AI Articles and Analysis in Finance

Episode Summary: Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off is in finance and hedge funds. In this episode, we speak with Numerai Founder Richard Craib, whose company is crowdsourcing a machine learning hedge fund. Their model is based on pooling data science talent from all over the world and using "anonymous" models to train financial data. These models compete against one another, and the winning models' creators are rewarded in bitcoin - a process based entirely on encryption and anonymity. Craib speaks about his overarching vision for the company, and also delves into his thoughts on the past, present, and future of AI applications in finance.
 

Episode Summary: Big data is often a buzz word, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information  - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, chief analytics officer and economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

Episode Summary: Fifteen years ago, investing in AI may have seemed a bit far-fetched, but today it's not at all a rare occurrence; however, it's more rare to find entire firms dedicated to investing entirely in AI. In today's episode, we're joined by Saman Farid, co-founder of Comet Labs, an investment firm focused on investment in AI companies across industries. He speaks about his investment hypothesis in the future of AI, why he’s decided to hone his funds in this domain, and the different domains where he believes AI is ripe to disrupt on a global level in the coming few years.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).

Episode Summary: There are hedge funds and financial institutions that already use real-time data and sentiment analysis from social media, articles and videos in real-time to potentially make better trading decisions - but what does it mean when those same companies can use real-time satellite information to detect company activities and make trades based on that data? In this episode, Research Director of Capital Markets at Celent Securities discusses the focus on emerging technologies in trading and finance. He talks about the way that analytics and machine learning have affected the ways banks operate, the kinds of data that hedge funds and individual investors now have at their fingertips, and what that means for the future implications of AI-related technology in the finance world.

Episode Summary: Tad Slaff is the founder of Inovance Financial Technologies, the creator of TRAIDE - a strategy creation platform that uses machine learning algorithms to help traders uncover patterns in assets and indicators and build more reliable trading strategies. In this episode, Tad speaks about the state of machine learning in finance today, and touches on how future applications of machine learning and trends may alter what gives an edge to one hedge fund or institutional investor over another.

Science Magazine’s report on Friday that an artificial intelligence system was caught stealing banking customers’ money may have made you rethink vesting your funds in the burgeoning technology. But have no fear – the article was an April Fool’s joke.

White collar professions were once considered safe from automation. It was blue collar work such as labor and manufacturing jobs that appeared at risk of becoming redundant in the wake of advancing technologies. But according to the Word Economic Fund – who held a conference last week in in Davos Switzerland – white collar work is not so secure as it seemed. AI systems continue to advance and challenge the status quo.