Price changes in financial products are largely random but can often be supplemented by features, providing additional structure that can be exploited for trading profits. Experienced traders are skilled at identifying these features and deploying profitable exploits, with the use of Q- function based reinforcement learning and DQNs. We spoke to David Samuel, Co-Founder of RKR Epsilon & Predictive Machines, to learn more about applying deep learning in financial markets.
Topics: Deep Learning, A I, Deep Learning Summit, Stock Market Prediction
Deep Learning neural networks, or "neural nets", have become a common term in discussions around artificial intelligence, since their use has revolutionised performance across multiple domains like computer vision and speech recognition. We spoke to Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about training neural nets, the recent uptake of machine learning, future applications and more.
Topics: Machine Learning, Neural Networks, Deep Learning, A I
While tech companies battle to be the first to create fully autonomous vehicles, there is a quiet evolution happening in the background that is building the foundation for these vehicles: data collection, with insights powered by artificial intelligence and machine learning. We spoke to John Cordell, Chief Product Officer at Xevo AI, to learn more about how connected cars of today will shape the fully autonomous vehicles of tomorrow.
Topics: Machine Learning, A I, Smart Transport, Connected Car
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