Events

Jun29

Dissertation Talk: Real World Robot Learning: Learned Rewards, Offline Datasets and Skill Re-use

Zoom
  • Avi Singh
Robots that can operate in an open, unstructured environment and perform a wide range of tasks have been a long-standing goal of artificial intelligence. In this talk, I will go over how we can build such robots using tools from deep learning and reinforcement learning. In particular, I will address some of the key bottlenecks to applying deep reinforcement learning to real world domains like...
Jul06

Dissertation Talk: Detecting Synthetic Faces by Understanding Real Faces

Zoom
  • Shruti Agarwal, EECS
The creation of sophisticated fake videos has been largely relegated to Hollywood studios or state actors. Recent advances in deep learning, however, have democratized the creation of sophisticated and compelling fake images, videos, and audios. This synthetically-generated media -- so-called deep fakes -- continue to capture the imagination of the computer-graphics and computer-vision...
Sep10

Deep Learning-Assisted Analysis of Anomalous Nanoparticle Surface Diffusion in Liquid Phase TEM: Nano Seminar Series

180 Tan Hall
  • Dr. Vida Jamali, UC Berkeley, CBE
Beginning with Robert Brown’s original observation in 1828, various techniques have been developed to study the hydrodynamics and interactions of particles in solution. These techniques have inspired or been followed by development of theories that are capable of describing these fundamental aspects of micron-scale particles. Yet, many of the underlying assumptions break down at the nanoscale...
Sep23

Tanner Lectures on Human Values: Excavating “Ground Truth” in AI: Epistemologies and Politics in Training Data

Alumni House
  • Kate Crawford, Miegunyah Distinguished Visiting Fellow, University of Melbourne
  • Trevor Paglen
  • Marion Fourcade, Professor, UC Berkeley, Department of Sociology
  • Angjoo Kanazawa, Assistant Professor, UC Berkeley, Department of EECS
The last decade has seen a dramatic capture of digital material for machine learning production. This data is the basis for sense-making in AI, not as classical representations of the world with individual meaning, but as mass collections: ground truth for machine abstractions and operations. OpenAI’s GPT-3 language model is trained on a corpus of 1 billion words, ImageNet contains over 14...
Sep24

Tanner Lectures on Human Values: Excavating “Ground Truth” in AI: Epistemologies and Politics in Training Data

Alumni House
  • Kate Crawford, Miegunyah Distinguished Visiting Fellow, University of Melbourne
  • Trevor Paglen
  • Marion Fourcade, Professor, UC Berkeley, Department of Sociology
  • Angjoo Kanazawa, Assistant Professor, UC Berkeley, Department of EECS
The last decade has seen a dramatic capture of digital material for machine learning production. This data is the basis for sense-making in AI, not as classical representations of the world with individual meaning, but as mass collections: ground truth for machine abstractions and operations. OpenAI’s GPT-3 language model is trained on a corpus of 1 billion words, ImageNet contains over 14...
Oct05
The inaugural ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) aims to highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities. The conference is organized by the...