A robot sat for the math test during China's national college entrance exam, or gaokao, in the southwestern city of Chengdu on Wednesday, June 7, 2017. (Photo: China News Service/An Yuan)
Arcelor Mittal, the world's largest steel maker, worked with NNAISENSE to greatly improve steel defect detection. Cameras take pictures of the steel which is rapidly coming out of the steel mill.
NNs learned to do a better job, than the traditional approach did using humans, in identifying high-quality steel that can be sold for a higher price through patterns in the pictures. Such pattern recognition technology is applicable for thousands of industries.
NNAISENSE and Acatis, a German fund, co-founded Quantenstein. Quantenstein uses Machine Learning to pick stocks and manage portfolios. The new fund Quantenstein is about to launch aims at 3% outperformance w.r.t. MSCI world index, keeping comparable volatility. During walk forward testing, Quantenstein's new fund obtained approximately 5% outperformance (initial investment in Jan 2006), information ratio of 1.0 and performance p.a. of 12%. The main difference of Quantenstein to other funds is that no human in the loop, all AI-driven end-to-end. The AI receives fundamental data of companies, produces portfolios and weights, and adjusts portfolios regularly. Conventional systems for long-term value investing usually have a first stage where stocks are selected, followed by a second stage where Markowitz or other "crafted" methods are used to produce portfolio weights. Quantenstein's system learns also the second stage, given the optimization objectives such as high information ratio or Sharpe ratio. In addition to intelligent investment field, AI is also used in marketing and security of financial sector.
NNAISENSE recently worked with Volkswagen AG's Audi auto department to create a miniaturized car that learned to park itself without a teacher. The system used cameras to teach itself how to drive on its own. It different from others which imitate human teachers and rely on Lidar and radar to find their way around based on predetermined parameters.
Professor Jürgen Schmidhuber says that, ultimately, the mission of NNAISENSE is to create a general purpose AI that continually learns new skills on top of old skills, and even learns to learn new skills more quickly. On the way to reaching this goal, Professor Jürgen Schmidhuber's team is working on a variety of problems in partnership with industry that test different aspects of the system they are building, and provide essential insights that inform their on-going research plan. While finance and automotive do present different challenges, there are common underlying principles: both require learning to make predictions based on complex high-dimensional data, and learning to make the right decisions based on those predictions.
