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Making New Drugs With a Dose of Artificial Intelligence

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SAN FRANCISCO — You can think of it as a World Cup of biochemical research.

Every two years, hundreds of scientists enter a global competition. Tackling a biological puzzle they call “the protein folding problem,” they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease.

Mohammed AlQuraishi, a biologist who has dedicated his career to this kind of research, flew in early December to Cancun, Mexico, where academics were gathering to discuss the results of the latest contest. As he checked into his hotel, a five-star resort on the Caribbean, he was consumed by melancholy.

The contest, the Critical Assessment of Structure Prediction, was not won by academics. It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company.

“I was surprised and deflated,” said Dr. AlQuraishi, a researcher at Harvard Medical School. “They were way out in front of everyone else.”

DeepMind specializes in “deep learning,” a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists.

“It is not that machines are going to replace chemists,” said Derek Lowe, a longtime drug discovery researcher and the author of In the Pipeline, a widely read blog dedicated to drug discovery. “It’s that the chemists who use machines will replace those that don’t.”

After the conference in Cancun, Dr. AlQuraishi described his experience in a blog post. The melancholy he felt after losing to DeepMind gave way to what he called “a more rational assessment of the value of scientific progress.”

But he strongly criticized big pharmaceutical companies like Merck and Novartis, as well as his academic community, for not keeping pace.

“The smartest and most ambitious researchers wanting to work on protein structure will look to DeepMind for opportunities instead of Merck or Novartis,” he wrote. “This fact should send chills down the spines of pharma executives, but it won’t, because they’re clueless, rudderless, and asleep at the helm.”

The big pharma companies see the situation differently. Though Merck is not exploring protein folding because its researchers believe its potential impact would be years away, it is applying deep learning to other aspects of its drug discovery process.

“We have to connect so many other dots,” said Juan Alvarez, associate vice president of computational and structural chemistry at Merck.

In the spring of 2016, after making headlines with A.I. systems that played complex games like the ancient board game Go, DeepMind researchers were looking for new challenges. So they held a “hackathon” at company headquarters in London.

Working with two other computer scientists, the DeepMind researcher Rich Evans homed in on protein folding. They found a game that simulated this scientific task. They built a system that learned to play the game on its own, and the results were promising enough for DeepMind to greenlight a full-time research project.

The protein folding problem asks a straightforward question: Can you predict the physical structure of a protein — its shape in three dimensions?

If scientists can predict a protein’s shape, they can better determine how other molecules will “bind” to it — attach to it, physically — and that is one way drugs are developed. A drug binds to particular proteins in your body and changes their behavior.

In the latest contest, DeepMind made these predictions using “neural networks,” complex mathematical systems that can learn tasks by analyzing vast amounts of data. By analyzing thousands of proteins, a neural network can learn to predict the shape of others.

This is the same deep learning technology that recognizes faces in the photos you post to Facebook. Over the past decade, the technology has reinvented a wide range of internet services, consumer products, robotic devices and other areas of scientific research.

Many of the academics who competed used methods that were similar to what DeepMind was doing. But DeepMind won the competition by a sizable margin — it improved the prediction accuracy nearly twice as much as experts expected from the contest winner.

DeepMind’s victory showed how the future of biochemical research will increasingly be driven by machines and the people who oversee those machines.

This kind of A.I. research benefits from enormous amounts of computing power, and DeepMind can lean on the massive computer data centers that underpin Google. The lab also employs many of the world’s top A.I. researchers, who know how to get the most out of this hardware.

“It allows us to be much more creative, to try many more ideas, often in parallel,” said Demis Hassabis, the chief executive and a co-founder of DeepMind, which Google acquired for a reported $650 million in 2014.

Universities and big pharmaceutical companies are unlikely to match those resources. But thanks to cloud computing services offered by Google and other tech giants, the price of computing power continues to drop. Dr. AlQuraishi urged the life-sciences community to shift more attention toward the kind of A.I. work practiced by DeepMind.

Some researchers are already moving in that direction. Many start-ups, like Atomwise in San Francisco and Recursion in Salt Lake City, are using the same artificial intelligence techniques to accelerate other aspects of drug discovery. Recursion, for instance, uses neural networks and other methods to analyze images of cells and learn how new drugs affect these cells.

The big pharma companies are also beginning to explore these methods, sometimes in partnership with start-ups.

“Everyone is trending up in this area,” said Jeremy Jenkins, the head of data science for chemical biology and therapeutics at Novartis. “It is like turning a big ship, and I think these methods will eventually scale to the size of our entire company.”

Mr. Hassabis said DeepMind was committed to solving the protein folding problem. But many experts said that even if it was solved, more work was needed before doctors and patients benefited in any practical way.

“This is a first step,” said David Baker, the director of the Institute for Protein Design at the University of Washington. “There are so many other steps still to go.”

As they work to better understand the proteins in the body, for instance, scientists must also create new proteins that can serve as drug candidates. Dr. Baker now believes that creating proteins is more important to drug discovery than the “folding” methods being explored, and this task, he said, is not as well suited to DeepMind-style A.I.

DeepMind researchers focus on games and contests because they can show a clear improvement in artificial intelligence. But it is not clear how that approach translates to many tasks.

“Because of the complexity of drug discovery, we need a wide variety of tools,” Dr. Alvarez said. “There is no one-size-fits-all answer.”

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Artificial intelligence pioneers win tech’s ‘Nobel Prize’

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Computers have become so smart during the past 20 years that people don’t think twice about chatting with digital assistants like Alexa and Siri or seeing their friends automatically tagged in Facebook pictures.

But making those quantum leaps from science fiction to reality required hard work from computer scientists like Yoshua Bengio, Geoffrey Hinton and Yann LeCun. The trio tapped into their own brainpower to make it possible for machines to learn like humans, a breakthrough now commonly known as “artificial intelligence,” or AI.

Their insights and persistence were rewarded Wednesday with the Turing Award, an honor that has become known as technology industry’s version of the Nobel Prize. It comes with a $1 million prize funded by Google, a company where AI has become part of its DNA.

The award marks the latest recognition of the instrumental role that artificial intelligence will likely play in redefining the relationship between humanity and technology in the decades ahead.

Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society,” said Cherri Pancake, president of the Association for Computing Machinery, the group behind the Turing Award.

Although they have known each other for than 30 years, Bengio, Hinton and LeCun have mostly worked separately on technology known as neural networks. These are the electronic engines that power tasks such as facial and speech recognition, areas where computers have made enormous strides over the past decade. Such neural networks also are a critical component of robotic systems that are automating a wide range of other human activity, including driving.

Their belief in the power of neural networks was once mocked by their peers, Hinton said. No more. He now works at Google as a vice president and senior fellow while LeCun is chief AI scientist at Facebook. Bengio remains immersed in academia as a University of Montreal professor in addition to serving as scientific director at the Artificial Intelligence Institute in Quebec.

“For a long time, people thought what the three of us were doing was nonsense,” Hinton said in an interview with The Associated Press. “They thought we were very misguided and what we were doing was a very surprising thing for apparently intelligent people to waste their time on. My message to young researchers is, don’t be put off if everyone tells you what are doing is silly.” Now, some people are worried that the results of the researchers’ efforts might spiral out of control.

While the AI revolution is raising hopes that computers will make most people’s lives more convenient and enjoyable, it’s also stoking fears that humanity eventually will be living at the mercy of machines.

Bengio, Hinton and LeCun share some of those concerns especially the doomsday scenarios that envision AI technology developed into weapons systems that wipe out humanity.

But they are far more optimistic about the other prospects of AI empowering computers to deliver more accurate warnings about floods and earthquakes, for instance, or detecting health risks, such as cancer and heart attacks, far earlier than human doctors.

“One thing is very clear, the techniques that we developed can be used for an enormous amount of good affecting hundreds of millions of people,” Hinton said.

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Lamborghini’s latest Huracán is a supercar with a supercomputer

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Over the past few decades, technology has made vehicles safer and easier to drive. Anti-lock brakes, traction control, torque vectoring and other bits of tech keep cars on the road instead of flying into a ditch when things get hairy. It’s why newer cars typically handle corners better than older cars.

At Lamborghini, they’ve taken things further with their new Lamborghini Dinamica Veicolo Integrata or LDVI system. The Engine Control Unit (ECU) takes data from the entire car and uses it to adjust how the new Huracán EVO Spyder drives in real time (actually in less than 20 milliseconds. But that’s about as close as you can get to real time). Cars have been doing some form of this for a while but the Italian automaker needs to be able to do this at incredible speeds and in environments your typical sedan or SUV doesn’t encounter.

At Lamborghini, they’ve taken things further with their new Lamborghini Dinamica Veicolo Integrata or LDVI system. The Engine Control Unit (ECU) takes data from the entire car and uses it to adjust how the new Huracán EVO Spyder drives in real time (actually in less than 20 milliseconds. But that’s about as close as you can get to real time). Cars have been doing some form of this for a while but the Italian automaker needs to be able to do this at incredible speeds and in environments your typical sedan or SUV doesn’t encounter.

With this technology, Lamborghini is able to take the raw power of an all-wheel-drive supercar with a V10 engine and 630 horsepower and tame it, just enough, so your average driver (who can shell out $287,400) can enjoy themselves behind the wheel of the all-wheel-steering vehicle without, you know, flying into a ditch.

To achieve this, the LVDI is actually a super fast central processing unit that takes in data about the road surface, the car’s setup, the tires and how the driver is driving the vehicle. It then uses that info to control various aspects of the Huracan.

The system works in concert with the Lamborghini Piattaforma Inerziale (LPI) version 2.0 hardware sensors. This system uses gyroscopes and accelerometers located at the car’s center of gravity. It measures the vehicle’s movements and shares that data with the LVDI computer.

Lamborghini says the system is so in tune with all aspects of a drive that it can actually predict the best driving setup for the next moment. In other words, if you’re behind the wheel flying around corners on a back road, the system will recognize your behavior as you enter a corner and adjust itself.

“Where it’s possible to do a bigger jump in the future is with the intelligent use of four-wheel drive and four-wheel steering and the movement and control of the torque wheel by wheel in a way that can be more predictable and that is what we have with the Huracan EVO,” said Maurizio Reggiani, chief technology officer of Automobili Lamborghini.

Lamborghini is thinking about a world beyond a completely gas-powered engine though — it has a pipeline for hybrid and electric vehicles. But Reggiani notes that Lamborghini will probably be the last automaker to leave behind a large growling power plant.

Putting all that power to the ground in a controllable way requires an incredible amount of technology — that’s where LVDI and other pieces of technology come in. The automaker believes the result is a driving experience that matches exactly what the driver wants, regardless of the mode the car is in. Whether it be Strada, Sport, or the track ready Corsa, the vehicle (in a controlled way) should deliver.

That control allows a driver to do something that typically takes months if not years to master: drifting. It goes against what the car wants to do — lose traction. But in Sport mode it’s possible. To do that, the vehicle has to figure out (in real time and safely) things like what angle it wants to slide. The Huracán EVO Spyder has to understand that you want to drift and not fight that. If it does, it will jerk the car (and driver) back into alignment.

Lamborghini Huracan EVO Spyder

To relive your Fast and Furious dreams, the automaker started where lots of companies start with new technology: In the simulator. But a computer can’t faithfully reproduce the real world. Mostly that has to do with tires, a variable that’s tough to predict because of the density of the rubber’s compound and its wear.

Then, of course, there’s the driver. We all drive differently but the experience must be the same for everyone. It’s important that even with all that technology, it’s still a driving experience. “We don’t want to have something that substitutes the driver. We want to have a car that is able to understand what the driver wants to do,” Reggiani said.

Lamborghini is known for large engines, intense growls, striking design and bank-busting prices. But the reality is all that power would be useless if drivers couldn’t actually control the car. The automaker’s latest system makes that possible for everyone. Sure, only a select few can own a Lamborghini, but everyone can appreciate a system that makes driving safer while simultaneously more fun.

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This device makes it easy for the elderly to stay in touch with their loved ones

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Only 20 percent of over-75s in the UK have a smartphone compared to 95 percent of 16-to-24-year-olds. Digital technologies change fast, become obsolete quickly and usually need you to spend a bit of time learning how to use them.

This helps explain why most older adults tend to use what they know best when it comes to communicating, which usually means a phone call via a landline or basic mobile, instead of a quick text or social media update.

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But it doesn’t have to be this way. My colleague Massimo Micocci and I have recently designed a more modern device we hope will help older people stay in more frequent touch with instant updates, but that has a familiar feel to it. By drawing on smart materials and what we call “design metaphors”, we hope to make new technology more accessible.

When older people don’t have access to instant messaging, a phone call or a visit may be the only way for friends and family to check their loved ones are well. And doing so more than (or even) once a day might not be feasible or wanted.

Similarly, older people might feel that ringing their relatives morning and night just to let them know they’re OK would be an inconvenience. And while you can buy specialized monitoring devices that record people’s movements around their home, these often feel like an invasion of privacy.

With this in mind, we developed something that lets older people broadcast their status to their families like a social media update. Our device (which is designed for research purposes rather than commercial development) looks like an analogue radio. But it lets users transmit information about their activity captured from a wearable heartbeat sensor in a way that is entertaining and intuitive, and only shared with selected group of followers.

The keep-in-touch. Author provided

The information includes how energetic their current activity is, for example whether they are conducting an active task such as gardening, or a relaxing and restful one such as reading a book.

By designing the device to evoke technology with which people will feel instantly familiar, we’re using the principle of design metaphor. Most people find it easier to interact with devices that resemble products they have already used.

In cognitive psychology, this is known as inferential learning, referring to when someone applies established knowledge in their brain to a new context. The design of our “radio” device makes it easier for users to work out how to use it, based on their previous interactions with traditional radios – even though it has a very different function.

Giving users control

There are plenty of systems that enable people to monitor older family members. But usually these are fully passive, where the older adults are observed directly through cameras and sensors around their homes. Or they are fully active, for example mobile phones that require the older adults to stop what they’re doing and respond right away.

Instead, our device lets people choose the level of communication they want. It runs in the background and doesn’t transmit detailed information such as images of people in their homes. This makes it a much less intrusive way of letting someone know you’re OK.

We also wanted to make the device very easy to understand, interpret and remember. So rather than having an information screen that showed text or images, we wanted to create a display that used so-called smart materials to convey what the user was doing.

In this context, smart materials are those that can change color, shape, viscosity or how much light they emit. Our research showed that light-emitting materials were the best way of conveying messages without words for both under and over-60s.

The “radio” is just a research prototype but it has allowed us to understand that the combination of innovative materials and familiar artefacts can be a successful way to encourage aging users to adopt new technologies. In this way, smart materials and design metaphors could help bridge the digital gap and promote innovation among older consumers.

This article is republished from The Conversation by Gabriella Spinelli, Reader in Design Innovation, Brunel University London under a Creative Commons license. Read the original article.

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