1/Several leaders share their hopes for AI in 2023, including finding key missing pieces that will enable algorithms to reason, building a personal data timeline, improving AI processes, discovering new principles for explainability, and using generative AI for active learning.
2/Yoshua Bengio wants to develop new architectures that can discover and reason with high-level concepts, rather than just brute force the learning process by scaling up existing models’ data and compute. deeplearning.ai/the-batch/yosh…
3/Alon Halevy envisions AI capturing massive amounts of data from your daily life -- photos, browsing, purchases -- and fusing it into a personal timeline that helps you track and achieve your goals, while preserving privacy.
deeplearning.ai/the-batch/data…
deeplearning.ai/the-batch/data…
4/Douwe Kiela points out key directions: Multimodality, grounding, and interaction so AI understands us better; alignment, attribution, and uncertainty to make models safer; data-centric AI to improve scaling; and better ways to evaluate AI models.
deeplearning.ai/the-batch/douw…
deeplearning.ai/the-batch/douw…
5/Been Kim discusses AI explainability. AI has taken an engineering-centric approach, where researchers devise techniques via trial and error, and she urges developing fundamental scientific principles that make explanations more trustworthy and accurate. deeplearning.ai/the-batch/been…
6/Reza Zadeh sees generative AI bringing progress to active learning, where a system picks its own examples to be labeled to improve the data. With generative AI, he sees a potential revolution in algorithms generating new data to request to be labeled.
deeplearning.ai/the-batch/reza…
deeplearning.ai/the-batch/reza…
7/Exciting times ahead, and thank you Yoshua Bengio, @AlonHalevy, @douwekiela, @_beenkim, and @Reza_Zadeh for writing for The Batch!
• • •
Missing some Tweet in this thread? You can try to
force a refresh
