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The state of AI in 2020: Democratization, industrialization, and the way to artificial general intelligence

In the State of AI Report 2020, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30%. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI.

The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand, and concentration of talent working in the field, large platforms, financing, and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.

Some researchers, Benaich and Hogarth noted, feel that progress in mature areas of machine learning is stagnant. Others call for advancing causal reasoning and claim that adding this element to machine learning approaches could overcome barriers.

Causality, Hogarth said, is arguably at the heart of much of human progress. From an epistemological perspective, causal reasoning has given us the scientific method, and it’s at the heart of all of our best world models. So the work that people like Judea Pearl have pioneered to bring causality to machine learning is exciting. It feels like the biggest potential disruption to the general trend of larger and larger correlation driven models.

The original article can be found here.

Regarding to AI and Causality, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.”  In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea also contributes to Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.