STAC's working definition of artificial intelligence (AI) encompasses both neural approaches like Deep Learning as well as non-neural machine learning approaches such as support vector machines and genetic algorithms.
The financial markets were early adopters of AI, and its use is growing: growing in the number of organizations using AI techniques, the range of techniques being deployed, and the purposes they are putting them to.
STAC focuses on two aspects of AI: what is possible and how to make what is possible doable.
With respect to the first question, there is ongoing--sometimes conflicting--dialog within the STAC Benchmark Council about use cases, including applying Deep Learning to trading, and whether AI is better for developing strategies directly or providing clues to human quants, and where Deep Learning fits in the pantheon of AI techniques.
There have also been overviews of artificial intelligence as a whole and Deep Learning in particular, as well as specific cases, such as using Deep Learning to predict market movements or to understand interactions that happen by voice, or using FPGA to accelerate strategy development through genetic algorithms.
The growth of AI in financial markets is a significant driver for other hot topics within the STAC community, including quant finance as a whole, the role of cloud computing, and the storage and memory revolution.
STAC is currently setting up a working group around artificial intelligence benchmarking. If you're interested, please contact us.