LeCun also discussed the future of AI hardware, stating that while GPU technology is currently the gold standard for AI, future chips may be neural, deep learning accelerators. He expressed skepticism about quantum computing, stating that many problems solvable with quantum computing can be solved more efficiently with classical computers. He also suggested that while quantum computing is a fascinating scientific topic, its practical relevance and the feasibility of creating useful quantum computers is less clear.
Key takeaways:
- Yann LeCun, chief AI scientist at Meta, believes that current AI systems are decades away from reaching some semblance of sentience, equipped with common sense that can push their abilities beyond merely summarizing mountains of text in creative ways.
- LeCun and other Meta AI executives have been heavily researching how the so-called transformer models used to create apps such as ChatGPT could be tailored to work with a variety of data, including audio, image and video information.
- Meta relied on 16,000 Nvidia A100 GPUs to train its Llama AI software. LeCun said that the GPU technology is still the gold standard when it comes to AI, but the computer chips of the future may not be called GPUs, they are just neural, deep learning accelerators.
- LeCun is skeptical about quantum computing, stating that the number of problems you can solve with quantum computing, you can solve way more efficiently with classical computers. He also questioned the practical relevance and the possibility of actually fabricating quantum computers that are actually useful.