Oriol Vinyals is the Research Director and Deep Learning Lead at DeepMind. Please support this podcast by checking out our sponsors: - Shopify: https://shopify.com/lex to get 14-day free trial - Weights & Biases: https://lexfridman.com/wnb - Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off - Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium EPISODE LINKS: Oriol's Twitter: https://twitter.com/oriolvinyalsml Oriol's publications: https://scholar.google.com/citations?user=NkzyCvUAAAAJ DeepMind's Twitter: https://twitter.com/DeepMind DeepMind's Instagram: https://instagram.com/deepmind DeepMind's Website: https://deepmind.com Papers: 1. Gato: https://deepmind.com/publications/a-generalist-agent 2. Flamingo: https://deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model 3. Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.14165 4. Emergent Abilities of Large Language Models: https://arxiv.org/abs/2206.07682 5. Attention Is All You Need: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (05:18) - AI (20:14) - Weights (26:33) - Gato (1:01:22) - Meta learning (1:15:21) - Neural networks (1:37:46) - Emergence (1:44:30) - AI sentience (2:08:27) - AGI
In this podcast episode, Lex Fridman is joined by Aurel, the research director and deep learning lead at DeepMind, to talk about history, philosophy, creativity, and intelligence in artificial intelligence, with some musings on consciousness, free will, and the future of humanity.
The idea of AI completely replacing human interviewers doesn't seem exciting, but self-play interventions might make it interesting to learn a topic through listening to interviews. Excitement labeling still has to come from humans as computational measures of excitement do not exist yet.
The speaker discusses the limitations of constantly training machine learning models from scratch and proposes the idea of growing models over time, similar to how the universe and species evolve.
The use of single models in machine learning, such as GPT-3 or BERT, has proven to be effective in teaching the model multiple tasks in various domains including language and vision.
The transformer model is a type of recurrent neural network that can take a sequence of observations, which could be words or images, and actions to predict the next action and observation.
The team behind Gato has developed a method for compressing images in a way that is more suitable for use in neural networks, allowing for more efficient data processing. However, the effectiveness of the system still has some room for further exploration and refinement.
The speaker explains the approach of machine learning when dealing with images and the quantization step, which is driven by the statistics of the images, the common patterns found, and the compression of images based on these statistics.
The Flamingo AI model's significant contribution to different projects is its exploratory nature, which makes it easier to upload and communicate images and convert words into vision. It provides few-shot learning and other capabilities which were not initially measured during development.
The possibility of freezing weights and combining different modalities in machine learning models is being explored, with hopes to have a set of weights that can be used to teach different tasks, such as playing StarCraft, in the future.
This podcast explores the power of transformers in machine learning, providing insights into why they are enabling machines to more accurately predict outcomes in complex data analysis.
The conversation explores the intersection of human endeavors and machine learning, delving into how people can work with models to come up with ideas that work, as well as how the tiny details of engineering play a huge role in technological advancements.
The threshold for performance in language models like transformers may be reached once the right questions are asked by the querying system. However, benchmarks need to capture the unpredictability and edge cases of the real world, which current benchmarks like ImageNet lack.
The complexity and magic of neural networks can be demystified with a better understanding of the math involved in creating models, which can be as simple as 10 lines of code and a dump of the internet. Language models have transformed our world, but there is still much to be explored about the role of AI systems in a human world.
The potential for AI to display sentience raises ethical concerns, and laws may be put in place to restrict engineering such systems unless they are explicitly designed to display it or are designated as a pet. However, science fiction has helped prepare society to consider and discuss these concerns.
The shape of the hardware needs to be based on AI methods that might be needed to scale. There is a notion of the hardware lottery for scale that might impact progress towards truly intelligent agents.
In this podcast episode, Ariel Vinales discusses the importance of balance in the development of AI and how it aligns with his core beliefs as a researcher. He and Lex also talk about their views and expectations for the singularity and how it will change the world.