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  • Big data AI startup Noble.AI raises a second seed round from a chemical giant
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    Noble.AI, an SF/French AI company that claims to accelerate decision making in R&D, has raised a new round of funding from Solvay Ventures, the VC arm of a large chemical company, Solvay SA. Although the round was undisclosed, TechCrunch understands it to be a second seed round, and we know the company has closed a total of $8.6 million to date. Solvay was previously an early customer of the platform, prior to this investment. The joint announcement was made at the Hello Tomorrow conference in Paris this week. As a chemical company, Solvay’s research arm generates huge volumes of data from various sources, which is part of the reason for the investment, confirmed the firm. Noble.AI’s “Universal Ingestion Engine” and “Intelligent Recommendation Engine” claim to enable the creation of high-quality data assets for these kinds of big data sets that can later be turned into recommendations for decision making inside these large businesses. Founder and CEO of Noble.AI, Dr. Matthew C. Levy, said he is “enthusiastic to see what unfolds in its next phase, tackling the most important and high-value problems in chemistry” via the partnership with Solvay. “Noble.AI has the potential to be a real game changer for Solvay in the way it enables us to utilize data from our 150-year history with new AI tools, resulting in a unique lever to accelerate our innovation,” said Stéphane Roussel, Solvay Ventures’ managing director. Prime Movers led a seed round in Noble.AI in late 2018, which was never previously disclosed to the press. Solvay Ventures is now leading this second seed round. The move comes in the context of booming corporate R&D spending, which in 2018 reached $782 billion among the top 1,000 companies, representing a 14 percent increase relative to 2017 and the largest figure deployed to R&D ever. However, R&D in corporates lags behind the startup world, so these strategic investments seem to be picking up pace. Source: TechCrunch Read more »
  • 3 Things That Will Help You Leverage AI
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    AI is the transformative technology of tomorrow, but leaders need to get it up and running today. Here's how.   If artificial intelligence isn't at the top of your priority list, it should be. Deloitte's "Tech Trends 2019: Beyond the digital frontier" report shows AI topping the list of tech trends that CIOs are eager to invest in. Deloitte predicts that the next two years will see a growing number of companies transition certain functions, such as insurance claim processing, to fully autonomous operations backed by AI. Terms like "cognitive technologies" and "machine learning" have become buzzwords, but these trends will strengthen--particularly as these systems begin to harness the scads of data available from which they can extract insights.   But AI's promise is more general than just data mining. Lu Zhang, founder and managing partner at Fusion Fund, describes the technology as applicable to a broad swath of commerce: "AI's application space has developed. The AI market has great potential across various industry verticals such as manufacturing, retail, healthcare, agriculture, and education." Even with this potential of AI for business, many business leaders feel held back from taking the actions necessary to implement it at their companies. So let's take a look at some things you can do now to overcome those barriers. 1. Get your C-suite on board the AI train. Any change is hard to create when the top of the organization is not fully on board. IDC found that 49 percent of enterprises surveyed cited problems related to stakeholders' reluctance to buy in as a barrier to AI adoption. The first step in setting up AI at your company is to make sure the members of the C-suite understand the value--particularly in the long term--that AI can bring.   The evidence is out there: An Accenture report predicts that AI could increase productivity by up to 40 percent by 2035. And when dealing with data, AI really shines, enabling exciting new opportunities to discover valuable business insights. For instance, a McKinsey Global Institute analysis found when AI combines demographic and past transaction data with information gleaned from social media monitoring, the resulting personalized product recommendations can lead to a doubling of the sales conversion rate. Aside from providing your company's leadership team industry data proving AI's worth, it's imperative to also show them evidence of the value for your business specifically. You can do this by implementing a small AI project, such as using a chatbot to help answer customer questions online. After seeing the success of one AI use case, your C-suite is more likely to be ready for further AI-driven digital transformations. 2. Pack quality data onto the train's cargo car. Of course, AI can only create value from data if you have data--and not just any data, but good data. Despite the world generating incomprehensible volumes of data every minute, 23 percent of respondents to a Vanson Bourne/Teradata survey of senior IT and business leaders reported that C-suite executives aren't using data to inform their decisions. Data has to be relevant to a company's business model, and sometimes the systems are not in place to capture the data business leaders need.   Nor is it just a matter of access to relevant data; data quality is critical as well. Data that contains many factual errors or omissions need to be cleaned before it can be fed to AI algorithms so that the insights derived from the data set reflect reality and not just data noise. To prepare your data beforehand, have your team scan it for missing or incomplete records, empty cells and misplaced characters, and data that's entered in a different format from everything else--any or all of which could throw off your algorithms. There are machine learning platforms with tools to help your team with this task, such as the DataRobot platform, which uses tools like Trifacta to facilitate the data prep process. 3. Hire--or train--the right crew members. Finally, make sure your team members have what it takes to launch your new AI initiative and keep it aligned with best practices. You'll want to put together a team that includes roles such as a systems architect, data engineer and/or data scientist, and a business analyst, among possible others. The team should be focused on creating scalable solutions that take advantage of the latest approaches in the fields of machine learning, deep learning, big data, SQL and NoSQL databases, and other areas of active development. Not that assembling such a team will be easy: the Vanson Bourne/Teradata survey found about a third of respondents cited talent as the bottleneck to advancing their AI plans. That isn't surprising given there may be only about 3,000 AI professionals who are actively seeking jobs--against about 10,000 available jobs in this country alone.   So if you're struggling to find people trained and experienced in working with AI, train members of your current team to fill that talent gap. Your developers can take advantage of Microsoft Professional Program for Artificial Intelligence, a program the software giant has made available to IT professionals who want to develop skills in AI and data science. And don't neglect the non-IT members of your team. A former AI leader at Google offers online AI training through Coursera that aims to give businesspeople a foundational knowledge of pattern recognition and machine learning. Spreading the AI savvy throughout your organization can only aid your efforts to put this groundbreaking technology to the best use. Companies that get on board with AI now will be at a critical advantage this time next year. Don't delay further--fire up your executives' enthusiasm, find your best data sets and use cases, and start putting together a first-rate team of AI experts. Done right, AI can be a complete game changer for many companies, from enterprises to small businesses. Source: Inc.com Read more »
  • 3 ways AI is already changing medicine
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    When Dr. Eric Topol joined an experiment on using artificial intelligence to get personalized nutrition advice, he was hopeful. For two weeks, Topol, a cardiologist at Scripps Research, dutifully tracked everything he ate, wore a sensor to monitor his blood-glucose levels, and even collected and mailed off a stool sample for an analysis of his gut microbiome. The diet advice he got back stunned him: Eat Bratwurst, nuts, danishes, strawberries, and cheesecake. Stay away from oatmeal, melon, whole-wheat fig bars, veggie burgers, and grapefruit. “It was crazy stuff,” Topol told me. Bratwurst and cheesecake are foods Topol generally shirks because he considers them “unhealthy.” And strawberries can actually be dangerous for Topol: He’s had kidney stones and has to avoid foods, such as berries, that are high in calcium oxalate, a chemical that can turn into stones. All in all, Topol discovered that most of the companies currently marketing personalized diets can’t actually deliver. It’s just one of the great insights in his new book about artificial intelligence, Deep Medicine. AI for diet is one of the most hyped applications of the technology. But in the book Topol uncovers more promising opportunities for artificial intelligence to improve health — some of which surprised me. He also challenges the most common narrative about AI in health: that radiologists will soon be replaced by machines. Instead of robots coming into medicine and further eroding what’s left of the doctor-patient relationship, Topol argues, AI may actually enhance it. I’ve boiled down three of Topol’s most surprising findings, after reading the book and talking with him. 1) AI for your eyes and colon Diagnosing disease is a notoriously difficult task, and doctors don’t always get it right — which is why there’s been a lot of excitement around the idea that AI might make the task both easier and more precise. But as the quest to create a medical tricorder — a portable device capable of diagnosing diseases in humans — continues, there’ve been serious developments in automating diagnostics, and even triage, in several pretty specific areas of medicine. Take ophthalmology. The top cause of loss of vision in adults worldwide is diabetic retinopathy, a condition that affects about a third of people with diabetes in the US. Patients should be screened for the condition, but that doesn’t always happen, which can delay sometimes diagnosis and treatment — and lead to more vision loss. Researchers at Google developed a deep learning algorithm that can automatically detect the condition with a great deal of accuracy, Topol found. According to one paper, the software had a sensitivity score of 87 to 90 percent and 98 percent specificity for detecting diabetic retinopathy, which they defined as “moderate or worse diabetic retinopathy or referable macular edema by the majority decision of a panel of at least seven US board-certified ophthalmologists.” Doctors at Moorfields Eye Hospital in London took that work a step further. They trained an algorithm that could recommend the correct treatment approach for more than 50 eye diseases with 94 percent accuracy. “They compared that to eye specialists, and the machine didn’t miss one referral, but the eye doctors did,” Topol said. “The eye doctors were only in agreement about the referrals 65 percent of the time. So that’s the beginning of moving from narrow AI to triage.” In another example, doctors in China used AI to diagnose polyps on the colon during a colonoscopy. In one arm of the randomized trial, the diagnosis was made by AI plus the gastroenterologist. In another arm, just the specialist made the diagnosis. The AI system significantly increased polyp detection (29 percent compared to 20 percent). And this was mainly because AI spotted what are known as “diminutive adenomas,” or tiny polyps — less than 5 mm in size — that are notoriously easy for doctors to miss. “Machine vision is starting to improve,” Topol said. And while we’re far from having a hand-held machine that can diagnose any disease, these small steps will probably eventually lead there, he added. 2) Avatars to help anxiety and depression When we talk about the impact of computers and the internet on our mental health, we often talk about the negative: that they can be alienating, isolating, anxiety-provoking. Yet Topol found good evidence of just the opposite: They can be comforting in some cases. In one elegant experiment, researchers at USC tested whether people would be willing to reveal their innermost secrets to an avatar named Ellie as compared to another human. “The shocking result — it wasn’t even a contest,” said Topol. “People far more readily would tell an avatar their deepest secret.” That experiment has since been replicated, and researchers are finding chat bots and avatars also seem to help people with symptoms of anxiety and depression. “It’s an interesting finding in the modern era,” said Topol. “I don’t think it would have been predicted. It’s like going to confession — you’re laying it out there and you feel a catharsis.” So why is this so important? “Some think it’s a breakthrough. Others are skeptical it’ll help. But there’s such an absurd mismatch between what we need to support people’s mental health conditions and what’s available,” Topol said. “So if this does work — and it looks promising — this could be a vital step forward to helping [more] people.” 3) AI could free up time for doctors As the average doctor appointment time has dwindled to a few minutes, so too has any intimacy or sense of connection that can develop between doctors and patients. Topol went into the book thinking AI — and bringing more machines into hospitals and clinics — might further dampen the human side of medicine. But by the end of his research, he ended up seeing a big opportunity: “I realized that as you can augment human performance at both the clinician level and the patient level, at a scale that is unprecedented, you can make time grow.” And giving more time to doctors, could, in theory mean the intimacy can come back. To “make time grow,” Topol said, AI can help with time-consuming tasks, like note-taking by voice. Notes can then be archived for patients to review — and a correction function could be built into the process so patients can flag any errors in their records. “These are all features that can enhance the humanistic encounter we’ve lost over time,” Topol said. AI can also free up time for specialists to meet with patients. Topol argues in the book that instead of AI replacing radiologists — widely viewed as the medical specialists most at risk of becoming extinct — AI will enhance them. “The average radiologist today reads between 50 and 100 films in a day. There’s a significant error rate and a third of radiologists at some point in their career get sued for malpractice,” he said. Enter deep learning. “You then have an amazing ability to scale where a radiologist could read 10 times as many films or 100 times as many films. But is that what we want? Or do want to use that capability [so radiologists] can start talking to patients, come out of the basement and review the results, sharing an expertise which they never otherwise get to.” So AI could liberate doctors in a tech-heavy specialty, like radiology, to help patients through a diagnosis — something that doesn’t happen now. Two big hurdles Topol is certainly an optimist about the power of AI to make things better — even about personalized diets. “Our health is not just absence of disease. It’s about the prevention of disease,” he told Vox. “And if we can use food as a medicine to help us prevent illness, that would be terrific. We’ll get there someday.” But you might still be skeptical — that’s fair. The health care system has been abysmal at doing the very basics of incorporating new technology into medical practice, like digitizing medical records. And Topol makes clear in the book that many of these promising technologies, like avatars for mental health or AI for colonoscopies, need to be further validated and refined in clinical studies, and followed up with as they move beyond the study phase and into the real world. To get there, there are also the privacy and data hurdles to contend with, which could make or break technologies like the avatar shrink. Machine learning is best when lots of data is fed into an algorithm — the more data, the better. “If we’re going to do deep learning and provide feedback, the only way it’ll work well is if we have all a person’s data: sensor data, genome data, microbiome data, [medical records]. It’s a long list.” But “people don’t have their [personal] data today in this country,” Topol said. “They can’t get all their medical records for every time they’ve been to a doctor or hospital. We’d want each person to have all their data from when they’re still in their moms’ womb.” Topol has some ideas for how to fix this too. US policymakers need to move in step with countries like Estonia, which found a way to allow people full control of their personal, including medical, data. Empowering people with their data could also help with security. Our data right now is stored on massive servers and clouds. “The gurus say the best chance of data being secure and maintained privately is to store it in the smallest units possible,” Topol said. “It’ll help guide your health in the times ahead.” Source: Vox Read more »
  • Why AI will make healthcare personal
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    For generations healthcare has been episodic – someone gets sick or breaks a bone, they see a doctor, and then they might not see another one until the next time they get sick or injured. Now, as emerging technologies such as artificial intelligence open up new possibilities for the healthcare industry in the Fourth Industrial Revolution, policymakers and practitioners are developing new ways to deliver continuous healthcare for better outcomes. Consumers already expect access to healthcare providers to be as smart and easy as online banking, retrieving boarding passes and making restaurant reservations, according to Kaiser Permanente CEO Bernard J Tyson. Nearly three-quarters of Americans with health insurance (72%), for example, say it’s important that their health insurance provider uses modern communication tools, such as instant message and two-way video. Innovative healthcare organizations such as Kaiser Permanente are listening. The company is not only looking at how they harness technology now, to provide patients with better access to care, but how it can be used in the future to diagnose and treat chronic disease early, so people have a better chance of leading longer and healthier lives. A future where personal digital healthcare assistants monitor every aspect of our health and well-being, and screening for and treating disease is tailored to our DNA, isn’t science-fiction. It’s getting closer every day. Achieving the dream of personalized healthcare for everyone won’t be without challenges. Not only will healthcare providers have to develop and adopt new technology, they will also have to collect, aggregate and share the vast amounts of patient data, and organize it into a usable form to train the AI systems to make intelligent diagnoses, advice and predictions. They will also have to address the very real privacy concerns raised by high-profile cases such as Google's acquisition of DeepMind Health, which may see the tech giant get access to 1.6 million National Health Service patients’ data in the UK. Digital assistants to provide a 24/7 helping hand Already, digital assistants such as Amazon’s Alexa and Apple’s Siri are using AI to handle routine tasks, from making restaurant reservations to scheduling meetings and returning phone calls. The digital assistants of the future will be full-time healthcare companions, able to monitor a patient’s condition, transmit results to healthcare providers, and arrange virtual and face-to-face appointments. They will help manage the frequency and dosage of medication, and provide reliable medical advice around the clock. They will remind doctors of patients’ details, ranging from previous illnesses to past drug reactions. And they will assist older people to access the care they need as they age, including hospice care, and help to mitigate the fear and loneliness many elderly people feel. Precision medicine to personalize treatment AI is also the driving force behind precision medicine, which uses information about a person’s environment, lifestyle and biology, including in their DNA, to diagnose and treat diseases. By analyzing a patient’s information, doctors are able to prescribe the treatments that are most likely to be effective, as well as minimize drug reactions and unwanted side effects. As the World Economic Forum’s head of precision medicine, Genya Dana, says, it’s “the right treatment for the right person at the right time.” Collecting the genetic information needed for precision medicine is already becoming easier and less expensive. It’s now possible to have your entire genome sequenced for less than $1,000 (and access it via a mobile phone app). In 2007, the cost was $350,000. In addition to improving people’s health outcomes, being able to quickly identify effective treatments could also help reduce the cost of healthcare by reducing the number of treatments and procedures doctors prescribe. This will become increasingly crucial as the world’s older population continues to grow. Globally, countries including the US, China, and Japan, as well as pharmaceutical companies, are investing billions on researching precision medicine. Reduced costs The great power of harnessing AI is that access to these innovations won’t just be limited to the wealthy few. More of the world’s population will benefit from these advances. In Africa, cancer is now the No 1 cause of death. The Rwandan government is working with the World Economic Forum to increase the country’s diagnostic capacity for detecting cancer. As examples such as Massachusetts General Hospital and Harvard Medical School’s breast cancer screening trial prove, AI can be used to accurately assess scans, make recommendations for treatment, and reduce unnecessary surgeries caused by false positives. With the right kind of policy and infrastructure in place, the potential benefits of AI-driven medicine would be enormous for Rwanda. Removing the opportunity for error AI is already contributing to reducing deaths due to medical errors. After heart disease and cancer, medical errors are the third-leading cause of death. Take prescription drug errors. In the US, around 7,000 people die each year from being given the wrong drug, or the wrong dosage of the correct drug. To help solve the problem, Bainbridge Health has designed a system that uses AI to take the possibility of human error out of the process, ensuring that hospital patients get the right drug at the right dosage. The system tracks the entire process, step-by-step, from the prescription being written to the correct dosage being given to the patient. Health insurance company Humana is using AI to augment its human customer service. The system can send customer service agents real-time messages about how to improve their interaction with callers. It’s also able to identify those conversations that seem likely to escalate and alert a supervisor so that they’re ready to take the call, if necessary. This means the caller isn’t put on hold, improving the customer experience and helping to resolve issues faster. These are both great examples of the kinds of problems that can be solved with AI. We’re going to be seeing more and more innovations like these. Where do we go from here? AI has the potential to revolutionize healthcare, but if we want to make sure that this leads to better healthcare outcomes for everyone, then we need to do three things. Firstly, governments and other organizations need to develop protocols for safely and sustainably building the emerging personal data economy. Only by allowing people to manage and trade their own data will we calm fears about data security and misuse, and ensure the good flow of high-quality data that healthcare providers will require to build smarter AI systems. Second, we need to maintain a strong ethical mindset when considering the moral implications of using technology to make decisions about people’s health and well-being. This includes being transparent about the algorithms employed and the data used to feed them, so that patients understand why a decision was made. And finally, we need to dream big. Diseases like polio and smallpox have been virtually eradicated in many parts of the world already, so why can’t we do the same with other diseases? Imagine a world without sickle cell anemia, without cancer! When thinking about the future of healthcare, the potential is immense. Now let’s make it happen. Source: World Economic Forum Read more »
  • A.I. Could Help Us Be More Human
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    Maricel Cabahug, the chief design officer at German business software giant SAP, says her company likes to think of its A.I.-driven products and services as coworkers for SAP’s clients. But that paradigm has its issues.     “How do we make [an A.I. product] so it doesn’t compete with you?” Cabahug asked during Fortune’s Brainstorm Design conference in Singapore last week. The potential for robots to replace humans has already been realized on a large scale in the manufacturing sector, she said, and mundane white-collar jobs are likely targets for automation, too. “Unlike a coworker you might train and who might one day be your boss, this coworker will never be better than you,” Cabahug assured conference attendees, whose companies might call such “coworkers” by another name: virtual assistants. With that premise in mind, SAP developed a “smart” tool called Inscribe, which allows users to interact with SAP’s management software via a stylus and therefore natural handwriting. Through Inscribe, a person can scribble out columns in a spreadsheet, add notes to sections they find interesting, and hand-write directives to the algorithms running the software. Cabahug described the technology as a “conversational experience” because SAP’s A.I. responds to prompts from the stylus and feeds the user information. Inscribe’s purpose, Cabahug said, is to help people be better at their job—not to do their job for them. Besides Inscribe, SAP’s solution to the problem of how humans interact with ever-advancing tech, the company also offers voice-activated solutions. “These types of interaction allow us to be more human,” Cabahug said. That’s a similar sentiment to one that Tim Brown, CEO of design consultancy IDEO, expressed during the first day of Brainstorm Design. Brown remarked that A.I. could stand for “augmented” rather than “artificial” intelligence, because its purpose is to help humans achieve more than we could do alone. Perhaps having a robotic co-worker won’t be so bad after all. For more coverage of Fortune’s Brainstorm Design conference, click here.   Source: Fortune Read more »
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