How Machine Learning Pushes Us to Define Fairness

https://hbr.org/2019/11/how-machine-learning-pushes-us-to-define-fairness?utm_source=twitter&utm_medium=social&utm_campaign=hbr

  • David Weinberger – As we look at higher levels of abstraction — from using sliders to adjust the mixes in the bins, to questions about optimizing possibly inconsistent values — ML is teaching us that fairness is not simple but complex, and that it is not an absolute but a matter of trade-offs. 
  • AI permeates capital markets

    https://www.finextra.com/newsarticle/34515/ai-permeates-capital-markets—study

    Greenwich Associates report – With 44% already using AI and another 17% reporting plans to implement it in trading in the next 12 to 24 months, a solid majority of market participants — including buy-side institutions, sell-side firms, exchanges, and others — soon will be using AI in the securities trading process.

    S&P Global Market Intelligence launches Textual Data Analytics through Xpressfeed

    https://martechseries.com/analytics/data-management-platforms/sp-global-market-intelligence-launches-textual-data-analytics-xpressfeed/

    S&P Global Market Intelligence announced the launch of Textual Data Analytics (TDA), a sophisticated new data offering which applies Natural Language Processing to generate sentiment scores and behavioral metrics based on company transcripts

    What’s the Difference Between AI, ML, Deep Learning, and Active Learning?

    https://www.datanami.com/2019/09/17/whats-the-difference-between-ai-ml-deep-learning-and-active-learning/

    Kiran VajapeyToday, the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably. While the terms are related, they mean different things. We map out how they all relate to one another, so your team can find the best candidates, best approaches and best frameworks as you embark upon your AI journey.

    Buy Side Turning to Technology to Meet Today’s Challenges

    https://tabbforum.com/opinions/buy-side-turning-to-technology-to-meet-todays-challenges/

    Monica Summerville – The practical reality of this means robotic process automation (RPA), as opposed to AI – e.g., machine learning (ML) and Natural Language Processing (NLP) techniques – are more likely to be implemented to start.

    FactSet Joins Forces with DataRobot

    https://investingnews.com/daily/tech-investing/fintech-investing/factset-joins-forces-with-datarobot/

    FactSet (NASDAQ:FDS), financial analytics company announced that it has partnered with DataRobot, an enterprise AI company. DataRobot’s services integrate predictive analytic tools into investment workflows, extending from predicting macroeconomic events to applications for bond performance. As quoted in the press release: “Clients are looking for more effective data and AI tools that will help them …