkling that protein folding just might be one of those first “everything elses.”
Hassabis was doing a postdoc at the Massachusetts Institute of Technology in 2009 when he heard about an online game called Foldit. Foldit was designed by researchers at the University of Washington and, like Fold@home, it was a “citizens science” project for protein folding. But instead of yoking together idle microchips, Foldit was designed to harness idle brains.
Foldit is a puzzle-like game in which human players, without any knowledge of biology, compete to fold proteins, earning points for creating shapes that are plausible. Researchers then analyze the highest-scoring designs to see if they can help complete unsolved protein structures. The game has attracted tens of thousands of players and, in a number of documented cases, produced better protein structures than protein-folding computer algorithms. “I thought it was fascinating from the standpoint of, can we use the addictiveness of games and the joy of them, and in the background not only are they having fun, but they are doing something useful for science,” Hassabis says.
But there was another reason Foldit would continue to capture Hassabis’s imagination. Games are a particularly good arena for a kind of A.I. training called reinforcement learning. This is where software learns from experience, essentially by trial and error, to get better at a task. In a computer game, software can experiment endlessly, playing over and over again, improving gradually until it reaches superhuman skill, without causing any real-world harm. Games also have ready-made and unambiguous ways to tell if a particular action or set of actions is effective: points and wins. Those metrics provide a very clear way to benchmark performance—something that doesn’t exist for many real-world problems, where the most effective move may be far more ambiguous and the entire concept of “winning” may not apply.
DeepMind was founded largely on the promise of combining reinforcement learning with a kind of A.I. called deep learning. Deep learning is A.I. based on neural networks—a kind of software loosely based on how the human brain works. In this case, instead of networks of actual nerve cells, the software has a bunch of virtual neurons, arranged in a hierarchy where an initial input layer takes in some data, applies a weighting to it, and passes it along to the middle layers, which do the same in turn, until it is eventually passed to an output layer that sums up all the weighted signals and uses that to produce a result. The network adjusts these weights until it can produce a desired outcome—such as accurately identifying photos of cats or winning a game of chess. It’s called “deep learning” not because the insights it produces are necessarily profound—although they can be—but because the network consists of many layers and so can be said to have depth.
DeepMind’s initial success came in using this “deep reinforcement learning” to create software that taught itself to play classic Atari computer games, such as Pong, Breakout, and Space Invaders, at superhuman levels. It was this achievement that helped get DeepMind noticed by big technology firms, including Google, which bought it for a reported £400 million (more than $600 million at the time) in 2014. It then turned its attention to Go, eventually creating the system AlphaGo, which defeated Sedol in 2016. DeepMind went on to create a more general version of that system, called AlphaZero, that could learn to play almost any two-player, turn-based game in which players have perfect information (so there is no element of chance or hidden information, such as face-down cards or hidden positions) at superhuman levels. Last year, it also built a system that could beat top human professional e-sports players at the highly complex real-time strategy game Starcraft 2.
Professional Go player Lee Sedol (left) shakes hands with Demis Hassabis on March 15, 2016, after the final match of the Google DeepMind Challenge Match, which pitted Sedol against the computer program AlphaGo. Jeon Heon-Kyun—Pool/Getty Images
But Hassabis says he always saw the company’s work with games as a way to perfect A.I. methods so they could be applied to real-world challenges—especially in science. “Games are just a training ground, but a training ground for what exactly? For creating new knowledge,” he says.
DeepMind is not a traditional business, with products and customers. Instead, it is essentially a research lab that tries to advance the frontiers of artificial intelligence. Many of the methods it develops, it publishes openly for anyone to use or build upon. But some of its advances are useful for its sister company, Google.
DeepMind has a whole team of engineers and scientists that help Google incorporate cutting-edge A.I. into its products. DeepMind’s technology has found its way into everything from Google Maps to the company’s digital assistant to the system that helps manage battery power on Android phones. Google pays DeepMind for this help, and Alphabet, its parent company, continues to absorb the additional losses that DeepMind generates. Those are not insignificant: The company lost £470 million in 2018 (about $510 million at the time), the last year for which its annual financial statements are publicly available through the U.K. business registry Companies House.
But DeepMind, which now employs more than 1,000 people, also has a whole other division that works only on scientific applications of A.I. It is headed by Pushmeet Kohli, a 39-year-old native of India, who worked on A.I. research for Microsoft before joining DeepMind. He says that DeepMind’s aim is to try to solve “root node” problems—data science-speak for saying it wants to take on issues that are fundamental to unlocking many different scientific avenues. Protein folding is one of these root nodes, Kohli says.
“The Olympics of protein folding”
In 1994, at a time when many scientists were first starting to use sophisticated computer algorithms to try to predict how proteins would fold, Moult, the University of Maryland biologist, decided to create a competition that could provide an unbiased way of assessing which of these algorithms was best. He called this competition the Critical Assessment of Protein Structure Prediction (CASP, for short), and it has been held biennially ever since.
It works like this: The Protein Structure Prediction Center, the organization that runs CASP and which is funded through the U.S. National Institute of General Medical Sciences, persuades researchers who do X-ray crystallography and other empirical studies to provide it with protein structures that have not yet been published anywhere, asking them to refrain from making the structures public until after the CASP competition. CASP then gives the DNA sequences of these proteins to the contestants, who use their algorithms to predict the protein’s structure. CASP then judges how close the predictions are to the actual structure the X-ray crystallographers and experimentalists found. The algorithms are then ranked by their average performance across all the proteins. “I call it the Olympics of protein folding,” Hassabis says. And, in 2016, shortly after AlphaGo beat Sedol, DeepMind set out to win the gold medal.
DeepMind established a small, crack team of a half-dozen machine learning researchers and engineers to work on the problem. “It’s part of our philosophy that we start with generalists,” Hassabis says. The company does not suffer from a lack of brain power. “Ex-physicists, ex-biologists, we just have them lying around generally,” Hassabis says with a wry smile. “They never know when their previous expertise suddenly is going to become useful.” Eventually the team grew to about 20 people.
Still, DeepMind decided it would be helpful to have at least one true protein-folding expert onboard. It found one in John Jumper. Skinny, with a mop of asymmetrically styled brown hair, Jumper is a boyish 35 and looks a bit like the bass guitarist in a late-1990s high school garage band. He earned a master’s degree in theoretical condensed matter physics from Cambridge before going on to work at D.E. Shaw Research in New York City, an independent research lab founded by hedge fund billionaire David Shaw. The lab specializes in computational biology, including the simulation of proteins. Jumper later got his Ph.D. in computational biophysics from the University of Chicago, studying under Karl Freed and Tobin Sosnick, two scientists known for advances in protein-fold modeling. “I had heard this rumor that DeepMind was interested in protein problems,” he says. He applied and got the job.
Hassabis’s and the DeepMind team’s first instinct was that protein folding could be solved in exactly the same way as Go—with deep reinforcement learning. But this proved problematic: For one thing, there were even more possible fold configurations than there are moves in Go. More importantly, DeepMind had mastered Go in large part by getting its A.I. system, AlphaGo, to play games against itself. “There isn’t quite the right analogy for that because protein folding is not a two-player game,” Hassabis says. “You’re sort of playing against Nature.”
John Jumper, a computational physicist who now helps lead DeepMind’s protein-folding team. The team’s challenge, Jumper says, was to push beyond just being the best in a competition: “We actually want a system that matters to biologists.” Courtesy of DeepMind
DeepMind soon established that there was a simpler way of making progress using a kind of A.I. training known as supervised deep learning. This is the sort of A.I. used in most business applications: From an established set of data inputs and corresponding outputs, a neural network learns how to match a given input to a given output. In this case, DeepMind had the protein structures—currently about 170,000 of them—that are publicly available in the Protein Data Bank (PDB), a public repository of all known three-dimensional protein shapes and their genetic sequences, to use as training data.
Some biologists had already used supervised deep learning to predict how proteins would fold. But the best of these A.I. systems were right only about 50% of the time, which wasn’t particularly helpful to biologists or medical researchers—especially since, for a protein whose structure was unknown, they had no way of determining whether a particular prediction was correct.
One promising technique rested on the idea that proteins can be grouped into families based on their evolutionary history. And within these families, it is possible to find pairs of amino acids that are distant from one another in a DNA sequence, yet seem to mutate at the same time. This phenomenon, which is called “coevolution,” is helpful because coevolved proteins are likely to be in contact within the protein’s folded structure. Jinbo Xu, a scientist at the Toyota Technological Institute in Chicago, pioneered using deep learning on this coevolutionary data to predict amino acid contacts. The approach is a bit like finding just the dots in a connect-the-dots game. Scientists still had to use other software to try to figure out the lines between those dots—and often they got this wrong. Sometimes they didn’t even get the dots right.
For the 2018 CASP competition, DeepMind took these basic ideas about coevolution and contact prediction but added two important twists. First, rather than trying to determine if two amino acids were in contact, a binary output (either the pair is in contact or isn’t), it decided to ask the algorithm to predict the distance between all the amino acid pairs in the protein.
To most molecular biologists, such an approach seemed counterintuitive—although Xu, to his credit, had independently proposed a similar method. After all, it was contact that mattered most. But to DeepMind’s deep learning experts it was immediately obvious that distance was a much better metric for a neural network to work on, Kohli says. “It is just a fundamental part of deep learning that if you have some uncertainty associated with a decision, it is much better to have the neural network incorporate that uncertainty and decide what to do about it,” he says. Distance, unlike contact, was a richer piece of information the network could adjust and play with.
The other twist DeepMind came up with was a second neural network that predicted the angles between amino acid pairs. With these two factors—distance and angles—DeepMind’s algorithm was able to work out a rough outline of a protein’s likely structure. It then used a different, non-A.I. algorithm to refine this structure. Putting these components together into a system it called AlphaFold, DeepMind crushed the competition in the 2018 CASP (called CASP13 because it was the 13th of the biennial contests). On the hardest set of 43 proteins in the competition, AlphaFold got the highest score on 25 of them. The next closest team scored highest on just three. The results shook the entire field: If there had been any doubt about whether deep learning methods were the most promising way to crack the protein-folding problem, AlphaFold ended them.
Going back to the whiteboard
Still, DeepMind was nowhere close to Hassabis’s goal: solving the protein-folding problem. AlphaFold was fairly inaccurate almost half the time. And, of the 104 protein targets in CASP13, it achieved results that were as good as X-ray crystallography in only about three cases. “We didn’t just want to be the best at this according to CASP, we wanted to be good at this. We actually want a system that matters to biologists,” Jumper says.
No sooner had the CASP 2018 results been announced than DeepMind redoubled its efforts: Jumper was put in charge of the expanded team. Rather than simply trying to build on AlphaFold, making incremental improvements, the team went back to the whiteboard and started to brainstorm radically different ideas that they hoped would be able to bring the software closer to the kind of accuracy X-ray crystallography yielded.
What followed, Jumper says, was one of scariest and most depressing periods of the entire project: nothing worked. “We spent three months not getting any better than our CASP13 results and starting to really panic,” he says. But then, a few of the things the researchers were trying produced a slight improvement—and within six months the system was notably better than the original AlphaFold. This pattern would continue throughout the next two years, Jumper says: three months of nothing, followed by three months of rapid progress, followed by yet another plateau.
Hassabis says a similar pattern had occurred with previous DeepMind projects, including its work on Go and the complex, real-time strategy video game Starcraft 2. The company’s management strategy for overcoming this, he says, is to alternate between two different ways of working. The first, which Hassabis calls “strike mode,” involves pushing the team as hard as possible to wring every ounce of performance out of an existing system. Then, when the gains from the all-out effort seem to be exhausted, he shifts gears into what he calls “creative mode.” During this period, Hassabis no longer presses the team on performance—in fact, he tolerates and even expects some temporary declines—in order to give the researchers and engineers the space to tinker with new ideas and try novel approaches. “You want to encourage as many crazy ideas as possible, brainstorming,” he says. This often leads to another leap forward in performance, allowing the team to switch back into strike mode.
A big birthday present
On Nov. 21 of 2019, Kathryn Tunyasuvunakool, a researcher at DeepMind who works on the protein folding team, turned 30. The day would prove to be memorable for another reason too. Tunyasuvunakool, who has a Ph.D. in computational biology from the University of Oxford, was the person on the team in charge of developing new test sets for the protein-folding A.I., now dubbed AlphaFold 2, that DeepMind was developing for the 2020 CASP competition. That morning, when she turned on her office computer, she received an assessment of the system’s predictions on a batch of about 50 protein sequences—all of them only recently added to the Protein Data Bank. She did a double take. AlphaFold 2 had been improving, but on this set of proteins the results were startlingly good—predicting the structure in many cases to within 1.5 angstroms, a distance equivalent to a tenth of a nanometer, or about the width of an atom.
Kathryn Tunyasuvunakool, a DeepMind research scientist, has helped to spearhead the company’s advances in the study of protein folding. Courtesy of DeepMind
Tunyasuvunakool, who calls herself “the team’s pessimist,” says her first response was not elation, but nausea. “I was feeling quite scared,” she says. The results were so good she was certain she had made a mistake—that when she was preparing the test set, she must have inadvertently allowed several proteins that the A.I. had already seen in the training data to slip in. That would have allowed AlphaFold 2 to essentially cheat, easily predicting the exact structure. Tunyasuvunakool recalls sitting in DeepMind’s cafeteria overlooking London’s St. Pancras Station and drinking cup after cup of herbal tea in an effort to calm herself. She and other team members then spent the rest of that day and late into the evening, and several days more, sitting at their workstations, painstakingly combing through AlphaFold 2’s training data to try to find the mistake.
There wasn’t one. In fact, the new system had made a giant leap forward in performance. AlphaFold 2 was completely different from its predecessor. Rather than an assemblage of components—one to predict the distance between amino acids and another to forecast the angles, with a third piece of software to tie them together —the A.I. now used a single neural network to reason directly from the DNA sequence. While the system still took in evolutionary information—figuring out if the protein in question had a likely common ancestor to others it had seen before, and scrutinizing the alignment between the target protein’s DNA sequence and other known sequences—it no longer needed explicit data about which amino acid pairs evolved together. “Instead of providing more information, we actually provided less,” Jumper says. The system was free to draw its own insights about when ancestry might determine a portion of the protein’s shape and when it might depart more radically from that heritage. In other words, it developed a kind of intuition based on its experience, in much the same way a veteran human scientist might.
At the heart of the new system was a mechanism called “attention.” Attention, as the name implies, is a way to get a deep learning system to focus on a certain set of inputs and weigh those more heavily. For a cat identification system, for instance, the system might learn to pay attention to the shape of the ears and also learn to look for evidence of whiskers near the nose. Jumper compares what AlphaFold 2 does to the process of solving a jigsaw puzzle where “you can snap together certain pieces and be pretty sure of it, and then what you end up with are different local islands of solution, and then you figure out how to join these up.” The middle of the network, Jumper says, has learned to reason about geometry and space and how to join up those amino acid pairs it thinks are close together based on its analysis of the DNA sequences.
DeepMind trained AlphaFold 2 on 128 “tensor processing cores,” the number-crunching brains found on 16 special computer chips engineered for deep learning that Google designed and uses in its data centers, running continuously for what the company says was a few weeks. (These 128 specialized A.I. cores are about equivalent to 100 to 200 of the powerful graphics processing chips that deliver eye-popping animation on an Xbox or PlayStation.) Once trained, the system can take a DNA sequence and spit out a complete structure prediction “in a matter of days,” the company says.
Among AlphaFold 2’s advantages over its predecessor is a confidence gauge: The system produces a score for how sure it is of its own predictions for each amino acid in a structure. This metric is crucial if AlphaFold 2 is going to be useful to biologists and medical researchers who will need to know when they can reasonably rely on the model and when to have more caution.
Despite the stunning test results, DeepMind was still not certain how good AlphaFold 2 was. But they got an important clue when the coronavirus pandemic struck. In March of this year, AlphaFold 2 was able to predict the structure for six understudied proteins associated with SARS-CoV-2, the virus that causes COVID-19, one of which scientists have since confirmed using an empirical method called cryogenic electron microscopy. It was a powerful glimpse of the kind of real-world impact DeepMind hopes AlphaFold 2 will soon have. (To read more about AlphaFold 2’s role in the fight against COVID-19, click here.)
An astonishing result
The CASP competition takes place between May and August. The Protein Structure Prediction Center releases batches of target proteins, and contestants then submit their structure predictions for evaluation. The rankings for this year’s competition were announced on Nov. 30.
Each prediction is scored using a metric called “global distance test total score,” or GDT for short, that in effect looks at how close, in angstroms, it is to a structure obtained by empirical methods such as X-ray crystallography or electron microscope. A score of 100 is perfect, but anything at 90 or above is considered equivalent to the empirical methods, Moult, the CASP director, says. The proteins are also classed into groups based on how difficult the CASP organizers think it is to get the structure.
When Moult saw AlphaFold 2’s results he was incredulous. Like Tunyasuvunakool months earlier, his initial thought was that there might be a mistake. Maybe some of the protein sequences in the competition had been published before? Or maybe DeepMind had somehow managed to get hold of a cache of unpublished data?
A computer-generated image of T1042, part of a protein from a virus that infects bacteria. The accuracy with which DeepMind’s AlphaFold 2 was able to predict the protein’s structure, as part of the 2020 CASP competition, represents a major breakthrough in the application of A.I. to biology and medical research. Courtesy of DeepMind
DeepMind achieved a major scientific breakthrough by using A.I. to predict the structure of proteins such as this down to an atom’s width of accuracy. Courtesy of DeepMind
As a test, he asked Andrei Lupas, director of the department of protein evolution at the Max Planck Institute for Developmental Biology in Tuebingen, Germany, to conduct an experiment. Lupas would ask AlphaFold 2 to predict a structure that he knew for certain had never been seen before because Lupas had never been able to work out from X-ray crystallography what a key piece of the protein looked like. For almost a decade, Lupas had puzzled over this missing link, but the correct shape had eluded him. Now, with AlphaFold’s prediction as a guide, Lupas says, he went back to the X-ray data. “The correct structure just fell out within half an hour,” he says. “It was astonishing.”
Since DeepMind’s success in 2018’s CASP, many academic researchers have flocked to deep learning techniques. As a result, the rest of the field’s performance has improved: On a median difficulty target, the other competitors now have an average best prediction GDT of 75, up 10 points from two years ago. But there was no comparison to AlphaFold 2: It scored a median 92 GDT across all proteins, and even on the most difficult proteins it achieved a median score of 87 GDT. Moult says AlphaFold 2’s predictions are “on par with empirical methods,” such as X-ray crystallography. That conclusion lead CASP to make a momentous declaration on Monday, Nov. 30: The 50-year-old protein-folding problem had been solved.
Venki Ramakrishnan, a Nobel Prize–winning structural biologist who is also the current president of The Royal Society, Britain’s most prestigious scientific body, says AlphaFold2 “represents a stunning advance” in protein folding. With AlphaFold 2, expensive and time-consuming empirical analysis with methods like X-ray crystallography and electron microscopes may become a thing of the past.
Janet Thornton, an expert in protein structure and former director of the European Molecular Biology Laboratory’s European Bioinformatics Institute, says that DeepMind’s breakthrough will allow scientists to map the entire human “proteome”—all the proteins found within the human body. Currently only a quarter of these proteins have been used as targets for drugs, but having the structure for the rest would create vast opportunities for the development of new therapies. She also says the A.I. software could enable protein engineering that might aid in sustainability efforts, allowing scientists to potentially create new crop strains that provide more nutritional value per acre of land planted, and also possibly allowing for the advent of enzymes that could digest plastic.
For now, though, the question remains about how exactly DeepMind will make AlphaFold 2 available. Hassabis says the company is committed to ensuring the software can “make the maximal positive societal impact.” But he says it has not yet determined how to do that, saying only that it will make an announcement sometime next year. Hassabis also tells Fortune that DeepMind is considering how it might be able to build a commercial product or partnership around the system. “This should be hugely useful for the drug discovery process and therefore Big Pharma,” he says. But exactly what form this commercial offering will take, he says, has not yet been decided either.
A commercial venture would be marked departure for DeepMind, which, since its sale to Alphabet, has not had to worry about generating revenue. The company briefly set up a division called DeepMind Health that was working with the U.K.’s National Health Service on an app that could identify hospital patients who were at risk of developing acute kidney injury. But the effort became embroiled in a controversy after news reports revealed DeepMind’s hospital partner had violated the U.K. data protection laws by giving the company access to millions of patients’ medical records. In 2019, DeepMind Health was formally absorbed into a new Google health division. At the time, DeepMind said cleaving off its health effort would allow it to remain true to its research roots without the distraction of having to build a commercial unit that might replicate areas, such as data security and customer support, where Google already had expertise.
Of course, if DeepMind were to launch a commercial product, it would not be the first A.I. research company to do so: OpenAI, the San Francisco–based research company that is perhaps DeepMind’s closest rival, has become increasingly business-oriented. Last year, OpenAI launched its first commercial product, an interface that lets companies use an A.I. that composes long passages of coherent text from a short, human-written prompt. The business value of that A.I., called GPT-3, remains unproven, while DeepMind’s AlphaFold 2 could have an immediate bottom-line impact for a pharmaceutical company or biotechnology startup. At a time when antitrust regulators are probing Alphabet, having a viable commercial product could be a good insurance policy for DeepMind in the event it ever loses the unconditional support of its deep-pocketed parent in some future breakup of the Googleplex.
One thing is certain: DeepMind isn’t done with protein folding. The CASP competition was set up around predicting the structure of single proteins. But in biology and medicine, it is usually protein interactions that researchers really care about. How does one protein bind with another or with a particular small molecule? Exactly how does an enzyme break a protein apart? The problem of predicting these interactions and bindings will likely become the primary focus of future CASP competitions, Moult says. And Jumper says DeepMind plans to work on those challenges next.
Reverberations from AlphaFold 2’s success are certain to be felt in areas far removed from protein folding, too, encouraging others to apply deep learning to big scientific questions: finding new subatomic particles, probing the secrets of dark matter, mastering nuclear fusion, or creating room-temperature superconductors. DeepMind has an active effort already underway on astrophysics, Kohli says. Facebook’s A.I. researchers just launched a deep learning project aimed at finding new chemical catalysts. Protein folding is the first foundational scientific mystery to fall to the power of artificial intelligence. It won’t be the last.


fortune--2020-11-30--DeepMind’s new protein-folding A.I. is already helping in the fight against COVID-19

What could happen to the economy if Biden canceled the majority of student loan debt ?

Artificial intelligence has just solved one of biology’s most vexing challenges: how to determine the three-dimensional shape of a protein. DeepMind, the London-based A.I. company that is owned by Google-parent Alphabet, has created an A.I. system it calls AlphaFold 2 that uses a protein’s DNA sequence to predict its folded shape, frequently to within an atom’s width of accuracy. Previously, this could be done only through time-consuming and expensive experiments. (Read more about how DeepMind accomplished this goal here.) In the future, the breakthrough is likely to speed the development of new medicines for everything from malaria to cancer. But AlphaFold 2 is already having an impact on the fight against today’s most pressing global health scourge: the COVID-19 pandemic.
Almost as soon as fears arose that a novel coronavirus was circulating in Wuhan, China, with the potential to become a global pandemic, DeepMind, like many companies, wanted to do something to help. The potential to use AlphaFold 2, DeepMind’s still-under-wraps new A.I. system, to gain insights into the virus was clear. But the system, which was being prepared for its debut in a global competition on protein structure prediction that was still months away, had not yet been fully tested.
The DNA sequence of SARS-CoV-2, the virus that causes COVID-19, had been published by Chinese researchers as early as Jan. 11. This allowed researchers to begin to scrutinize proteins associated with the virus as possible targets for vaccines and treatments. Most of these efforts focused on the coronavirus’s distinctive spike protein, an obvious candidate for medicines since its structure and function were similar to that of other coronaviruses and well understood.
DeepMind’s protein-folding team quickly realized, though, that there were a number of proteins associated with the new virus whose structures were unknown to researchers. If DeepMind could turn AlphaFold 2 loose on these, it might be able to play a small but important part in helping to combat the virus.
John Jumper, the senior research scientist who leads DeepMind’s protein-folding team, describes the process of using AlphaFold 2 on the SARS-CoV-2 proteins as “terrifying.” “We had a system that we had tested internally, and we had numbers internally that said, ‘We think this is good, and we expect it is better than the field,’” he says. “But there was always the possibility that we could be wrong.” Especially worrisome, Jumper says, is that biology researchers at a number of academic labs had also tried to use algorithms to get structures for some of the same SARS-CoV-2 proteins, and their results looked very different from AlphaFold 2’s predictions.
To give themselves some assurance that AlphaFold 2 was on the right track, Jumper says, the team launched a crash program to equip the A.I. system with an internal confidence gauge: In other words, it would ask the A.I. software to produce an assessment of how good it thought its own predictions were for any given part of a protein. DeepMind had always planned to build this feature, Jumper says, since the metric would be crucial if AlphaFold 2 was ever going to be useful to medical researchers and biologists. But DeepMind hadn’t actually gotten around to it yet. “We had decided that we’d do confidence at the end, when we knew the final system. But suddenly it was, Okay, we need it now,” he says. “Very luckily the first thing we tried worked.”
Kathryn Tunyasuvunakool, a DeepMind research scientist, has helped to spearhead the company’s advances in the study of protein folding. Courtesy of DeepMind
Ultimately, AlphaFold 2 spat out high-confidence predictions for six previously unmapped SARS-CoV-2 proteins. Still, DeepMind remained trepidatious enough about the predictions that it decided to seek advice from virus experts and structural biologists at London’s Francis Crick Institute, one of Europe’s largest and most advanced biomedical research labs. The Crick scientists told DeepMind that AlphaFold 2’s structures seemed plausible and encouraged the company to publish them. It did so in early March.
Kathryn Tunyasuvunakool, the DeepMind research scientist responsible for preparing the training and test data for AlphaFold 2, remembers staring at the protein shapes the A.I. had predicted over and over again the night before they were to be released. “I think it was the most stressful thing I’ve ever done in my career,” she says.
One morning three months later, Tunyasuvunakool woke up to find a message from Jumper. Scientists at the University of California at Berkeley had just used an electron microscope to obtain the structure for one of the six proteins AlphaFold 2 had analyzed: ORF3a, a protein thought to help the virus, once it replicates, to break out of its host cell. The protein may also play a role in triggering the inflammatory response the human immune system mounts in response to the infection, a response that contributes to the physical damage inflicted on patients in severe COVID-19 cases. The structure had just been added to the Protein Data Bank (PDB), a public repository of protein information, overnight.
Tunyasuvunakool says she raced to her laptop and opened up the PDB website. She had spent so long staring at AlphaFold 2’s SARS-CoV-2 predictions when DeepMind was trying to decide whether to publish them, that she didn’t even have to compare each amino acid position. The second she saw the protein, she recalls, she just knew: AlphaFold 2 had pretty much nailed it.
AlphaFold 2’s predictions have even improved since March. Experimental methods have shown it predicted the structure of another SARS-CoV-2 protein, called ORF8, with a high degree of accuracy as well. And DeepMind has released updated predictions for the four other SARS-CoV-2 proteins whose structures have still not been confirmed through empirical methods.
Researchers are now at work on figuring out whether ORF3a and the other proteins AlphaFold 2 predicted structures for could be important for developing treatment or vaccines.
This story has been updated to include the fact that AlphaFold 2 also predicted the structure of the protein ORF8 with a high degree of accuracy.


fortune--2020-11-30--Federal agencies warn against anticipated fake COVID vaccines and treatments

The coronavirus vaccine inching toward approval in the U.S. is desperately anticipated by weary Americans longing for a path back to normal life. But criminals are waiting, too, ready to use that desperation to their advantage, federal investigators say.
Homeland Security investigators are working with Pfizer, Moderna, and dozens of other drug companies racing to complete and distribute the vaccine and treatments for the virus. The goal: to prepare for the scams that are coming, especially after the mess of criminal activity this year with phony personal protective equipment, false cures and extortion schemes.
“We’re all very excited about the potential vaccine and treatments,” said Steve Francis, assistant director for global trade investigations with Homeland Security Investigations. “But I also caution against these criminal organizations and individuals that will try to exploit the American public.”
No vaccine has yet been approved by the U.S. Food and Drug Administration. The FDA has approved the first treatment for COVID-19, the antiviral drug remdesivir. With vaccines and treatments both, it has warned about the potential for fraud.
“The FDA is particularly concerned that these deceptive and misleading products might cause Americans to delay or stop appropriate medical treatment, leading to serious and life-threatening harm,” the agency said in a recent statement.
The drug companies are to have safeguards and brand-protection features in place to help avoid fraud, but that may not be available until the second generation of vaccine because everything is operated on such an emergency basis, said Karen Gardner, chief marketing officer at SIPCA North America, a company that works as a bridge between the government, businesses, and consumers. She said that makes it more important to educate health care providers on what the real thing looks like.
“When you have anything in high demand and limited supply, there is going to be fraud,” she said. Desperation will drive people around normal channels.
Meanwhile, investigators are learning about how the vaccine will be packaged and getting the message out to field agents, creating a mass database of information from more than 200 companies, so they can be prepared to spot fakes and crack down on dangerous fraud. They are monitoring tens of thousands of false websites and looking for evidence of fake cures sold online.
Earlier this year as cases exploded, hospitals and governments grew short on masks, gloves, and other protective gear. Scams grew, too. Tricksters preyed on unwitting citizens to hand over money for goods they’d never receive.
Homeland Security Investigations started using its 7,000 agents in tandem with border, FDA and FBI officials to investigate scams, seize phony products and arrest hundreds of people. The effort is headquartered at the National Intellectual Property Rights Coordination Center, a government watchdog aimed at enforcement of its international trade laws and combating intellectual property theft.
The agency has already analyzed more than 70,900 websites suspected as being involved in some type of COVID-19 fraud. Millions of fake or unapproved personal protective equipment products and antiviral pharmaceuticals were seized. Homeland Security Investigations made more than 1,600 seizures of products worth more than $27 million and made more than 185 arrests.
Home test kits, for example, were only recently made available to the public in the past few weeks. But investigators seized tens of thousands of fake kits in the months before. On the dark web, scammers were selling domain names like “coronaprevention.org,” attractive to counterfeiters. In the U.S. alone, more than 1,000 fake websites a day have been removed during the pandemic.
A vaccine can’t come fast enough, as virus cases have topped 13 million in the U.S. and many cities have started restricting movement again as the country heads into winter. The pandemic has killed more than 1.4 million people worldwide, more than 266,000 of them in the U.S., according to figures compiled by Johns Hopkins University. But Francis and other investigators are worried that desperation will make Americans more susceptible.
If the FDA allows emergency use of a vaccine, there will be limited, rationed supplies before the end of the year.
Gen. Gus Perna, in charge of the government’s efforts to distribute the vaccine, said on CBS’ “60 Minutes” the government was prepared to distribute the vaccine within 24 hours of approval. There’s a stockpile of the prospective vaccine itself plus kits of needles, syringes and alcohol swabs needed to give the dose. The secret stash is watched by armed guards.
“We have taken extraordinary precaution in this area,” he said. “It’s such a commodity to us, we’re taking the full steps to make sure that the vaccine’s secure.”
Who is first in line has yet to be decided. But Health and Human Services Secretary Alex Azar said the hope is that enough doses are available by the end of January to vaccinate adults over age 65, who are at the highest risk from the coronavirus, and health care workers. Dr. Anthony Fauci, the U.S. government’s top infectious-diseases expert, said it may take until spring or summer before anyone who is not high risk and wants a shot can get one.
States already are gearing up for what is expected to be the biggest vaccination campaign in U.S. history. First the shots have to arrive where they’re needed, and Pfizer’s must be kept at ultra-cold temperatures—around minus 94 degrees Fahrenheit, or minus 70 degrees Celsius. Moderna’s vaccine also starts off frozen, but the company said it can be thawed and kept in a regular refrigerator for 30 days, easing that concern.
Governments in other countries and the World Health Organization, which aims to buy doses for poor nations, will have to decide separately if and when vaccines should be rolled out broadly.
Meanwhile, Homeland Security investigators and others are trying to send the message now to the public before the vaccines are approved and begin distribution. They say people should only get a vaccine from an approved medical provider. They shouldn’t respond to calls seeking personal information. And they shouldn’t click on social media posts purporting to sell cures.
“If it sounds too good to be true, it is,” Francis said.


fortune--2020-11-30--In Trump’s push to exclude immigrants from the census count, the Supreme Court hints it will move ca

In Trump’s push to exclude immigrants from the census count, the Supreme Court hints it will move cautiously

The U.S. Supreme Court seemed unlikely to issue an immediate decision blocking President Trump’s push to exclude undocumented immigrants from the census, as key justices suggested they wanted to wait to see what the impact would be.
In an 80-minute telephone argument session on Monday, the justices suggested they will move cautiously in a case that could affect the allocation of congressional seats and federal dollars. The Trump administration is in a race to finish the count, and submit the report to Congress, before President-elect Joe Biden takes over the presidency on Jan. 20.
Acting U.S. Solicitor General Jeffrey Wall told the justices the administration is now trying to identify categories of people who might be excluded, such as those now in federal detention, rather than aiming to omit the estimated 10.5 million people who are in the country illegally. Wall said it wasn’t clear that effort would have any impact on the allocation of congressional seats, making an argument that resonated with some justices.
“Doesn’t that cut in favor of waiting, that maybe there’s no injury here because we’re not really sure what the contours of the decision would be?” Justice Amy Coney Barrett asked.
Three lower courts have said the administration is violating either federal law or the Constitution by trying to exclude undocumented immigrants. Critics say Trump is trying to manipulate the census numbers at the expense of Democratic-leaning areas with high immigrant populations. The push could mean fewer seats for Texas, California and possibly New York and New Jersey.
Trump is seeking to change more than 200 years of practice in a nation that has always counted non-citizen residents, even those in the U.S. illegally. The case marks the last major immigration showdown at the Supreme Court for Trump after a presidential term marked by divisive legal fights.
The challenge is just one of several hurdles Trump will have to overcome to accomplish his goal. With the pandemic slowing the count, Census Bureau officials have told the Commerce Department they can’t produce the state population totals until after Trump leaves office, according to a New York Times report. And even if Trump can send Congress the numbers, lawmakers could still reject the tally, and Biden might be able to submit a revised report after he becomes president.
The president is defending his July 21 memorandum, which made it administration policy to exclude undocumented immigrants from the count. The memo tells the commerce secretary to send the president a tally that excludes that group, along with a separate set of numbers showing the total population.
The high court could decide the case on procedural grounds, leaving open the possibility of another challenge later. Trump contends that opponents don’t currently have “standing” to challenge the plan because they lack a concrete stake in the outcome.
The court has put the case on a fast track, making a ruling possible by the end of the year.


fortune--2020-11-30--FCC chief who ended net neutrality says he’ll quit on Jan. 20

U.S. Federal Communications Commission Chairman Ajit Pai said he’ll leave the agency Jan. 20, eliminating the possibility of a holdover Republican majority at the agency that could have temporarily stymied changes sought by the incoming Biden administration.
Since being elevated to the chairmanship by President Donald Trump in 2017, Pai, a Republican, has led the commission in dismantling net neutrality regulations and pushed for fast wireless broadband service.
Pai’s term as a commissioner extends to July 2021 and he could have stayed on as a commissioner, without the chairman’s power to set the agency’s agenda, but has opted not to.
With another Republican leaving after his term expired, the FCC will have a 2-to-1 Democratic majority on Inauguration Day unless the Senate confirms Nathan Simington, a Republican whose nomination is to be considered Dec. 4 by the Commerce Committee.
Under Pai the FCC in 2017 revoked so-called net neutrality rules put in place by the Obama administration that barred broadband service providers from interfering with web traffic — for instance by slowing competitors’ content.
Pai’s change gutted the FCC’s authority over internet service providers. It was welcomed by carriers such as AT&T Inc. and Verizon Communications Inc.
Since the vote, Pai has pointed to increased broadband availability as vindication. Critics have cited competing data and insisted rules are needed. The debate is likely to continue into the term of President-elect Joe Biden.
Pai pressed to assign more airwaves for high-speed mobile broadband. The FCC sold airwaves in an auction that attracted more than $4 billion in bidding that concluded in August, and has scheduled a sale for December.
Pai, 47, promised “light-touch” regulation, and he used his time in office to reduce FCC rules. He has been a member of the five-person commission since 2012.


fortune--2020-11-30--U.S. air travel spiked over Thanksgiving weekend despite CDC guidance to stay home

U.S. air travelers rose to the most in more than eight months as flyers brushed aside the advice of public health officials to avoid trips around the Thanksgiving holiday.
Passengers at domestic airport checkpoints totaled 1,176,091 on Sunday, the U.S. Transportation Security Administration said Monday. That’s the most since March, when the coronavirus pandemic gutted travel demand.
The uptick in airline travel came even as public health officials and state leaders urged people to stay home and limit holiday gatherings to prevent a further surge in COVID-19 cases. Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, before the holiday said that people should “think twice” about traveling.
Despite the increase, Sunday’s passenger total was only 41% of last year’s level. Before the holiday week, travelers in November had been at about 35% of 2019 levels.
American Airlines, United Airlines, and Southwest Airlines had warned before Thanksgiving that bookings were softening with news of growing infections, hospitalizations and deaths due to COVID-19.
A Standard & Poor’s index of major U.S. airlines fell 1.8% at 11:21 a.m., paced by American’s 4.3% drop to $14.33.
Driving was also off last week, with U.S. gasoline demand falling more than 25% compared with year-earlier levels, according to GasBuddy.


fortune--2020-11-30--Cyber Monday sales could hit  a record $12.7 billion. Can retailers handle the surge?

Coming on the heels of the most e-commerce intensive Black Friday yet, Cyber Monday promises to be a big new test of whether physical retailers will succeed at managing surging online demand this holiday season.
Adobe Analytics, which tracks digital transactions on 80 major e-commerce sites, expects online sales could reach as much as $12.7 billion on Monday, up 35% from a year earlier. That is likely to further strain retailers coping with unusually high online sales this year.
As Fortune reported Friday, malls across the country were very quiet as shoppers stayed on the couch rather than dive into crowds at a time of surging COVID-19 infection rates. Indeed, according to RetailNext, the number of in-store shoppers on Black Friday was down by 48% from the previous year.
Instead shoppers went online: According to Adobe Analytics, online sales hit $9 billion Friday, a 22% increase over last year. That was below expectations because of retailers doling out online deals since mid-October, far earlier than usual.
And this year, once again, retailers are offering deep discounts on Cyber Monday on everything from televisions to Crock-Pots to Barbie Playsets. But this time around, it’s not only to compete with Amazon, whose sales rose 30% over the long weekend. For many retailers, this strategy is intended to bring sales forward as much as possible to reduce any surge in December that would lead to running out of stock and delayed packages.
“There is absolutely no way that the carriers will be able to keep up with the volume,” Moody’s vice president Charlie O’Shea tells Fortune. And so even as shoppers have been sifting through deals for weeks, they will turn out online on Monday because of that. “How early do I buy to make sure I get it, and am I going trust their ability to get it on time?” O’Shea continues.
Some in the industry have coined the term “shipageddon,” where there is simply no more capacity to ship any additional items. Salesforce has forecast some 5% of packages shipped between mid-November and Christmas Eve could be late because of demand outstripping capacity.
Abercrombie & Fitch, for instance, has told shoppers on its website that Dec. 4 is the cutoff to be sure items will arrive by Christmas Eve—unless they’re willing to pay extra shipping. Otherwise, they will be nudged to use curbside pickup.
Indeed, retailers like Target, Best Buy, and Walmart are banking on their expansive curbside pickup setup to save on shipping costs, provide online shoppers with additional options, and offer customers more certainty about getting their items. Those investments have paid off handsomely: In its most recent quarter, Best Buy saw online sales grow 174%, while at Target they rose 155%, and at Walmart they nearly doubled.
And so Cyber Monday won’t be anticlimactic despite the stream of deals in the last few weeks. Retailers want to get people shopping online now, given there are only three and a half weeks left before Christmas. Otherwise shoppers will have to pay more or risk not getting their items on time.
“Shoppers are encouraged to do their gift buying soon, as shipping in time for Christmas will get more expensive in the coming weeks,” says Taylor Schreiner, director at Adobe Digital Insights.


fortune--2020-11-30--The biggest tech news you missed over Thanksgiving

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Good morning, Term Sheet readers. I’m glad to be back from Thanksgiving break, when I finally gave up trying to like turkey and opted for lasagna.
Much like lasagna (forgive the pun), the news layered on over the long U.S. holiday as investors continued to take advantage of these strange market conditions to IPO or merge with other businesses.
Here are five things you need to know to jumpstart your week:
(FINANCIAL) DATA IS EVERYTHING: In a mega Monday merger, S&P Global agreed to acquire market data provider IHS Markit for an eye-popping $44 billion, including $4.8 billion in debt. The transaction will be completed entirely in S&P Global stock. Meanwhile, the London Stock Exchange is seeking clearance for its plans to acquire data provider Refinitiv for $27 billion.
AIRBNB AND DOORDASH SEEK EVEN HIGHER VALUATIONS: Airbnb, which was valued at $31 billion in 2017 before succumbing to the pandemic with an $18 billion tag earlier this year, has recovered surprisingly quickly and may be seeking a valuation of $30 billion to $33 billion in its roadshow this week, the Wall Street Journal reports. And DoorDash is now seeking a valuation of $25 to $28 billion, after being last valued at $16 billion in private markets. Read more.
OH, COINBASE: On Sunday, the New York Times published an article based on conversations and documents from 23 current and former Coinbase employees alleging racism and discrimination against Black employees. Coinbase sought to get ahead of the story, releasing a statement saying, “We expect the story will paint an inaccurate picture that lacks complete information and context.” But it is worth noting: About 3% of the company’s employees are Black—less than half the average of the rest of the tech industry. Read more.
SALESFORCE-SLACK TALKS: Late last week, reports emerged that Salesforce was in talks to acquire Slack for over $17 billion. Salesforce shares have soared this year—making for valuable M&A currency—while Slack’s work-from-home boost teetered in recent months as investors worried about competition from Microsoft. A deal could come as soon as this week.
TONY HSIEH DIES AT 46: On Friday, Tony Hsieh, the former CEO of Zappos.com, died after being injured from a house fire in Connecticut. The Harvard graduate joined the online shoe company in 1999 and saw it through its sale to Amazon in 2009 for $1.2 billion and only retired as its CEO earlier this year. He was known for his management experiments that were hailed both wacky and visionary.
Lowercase Capital’s Chris Sacca wrote on Twitter, “Tony Hsieh might be the most original thinker I’ve ever been friends with. He questioned every assumption and shared everything he learned along the way. He genuinely delighted in making anyone and everyone happy. The earth has lost a beautifully weird and helpful person. RIP.”
“My favorite Tony Hsieh memory was watching him beat us all breezily in poker and then give our money to the dealers and servers as tips,” tweeted Mark Pincus, co-founder of Zynga. “I will miss his quiet smiling confidence.”
Lucinda Shen
Twitter: @shenlucinda
Email: lucinda.shen@fortune.com
VENTURE DEALS
- SpaceX, the space travel company, previously said it raised $1.9 billion. That round was led the round by Sequoia, per The Information. Read more.
- Empower Education Online, a Chinese provider of edtech software, raised $265 million in Series C funding. Hillhouse Capital led the round. Read more.
- Cars24, an India-based platform for buying and selling pre-owned vehicles, raised $200 million in Series E funding. DST Global led the round.
- HungryPanda, a London-based maker of a food delivery app for Chinese consumers outside of China, raised $70 million in Series C funding. Kinnevik led the round and was joined by investors including 83North and Felix Capital.
- Aurora Solar, a San Francisco-based company developing tools for solar professionals to design and sell solar remotely, raised $50 million in Series B funding. ICONIQ Capital led the round and was joined by investors including Energize Ventures, Fifth Wall, and Pear VC.
- Materialize, a New York-based database streaming company, raised $40 million in funding. Kleiner Perkins led a $32 million Series B round. Lightspeed Venture Partners led the Series A investment in 2019.
- Mursion, a San Francisco-based VR interpersonal training company, raised $20 million in funding from Leeds Illuminate. Read more.
- Primer, a U.K.-based fintech focused on consolidating payments for merchants, raised £14 million ($18.7 million) in Series A funding. Accel led the round. Read more.
- Elevian, an Allston, Mass.-based biotech company developing medicines for age-related diseases, raised $15 million and includes conversion of prior notes. Prime Movers Lab led the round and was joined by investors including Bold Capital Partners, For Good Ventures, Kizoo Ventures, Lauder Partners, Longevity Fund, SavEarth Fund, and WTI.
- Hypr, a self-driving car startup founded by former Zoox CEO Tim Kentley Kaly, raised $10 million in seed funding. R7 Ventures led the round. Read more.
- Cast.ai, a Miami, Fla.-based cloud management software provider, raised $7.7 million in seed funding. Investors included TA Ventures, DNX, and Florida Funders. Read more.
- Homeppl, a London-based tenant referencing system startup, raised $2 million. Investors include Ascension Ventures, Fair By Design, and JLR Star.
PRIVATE EQUITY
- Abris Capital Partners agreed to acquire Scanmed S.A., a Poland-based healthcare network operator, from Life Healthcare Group Holdings. The deal gives Scanmed an enterprise value of about 340 million zloty ($90.7 million).
- Sycamore Partners is finalizing plans to buy assets of retailer Ascena Retail Group Inc., including the Ann Taylor and Loft brands, per Bloomberg citing sources. Read more.
EXITS
- Unilever agreed to acquire SmartyPants Vitamins, a Los Angeles-based vitamin, mineral and supplement company. Investors including Morgan Stanley and North Castle backed SmartyPants.
- Advent International is planning to revive sales talks of Unit4, a Netherlands-based software company that could be valued at above $2.3 billion, per Bloomberg citing sources. Read more.
OTHER
- Suning.com, the Chinese home goods retailer, is considering selling a stake in its e-commerce business that could value the segment at about $6 billion. Read more.
- Intuit’s (Nasdaq: INTU) $7 billion bid to acquire Credit Karma, a fintech company, received Department of Justice approval for their union with the divestiture of Credit Karma’s tax business. Square (NYSE: SQ) will acquire the tax business for $50 million.
- Biogen (Nasdaq: BIIB) plans to take a $650 million stake in Sage Therapeutics (Nasdaq: SAGE), a Cambridge, Mass.-based biopharmaceutical company, and pay $875 million to jointly develop treatments for depression and other disorders.
- Alibaba and Tencent’s separate talks to acquire a controlling stake in iQIYI (Nasdaq: IQ) from Baidu, though those conversations have stalled over valuation and regulatory concerns, per Reuters. Read more.
- General Motors (NYSE: GM) is forgoing a potential 11% equity stake in Nikola (Nasdaq: NKLA) and dropped plans to make the electric truck maker’s pickup, the Badger. Read more.
IPO
- 17 Education & Technology Group, a Chinese provider of K-12 tutoring services, says it plans to raise $288 million with China Pinnacle Equity Management planning to acquire some $80 million worth in ADSs in the offering. Read more.
SPAC
- Perella Weinberg Partners is in talks to go public via merger with FinTech Acquisition Corporation IV, a SPAC set up by Betsy Cohen. Read more.
- CBRE Acquisition Holdings, a SPAC formed by CBRE, lowered the size of its raise and now plans to raise $350 million. Read more.
- Marquee Raine Acquisition, a blank check company formed by The Raine Group seeking a target in the TMT industry, filed to raise $325 million. Read more.
F+FS
- Firstminute Capital, a London-based fund, raised $111 million (£87 million) for its second early-stage venture fund.


fortune--2020-11-30--In a major scientific breakthrough, A.I. predicts the exact shape of proteins

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Researchers have made a major breakthrough using artificial intelligence that could revolutionize the hunt for new medicines.
The scientists have created A.I. software that uses a protein’s DNA sequence to predict its three-dimensional structure to within an atom’s width of accuracy.
The achievement, which solves a 50-year-old challenge in molecular biology, was accomplished by a team from DeepMind, the London-based artificial intelligence company that is part of Google parent Alphabet. Until now, DeepMind was best known for creating A.I. that could beat the best human players at the strategy game Go, a major milestone in computer science.
DeepMind achieved the protein shape breakthrough in a biennial competition for algorithms that can be used to predict protein structures. The competition asks participants to take a protein’s DNA sequence and then use it to determine the protein’s three-dimensional shape. (For an exclusive account of how DeepMind accomplished this goal, read this Fortune feature.)
Across more than 100 proteins, DeepMind’s A.I. software, which it called AlphaFold 2, was able to predict the structure to within about an atom’s width of accuracy in two-thirds of cases and was highly accurate in most of the remaining one-third of cases, according to John Moult, a molecular biologist at the University of Maryland who is director of the competition, called the Critical Assessment of Structure Prediction, or CASP. It was far better than any other method in the competition, he said.
Demis Hassabis, DeepMind’s cofounder and chief executive officer, said the company wants “to make the maximal positive societal impact with these technologies.” But he said DeepMind had not yet determined how it would provide academic researchers with access to the protein structure prediction software or whether it would seek commercial collaborations with pharmaceutical and biotechnology firms. He said the company would announce “further details on how we’re going to be able to give access to the system in a scalable way” sometime next year.
“This computational work represents a stunning advance on the protein-folding problem,” Venki Ramakrishnan, a Nobel Prize–winning structural biologist who is also the outgoing president of the Royal Society, Britain’s most prestigious scientific body, said of AlphaFold 2.
Janet Thornton, an expert in protein structure and former director of the European Molecular Biology Laboratory’s European Bioinformatics Institute, said that DeepMind’s breakthrough opened up the way to mapping the entire “human proteome”—the set of all proteins found within the human body. Currently, only about a quarter of human proteins have been used as targets for medicines, she said. Now, many more proteins could be targeted, creating a huge opportunity to invent new medicines.
Thornton also said that DeepMind’s A.I. system would have profound implications for scientists who create synthetic proteins and that these could have big impacts too: everything from creating new genetically modified crop strains that will be far more nutritious to new enzymes that could help clean up the environment by digesting plastics.
Proteins are the basic mechanisms of biological processes. They are formed from long chains of amino acids, coded for in DNA, but once manufactured by a cell, they fold themselves spontaneously into complex shapes that often resemble a tangle of cord, with ribbons and curlicue-like appendages. The exact structure of a protein is essential to its function. It is also critical for designing small molecules that might be able to bind with the protein and alter this function, which is how new medicines are created.
Until now, the primary way to obtain a high-resolution model of a protein’s structure was through a method called X-ray crystallography. In this technique, a solution of proteins is turned into a crystal, itself a difficult and time-consuming process, and then this crystal is bombarded with X-rays, often from a large circular particle accelerator called a synchrotron. The diffraction pattern of the X-rays allows researchers to build up a picture of the internal structure of the protein. It takes about a year and costs about $120,000 to obtain the structure of a single protein through X-ray crystallography, according to an estimate from the University of Toronto.
More recently, two other experimental methods—nuclear magnetic resonance and cryogenic electron microscopy—have also been used. They can be faster and less expensive but tend to produce models that are less precise than X-ray crystallography.
It takes AlphaFold 2 “a matter of days” to calculate each protein structure using what John Jumper, the researcher who leads the protein-folding team at DeepMind, characterized as “modest” computing resources. Training the system required 128 specialized A.I. computing units on 16 chips created by Google, called tensor processing units, running continuously for “roughly a few weeks,” Jumper said. He noted that this is much less computing power than has been required for many other recent A.I. breakthroughs, including DeepMind’s previous work on Go.
In 1972, Nobel Prize–winning chemist Christian Anfinsen postulated that DNA alone should fully determine what final structure a protein takes—a supposition that set off the decades-long quest to find a mathematical model that could do what Anfinsen was proposing. The problem was, however, that even though the laws of physics control how a protein folds, there are so many possible permutations that biologist Cyrus Levinthal famously estimated it would take longer than the age of the known universe to puzzle out a single protein’s structure through random trial and error.
But DeepMind’s AlphaFold 2 has now essentially done what Anfinsen suggested. AlphaFold 2 is “on par” with X-ray crystallography across more than two-thirds of the proteins in the CASP competition, Moult said. Now the hope is that researchers will be able to use AlphaFold 2, or at least the same method, to go directly from a protein’s DNA sequence, which has become relatively easy and inexpensive to obtain, to knowing its 3D shape, without having to use X-ray crystallography or other physical experiments at all.
Andrei Lupas, director of the department of protein evolution at the Max Planck Institute for Developmental Biology in Tübingen, Germany, who served as one of the assessors for this year’s CASP competition, called DeepMind’s results “astonishing.”
As part of CASP’s efforts to verify the capabilities of DeepMind’s system, Lupas used the predictions from AlphaFold 2 to see if it could solve the final portion of a protein’s structure that he had been unable to complete using X-ray crystallography for more than a decade. With the predictions generated by AlphaFold 2, Lupas said he was able to determine the shape of the final protein segment in just half an hour.
AlphaFold 2 has also already been used to accurately predict the structure of a protein called ORF3a that is found in SARS-CoV-2, the virus that causes COVID-19, which scientists might be able to use as a target for future treatments.
Lupas said he thought the A.I. software would “change the game entirely” for those who work on proteins. Currently, DNA sequences are known for about 200 million proteins, and tens of millions more are being discovered every year. But 3D structures have been mapped for less than 200,000 of them.
AlphaFold 2 was only trained to predict the structure of single proteins. But in nature, proteins are often present in complex arrangements with other proteins. Jumper said the next step was to develop an A.I. system that could predict complicated dynamics between proteins—such as how two proteins will bind to one another or the way that proteins in close proximity morph one another’s shapes.
DeepMind had entered and won the CASP competition two years ago. But at the time, using an A.I. system called AlphaFold that was configured differently, it was only able to achieve an average “global distance test total score” (GDT) —a measure that is approximately equivalent to the percentage of each protein that it accurately maps—of 58 on the hardest class of proteins.
Although this was about six points better than the next best team, it was not a result that was competitive with empirical methods like X-ray crystallography. This year, even on these hardest proteins, DeepMind achieved a median GDT of 87, which is close to being as good as crystallography and was about 26 points better than its nearest competitor.


fortune--2020-11-30--Trump’s path to a second term via faithless electors has ‘zero practical chance’

On Thursday, President Donald Trump said he’d accept defeat if President-elect Joe Biden wins 270 electoral votes when members of the Electoral College vote on Dec. 14.
Trump’s court challenges have been unsuccessful at flipping Biden’s wins in battleground states like Georgia and Wisconsin. That leaves convincing Biden electors to ditch him as the President’s last remaining path to a second term. While members of the Electoral College—with the exception of Maine and Nebraska—are pledged to vote for the highest vote getter in their state, most states do nothing to stop them from breaking that pledge.
Biden won 25 states representing 306 electoral votes to Trump’s 25 states with 232 electoral votes. In order to pull off an upset, Trump would need 37 Biden electors to flip and cast “faithless” ballots.
The obstacle for Trump? No presidential candidate has seen more than six electors flip—which happened to James Madison in 1808. And only around 160 faithless ballots have been cast in U.S. history.
In 2016, a total of 10 electors cast faithless ballots, with seven—the highest since 1912—actually counting. Trump lost two electoral votes, and Hillary Clinton lost five. But even that historically high faithless ballot count is still 30 short of what Trump would need this go-around.
And pulling off the feat would be even harder in 2020.
Following the 10 faithless votes in 2016, more states adopted laws that prevent this. According to fairvote.org, a total of 14 states, including Iowa and Michigan, now have laws that will cancel electors’ ballots if they don’t cast it for the statewide winner. Those state laws were upheld by the U.S. Supreme Court in July when it ruled on the Chiafalo v. Washington case. The Supreme Court also deemed state penalties on unfaithful electors as legal.
“In sum, there is zero practical chance that Trump can be reelected by changing the minds of Biden electors in the Electoral College,” wrote Tom Goldstein, publisher of the SCOTUSblog, on Saturday.
Goldstein’s review of state laws finds that 199 Biden electors are already promised to the Democratic nominee by law. That means Trump would have to flip 35% of Biden remaining electors while also not losing any of his own electors.
The President’s best chance at winning enough faithless electors might be to get Republican-controlled state legislatures in states like Wisconsin and Georgia to intervene. The Constitution gives state legislatures the power to determine how electors for their state are chosen, with these elected bodies usually selecting their slates prior to elections. However, some Republicans have discussed asking state legislatures to throw out Democratic slates and put up pro-Trump electors in their place. Currently, no state legislature is actively pursing such an unprecedented move.
In total, 538 electors make up the Electoral College. They’ve never changed the final outcome for President. However, in 1836, they nearly changed the vice president outcome—which electors are also required to vote for. Enough electors flipped from Democratic nominee Richard Johnson that it forced the vice presidential race to the Senate—where he was confirmed.
If none of the vice presidential candidates top 270 electoral votes, the Senate gets to pick. Meanwhile, if neither presidential candidate reaches 270 electoral votes, the vote moves to the House of Representatives. But instead of voting as individual representatives, they’d have to vote as a state—with each state getting one vote. While Democrats have the most House seats, Republicans currently control the most state delegations. The President would be favored in such a scenario, as long as representatives voted along party lines.


fortune--2020-11-30--Game over for the ‘Wargames’ hacking law?

When a law that governs technology stays on the books for decades, the results can be a puzzling mess. Later today, the Supreme Court will take up a dispute over the 1984 Computer Fraud and Abuse Act, which has become messy in the hands of prosecutors and corporations seeking to punish people using computers in ways they don’t approve of.
The origin of the law is simple yet absurd: The president of the United States watched the 1983 movie Wargames and got scared about hackers. I am not kidding. He then started pressing members of Congress to do something. So, they did.
The resulting law imposed criminal penalties for accessing computer systems without authorization. That was a clear danger back in the 1980s before the Internet was available to most people. Wargames fictional hacker David Lightman (played with great, nervous energy by actor Matthew Broderick) would surely be prosecutable under the CFAA for dialing into WOPR, the fictional nuclear war simulation program, even if the program was welcoming. “Shall we play a game,” WOPR famously asked Lightman. Great quote that I try not to overuse in this newsletter.
But over time, as the Internet spread, accessing other people’s computers became considerably more common (every time you read a web page, order an Uber, or stream on Netflix, you’re technically tapping into someone else’s computer). That’s raised questions about just what accessing someone else’s computer “without authorization” means under the law.
One of the most debatable prosecutions: In 2011, CFAA was used to prosecute programmer and tech activist Aaron Swartz after he downloaded millions of articles from a subscription database. Facing possibly decades of jail time, Swartz took his own life. And there are zillions of other cases that don’t seem in the spirit of what lawmakers intended back in the 1980s when they were trying to avoid future David Lightmans accidentally starting a nuclear war.
What if student researchers found grave vulnerabilities in a transit system’s fare collection computers? Or what if you changed the headline on a news story as a prank? What if you just violated the “terms of service” of Facebook by lying about your home city? Even that could be considered unauthorized access under the law, according to cyberlaw expert Orin Kerr. He’s blogged that, depending on your interpretation, the law “either makes most people or very few people criminals.”
The case the Supreme Court is hearing today, Van Buren v. United States, concerns an allegedly bad cop in Georgia who took $6,000 from a pal to look up a license plate number in the state’s crime information database. CFAA critics hope the Supreme Court will use the case to curtail the law’s reach or, at least, set a national standard for what constitutes unauthorized access. Stay tuned for a decision.
Aaron Pressman
@ampressman
aaron.pressman@fortune.com
NEWSWORTHY
RIP to a startup legend. Retired Zappos CEO Tony Hsieh, a beloved figure in the tech world, died on Sunday at age 46 after being caught in a house fire in Connecticut. Known for his devotion to customer service, Hsieh had run the popular shoe sales site even after selling it to Amazon in 2009. He retired in August.
The ghosts of shoppers past. Fortune's intrepid retail reporter Phil Wahba hit the malls on Friday and discovered that Black Friday has shifted mostly online thanks to COVID-19. "At the sprawling Newport Centre in Jersey City, N.J., across the Hudson River, it was a ghost town: not a single customer inside its Sears store, and only a smattering of shoppers at its J.C. Penney or Kohl's," Wahba notes. Speaking of online sales, Amazon is paying its frontline staff a $300 bonus in December, the second bonus payment since the company ended an extra $2 per hour for wages at the end of May. The announcement came as unionized Amazon workers in countries like Germany and Australia staged protests for better working conditions on Friday.
Pop goes my heart. Renewable fuel-powered truck startup Nikola got some bad news on Monday morning. Turns out, General Motors will not agree to manufacture an electric pickup truck for Nikola in return for an equity stake. Instead, GM will only consider using Nikola's hydrogen tech in its own trucks under a non-binding agreement. Nikola stock, which went public by merging with a SPAC in June, plunged 21% in premarket trading.
You have 17 billion unread messages. Trying to catch Microsoft in the productivity software wars, Salesforce is pursuing an acquisition of messaging service Slack, the Wall Street Journal reports. Data Sheet's Tuesday guy, Robert Hackett, explains why Salesforce "wants to slurp up Slack."
Check the numbers. Two of the largest providers of financial data could be combining. S&P Global is offering a reported $44 billion for IHS Markit in order to create a titan of market-moving information. Elsewhere on Wall Street, prepare for the IPO onslaught. DoorDash wants to raise almost $3 billion in its initial public offering at a valuation of $30 billion. Airbnb is targeting a similar value in its IPO. I reviewed the pros and cons of those two IPOs and a few others last week.
FOOD FOR THOUGHT
To become truly popular, electric vehicles need access to a much bigger network of charging stations, writer Steve LeVine argues on his blog, The Mobilist. Should the government step in and speed up the construction process?
[Mike] Calise, the executive from Tritium, said the build-out should begin like the Eisenhower-era highway system, with the construction of EV charging corridors. These would involve the construction of EV charging stations every 10 miles or so along the country’s main highways. From there, spokes can be filled in where people live and work, especially inner cities.
The problem is that they are expensive, requiring the government to get behind them, Calise said. President-elect Joe Biden is proposing that the federal government subsidize the construction of 500,000 charging stations. If Congress approves funding, charging will be accelerated. “You will start to see them at all the gas stations, convenience stores, hotels, ports, stadiums, fleet depots,” Calise said. “The pure gas station is going to be disrupted.”
IN CASE YOU MISSED IT
This new VR simulator helps you prepare for the most awkward office encounters By Lee Clifford
Tesla can now sell its made-in-China SUVs in China By Naomi Xu Elegant
As libraries fight for access to e-books, a new copyright champion emerges By Jeff John Roberts
Everything to know about T-Mobile, Verizon, and AT&T’s 5G plans By Aaron Pressman
The world’s largest wind farm is truly enormous, and it’s one step closer to reality Katherin Dunn
MeWe is a fast-growing social hub for conservatives. What you need to know By Danielle Abril
The email security market is littered with false claims. How to fix it By Kevin O'Brien
(Some of these stories require a subscription to access. Thank you for supporting our journalism.)
BEFORE YOU GO
The Great British Baking Show (or Bake Off in the U.K.) concluded last week and crowned its latest winner as the best amateur baker of the year. There was a little too much controversy this year, but it's hard to be disappointed with the final outcome (spoiler warning: That link names the winner).
Also, the mysterious Utah monolith I mentioned last email has now vanished. Maybe it wanted to hit the malls before the crowds returned?


fortune--2020-11-30--How water-resistant is the iPhone? Italian watchdog says Apple’s claims are overblown

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Apple has been offering water-resistant iPhones for several years now, but are its protection claims actually justified?
The Italian Antitrust Authority (AGCM) thinks Cupertino has been overstating the handsets’ water resistance. The agency, which is in charge of consumer protection in Italy, on Monday fined Apple €10 million ($12 million) for misleading its customers—and for not providing standard warranty assistance to customers whose iPhones were damaged by liquids.
Since 2016’s iPhone 7 range, Apple’s smartphones have been promoted as offering at least “IP67” protection. (The “IP” part stands for “ingress protection,” while the first digit refers to dust protection and the second to water protection.)
A phone with IP67 protection can be continually immersed in water of up to 1 meter (3 feet 3 inches) in depth. According to Apple’s promotional material, this is fine for up to 30 minutes. For 2018’s iPhone XS and subsequent models, the advertised protection is IP68, meaning a maximum depth of over 1 meter—it grades up from the XS’s claimed 2-meter water resistance to the new iPhone 12’s 6 meters.
Lab conditions
Now, despite what many online sources allege, Apple’s water resistance does not equal waterproofness, which is why Apple never claims its iPhones are waterproof—it doesn’t even use that word for the Apple Watch, which is meant to be worn while swimming.
But, focusing on the models from the iPhone 8 to last year’s iPhone 11, the Italian regulator said Apple still wasn’t being cautious enough.
It said the tech giant broke the rules by failing to tell its customers that the claimed water resistance is not normally achievable.
So an iPhone 8 can theoretically stay a meter underwater for half an hour and emerge unscathed, but only if the water is entirely still and pure, like in a laboratory setting.
Apple does give a disclaimer that the iPhone’s guarantee won’t cover damage caused by liquids, but the agency said the company’s “emphatic advertising boast of water resistance” still means customers were deceived.
On top of that, the AGCM said Apple was infringing on customers’ legal warranty rights by refusing to help them out when their phones were damaged by water or other liquids. It told Apple to pay a total of €10 million for the infringements, and to publish a notice of the ruling on its Italian website’s iPhone section.
Apple refused to comment on the fine.


fortune--2020-11-30--These women are at the center of the publishing industry’s uncertain future

This is the web version of The Broadsheet, a daily newsletter for and about the world’s most powerful women. Sign up to get it delivered free to your inbox.
Good morning, Broadsheet readers! The Biden comms shop will be women-run, Sarah Fuller makes college football history, and the publishing industry faces an uncertain future. Have a mindful Monday.
– Bookmark this. Last week ended with a seismic shift for the book business. Penguin Random House will pay $2 billion to acquire Simon & Schuster—creating the first true megapublisher.
The deal, if it makes it through antitrust concerns, has the potential to completely reshape publishing—and, of course, Simon & Schuster, which is headed by publisher Dana Canedy. She was No. 50 on our Most Powerful Women list this year. In her writeup, we noted the outsize impact of the business compared to its size, citing how “what we read is powerful, with far-reaching impact on our culture and beliefs”—influence that certainly comes into play for this acquisition, too.
But the merger of the two biggest publishers isn’t the only story right now with implications for writers and readers. Fortune‘s Jeff John Roberts has a piece on the battle between the Internet Archive and the American Association of Publishers. Lila Bailey, the Internet Archive’s chief lawyer, is representing the nonprofit as it fights for its ability to share ebooks with the public—in essence, to decide the future of libraries online.
The suit stems from an early-COVID project called the National Emergency Library; the Internet Archive sought to make ebooks more accessible as libraries closed at the height of March’s pandemic chaos.
Bailey acknowledges the economic realities that have led publishers to take a hard line on copyright issues, as is happening in this case, but says that battling nonprofits like the Internet Archive won’t solve the industry’s problems. The American Association of Publishers didn’t respond to Fortune‘s request for comment.
“This case will determine what libraries will be,” says Bailey—just as the Penguin Random House/Simon & Schuster merger will decide the future of publishing. Read the full Fortune story here.
Emma Hinchliffe
emma.hinchliffe@fortune.com
@_emmahinchliffe
ALSO IN THE HEADLINES
- Setting up shop. The Biden transition team announced a few more appointments, including seven that will create the White House's first all-female senior communications team. Jennifer Psaki will be White House press secretary; Kate Bedingfield will be communications director; Karine Jean-Pierre will be principal deputy press secretary; and Symone Sanders will be chief spokesperson for Vice President-elect Kamala Harris. President-elect Joe Biden is also expected to name two women to his economic team: Neera Tanden as director of the Office of Management and Budget and Cecilia Rouse as chairwoman of the Council of Economic Advisors.
- A CEO's challenge. Bloomberg Markets has a new interview with Citi's Jane Fraser, who will become the first female CEO of a Wall Street bank in February (and whom Fortune featured on its November cover). One of the first challenges she'll confront is how banks factor into the COVID recovery. "We do anticipate [the recovery] to be quite sluggish," she says. "We’re certainly seeing it in consumer [banking]: You’ve got the 'haves' and you’ve got those who are really struggling. I think it goes to that sense of, what are the values of the industry?" Bloomberg
- Kick, goal. Vanderbilt student athlete Sarah Fuller made college football history this weekend when she delivered the second half's opening kickoff in a game against Missouri. The football team asked Fuller, a women's soccer goalkeeper, to play after its kicking unit couldn't practice because of a coronavirus quarantine. Fuller is the first woman to play in a Power 5 football game. ESPN
- Uneven playing field. Britain's nationwide lockdown has exposed the uneven value it places on men's sports versus women's. When the government granted lockdown exemptions for "elite sport" purposes, the top six tiers of men's soccer kept training and competing, but only two women's leagues received the same privilege. New York Times
MOVERS AND SHAKERS: Refinery29 promoted Kathleen Newman-Bremang to senior editor, Unbothered.
IN CASE YOU MISSED IT
- Cash is king? The U.S. enacted sanctions on specific leaders in mainland China and Hong Kong over the national security law that Beijing imposed on Hong Kong after protests rocked the city. Now Hong Kong Chief Executive Carrie Lam says she has "piles of cash" at home because, as one of the leaders targeted by sanctions, she can't access banking services. Financial Times
- Hedge bets. Aberdeen Standard Investments, one of the biggest asset managers in Europe, is launching a new fund that will invest in hedge funds run by women. The fund is meant to capitalize on investor interest in diversity and socially conscious investing and to take advantage of women-led hedge funds' strong returns. Financial Times
- Early action. One of Kate Middleton's signature causes as a member of the Royal Family is early childhood development. The Duchess of Cambridge released findings from a major survey of U.K. parents, finding that parents' stress and loneliness has increased during the pandemic—and that their parents' loneliness affects children under five. Guardian
ON MY RADAR
Saweetie, City Girls, and the female rapper renaissance New York Times
The Queen expresses an interest in blockchain Financial Times
The reigning queen of pandemic yoga New York Times
PARTING WORDS
"[People] want the frilly apron, and they want to believe that I would have been home either way, cooking. And I wouldn’t have been. I’m home cooking because I’ve been able to make it an economically viable career."
-Smitten Kitchen author Deb Perelman