Artificial intelligence (AI) and machine learning are increasingly becoming a part of drug discovery and development beginning with identifying new compounds to structuring and designing clinical trials and targeting clinical trial populations. A recent example came out of Linköping University in Sweden. The investigators utilized an artificial neural network to create maps of biological networks based on how different genes or proteins interact with each other. The AI was then taught to find patterns of gene expression. And in mid-February, a drug developed using AI began testing in human clinical trials.

There is a saying that has emerged among the tech set in recent years: AI is the new electricity. The platitude refers to the disruptive power of artificial intelligence for driving advances in everything from transportation to predicting the weather. Of course, the computers and data centers that support AI's complex algorithms are very much dependent on electricity. While that may seem pretty obvious, it may be surprising to learn that AI can be extremely power-hungry, especially when it comes to training the models that enable machines to recognise your face in a photo or for Alexa to understand a voice command. The scale of the problem is difficult to measure, but there have been some attempts to put hard numbers on the environmental cost.

Kevin Wo, Managing Director at Microsoft Singapore speaks to team Grab, one of the winners from Microsoft's AI for Accessibility Hackathon, about their communication solution for people with autism. In October 2019, Microsoft organized its first-ever AI for Accessibility Hackathon in Asia Pacific, bringing together over 400 participants in eight countries to conceptualize and prototype AI solutions that can transform the daily life, employment and communication of people with disabilities. Our partners and customers worked alongside nonprofits to tackle 23 problem statements, identifying opportunities to use AI to build a more inclusive society.

Kneron, Inc., a leading on-device edge artificial intelligence (AI) company based in San Diego, California, was named by CB Insights to the fourth annual AI 100 ranking, showcasing the 100 most promising private AI companies in the world. "To be listed on the AI 100 is an honor," stated Albert Liu, Kneron's Cofounder and CEO. "It represents our determination to expand AI inferencing from the cloud to the edge so that private user data can be more secure, and edge AI devices and applications can be more ubiquitous in our everyday lives. We're excited and inspired to see our work being recognized by CB Insights." Kneron's on-device edge AI solutions include AI chips and AI software models that enhance smart devices with AI functions without the constant need to be connected to a cloud-based AI service because the AI inferencing happens where the data is collected.

Trust and people's judgement will generate a higher value than pure technology in an era dominated by artificial intelligence and machine learning, Genpact CEO NV'Tiger' Tyagarajan said. New York-based Tyagarajan told ET in an interview that, going forward, AI would stand for Augmented Intelligence and not Artificial Intelligence. He also talked about the impact of the recent Delhi violence on India's global image and the scare from the Covid-19 virus. How are you striking a balance with tech like AI and ML? Our focus on processes and understanding of the industry hasn't changed since 2005.

Josh Swamidass is the Washington University computational biologist and intelligent design critic who debated with biochemist Michael Behe last week at Texas A&M. Their exchange is now up on YouTube. Jonathan Witt reported on the contents here. Dr. Swamidass then headed to Seattle where he spoke last night at the University of Washington on "Human Identity and the Meaning of Artificial Intelligence: A Conversation with a Secular Humanist and a Scientist Christian." It was good to see Josh in the flesh.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences.

Thanks to the 30cm (1ft) unit named AV1, children at St Oswald's in Gosforth can communicate remotely with family and friends, ensuring they don't feel isolated.

The same science that powers Google searches, Siri and Alexa, and self-operating cars may steer laboratory analysis in the University of Wyoming's College of Agriculture and Natural Resources. Microsoft has awarded two $15,000 grants through its AI for Earth program to scientists in two departments in the college. Researchers Todd Schoborg and Jay Gatlin, both in molecular biology, will examine biomedical imaging datasets to understand the molecular basis of human disease. Brant Schumaker, in the Department of Veterinary Sciences, will evaluate migration congregation points and potential for Chronic Wasting Disease (CWD) transmission. Scientists in both departments will collaborate with Lars Kotthoff, an assistant professor in the Department of Computer Science, whose research combines artificial intelligence (AI) and machine learning.

We consider the problem faced by a recommender system which seeks to offer a user with unknown preferences an item. Before making a recommendation, the system has the opportunity to elicit the user's preferences by making queries. Each query corresponds to a pairwise comparison between items. We take the point of view of either a risk averse or regret averse recommender system which only possess set-based information on the user utility function. We investigate: a) an offline elicitation setting, where all queries are made at once, and b) an online elicitation setting, where queries are selected sequentially over time. We propose exact robust optimization formulations of these problems which integrate the elicitation and recommendation phases and study the complexity of these problems. For the offline case, where the problem takes the form of a two-stage robust optimization problem with decision-dependent information discovery, we provide an enumeration-based algorithm and also an equivalent reformulation in the form of a mixed-binary linear program which we solve via column-and-constraint generation. For the online setting, where the problem takes the form of a multi-stage robust optimization problem with decision-dependent information discovery, we propose a conservative solution approach. We evaluate the performance of our methods on both synthetic data and real data from the Homeless Management Information System. We simulate elicitation of the preferences of policy-makers in terms of characteristics of housing allocation policies to better match individuals experiencing homelessness to scarce housing resources. Our framework is shown to outperform the state-of-the-art techniques from the literature.