For as long as artificial intelligence and machine learning tools have been moving into the workforce, there have been rumblings of robots taking over the work of people, and the impact that could have on their career prospects. However, new studies undertaken by global professional services brand Genpact of 5,000 respondents in the United Kingdom, United States, and Australia, shows that the level of concern among the workers themselves is not very high. Roughly twenty percent of those surveyed in the UK felt that their jobs were threatened by AI, with only six percent feeling this strongly. But, although they did not feel overly cautious about their own prospects, they saw the potential disadvantages for the next generation of workers, with over fifty percent responding there was a threat to their children's careers, and over eighty percent stating that new skills will be needed for those workers in order to succeed in an AI advanced environment. The reason for this caution can be found in the training, or lack thereof, in the use of AI.
Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).
The Pittsburgh Supercomputing Center received five @HPCwire awards, including one for poker AI'Libratus' The Pittsburgh Supercomputing Center (PSC) received not one, but five HPCwire awards at the 2017 International Conference for High-Performance Computing (HPC), Networking, Storage and Analysis (SC17) on Sunday, Nov. 12. One of the three Readers' Choice Awards that PSC received was for Best Use of AI: CMU School of Computer Science "Libratus" AI on PSC's "Bridges" wins Brains vs. AI competition. HPCwire represents the leading trade publication in the supercomputing community and their annual Readers' and Editors' Choice Awards, given out at the start of the annual supercomputing conference, are well respected in that community. The awards are determined based on a nomination and voting process among the HPCwire community as well as selections from the publication's editors. In addition to Best Use of AI, PSC received two more Readers' Choice Awards -- Outstanding Leadership in HPC (Nick Nystrom, Interim Director, PSC) and Best Use of HPC in Energy (PSC with Texas A&M uses OpenFOAM on PSC Bridges & Texas Advanced Computing Center's Stampede to better understand coolant & heat transfer in high-temperature-jet reactors).
At a time when technologies like Artificial Intelligence are becoming the new world order, Karnataka is betting big to prepare itself for these new drivers of employment. Drones that monitor crop health, medical devices for early detection of cancer and apps that help visually impaired read and identify objects were some of the AI--based innovations on display at the Bengaluru Tech Summit 2017. Many of these companies pitched their products and services to an audience of top business executives, government officials, and investors at Karnataka government's flagship event held in Palace Grounds here. "We are at the beginning of what is called as fourth industrial revolution," said Kris Gopalakrishnan, co-founder of software giant Infosys. He said multinational companies are setting up research and development facilities here because they are able to find professionals at a scale who understand technologies such as AI and Machine Learning.
The pace of artificial intelligence technology adoption in healthcare varies considerably. Some medical establishments are undertaking small incremental changes; others centers have seen several years of innovation; and a proportion remain tied to the traditional healthcare model of the 1990s. This is the view of Dr. Ameet Bakhai, deputy director of research at the Royal Free London NHS Foundation Trust. Dr. Bakhai was expressing his views in advance of a major conference that is set to look at artificial intelligence in healthcare: Digital Healthcare Transformation Summit 2017, which takes place in London in December. A key theme is that although there are more advanced machines, from ultra-high-resolution imaging instruments to surgical robots, these tend to remain fully controlled by humans rather than with decisions made by artificial intelligence.
Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published today in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).
AI is a term that gets bandied about a lot these days. It's the capability du jour, the follow-up hit to "big data." But what does it really mean? Luis Perez-Breva is a lecturer and research scientist at MIT's School of Engineering and the originator and lead Instructor of the MIT Innovation Teams Program. He's the author of Innovating: A Doer's Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong.
A Guide to AI Accelerators and Incubators I. Rationale for the post Well, let's be completely honest: the current startups landscape is incredibly messy. There are plenty of ways to get funded to start your own company--but how many of them are not simply'dumb money'? How many of them give you some additional value and really help you scale your business? This problem is particularly relevant for emerging exponential technologies such as artificial intelligence, machine learning and robotics. For those specific fields, highly specialized investors/advisors are essential for the success of the venture.
High Performance Computing (HPC) has historically depended on numerical analysis to solve physics equations, simulating the behavior of systems from the subatomic to galactic scale. Recently, however, scientists have begun experimenting with a completely different approach. It turns out that Machine Learning (ML) models can be far more efficient and even more accurate than the time-tested, number-crunching simulations in use today. Once a Deep Neural Network (DNN) is trained, using the virtually unlimited data sets from traditional analysis and direct observation, it can predict or estimate the outcome of a simulation–without actually running it. Early results indicate that by combining ML and traditional simulation, these "synthesis models" can improve accuracy, accelerate time to solution, and significantly reduce costs.