Mar 15, 2023
In this podcast episode,
Ilya Sutskever, the co-founder and chief scientist at OpenAI, discusses his vision for
the future of artificial intelligence (AI), including large
language models like GPT-4.
Sutskever starts by explaining the importance of AI research and
how OpenAI is working to advance the field. He shares his views on
the ethical considerations of AI development and the potential
impact of AI on society.
The conversation then moves on to large language models and their
capabilities. Sutskever talks about the challenges of developing
GPT-4 and the limitations of current models. He discusses the
potential for large language models to generate a text that is
indistinguishable from human writing and how this technology could
be used in the future.
Sutskever also shares his views on AI-aided democracy and how AI
could help solve global problems such as climate change and
poverty. He emphasises the importance of building AI systems that
are transparent, ethical, and aligned with human values.
Throughout the conversation, Sutskever provides insights into the
current state of AI research, the challenges facing the field, and
his vision for the future of AI. This podcast episode is a
must-listen for anyone interested in the intersection of AI,
language, and society.
Timestamps:
(00:04) Introduction of Craig Smith and Ilya Sutskever.
(01:00) Sutskever's AI and consciousness interests.
(02:30) Sutskever's start in machine learning with Hinton.
(03:45) Realization about training large neural networks.
(06:33) Convolutional neural network breakthroughs and
imagenet.
(08:36) Predicting the next thing for unsupervised learning.
(10:24) Development of GPT-3 and scaling in deep learning.
(11:42) Specific scaling in deep learning and potential
discovery.
(13:01) Small changes can have big impact.
(13:46) Limits of large language models and lack of
understanding.
(14:32) Difficulty in discussing limits of language models.
(15:13) Statistical regularities lead to better understanding of
world.
(16:33) Limitations of language models and hope for reinforcement
learning.
(17:52) Teaching neural nets through interaction with humans.
(21:44) Multimodal understanding not necessary for language
models.
(25:28) Autoregressive transformers and high-dimensional
distributions.
(26:02) Autoregressive transformers work well on images.
(27:09) Pixels represented like a string of text.
(29:40) Large generative models learn compressed representations of
real-world processes.
(31:31) Human teachers needed to guide reinforcement learning
process.
(35:10) Opportunity to teach AI models more skills with less
data.
(39:57) Desirable to have democratic process for providing
information.
(41:15) Impossible to understand everything in complicated
situations.
Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter:
https://twitter.com/EyeOn_AI