The Future of Artificial Intelligence

Professor Joseph Gonzales, specialized in ML and data systems, highlights the evolution of the AI field since he was first a graduate student to now. He highlights the change from statistical graphical models to more computationally expensive data driven models. 

As someone coming into the field, it's interesting to realize the innovations that enabled and continue to motivate the growth of deep learning.

He highlights key innovations:

• python data processing --> availability to data
• big data systems for ML
• development of model APIs (scikit-learn, XGBoost, Keras)

According to Prof. Gonzales, the next step in ML is "reliably deploying and managing these trained models to render predictions in real-world settings." 

In a talk at UC Berkeley this Tuesday, Yann LeCun, one of the founding fathers of deep learning, discussed approaches for more generalizable and autonomous AI.

Current deep learning frameworks require error training to learn very specific tasks and often fail to generalize to even out of distribution input on the same task. Specifically with reinforcement learning, we need a model to "fail" hundreds of times for it to start learning.

As a potential lead away from specialized AI, LeCun proposes a novel architecture composed of five sub-models mirroring the different parts of our brain. Specifically, one of the modules would ressemble memory as a “world model module”. Instead of each model learning a representation of the wold specific to their task, this framework would maintain a world model usable across tasks by different module.

See full paper: https://openreview.net/pdf?id=BZ5a1r-kVsf