This paper describes the advantages and costs of patenting, as well as what can be patented today in the AI domain and what instead needs or should be kept undisclosed.
With recent breakthroughs in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), financial companies are beginning to pass the task of identifying and executing trades on to automated systems. Algorithmic Trading (AT), smart stock advisors, and ‘self-learning’ Reinforcement Learning (RL) agents now powerful tools in the financial decision-making process. In this white paper, we’ll discuss how financial companies can benefit from the ongoing AI revolution in their investment and technical analysis, portfolio management and diversification, and other important areas of investment decision-making. Hopefully, by the end of the paper, you’ll have a better understanding of how AI can boost a financial investment strategy.
This article provides a series of forecasts regarding the development of AI and robotics. We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. The interesting aspect of those is that are predictions made one year ago, and many turned out to be true.
This paper illustrates tips and tools to run a data science practice within an organization. It will also give some tools to understand the stage of data science maturity of the company.