Lecture, Keynote
Deep Learning State of the Art (2020)
Lex FridmanPamela McCordick, Alan Turing, Frank Rosenblatt, Yann LeCun, Geoffrey Hinton, Yoshua Bengio, Walter Pitts, Warren McCulloch, Alexei Evaknenko, V.G. Lapa, John Hopfield, Juergen Schmidhuber, Rodney Brooks, Sebastian Reuter, Jacob, Noah Brown, Chris Ferguson, Darren Elias, Jeremy Howard, Ian Goodfellow, Aaron Corville, Andrew Trask, Francois Chollet, David Silver, Robbie Allen, Victor Flevin, Ilias Esquiver, Peter Singer, George Washington, Stalin
- Predicts 2020 as a year of significant activity and progress following previous years, anticipating a shift in the popular press to declare the end of the deep learning era.
- Envisions a community environment by 2020 characterized by reduced hype, criticism, derision, and jealousy, replaced by solid research, respect, open-mindedness, and collaboration.
- Anticipates increased research output in reasoning, common sense, active learning, lifelong learning, multimodal/multitask learning, open domain conversation, and algorithmic ethics including fairness, privacy, and bias.
- Expects continued advancements in deep reinforcement learning applications, robotics, and specifically legged robotics and robot manipulation.
- Forecasts TensorFlow 2.0 and PyTorch 1.3 converging to combine strengths while eliminating weaknesses, with support for Python 2 ending on January 1st, 2020.
- Hopes for research to become framework-agnostic to facilitate easy model transfer between TensorFlow and PyTorch.
- Expects OpenAI and DeepMind to advance reinforcement learning frameworks to maturity levels comparable to OpenAI Gym by 2020.
- Predicts greater abstractions enabling scientists outside machine learning to utilize deep learning without extensive Python programming knowledge.
- Anticipates reasoning and common sense reasoning becoming central components of transformer-based language model work.
- Expects transformer context windows to expand from hundreds or thousands of words to tens of thousands, allowing for the processing of entire stories.
- Predicts the success of transformer architectures transferring to visual information and video analysis.
- Expects reinforcement learning to be utilized for exploring social behaviors in agents and their echo in human systems.
- Envisions applied deep learning innovation in autonomous vehicles focusing on active, multitask, lifelong, and online learning.
- Forecasts increased over-the-air updates for autonomous vehicles as a prerequisite for solving autonomy problems.
- Hopes for more public datasets of edge cases released by automotive companies alongside simulators like Carla and NVIDIA Drive Constellation.
- Expects reduced fear of AI and enhanced government-expert discourse regarding privacy, cybersecurity, and transparency.
- Identifies recommendation systems as the most exciting and powerful AI space for the next couple of decades due to societal impact.
- Anticipates breakthroughs in open domain conversation within the Alexa Prize, despite current estimates suggesting such achievement is two or three decades away.
- Predicts that within approximately 20 years, society will attribute rights to robots similar to those currently afforded to animals.
- Expects Artificial General Intelligence (AGI) systems to exist as companions and digital assistants rather than posing a threat to human life.
- Predicts AI systems will never become human masters, with the primary danger lying in tech company owners using these tools for control.