"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Since complex diseases such as cancer, diabetes and so on pose a very big threat to human health, they have been extensively studied in the past decades1. However, the underlying pathogenesis of complex diseases is still not clearly known. With the rapid development of genomics technologies, the big data of variations on DNA level such as SNP and CNV (copy number variation) allow comprehensive characterization of complex diseases and provide potential biomarkers to predict the status of complex diseases. Due to the'missing heritability' and lack of reproducibility, the exploration of relationships between SNPs and complex diseases have been transferred from single variation to biomarkers interactions which are defined as epistasis2. First, as the number of variants increases, the combination space expands exponentially, resulting in the'curse of dimensionality' problem.
Bottom Line: Zero Trust Security (ZTS) starts with Next-Gen Access (NGA). Capitalizing on machine learning technology to enable NGA is essential in achieving user adoption, scalability, and agility in securing applications, devices, endpoints, and infrastructure. Zero Trust Security provides digital businesses with the security strategy they need to keep growing by scaling across each new perimeter and endpoint created as a result of growth. ZTS in the context of Next-Gen Access is built on four main pillars: (1) verify the user, (2) validate their device, (3) limit access and privilege, and (4) learn and adapt. The fourth pillar heavily relies on machine learning to discover risky user behavior and apply for conditional access without impacting user experience by looking for contextual and behavior patterns in access data.
While AI has made waves in diagnosing certain diseases better than doctors, there's another area where the tech is being applied that might eventually have even greater impacts on health. Today, at least 18 pharmaceutical companies and more than 75 startups are applying machine learning to drug discovery--the complex, expensive process of identifying and testing new drug compounds. These companies are betting hundreds of millions of dollars that AI will reduce costs, shorten timelines, and lead to new and better drugs. At the Forum on Monday, Exscientia founder and CEO Andrew Hopkins, formerly a professor at the University of Dundee in Scotland and a 10-year veteran of Pfizer, spoke about how AI can lead to improvements in drug development. Exscientia, formed in 2012, uses AI-driven systems to automate drug design while still "mimicking human creativity," says Hopkins.
Artificial intelligence, or AI, is undergoing a period of massive expansion. This is not because computers have achieved human-like consciousness, but because of advances in machine learning, where computers learn from huge databases how to classify new data. At the cutting edge are the neural networks that have learned to recognise human faces or play Go. Recognising patterns in data can also be used as a predictive tool. AI is being applied to echocardiograms to predict heart disease, to workplace data to predict if employees are going to leave, and to social media feeds to detect signs of incipient depression or suicidal tendencies.
AI is helping businesses understand "what will happen in the future and how they can stay ahead," says Oracle NetSuite EVP Jim McGeever. He now runs his own firm, Evans Strategic Communications LLC.) CLOUD WARS -- A few months after upgrading its huge portfolio of SaaS apps with "adaptive intelligence" capabilities for the digital economy, Oracle is doing the same for its entire NetSuite family of integrated applications aimed at small and mid-sized businesses. The NetSuite announcement means that while Oracle is still well behind SaaS leader Salesforce.com in revenue, Oracle now offers not only the broadest set of SaaS apps on the market--a truly end-to-end integrated portfolio--but also has the largest family of AI- and machine-learning-enhanced applications suitable for customers ranging in size from the world's largest corporations down to small businesses. The impact will be significant because in cloud ERP alone, NetSuite has 40,000 organizations--standalone companies as well as subsidiaries of big corporations--running its products across 160 countries. And when this NetSuite AI initiative is paired up with the significant commitment Oracle's making to ensure that AI and machine learning are fully infused into all of its IP rather than being a separate application, it's clear that Oracle wants to ensure there is zero daylight between today's AI phenomenon and the company's extensive cloud product lineup--including NetSuite.
Deep learning is a part of AI and machine learning that is "based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised," according to Wikipedia. Deep Learning, rather than following rigid hierarchies, is modeled on the neurons of the brain. Are our systems ready to learn? In a world that is just getting started with AI, deep learning is another leap in sophistication.
Artificial intelligence may still be in its infancy, but it's moving fast. Nowhere is this more apparent than in the data-rich health sector. AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world's biggest global health issues. The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn (£97bn-£270bn) a year over current health spending. AI startups raised $15.2bn last year alone, adding to investments made by tech giants like Google, Facebook, and Alibaba and a host of research institutions.
One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.
Communication with computing machinery has become increasingly'chatty' these days: Alexa, Cortana, Siri, and many more dialogue systems have hit the consumer market on a broader basis than ever, but do any of them truly notice our emotions and react to them like a human conversational partner would? In fact, the discipline of automatically recognizing human emotion and affective states from speech, usually referred to as Speech Emotion Recognition or SER for short, has by now surpassed the "age of majority," celebrating the 22nd anniversary after the seminal work of Daellert et al. in 199610--arguably the first research paper on the topic. However, the idea has existed even longer, as the first patent dates back to the late 1970s.41 Previously, a series of studies rooted in psychology rather than in computer science investigated the role of acoustics of human emotion (see, for example, references8,16,21,34). Blanton,4 for example, wrote that "the effect of emotions upon the voice is recognized by all people. Even the most primitive can recognize the tones of love and fear and anger; and this knowledge is shared by the animals. The dog, the horse, and many other animals can understand the meaning of the human voice. The language of the tones is the oldest and most universal of all our means of communication." It appears the time has come for computing machinery to understand it as well.28 This holds true for the entire field of affective computing--Picard's field-coining book by the same name appeared around the same time29 as SER, describing the broader idea of lending machines emotional intelligence able to recognize human emotion and to synthesize emotion and emotional behavior.
A growing body of research has demonstrated that algorithms and other types of software can be discriminatory, yet the vague nature of these tools makes it difficult to implement specific regulations. Determining the existing legal, ethical and philosophical implications of these powerful decision-making aides, while still obtaining answers and information, is a complex challenge. Harini Suresh, a PhD student at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), is investigating this multilayered puzzle: how to create fair and accurate machine learning algorithms that let users obtain the data they need. Suresh studies the societal implications of automated systems in MIT Professor John Guttag's Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports. Here, she discusses her research motivations, how a food allergy led her to MIT, and teaching students about deep learning.