Robotics is no longer just a hobby but serious stuff. NVIDIA’s Deep Learning Institute is now working with online learning provider Udacity to develop a programme that will immerse students in the field of robotics, giving them career-ready skills.
The need for deep learning skills is increasing as more and more companies and industries hop on the bandwagon. Launch a little more than a year ago, NVIDIA’s Deep Learning Institute (DLI) has already trained tens of thousands of students, developers and data scientists.
And the company is expanding its DLI offerings with:
- New partnerships: Team up with Booz Allen Hamilton and deeplearning.ai to train thousands of students, developers and government specialists in artificial intelligence (AI).
- New University Ambassador Program: Instructors worldwide can teach students critical job skills and practical applications of AI at no cost.
- New courses: More courses are added to teach domain-specific applications of deep learning for finance, natural language processing, robotics, video analytics, and self-driving cars.
Singapore’s aim to be an artificial intelligence (AI) hub has been boosted with two initiatives — the setting up of a shared AI platform for researchers and the awarding of scholarships to develop AI talents.
At the NVIDIA AI Conference in Singapore yesterday, NVIDIA and Singapore’s National Supercomputing Centre (NSCC) agreed to establish a platform to bolster AI capabilities among its academic, research and industry stakeholders and in support of AI Singapore (AISG), a national programme set up in May to drive AI adoption, research and innovation in Singapore.
Called AI.Platform@NSCC, it will provide AI training, technical expertise and computing services to AISG, which brings together all Singapore-based research and tertiary institutions, including the National University of Singapore (NUS), Nanyang Technological University (NTU), Singapore University of Design and Technology (SUTD), Singapore Management University (SMU), as well as research institutions in the Agency for Science, Technology and Research (A*STAR).
NVIDIA is bringing its wealth of artificial intelligence (AI) solutions and expertise to the International Conference on Machine Learning (ICML) in Sydney.
Held at Sydney International Convention Centre from August 6 to 11, the event is expected to attract up to 3,000 participants, primarily faculty, researchers and PhD students in machine learning, data science, data mining, AI, statistics, and related fields.
The NVIDIA booth (Level 2, The Gallery, Booth #4) will feature many firsts in Australia, such as demos on 4K style transfer, a deep neural network to extract a specific artistic style from a source painting, and then synthesises this information with the content of a separate video; self-driving auto using the Drive PX2 AI car computing platform; Deepstream SDK that simplifies development of high performance video analytics applications powered by deep learning; and NVIDIA Isaac, the AI-based software platform lets developers train virtual robots using detailed and highly realistic test scenarios.
Interest in deep learning is growing so strongly that NVIDIA expects to train 100,000 developers this year — that’s 10 times more than last year —through its Deep Learning Institute (DLI).
According to research firm IDC, 80 percent of all applications will have an artificial intelligence (AI) component by 2020.
Greg Estes, Vice President of Developer Programs at NVIDIA, noted that there is a hunger for deep learning training. He cited the example of a DLI training at India Institute of Technology (IIT) in India where people came at 7.30am to try to sign up for a fully subscribed course.
Facebook is developing new artificial intelligent (AI) systems to help manage the vast amount of information — such as text, images and videos — generated daily so people can better understand the world and communicate more effectively, even as the volume of information increases.
It has worked with NVIDIA on Caffe2, a new AI deep learning framework that allows developers and researchers to create large-scale distributed training scenarios and build machine learning applications for edge devices.
Providing AI-powered services on mobile is a complex data processing task that must happen within the blink of an eye. Increasingly, the processing of lightning-fast AI services requires GPU-accelerated computing, such as that offered by Facebook’s Big Basin servers, as well as highly optimised deep learning software that can leverage the full capability of the accelerated hardware.