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How machine learning can help to future-proof clinical trials in the era of COVID-19

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The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future. In an article published in Statistics in Biopharmaceutical Research, an international collaboration of data scientists and pharmaceutical industry experts--led by the Director of the Cambridge Center for AI in Medicine, Professor Mihaela van der Schaar of the University of Cambridge--describes the impact that COVID-19 is having on clinical trials, and reveals how the latest machine learning (ML) approaches can help to overcome challenges that the pandemic presents. The paper covers three areas of clinical trials in which ML can make contributions: in trials for repurposing drugs to treat COVID-19, trials for new drugs to treat COVID-19, and ongoing clinical trials for drugs unrelated to COVID-19. The team, which includes scientists from pharmaceutical companies such as Novartis, notes that "the pandemic provides an opportunity to apply novel approaches that can be used in this challenging situation."


Overcome Distrust of AI With These 4 Principles

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Artificial intelligence (AI) and machine learning technologies are becoming increasingly incorporated into consumer products and enterprise solutions alike. As AI applications quickly advance into large-scale and more diverse use cases, it's becoming imperative that ethics guide its development, deployment and applications. This is especially important as we increasingly apply AI to use cases that impact individual lives and livelihoods -- including healthcare, criminal justice, public welfare and education. It's clear that to continue the widespread adoption of AI on both a consumer and enterprise level -- and subsequently spur continued innovation in the technology -- AI technologies and applications need to be trustworthy and transparent. Survey after survey have revealed substantial consumer mistrust of AI technologies.


Choosing a Deep Learning Framework

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Implementing deep learning algorithms from scratch using Python and NumPY is a good way to understand what these deep learning algorithms are really doing by unfolding the deep learning black box. However, it is increasingly not practical, at least for most people like me, is not practical to (CNN) or recurring neural networks (CNN) or such complex models, such as convolutional neural networks implement everything yourself from scratch. Even though you understand how to do multiplayer and you are able to build a large multiplication and you are probably not want to implement your own matrix multiplication function but instead, you want to call a numerical linear algebra library that could be more more efficiently for you. I think this is crucially important when you are in the middle of Deep Learning pipeline. So let's take a look at the frameworks out there ... Today, there are many deep learning frameworks that make it easy for you to implement neural networks, and here are some of the leading ones.


Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence): Russell, Stuart, Norvig, Peter: 9780134610993: Amazon.com: Books

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Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow.


Melanie Mitchell on AI: Intelligence is a Complex Phenomenon (257)

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Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science at Portland State University. Prof. Mitchell is the author of a number of interesting books such as Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans. One interesting detail of her academic bio is that Douglas Hofstadter was her Ph.D. supervisor. During this 90 min interview with Melanie Mitchell, we cover a variety of interesting topics such as: how she started in physics, went into math, and ended up in Computer Science; how Douglas Hofstadter became her Ph.D. supervisor; the biggest issues that humanity is facing today; my predictions of the biggest challenges of the next 100 days of the COVID19 pandemic; how to remain hopeful when it is hard to be optimistic; the problems in defining AI, thinking and human; the Turing Test and Ray Kurzweil's bet with Mitchell Kapor; the Technological Singularity and its possible timeline; the Fallacy of First Steps and the Collapse of AI; Marvin Minsky's denial of progress towards AGI; Hofstadter's fear that intelligence may turn out to be a set of "cheap tricks"; the importance of learning and interacting with the world; the [hard] problem of consciousness; why it is us who need to sort ourselves out and not rely on God or AI; complexity, the future and why living in "Uncertain Times" is an unprecented opportunity. Intelligence is a very complex phenomenon and we should study it as such.


Provably exact artificial intelligence for nuclear and particle physics

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The Standard Model of particle physics describes all the known elementary particles and three of the four fundamental forces governing the universe; everything except gravity. These three forces--electromagnetic, strong, and weak--govern how particles are formed, how they interact, and how the particles decay. Studying particle and nuclear physics within this framework, however, is difficult, and relies on large-scale numerical studies. For example, many aspects of the strong force require numerically simulating the dynamics at the scale of 1/10th to 1/100th the size of a proton to answer fundamental questions about the properties of protons, neutrons, and nuclei. "Ultimately, we are computationally limited in the study of proton and nuclear structure using lattice field theory," says assistant professor of physics Phiala Shanahan.


AI technology can predict vanadium flow battery performance and cost

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Vanadium flow batteries (VFBs) are promising for stationary large-scale energy storage due to their high safety, long cycle life, and high efficiency. The cost of a VFB system mainly depends on the VFB stack, electrolyte, and control system. Developing a VFB stack from lab to industrial scale can take years of experiments due to complex factors, from key materials to battery architecture. Novel methods to accurately predict the performance and cost of a VFB stack and further system are needed in order to accelerate the commercialization of VFBs. Recently, a research team led by Prof. Li Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences proposed a machine learning-based strategy to predict and optimize the performance and cost of VFBs.


Mobility and Event-Based Pricing

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Pricing services is often arcane, taking into account many factors beyond simple supply and demand. As our world becomes more complicated and connected, it's likely that mobility and event-based Pricing will increase and become more complicated. It costs more to ride a subway train during rush hour than it does at 9pm. "Congestion pricing" has traditionally been used to try and steer people who are not in a hurry out of peak hours. The same principle is also applied to tolls, congestion taxes, and opening or closing HOV lanes in specific directions. So, what will the future likely bring?


Robot Pets Helping Reduce Isolation Among Seniors – IAM Network

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By WVUA 23 Digital Reporter Anne Houtz Senior citizens, especially those in nursing homes or with family living far away, have had a higher risk of social isolation this year. This growing issue can be fixed by companionship, and the West Alabama Area Agency on Aging has developed a unique and heartwarming solution. With the help of a grant funded by the Alabama Department of Senior Services, the AAA has purchased robotic pets to help reduce risks of intensifying social isolation – and it is already helping a great deal. The West Alabama AAA has purchased 40 pets and already distributed 28 of them to clients. The robotic pets can interact with their owners through touch and voice activation, brightening the days of those who would otherwise go all day without any interaction.


Engineers Develop New Machine-Learning Method Capable of Cutting Energy Use – IAM Network

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Dor Skuler is the co-founder and CEO of Intuition Robotics, a company redefining the relationship between humans and machines. They build digital companions including ElliQ – the sidekick for happier aging which improves the lives of older adults. Intuition Robotics is your fifth venture. What inspired you to launch this company? Throughout my career, I've enjoyed finding brand new challenges that are in need of the latest technology innovations.