We'll discuss what needs to happen to bring AI-driven investing to the financial services industry. We'll talk about hardware and software innovations led by companies like Google and NVIDIA that have increased the learning rate for industry participants. We'll also cover technical challenges faced by asset-management firms when implementing machine learning methods that require a complex infrastructure and large amounts of data. This makes for an incredibly tricky use case in the unforgiving, fast-paced environment of trading, where the signal-to-noise ratio is low. We will share three aspects of software engineering to consider for a machine learning-driven investing framework, and show a service architecture design for simulations to be run at an amazingly fast speed.
We'll talk about how artificial intelligence has led to market-leading innovation in trading and the huge opportunity of using deep learning in trading today. There are three dominant trades: fast information extraction ("speed trade"), trade construction ("stat arb"), and prediction ("market timing"). AI has been very successful in all three aspects. We have been key innovators in the speed trade, having started with a $10,000 risk limit and, over the last 10 years, making more than $1.4 billion in profits. The reason is a purist adherence to AI. There is a huge opportunity for using deep learning in the prediction part of the trade, which is not latency sensitive and is mostly about high accuracy. Our mission is to make investing a science, a research-driven utility, and not a competition or a game that it is today. Deep learning has had a lot of success in bringing method to social science settings. We believe over the next five to 10 years that every trading operation will become deep learning based. However, at this time there is a lot of opportunity for innovation using deep learning in trading.