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.
You browse an e-commerce site on your mobile device, looking for a pair of shoes. Then, with every swipe on your phone, you see ads from other retailers offering you shoes, shoes and more shoes. Are you flattered that the retailer shared your session cookie with third parties? Or do you shake your head, annoyed that these ads are following you everywhere? You visit an online retailer and can't find what you're looking for.
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.
These days it seems that nearly every product and startup boasts some kind of A.I. capability, but when it comes to advancing this domain beyond simplistic machine learning technologists at MIT Technology Review's Future Compute conference say these A.I. will need to be more human than not. When discussing A.I. during the conference's first day on December 2nd, speakers focused on two distinct paths for this technology: more human-like A.I.'s as well as more computer-like humans. This dual approach was presented as a potential future for human-machine symbiosis. But what exactly does that all mean, and is it even a good thing? A research Scientist from Oak Ridge National Laboratory, Catherine Schuman began the conversation by presenting her work on neuromorphic computing.
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.
This article will take you through how these companies can automate several procedures like menu digitization or invoice processing that are traditionally done manually to save time and operational costs. We have all had moments when we suddenly crave a good dessert. Getting that big tub of ice-cream after a long day at work would've been an inconvenience a few years ago. But food delivery apps can get it to you at a lightning fast speed. With companies like DoorDash, DeliveryHero, GrubHub, FoodPanda, Swiggy, Zomato and Uber Eats competing for a greater market share in the food delivery market, adopting technology that aids companies to scale up their operations has become a necessity to stay relevant.
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?