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#431559 Drug Discovery AI to Scour a Universe of ...

On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe.
The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.
Yes, this includes wow-the-stars-are-freaking-amazing-tonight drugs, but also the kinds of drugs that make us well again when we’re sick. The number of possible organic compounds with “drug-like” properties dwarfs the number of stars in the universe by over 30 orders of magnitude.
Next to this multiverse of possibility, the chemical configurations scientists have made into actual medicines are like the smattering of stars you’d glimpse downtown.
But for good reason.
Exploring all that potential drug-space is as humanly impossible as exploring all of physical space, and even if we could, most of what we’d find wouldn’t fit our purposes. Still, the idea that wonder drugs must surely lurk amid the multitudes is too tantalizing to ignore.
Which is why, Alex Zhavoronkov said at Singularity University’s Exponential Medicine in San Diego last week, we should use artificial intelligence to do more of the legwork and speed discovery. This, he said, could be one of the next big medical applications for AI.
Dogs, Diagnosis, and Drugs
Zhavoronkov is CEO of Insilico Medicine and CSO of the Biogerontology Research Foundation. Insilico is one of a number of AI startups aiming to accelerate drug discovery with AI.
In recent years, Zhavoronkov said, the now-famous machine learning technique, deep learning, has made progress on a number of fronts. Algorithms that can teach themselves to play games—like DeepMind’s AlphaGo Zero or Carnegie Mellon’s poker playing AI—are perhaps the most headline-grabbing of the bunch. But pattern recognition was the thing that kicked deep learning into overdrive early on, when machine learning algorithms went from struggling to tell dogs and cats apart to outperforming their peers and then their makers in quick succession.
[Watch this video for an AI update from Neil Jacobstein, chair of Artificial Intelligence and Robotics at Singularity University.]

In medicine, deep learning algorithms trained on databases of medical images can spot life-threatening disease with equal or greater accuracy than human professionals. There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease. And, as Zhavoronkov noted, with more applications and a longer track record that trust is coming.
“Tesla is already putting cars on the street,” Zhavoronkov said. “Three-year, four-year-old technology is already carrying passengers from point A to point B, at 100 miles an hour, and one mistake and you’re dead. But people are trusting their lives to this technology.”
“So, why don’t we do it in pharma?”
Trial and Error and Try Again
AI wouldn’t drive the car in pharmaceutical research. It’d be an assistant that, when paired with a chemist or two, could fast-track discovery by screening more possibilities for better candidates.
There’s plenty of room to make things more efficient, according to Zhavoronkov.
Drug discovery is arduous and expensive. Chemists sift tens of thousands of candidate compounds for the most promising to synthesize. Of these, a handful will go on to further research, fewer will make it to human clinical trials, and a fraction of those will be approved.
The whole process can take many years and cost hundreds of millions of dollars.
This is a big data problem if ever there was one, and deep learning thrives on big data. Early applications have shown their worth unearthing subtle patterns in huge training databases. Although drug-makers already use software to sift compounds, such software requires explicit rules written by chemists. AI’s allure is its ability to learn and improve on its own.
“There are two strategies for AI-driven innovation in pharma to ensure you get better molecules and much faster approvals,” Zhavoronkov said. “One is looking for the needle in the haystack, and another one is creating a new needle.”
To find the needle in the haystack, algorithms are trained on large databases of molecules. Then they go looking for molecules with attractive properties. But creating a new needle? That’s a possibility enabled by the generative adversarial networks Zhavoronkov specializes in.
Such algorithms pit two neural networks against each other. One generates meaningful output while the other judges whether this output is true or false, Zhavoronkov said. Together, the networks generate new objects like text, images, or in this case, molecular structures.
“We started employing this particular technology to make deep neural networks imagine new molecules, to make it perfect right from the start. So, to come up with really perfect needles,” Zhavoronkov said. “[You] can essentially go to this [generative adversarial network] and ask it to create molecules that inhibit protein X at concentration Y, with the highest viability, specific characteristics, and minimal side effects.”
Zhavoronkov believes AI can find or fabricate more needles from the array of molecular possibilities, freeing human chemists to focus on synthesizing only the most promising. If it works, he hopes we can increase hits, minimize misses, and generally speed the process up.
Proof’s in the Pudding
Insilico isn’t alone on its drug-discovery quest, nor is it a brand new area of interest.
Last year, a Harvard group published a paper on an AI that similarly suggests drug candidates. The software trained on 250,000 drug-like molecules and used its experience to generate new molecules that blended existing drugs and made suggestions based on desired properties.
An MIT Technology Review article on the subject highlighted a few of the challenges such systems may still face. The results returned aren’t always meaningful or easy to synthesize in the lab, and the quality of these results, as always, is only as good as the data dined upon.
Stanford chemistry professor and Andreesen Horowitz partner, Vijay Pande, said that images, speech, and text—three of the areas deep learning’s made quick strides in—have better, cleaner data. Chemical data, on the other hand, is still being optimized for deep learning. Also, while there are public databases, much data still lives behind closed doors at private companies.
To overcome the challenges and prove their worth, Zhavoronkov said, his company is very focused on validating the tech. But this year, skepticism in the pharmaceutical industry seems to be easing into interest and investment.
AI drug discovery startup Exscientia inked a deal with Sanofi for $280 million and GlaxoSmithKline for $42 million. Insilico is also partnering with GlaxoSmithKline, and Numerate is working with Takeda Pharmaceutical. Even Google may jump in. According to an article in Nature outlining the field, the firm’s deep learning project, Google Brain, is growing its biosciences team, and industry watchers wouldn’t be surprised to see them target drug discovery.
With AI and the hardware running it advancing rapidly, the greatest potential may yet be ahead. Perhaps, one day, all 1060 molecules in drug-space will be at our disposal. “You should take all the data you have, build n new models, and search as much of that 1060 as possible” before every decision you make, Brandon Allgood, CTO at Numerate, told Nature.
Today’s projects need to live up to their promises, of course, but Zhavoronkov believes AI will have a big impact in the coming years, and now’s the time to integrate it. “If you are working for a pharma company, and you’re still thinking, ‘Okay, where is the proof?’ Once there is a proof, and once you can see it to believe it—it’s going to be too late,” he said.
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#431553 This Week’s Awesome Stories From ...

ROBOTS
Boston Dynamics’ Atlas Robot Does Backflips Now and It’s Full-Tilt InsaneMatt Simon | Wired “To be clear: Humanoids aren’t supposed to be able to do this. It’s extremely difficult to make a bipedal robot that can move effectively, much less kick off a tumbling routine.”

TRANSPORTATION
This Is the Tesla Semi TruckZac Estrada | The Verge“What Tesla has done today is shown that it wants to invigorate a segment, rather than just make something to comply with more stringent emissions regulations… And in the process, it’s trying to do for heavy-duty commercial vehicles what it did for luxury cars—plough forward in its own lane.”
PRIVACY AND SECURITY
Should Facebook Notify Readers When They’ve Been Fed Disinformation?Austin Carr | Fast Company “It would be, Reed suggested, the social network equivalent of a newspaper correction—only one that, with the tech companies’ expansive data, could actually reach its intended audience, like, say, the 250,000-plus Facebook users who shared the debunked YourNewsWire.com story.”
BRAIN HEALTH
Brain Implant Boosts Memory for First Time EverKristin Houser | NBC News “Once implanted in the volunteers, Song’s device could collect data on their brain activity during tests designed to stimulate either short-term memory or working memory. The researchers then determined the pattern associated with optimal memory performance and used the device’s electrodes to stimulate the brain following that pattern during later tests.”
COMPUTING
Yale Professors Race Google and IBM to the First Quantum ComputerCade Metz | New York Times “Though Quantum Circuits is using the same quantum method as its bigger competitors, Mr. Schoelkopf argued that his company has an edge because it is tackling the problem differently. Rather than building one large quantum machine, it is constructing a series of tiny machines that can be networked together. He said this will make it easier to correct errors in quantum calculations—one of the main difficulties in building one of these complex machines.”
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#431414 This Week’s Awesome Stories From ...

QUANTUM COMPUTING IBM Raises the Bar With a 50-Qubit Quantum ComputerWill Knight | MIT Technology Review “50 qubits is a significant landmark in progress toward practical quantum computers. Other systems built so far have had limited capabilities and could perform only calculations that could also be done on a conventional supercomputer. A 50-qubit machine can do things that are extremely difficult to simulate without quantum technology.”
ARTIFICIAL INTELLIGENCE AI Startup Embodied Intelligence Wants Robots to Learn From Humans in Virtual RealityEvan Ackerman | IEEE Spectrum “This is a defining problem for robotics right now: Robots can do anything you want, as long as you tell them exactly what that is, every single time… This week, Abbeel and several of his colleagues from UC Berkeley and OpenAI are announcing a new startup (with US $7 million in seed funding) called Embodied Intelligence, which will ‘enable industrial robot arms to perceive and act like humans instead of just strictly following pre-programmed trajectories.’”
TRANSPORTATION Uber’s Plan to Launch Flying Cars in LA by 2020 Really Could Take OffJack Stewart | Wired“After grabbing an elevator, passengers will tap their phones to pass through a turnstile and access the roof. Presumably they’ve been prescreened, because there’s no airport-style security in evidence. An agent in an orange vest takes a group of four passengers out to the waiting aircraft. There’s a pilot up front, and a small overhead display with the estimated arrival time.”
ROBOTICS This Robot Swarm Finishes Your Grocery Shopping in MinutesJesus Diaz | Fast Company “At an Ocado warehouse in the English town of Andover, a swarm of 1,000 robots races over a grid the size of a soccer field, filling orders and replacing stock. The new system, which went live earlier this year, can fulfill a 50-item order in under five minutes—something that used to take about two hours at human-only facilities. It’s been so successful that Ocado is now building a new warehouse that’s three times larger in Erith, southeast of London.”
BIOTECH Meet the Scientists Building a Library of Designer DrugsAngela Chen | The Verge“One of the most prominent categories of designer drugs are those intended to mimic marijuana, called synthetic cannabinoids. Marijuana, or cannabis, is widely considered one of the safest drugs, but synthetic cannabinoids are some of the most dangerous synthetic drugs.”
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#431389 Tech Is Becoming Emotionally ...

Many people get frustrated with technology when it malfunctions or is counterintuitive. The last thing people might expect is for that same technology to pick up on their emotions and engage with them differently as a result.
All of that is now changing. Computers are increasingly able to figure out what we’re feeling—and it’s big business.
A recent report predicts that the global affective computing market will grow from $12.2 billion in 2016 to $53.98 billion by 2021. The report by research and consultancy firm MarketsandMarkets observed that enabling technologies have already been adopted in a wide range of industries and noted a rising demand for facial feature extraction software.
Affective computing is also referred to as emotion AI or artificial emotional intelligence. Although many people are still unfamiliar with the category, researchers in academia have already discovered a multitude of uses for it.
At the University of Tokyo, Professor Toshihiko Yamasaki decided to develop a machine learning system that evaluates the quality of TED Talk videos. Of course, a TED Talk is only considered to be good if it resonates with a human audience. On the surface, this would seem too qualitatively abstract for computer analysis. But Yamasaki wanted his system to watch videos of presentations and predict user impressions. Could a machine learning system accurately evaluate the emotional persuasiveness of a speaker?
Yamasaki and his colleagues came up with a method that analyzed correlations and “multimodal features including linguistic as well as acoustic features” in a dataset of 1,646 TED Talk videos. The experiment was successful. The method obtained “a statistically significant macro-average accuracy of 93.3 percent, outperforming several competitive baseline methods.”
A machine was able to predict whether or not a person would emotionally connect with other people. In their report, the authors noted that these findings could be used for recommendation purposes and also as feedback to the presenters, in order to improve the quality of their public presentation. However, the usefulness of affective computing goes far beyond the way people present content. It may also transform the way they learn it.
Researchers from North Carolina State University explored the connection between students’ affective states and their ability to learn. Their software was able to accurately predict the effectiveness of online tutoring sessions by analyzing the facial expressions of participating students. The software tracked fine-grained facial movements such as eyebrow raising, eyelid tightening, and mouth dimpling to determine engagement, frustration, and learning. The authors concluded that “analysis of facial expressions has great potential for educational data mining.”
This type of technology is increasingly being used within the private sector. Affectiva is a Boston-based company that makes emotion recognition software. When asked to comment on this emerging technology, Gabi Zijderveld, chief marketing officer at Affectiva, explained in an interview for this article, “Our software measures facial expressions of emotion. So basically all you need is our software running and then access to a camera so you can basically record a face and analyze it. We can do that in real time or we can do this by looking at a video and then analyzing data and sending it back to folks.”
The technology has particular relevance for the advertising industry.
Zijderveld said, “We have products that allow you to measure how consumers or viewers respond to digital content…you could have a number of people looking at an ad, you measure their emotional response so you aggregate the data and it gives you insight into how well your content is performing. And then you can adapt and adjust accordingly.”
Zijderveld explained that this is the first market where the company got traction. However, they have since packaged up their core technology in software development kits or SDKs. This allows other companies to integrate emotion detection into whatever they are building.
By licensing its technology to others, Affectiva is now rapidly expanding into a wide variety of markets, including gaming, education, robotics, and healthcare. The core technology is also used in human resources for the purposes of video recruitment. The software analyzes the emotional responses of interviewees, and that data is factored into hiring decisions.
Richard Yonck is founder and president of Intelligent Future Consulting and the author of a book about our relationship with technology. “One area I discuss in Heart of the Machine is the idea of an emotional economy that will arise as an ecosystem of emotionally aware businesses, systems, and services are developed. This will rapidly expand into a multi-billion-dollar industry, leading to an infrastructure that will be both emotionally responsive and potentially exploitive at personal, commercial, and political levels,” said Yonck, in an interview for this article.
According to Yonck, these emotionally-aware systems will “better anticipate needs, improve efficiency, and reduce stress and misunderstandings.”
Affectiva is uniquely positioned to profit from this “emotional economy.” The company has already created the world’s largest emotion database. “We’ve analyzed a little bit over 4.7 million faces in 75 countries,” said Zijderveld. “This is data first and foremost, it’s data gathered with consent. So everyone has opted in to have their faces analyzed.”
The vastness of that database is essential for deep learning approaches. The software would be inaccurate if the data was inadequate. According to Zijderveld, “If you don’t have massive amounts of data of people of all ages, genders, and ethnicities, then your algorithms are going to be pretty biased.”
This massive database has already revealed cultural insights into how people express emotion. Zijderveld explained, “Obviously everyone knows that women are more expressive than men. But our data confirms that, but not only that, it can also show that women smile longer. They tend to smile more often. There’s also regional differences.”
Yonck believes that affective computing will inspire unimaginable forms of innovation and that change will happen at a fast pace.
He explained, “As businesses, software, systems, and services develop, they’ll support and make possible all sorts of other emotionally aware technologies that couldn’t previously exist. This leads to a spiral of increasingly sophisticated products, just as happened in the early days of computing.”
Those who are curious about affective technology will soon be able to interact with it.
Hubble Connected unveiled the Hubble Hugo at multiple trade shows this year. Hugo is billed as “the world’s first smart camera,” with emotion AI video analytics powered by Affectiva. The product can identify individuals, figure out how they’re feeling, receive voice commands, video monitor your home, and act as a photographer and videographer of events. Media can then be transmitted to the cloud. The company’s website describes Hugo as “a fun pal to have in the house.”
Although he sees the potential for improved efficiencies and expanding markets, Richard Yonck cautions that AI technology is not without its pitfalls.
“It’s critical that we understand we are headed into very unknown territory as we develop these systems, creating problems unlike any we’ve faced before,” said Yonck. “We should put our focus on ensuring AI develops in a way that represents our human values and ideals.”
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#431158 This AI Assistant Helps Demystify ...

In an interview at Singularity University’s Global Summit in San Francisco, Anita Schjøll Brede talked about how artificial intelligence can help make scientific research accessible to anyone working on a complex problem.
Anita Schjøll Brede is the CEO and co-founder of Iris AI, a startup that’s building an artificially intelligent research assistant, which was recently named one of the most innovative AI startups of 2017 by Fast Company. Schjøll Brede is also faculty at Singularity University Denmark and a 2015 alumni of the Global Solutions Program.
“Ultimately, we’re building an AI that can read, understand, and connect the dots,” Schjøll Brede said. “But zooming that back into today, we’re building a tool for R&D, research institutions, and entrepreneurs who have big hairy problems to solve and need to apply research and science to solve them. We’re semi-automating the process of mapping out what you should read to solve the problem or to see what research you need to do to solve the problem.”
Watch the interview for more on Iris AI’s technology and to hear Schjøll Brede’s take on whether AI researchers share a moral responsibility for the systems they build.

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