Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
The era of artificial intelligence (AI) is officially here. The AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2025, at a CAGR of 36.62% between 2018 and 2025, according to a recent report. AI's phenomenal growth across different industries is being fueled by unprecedented computing power, ever-increasing amounts of data--billions of gigabytes every day--and sophisticated deep-learning algorithms. According to the AI Index report, the number of active U.S. startups developing AI systems has increased 14 times whereas the annual VC investment into such startups has increased only 6 times since 2000. Moreover, the share of jobs requiring AI skills in the U.S. has grown 4.5 times since 2013.
Future Today Institute founder Amy Webb has released her annual tech trends report, and much of it focuses on the continuing impact of artificial intelligence. Other trends highlighted by the report include space travel, human gene editing, and a global shortage of data scientists. Webb, a quantitative futurist and professor of strategic foresight at the NYU Stern School of Business, released the report today in a presentation at SXSW in Austin, Texas. Now in its 11th year, the report identifies 225 trends across 20 industries, with roughly 70 of those trends related directly to AI. In 2018, Webb expects the AI cloud and marketplaces for algorithms will continue to grow and the first personal robots will come to market.
See the GTC session schedule. Major sponsors include Facebook, IBM, Cisco, Dell EMC, Google Cloud, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro. Show attendees can vote for the world's top AI startups at NVIDIA's Inception Awards Finale on Tuesday, March 27, from 4:30-5:30 pm Pacific time. NVIDIA Deep Learning Institute certified instructors will deliver more than 100 hours of training to thousands of data scientists, using the latest AI frameworks and software development kits. Two Global Impact Award winners will receive $200,000 in prizes for their pioneering work addressing important social and humanitarian problems using GPU computing.
Every telco seems to be pushing out news about 5G these days. With Mobile World Conference taking place, and as noted having morphed from a consumer show to a a business event, this is hardly surprising. Alongside 5G, machine learning, AI and cloud are seen as the technologies that will revolutionize domains such as autonomous vehicles and smart cities. But is 5G really a game changer? ZDNet discussed with leaders in the field, focusing on the impact of 5G on data collection, storage, processing, and applications and the interplay with cloud and AI.
There's been no shortage of hype about 5G from experts at this week's Mobile World Congress in Barcelona, along with a sober assessment of how far off it really is. Every telco seems to be pushing out news about 5G these days. With Mobile World Conference taking place, and as noted having morphed from a consumer show to a a business event, this is hardly surprising. Alongside 5G, machine learning, AI and cloud are seen as the technologies that will revolutionize domains such as autonomous vehicles and smart cities. But is 5G really a game changer?
Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.