The convergence of machine learning and business intelligence is upon us, as BI tool makers increasingly are exposing ML capabilities to users, and users are performing ML activities in their BI tools. That's according to the latest Gartner report on analytics and BI tools, which was released this week. In its February 11 Magic Quadrant for Analytics and Business Intelligence (ABI) Platforms, the storied Stamford, Connecticut analyst firm did its best to quantify and qualify the trends in the sector. While BI and ML have largely existed on parallel tracks, with BI seeking to report what happened and ML seeking to predict what will happen, Gartner sees the two disciplines converging, at least as far as the toolsets are concerned. Not all ML work will occur within BI tools, of course.

"There's this folklore mythology around if Amazon launches a business in a certain area, it means that all the other businesses in those areas are not going to be as successful," Jassy said at the Goldman Sachs Technology and Internet Conference in San Francisco yesterday. "I just haven't seen it." There are only two significant industries that Amazon has "disrupted," according to Jassy: retail with Amazon.com, and technology infrastructure with AWS. His remarks come as federal and state regulators are conducting antitrust probes to determine whether Amazon and other technology giants stifle competition and innovation. "In both cases, they were models that were pretty antiquated, and customers weren't so happy with those models, and somebody was going to end up reinventing them," Jassy said.

In 1997, Hiroaki Kitano, a research scientist at Sony, helped organize the first Robocup, a robot soccer tournament that attracted teams of robotics and artificial intelligence researchers to compete in the picturesque city of Nagoya, Japan. At the start of the first day, two teams of robots took to the pitch. As the machines twitched and surveyed their surroundings, a reporter asked Kitano when the match would begin. "I told him it started five minutes ago!" he says with a laugh. Such was the state of AI and robotics at the time.

Mila is happy to announce the nomination of five new faculty members to the Mila team! Guillaume Lajoie was appointed Core Academic Member. Guillaume is an assistant professor in the Department of Mathematics and Statistics (DMS) at the Université de Montréal. His research is rooted at the intersection of AI and neuroscience where he pursues questions surrounding neural network dynamics and computations, with some applications to neuroengineering.

The young digerati will lead innovation, but they'll also need to develop business awareness, an entrepreneurial attitude, bottom-line focus, and ethical intelligence. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Throughout history, new technologies have demanded step shifts in the skills that companies need. Like the First Industrial Revolution's steam-powered factories, the Second Industrial Revolution's mass-production tools and techniques, and the Third Industrial Revolution's internet-based technologies, the Fourth Industrial Revolution -- currently being driven by the convergence of new digital, biological, and physical technologies -- is changing the nature of work as we know it. Now the challenge is to hire and develop the next generation of workers who will use artificial intelligence, robotics, quantum computing, genetic engineering, 3D printing, virtual reality, and the like in their jobs.

AI-based efforts like this could help to conserve drug researchers' time and resources, says Mike Tarselli, scientific director of the Society for Laboratory Automation and Screening. "The use of AI to augment human capacity, to address a pressing public health concern using existing data without re-deploying a full team, should be a boon to researchers." The BenevolentAI report is "more a testimony to good literature searching and curation" than "a triumph of artificial intelligence," according to veteran drug-discovery researcher and blogger Derek Lowe. He says anyone could search through the extensive kinase literature to find good drug candidates, but he acknowledges that the researchers likely sped up their search with a well-organized database and software good at searching through it.

Heralded as an easy fix for health services under pressure, data technology is marching ahead unchecked. But is there a risk it could compound inequalities? When Adewole Adamson received a desperate call at his Texas surgery one afternoon in January 2018, he knew something was up. The call was not from a patient, but from someone in Maryland who wanted to speak to the dermatologist and assistant professor in internal medicine at Dell Medical School in the University of Texas about black people and skin cancer. Over the next few weeks, over a series of phone calls, Adamson would learn a lot about the caller.

Artificial Intelligence and IoT applications have always helped in disease diagnosis as well as various ailment process. With the increasing technological advancements, it is clearly evident that the workflow and mechanism of medical electronics has improved a lot and is expected to achieve laurels in the near future. An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes. This has been shown by a new study led by researchers at Linköping University, published in Nature Communications. The scientists hope that the method can eventually be applied within precision medicine and individualised treatment.

Work-related mental health has become a pressing issue for businesses globally. In the UK, a government-backed Labour Force Survey found that the total number of cases of work-related stress, depression, or anxiety in 2018 and 2019 was 602,000, resulting in a loss of 12.8 million working days. A recent WHO-led study also estimated that depression and anxiety disorders are costing the global economy $1 trillion USD each year in lost productivity. The good news is that artificial intelligence (AI) and machine-based learning have the potential to help in the workplace significantly, once they truly come of age. In its current form, AI is primarily a support mechanism.

Music making is increasingly digitized here in 2020, but some analog audio effects are still very difficult to reproduce in this way. One of those effects is the kind of screeching guitar distortion favored by rock gods everywhere. Up to now, these effects, which involve guitar amplifiers, have been next to impossible to re-create digitally. That's now changed thanks to the work of researchers in the department of signal processing and acoustics at Finland's Aalto University. Using deep learning artificial intelligence (A.I.), they have created a neural network for guitar distortion modeling that, for the first time, can fool blind-test listeners into thinking it's the genuine article.