If you're a content creator, you're not just striving to do your best work. You want to make your best work better. At the SIGGRAPH conference, which runs July 31-Aug. From Siri to Smart Cars to Netflix recommendations, AI is infused in our daily lives. Now, thanks to the computational power of NVIDIA GPUs, new AI accelerated workflows are optimizing content creation, saving artists and studios time and money, and driving greater creativity.

Tesla announced it will livestream the deliveries of the first Model 3 cars this Friday. You'll be able to watch Elon Musk's company hand over its Model 3 sedan to new owners. Earlier this month, Musk tweeted Tesla would hold a "party for first 30 customers" on July 28. The event should put Tesla fans at ease, since the company's second car, the Model X, was delayed by more than a year. It should especially be a relief for the others who have already reserved a car and are expecting to have it delivered next year.

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNA-Seq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively.

Driscoll's is so secretive about its robotic strawberry picker it won't let photographers within telephoto range of it. But if you do get a peek, you won't see anything humanoid or space-aged. AgroBot is still more John Deere than C-3PO -- a boxy contraption moving in fits and starts, with its computer-driven sensors, graspers and cutters missing 1 in 3 berries. Such has been the progress of ag-tech in California, where despite the adoption of drones, iPhone apps and satellite-driven sensors, the hand and knife still harvest the bulk of more than 200 crops. Now, the $47-billion agriculture industry is trying to bring technological innovation up to warp speed before it runs out of low-wage immigrant workers.

To say that radiology in the NHS is drowning in work volume is an understatement. In May 2016 the Royal College of Radiologists highlighted the results of a national workforce survey, including the fact that 230,000 studies a month were stuck in a backlog of over 31 days and that spending to reduce these backlogs had increased by 57%. The story is the same in Scotland, where over £5.25 million per annum is spent on outsourcing. No-one seems to have the answer to this crisis, and with the current state of NHS underfunding it doesn't look likely that one will emerge any time soon. Over the pond, however, there is a huge push towards solving radiology's problems -- and the UK is being left behind.

Posted By C. M. Rubin on Jun 20, 2017 "We need courageous cathedral builders! We also need to address traditional experts' biases clinging to their narrow domains, parents' old personal experiences biasing their views, and teachers' and administrators' lack of training and leadership, respectively." All around us we are witnessing disruptive automation that is changing lives and taking away the jobs many have relied on to make a living. According to a recent report by PwC, within 15 years, artificial intelligence will take over 38% of U.S. jobs. But will this trend continue even further, and to what extent does AI pose a threat to most of our jobs?

Tanmay Bakshi fell in love with computers at five, released his first iPhone app at nine, and now at just 13 years old is working with IBM on artificial intelligence. The Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding. He is currently in Australia for the IBM Watson Summit, which brings together experts in artificial intelligence to discuss how the technology can help people and businesses in the future. "If you think about it, really anything would fascinate a five-year-old, especially a computer," Tanmay told News Breakfast. "Just looking at the colours change on screen or even displaying my name on screen, whatever it might be, as a five-year-old that really fascinated me."

More than 70% of Australians play video games, and I'm one of them. With its spread from computers to TVs to mobile phones, the global games industry is now worth over $100bn, with an Australian market alone worth $3bn. But the value of games goes beyond just money. Interactive entertainment and the "serious games" that share lessons and skills have psychological and social benefits. We play to bond with other players, to build communities, to learn, to experience worlds beyond our imagination, and – as in the Dionysian theatres of old – to enjoy a temporary catharsis and channel feelings that otherwise preoccupy us into something vicarious.

While data science has emerged as an ambitious new scientific field, related debates and discussions have sought to address why science in general needs data science and what even makes data science a science. Following a comprehensive literature review,5,6,10,11,12,15,18 I offer a number of observations concerning big data and the data science debate. For example, discussion has covered not only data-related disciplines and domains like statistics, computing, and informatics but traditionally less data-related fields and areas like social science and business management as well. Data science has thus emerged as a new inter- and cross-disciplinary field. Although many publications are available, most (likely over 95%) concern existing concepts and topics in statistics, data mining, machine learning, and broad data analytics. This limited view demonstrates how data science has emerged from existing core disciplines, particularly statistics, computing, and informatics. The abuse, misuse, and overuse of the term "data science" is ubiquitous, contributing to the hype, and myths and pitfalls are common.4 While specific challenges have been covered,13,16 few scholars have addressed the low-level complexities and problematic nature of data science or contributed deep insight about the intrinsic challenges, directions, and opportunities of data science as an emerging field. Data science promises new opportunities for scientific research, addressing, say, "What can I do now but could not do before, as when processing large-scale data?"; "What did I do before that does not work now, as in methods that view data objects as independent and identically distributed variables (IID)?"; "What problems not solved well previously are becoming even more complex, as when quantifying complex behavioral data?"; and "What could I not do better before, as in deep analytics and learning?"