We're working on an infographic and article for the Huffington Post about the trends and limitations of autonomous vehicles, and we'd love for our readers to be involved! Our aim is to gather different perspectives and knowledge to create an informative report on the future of smart artificial intelligence in the transport industry. Read on for details on contributing.
Topics: Machine Learning, A I, Sensors, Connected Car
GPUs have been integral to advancements in artificial intelligence, specifically deep learning, in recent years. We spoke to Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, to learn more about his work and the spreading impact of deep learning, as well as the software and hardware driving the AI revolution.
Topics: Machine Learning, Neural Networks, Deep Learning, Hardware
Thanks to the collision of machine learning and IoT, driverless technology is advancing rapidly, and advancements are expected to continue on this trajectory with technology giants like Google, Intel et al working on creating the smartest vehicle on the market. We interviewed Teymur Sadikhov, Senior Vehicle Intelligence Engineer in Autonomous Driving, to learn more about recent advancements and key challenges in autonomous vehicle technologies.
Topics: Machine Learning, Deep Learning, A I, Connected Car
While supervised neural nets trained on huge datasets can achieve impressive performances in tasks such as computer vision and speech recognition, they are often criticized because their internal representations are lacking in interpretability. We spoke to Charlie Tang, Research Scientist at Apple and Deep Learning Summit speaker, about his work in the field that aims to address these concerns.
Topics: Machine Learning, Neural Networks, Deep Learning, A I
At the heart of artificial intelligence (AI) is data. AI needs knowledge. Read the takeaways from Yoshua Bengio's Keynote "Deep Learning Frameworks" from the Deep Learning Summit, Boston 2016, to learn how Deep Learning has progressed through time starting from supervised learning, progressing to speech recognition and computer vision, until it reached human level processing.
Topics: Big Data, Deep Learning, A I, Deep Learning Summit
In January, we held the Deep Learning Summit and Virtual Assistant Summit in San Francisco, with presentations from experts at OpenAI, Google, Facebook, Stanford University, Netflix, x.ai, Yale University, Slack, Jibo, UC Berkeley and more. Read on for presentations by Ilya Sutskever, Research Director at OpenAI, and Anjuli Kannan, Software Engineer at Google.
Topics: Machine Learning, Deep Learning, Virtual Assistant Summit, A I
RE•WORK announces its 11th global Deep Learning Summit in Singapore taking place on 27-28 April 2017. RE•WORK will bring together AI pioneers from various industries to explore the latest advancements in the deep learning. Topics covered at this event will include Deep Learning Algorithms,Autonomous Vehicles,AI for Business Efficiency, Deep Learning for Enterprise, and Predictive Intelligence.
Topics: Deep Learning, Press Releases, Deep Learning in Finance Summit, FinTech
Although deep learning models are giving increasingly advanced results in diverse problems, their lack of interpretability is a major problem, especially in fields such as genomics. We spoke to Avanti Shrikumar, a PhD student in Computer Science at Stanford University, to learn more.
Topics: Big Data, Deep Learning, Data Mining, MedTech
The technologies driving forward a new era of autonomous vehicles have been accelerating exponentially in the past few years. The futuristic cars that until recently were only found in science fiction could be with with us sooner than you think, with the global connected car market size is expected to reach $180 billion by 2022. We spoke to Bryan Mistele, President and CEO of INRIX, about how breakthroughs in location technology, connectivity and big data are poised to transform urban mobility.
Topics: Machine Learning, Deep Learning, A I, Smart Transport
Today’s healthcare system was not built for a seamless integration of rapidly emerging technologies, such as machine learning innovations. In this video presentation from the 2017 Deep Learning Summit in San Francisco, Will Jack, CEO of Remedy Health, explores the difficulties of integration and deployment, and how interpretable models can better tackle tasks such as diagnosis, physician education and treatment planning.
Topics: Healthcare, Deep Learning, Diagnostics, A I