Deep Learning - the Key Technology of Artificial Intelligence

Activity: Talk or presentationInvited talkscience-to-public

Description

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, language and vision benchmarks. Consequently it became the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or early elements of a sequence, therefore limits model selection. Most major advances in Deep Learning can be related to avoiding the vanishing gradient like unsupervised stacking, ReLUs, residual networks, highway networks, and LSTM networks. Currently, LSTM recurrent neural networks exhibit overwhelmingly successes in different AI fields like speech, language, and text analysis. LSTM is used in Google’s translate and speech recognizer, Apple’s iOS 10, facebook’s translate, and Amazon’s Alexa. We use LSTM in collaboration with Zalando and Bayer, e.g. to analyze blogs and twitter news related to fashion and health. In the AUDI Deep Learning Center, which I am heading, and with NVIDIA we apply Deep Learning to advance autonomous driving. In collaboration with Infineon we use Deep Learning for perception tasks, e.g. based on radar sensors. With Deep Learning we won the NIH Tox21 challenge and deploy it to toxicity and target prediction in collaboration with pharma companies like Janssen, Merck, Novartis, AstraZeneca, GSK, Bayer together with hardware-related companies like Intel, HP, Infineon, NVIDIA and others.
Period06 Nov 2019
Event titleunbekannt/unknown
Event typeOther
LocationAustriaShow on map

Fields of science

  • 101031 Approximation theory
  • 102 Computer Sciences
  • 305901 Computer-aided diagnosis and therapy
  • 102033 Data mining
  • 102032 Computational intelligence
  • 101029 Mathematical statistics
  • 102013 Human-computer interaction
  • 305905 Medical informatics
  • 101028 Mathematical modelling
  • 101027 Dynamical systems
  • 101004 Biomathematics
  • 101026 Time series analysis
  • 202017 Embedded systems
  • 101024 Probability theory
  • 305907 Medical statistics
  • 102019 Machine learning
  • 202037 Signal processing
  • 102018 Artificial neural networks
  • 103029 Statistical physics
  • 202036 Sensor systems
  • 202035 Robotics
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 101019 Stochastics
  • 101018 Statistics
  • 101017 Game theory
  • 101016 Optimisation
  • 102001 Artificial intelligence
  • 101015 Operations research
  • 102004 Bioinformatics
  • 101014 Numerical mathematics
  • 102003 Image processing

JKU Focus areas

  • Digital Transformation