AI!=ML // ML is just a statistics, wrapped by marketers as AI
Here is a summary of the key points from Jeff Dean’s talk:
- Machine learning has advanced rapidly in the last decade, enabling major improvements in areas like computer vision, speech recognition, language understanding, and generation of images/video from text prompts.
- Increasing the scale of compute resources, data, and model sizes has been a key driver of these advances. Specialized hardware like Google’s Tensor Processing Units have accelerated large model training.
- Models like the Transformer and language models like GPT and Google’s Gemini have achieved state-of-the-art performance across many academic benchmarks in text, images, and multimodal understanding tasks.
- Techniques like chain-of-thought prompting can improve the reasoning abilities of large language models on complex tasks.
- Beyond consumer applications, machine learning is being applied to scientific domains like materials discovery, medical imaging diagnostics, and education/tutoring systems.
- As machine learning is deployed more widely, considerations around fairness, accountability, privacy and societal impact are increasingly important areas of research and practice.
- While large language/multimodal models are a major focus, there remain many open research areas around data quality, curricula learning, interpretability and exploring novel model architectures beyond Transformers.
“The automatic operations of System 1 generate surprisingly complex patterns of ideas, but only the slower System 2 can construct thoughts in an orderly series of steps.”– Daniel Kahneman in “Thinking, Fast and Slow”
