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."
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.
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 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?
Nvidia unveiled a new federated learning edge computing reference application for radiology to help hospitals crunch medical data for better disease detection while protecting patient privacy. Called Clara Federal Learning, the system relies on Nvidia EGX, a computing platform which was announced earlier in 2019. It uses the Jetson Nano low wattage computer which can provide up to one-half trillion operations per second of processing for tasks like image recognition. EGX allows low-latency artificial intelligence at the edge to act on data, in this case images from MRIs, CT scans and more. Nvidia made its announcement of Clara on Sunday at the Radiological Society of North America conference in Chicago.
Employers engage artificial intelligence solutions amid a talent shortage. As employers grapple with a widespread labor shortage, more are turning to artificial intelligence tools in their search for qualified candidates. Hiring managers are using increasingly sophisticated AI solutions to streamline large parts of the hiring process. The tools scrape online job boards and evaluate applications to identify the best fits. They can even stage entire online interviews and scan everything from word choice to facial expressions before recommending the most qualified prospects.
ESA's SpaceBok robot is designed to walk, hop, and run in low-gravity environments. From free-flying droids to humanoids, from crawlers to inflatable torsos, space robots of myriad types are now being considered for missions in low Earth orbit, on interplanetary spacecraft, and on other worlds. It might sound like a prop list from a Star Wars movie, but space agencies and their contractors are developing a panoply of robotic assistants with a serious aim in mind: to boost the productivity and safety of astronauts. The idea behind robot assistants is multifaceted: one aim is to offload time-consuming repetitive tasks like space station cleaning and inventory making from crew members to free-flying or humanoid robots. Ground robots controlled from, say, spacecraft orbiting the Moon or Mars could construct human habitats ahead of a landing, or perform reconnaissance ahead of human exploration missions.
If I told you only 4% of all high school students in the U.S. were taking science or math classes, you'd be aghast. If 96% of students were not getting science or math classes, you could reasonably argue it does not exist in any practical sense. Over the last few months, several reports provided new insights about U.S. high school computer science (CS): California and Texas are two largest states based on U.S. population, but we can't generalize to everyone based on those states. We don't have data on who is taking CS across the U.S., due to our state-centric, decentralized model of primary and secondary school education. California and Texas are among the leaders in implementing CS education.
I recently had the opportunity to take a ride in a Waymo self-driving car in Chandler, AZ. I had been looking forward to this experience, not only to see how well the technology worked but also what the experience might be like as a passenger. Upon my arrival at the Waymo facility, I had apparently approached the side of the building where the Waymo cars go at the end of their duty cycles to be refueled and inspected. As I drove in, I was more or less surrounded by incoming Waymo vehicles. I relaxed as they navigated their way around me.
For the running example in Figure 1, this abstraction would replace the application-specific identifiers triangle and EQUILATERAL with generic placeholders, such as VAR1 and VAR2. After this abstraction, both approaches use an RNN-based sequence-to-sequence network that predicts how to modify the abstracted code. Given the increasing interest in learning-based approaches toward software engineering problems, we will likely see more progress on learning-based repair in the coming years. Key challenges toward effective solutions include finding an appropriate representation of source code changes and obtaining large amounts of high-quality human patches as training data.