Industry releases more "significant" machine learning models than academia, with 32 released in 2022 compared with three. Until 2014, most significant machine learning models were released by academic institutions. Performance on traditional benchmarks is saturating. State-of-the-art results continue to be published, but year-over-year improvement on many benchmarks continues to be marginal. Training of AI models has environmental implications, with BLOOM's training run emitting 25 times more carbon than a single air traveller on a one-way trip from New York to San Francisco.

The annual report keeps track, collects e displays AI-related data, to support meaningful decisions, and advance AI responsibly and ethically. The AI Index Report supports many different organizations to track progress in artificial intelligence. These organizations include: the Center for Security and Emerging Technology at Georgetown University, LinkedIn, NetBase Quid, Lightcast, and McKinsey. The AI Index Report also expanded its tracking of global AI legislation from 25 countries in 2022 to 127 in 2023. The demand for AI-related job skills is increasing in virtually all industries (in the US).

We have been building models to follow historical trends and make predictions on what will happen based on past data. Now, we have machines that can observe the environment, learn the "unspoken rules" of the environment and adapt its actions to explore & exploit the environment like a human; just don't hold your breath for artificial general intelligence (yet). AI in 2018 is not just about games and we now see real-world applications. In healthcare, deep learning models can perform as well as a human expert in analyzing electron microscopy or in detecting eye diseases. For the environment and climate, AI is helping to build better climate models, mapping millions of solar roofs in the US, detecting hyperspectral signatures of underwater objects to monitor ocean health & many other animal conservation works.

Finally for the past few months I have been working on AI for medicine and healthcare. I am also happy to see that the rate of innovation in less "traditional" domains like healthcare is quickly picking up. AI and ML have been applied to medicine with years, starting with expert and Bayesian systems in the 60s and 70s. However, I often find myself citing papers that are only a few months old. Some of the recent innovations presented this year include the use of Deep RL, GANs, or Autoencoders to represent patient phenotypes.