Geo-data firm Fugro collects and analyses information about the Earth and the structures built upon it. It surveys the land and in the case of mapping objects on the sea floor, Fugro uses side scan sonar, collected via boats, to gather information. One project sees Fugro search the sea for boulders to help its customers determine whether they can set up an offshore windfarm. "Windfarm companies want to know where the impediments and where the potential sites they can build windfarms are," Fugro senior innovation engineer Marcus Nepveaux said, speaking at AWS re:Invent in Las Vegas. "So we go in, we map the sea floor for them, tell them where the big rocks or the little rocks are … they may be as small as a foot, and as big as we can detect."

James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning. His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry.

It is becoming increasingly clear that for most working people, a proportion of the working tasks they currently perform will be either completely replaced by machines (AI if the tasks are cognitive, robots if they are manual) or augmented by a human-machine interface. While there is less clarity about the types of tasks that will remain within the human domain, we can make some predictions. We know that, right now and in the foreseeable future, machines are generally poor at understanding a person's mood, at sensing the situation around them, and at developing trusting relationships. So as the World Economic Forum report on future skills argued, it is human "soft skills" that will become increasingly valuable -- skills such as empathy, context sensing, collaboration, and creative thinking. That means that millions of people across the world will have to make the transition toward becoming a great deal better versed in these soft skills.

Enterprises are creating more and more videos and using them for various informational purposes, including marketing, training of customers, partners & employees and internal communications. However, videos are considered as the blackholes of the internet because it is very hard to see what's inside them. The opaque nature of videos equally impacts end users who spend a lot of time navigating to their point of interest, leading to severe underutilization of videos as a powerful medium of information. In this talk, we will describe visual processing pipeline of VideoKen platform which includes Graph-based algorithm along with deep scene text detection to identify key visual frames in the video, FCN-based algorithm for semantic segmentation of screen content in visual frames, Transfer-learning based visual classifier to categorize screen content into different categories such as slides, code walkthrough, demo, handwritten, etc. and Algorithm to detect visual coherency and select indices from the video. We will discuss challenges and experiences in implementing/iterating on these algorithms using our experience with processing 100K video hours of content.

Mumbai: The financial sector in India is driving investments into chatbots and artificial intelligence (AI) to augment customer service, but bankers are convinced that there would not be job losses as these new tools will only complement staff. When it comes to AI it is not upstarts but big guns of banking with resources, which are driving investments. State Bank of India (SBI) is working with IBM to make use of Watson -- an answering computer software to assist staff and employees. HDFC Bank has tied up with artificial intelligence firm Niki (funded by Ratan Tata and Ronnie Screwvala) to bring in conversational banking. Last week, Yes Bank partnered Payjo to launch AI-led digital initiatives.

Sophisticated machine learning applications require not only enormous amounts of training data, but powerful computer hardware on which to train. An analysis conducted by San Francisco research firm OpenAI found that since 2012, the amount of compute used in the largest training runs has been increasing exponentially with a 3.4-month doubling time, and that it's grown by more than 300,000 times over that same time period. The trend spurred the development of supercomputers like the U.S. Department of Energy's Sierra and Summit, which leverage dedicated accelerator chips to speed up AI computation. Now, IBM's Hardware Center, in collaboration with New York State, SUNY Polytechnic Institute, and other members of IBM's AI Hardware Center, has delivered a new machine for the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) that's optimized for state-of-the-art machine learning workloads. It's dubbed Artificial Intelligence Multiprocessing Optimized System, or AiMOS (in honor of Rensselaer cofounder Amos Eaton), and it will principally tackle projects in biology, chemistry, the humanities, and related domains underway at the new IBM Research AI Hardware Center on the SUNY campus in Albany.

Artificial Intelligence ("AI") swallows vast troves of data, so, as its definition suggests, it enables "the capability of a machine to imitate intelligent human behavior."1 Much like humans learn over time by exposure to different experiences and new information, AI systems can be fed enough data so that they can eventually draw conclusions and make inferences. Given AI's data diet, it is saddled with a host of privacy regulations, which vary depending on the nature of the data and its uses. This article highlights three compliance tensions between AI and the European privacy regime, the General Data Protection Regulation ("GDPR"), which contains various privacy-related principles for how personal data must be processed and provides certain data subject rights. With GDPR fines reaching as high as 4 percent of annual global turnover, or 20 million euros (whichever is higher), carriers insuring them should endeavor to understand these complex risks.

Artificial intelligence is among the most fascinating ideas of our time. It has captured the imagination of visionaries, science fiction writers, engineers and wall street analysts alike. In fact, artificial intelligence is in many ways a catalyst for the data revolution – something that has disrupted every aspect of modern life. As with all new technologies, some are faster to embrace them, and others are much slower. Is automotive manufacturing one of the faster ones or would it be among the last?

No, the robots are not coming for your job as they ready to take over the world ... yet. But the future of the world's workforce will mark a significant shift and work will be heavily reliant on the teamwork of human and machine, noted the just-released IDC white paper, Content Intelligence for the Future of Work. And we're not quite in sci-fi film territory either, said Holly Muscolino, research vice president of content and process strategies and the future of work at IDC. "A software robot (or'digital worker') is essentially a software program that automates a task that has previously been accomplished by a human worker," Muscolino explained. "The term'robot' is used to signify the role that these software solutions play in automation, however, beyond that, there is no relationship between a software robot and the physical robots that we may see on the manufacturing line, patrolling supermarket aisles on starring in'Star Wars'' movies." Muscolino added, "A variety of software technologies are classified as'digital workers.' The technology gaining the most airtime today is robotic process automation (RPA), but other automation technologies, and AI-enabled technologies, like digital assistants and chatbots, are also classified as'digital workers'."

Thanks to advances in artificial intelligence, computers can now assist doctors in diagnosing disease and help monitor patient vital signs from hundreds of miles away. Now, CU Boulder researchers are working to apply machine learning to psychiatry, with a speech-based mobile app that can categorize a patient's mental health status as well as or better than a human can. "We are not in any way trying to replace clinicians," says Peter Foltz, a research professor at the Institute of Cognitive Science and co-author of a new paper in Schizophrenia Bulletin that lays out the promise and potential pitfalls of AI in psychiatry. "But we do believe we can create tools that will allow them to better monitor their patients." Nearly one in five U.S. adults lives with a mental illness, many in remote areas where access to psychiatrists or psychologists is scarce.