IEEE VIC CIS Talk on Synthetic Data for AI
IEEE VIC CIS Chapter
Category: Distinguished Lecturer Program (DLP)
Title: Opportunities, Challenges, and Threats Using Synthetic Data for AI
Speaker: Dr Narayan Srinivasa (IEEE Distinguished Speaker, IEEE Fellow), Intel Corporation, Santa Clara, USA
Time: 12.00 – 1.30 pm (AEST) Friday 26th July 2024
VTool Link: https://events.vtools.ieee.org/m/427565
For further details, contact: Malka N. Halgamuge, Chair VIC CIS (malka_nisha@ieee.org)
VIC CIS Chapter website: https://r10.ieee.org/victorian-cis
This is a part of the IEEE Victorian Computational Intelligence Society (CIS) series of talks. The online delivery is kindly hosted by IEEE Victorian Section.
Date and Time
Location
Hosts
Registration
- Date: 26 Jul 2024
- Time: 12:00 PM to 01:00 PM
- All times are (UTC+10:00) Canberra
-
Add Event to Calendar
- Contact Event Host
- Co-sponsored by IEEE VIC CIS Chapter; IEEE VIC Section
- Starts 14 July 2024 08:00 PM
- Ends 26 July 2024 01:00 PM
- All times are (UTC+10:00) Canberra
- No Admission Charge
Speakers
Narayan of Intel Corporation
Opportunities, Challenges, and Threats Using Synthetic Data for AI
Both industry leaders and government bodies are channeling substantial investments into artificial intelligence (AI) to enhance productivity and drive automation. Progressive companies are already setting the pace. A 2019 Forrester study projected that by 2025, approximately 75% of businesses will have integrated AI into their technology and operational frameworks, making it a core component across all functions.
A critical element for AI development is data – and not just any data, but vast amounts of high-quality, precise data are essential for constructing AI models that are effective in real-world applications. While tech giants such as Microsoft, Google, Meta, and Amazon readily amass this kind of data through their products and services, many other companies face challenges in accessing and/or creating the necessary datasets.
In my presentation, I will explore the role of synthetic data generation, powered by AI, as a solution to these data accessibility issues. Additionally, I will examine the potential benefits, obstacles, and risks associated with these methods, particularly the widespread distribution of AI capabilities and the acceleration of AI integration across various sectors.
Biography:
Narayan Srinivasa received his Ph. D. from the University of Florida in Gainesville in 1994. He was a Beckman Post-Doctoral Fellow in the Human-Computer Intelligent Interaction group at the Beckman Institute, University of Illinois at Urbana Champaign from 1994-1997. Between 1998-2015, he was with HRL Laboratories in Malibu CA where he became Principal Scientist and the Director for Neural and Emergent Systems. At HRL, he worked on a wide range of AI projects including visual perception and computer vision, signal processing and sensor fusion, brain-inspired computing, and robotics. He served as Principal Investigator on different DARPA programs including BICA, SyNAPSE, Physical Intelligence, and UPSIDE. He joined Intel Labs in 2016 as Chief Scientist to lead the development of neuromorphic technology and played a key role in the development of the Loihi neuromorphic chip. He is currently Senior Principal AI Engineer and Director for Machine Intelligence Research Programs at Intel Labs. He is responsible for accelerating Intel Labs' research on problems with high risk but also with a potential for high rewards to Intel. He has 120 issued US patents with several patents pending and over 100 articles in journals, magazines, and conferences. He is a Fellow of the IEEE
Address:Intel Corporation, 2200 Mission College Boulevard, Santa Clara, CA, United States, 95054
Virtual ONLY