Announcements: We are honored that Google's TensorFlow Probability (TFP) Team has chosen our advisory services to design and develop educational materials about the application of TFP to computational problems in finance. TFP is Google's latest, open source, probabilistic machine learning library.
Our first blog post and TFP model is about the trinity of errors inherent in all financial models and the urgent need to use probabilistic modeling and machine learning technologies to mitigate them. We are delighted that this blog post has received glowing reviews in the AI/data science communities.
In 2005, Deepak invented a bayesian machine learning method and system for managing the risks and returns of project portfolios. It is a unique framework and has been cited by IBM (two times), Accenture and Kyung Hee University's Industry-Academia Collaboration Foundation (South Korea). See here for more details.
Humans and machines have found it extremely difficult to predict asset price returns. Low signal to noise ratio and a continually changing trading environment are the two main reasons for this low level of success.
We have successfully tackled this intractable prediction problem for over a decade and gained invaluable insights and experience into the theory and application of AI/ML technologies.
We welcome challenging prediction problems to which we can apply our expertise. It is also our experience that such challenges only lead to deeper insights and understanding of AI/ML for all involved in these exciting projects.
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