Financial prediction problems, such as pricing securities and risk management, usually involve large data sets with complex interactions, making it difficult or impossible to specify in a full economic model. By applying deep learning methods we can produce more useful results more traditional methods. We spoke to Scott Treloar, Founder of Noviscient, to learn more.
Topics: Deep Learning, A I, Deep Learning in Finance Summit, FinTech
Using games to train machine learning models has proven to be increasingly successful in recent years, and has helped to spread public understanding of algorithms and deep learning methods through mainstream media coverage, such as Google DeepMind's use of Atari, Montezuma's Revenge, Space Invaders and more. We spoke to Junhyuk Oh from the University of Michigan to learn more.
Topics: Machine Learning, Deep Learning, A I, Machine Intelligence
Today, AI-powered machines can defeat the most skilled players in chess, game of Go, Jeopardy and poker. They can outperform best doctors in diagnosing most complex health conditions, yet when it comes to understanding a wide range of human emotions, AI is not very intuitive.
Topics: Big Data, A I, Machine Intelligence, Virtual Assistants
Amongst the lineup of academic influencers and industry leaders at the second annual Deep Learning in Healthcare Summit in London, Nils Hammerla, the Machine Learning Lead at Babylon Health presented 'Deep Learning in Health – It's Not All Diagnostics'. Here, Nils gives a little more insight into his work.
Topics: Personalised Medicine, Deep Learning, Healthcare
Last Tuesday, the Deep Learning in Healthcare Summit London took place at LSO St Luke’s. RE•WORK hosted 40 speakers and 200 attendees over the course of the 2-day summit to explore the latest developments and applications of Deep Learning (DL) within healthcare, medicine and diagnostics.
Topics: Personalised Medicine, Deep Learning, Healthcare, MedTech
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