Leaders in AI applications will talk about their personal paths from being research-focused grad students to results-focused product leaders. They will share lessons learned from which parts of academia did (and did not) carry over to making AI work in the real-world, and provide guidance to people pursuing a similar path.
Technology's tentacles have encroached every aspect of our lives. Sitting in the comfort of your home you can tune in to live discussions and gain new understanding about technologies that are reshaping our world view. NDTV Tech Conclave 2018 saw a congregation of leading minds in the technology, mobile, and digital industries. The conclave aimed to showcase and create opportunities by bringing together many of the top entrepreneurs, investors, enterprise leaders, academics, and policymakers from around the world. The moderator of this session outlined two diametrically opposite views of AI and threw it open to the panelists.
A panel of industry experts gathered at RSA 2018 in San Francisco to explore the role that machine learning and artificial intelligence is playing in the current cyber landscape. After opening the discussion by asking the panel to each give their own definition of what machine learning is, Ira asked the speakers to define what types of applications are most appropriate for the use of machine learning and AI. Hillard: The places where it is most mature is around speech and image processing, and also around fraud detection. "The technology should be an enabler to solving a problem but sometimes it gets lost in what's being accomplished." Friedrichs: Most people have woken up to the fact that machine learning and AI are not the panacea that marketing tells us they are, but they can add to the feature set of a product.
The key to getting better at deep learning (or most fields in life) is practice. Each of these problem has it's own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.
Freedman, Richard G. (University of Massachusetts Amherst) | Chakraborti, Tathagata (Arizona State University) | Talamadupula, Kartik (IBM Research) | Magazzeni, Daniele (King's College London) | Frank, Jeremy D. (NASA Ames Research Center)
The User Interfaces and Scheduling and Planning (UISP) Workshop had its inaugural meeting at the 2017 International Conference on Automated Scheduling and Planning (ICAPS). The UISP community focuses on bridging the gap between automated planning and scheduling technologies and user interface (UI) technologies. Planning and scheduling systems need UIs, and UIs can be designed and built using planning and scheduling systems. The workshop participants included representatives from government organizations, industry, and academia with various insights and novel challenges. We summarize the discussions from the workshop as well as outline challenges related to this area of research, introducing the now formally established field to the broader user experience and artificial intelligence communities.