I met Dan Selsam when we were toddlers. He liked solving math problems. We both liked the show "ThunderCats." I became a comedy writer. Dan became a computer scientist.

If you've got coin on who'll win at the 62nd annual Grammys this Sunday, predictive tips are available, and it's not insider trading, it's artificial intelligence (AI). Last year, Boston-based data science company DataRobot successfully chose 2019's song of the year, Childish Gambino's "This is America." This year, DataRobot hopes to revisit that win. Some of the top nominees are Lizzo, who has eight nominations, Lil Nas X with six nominations, and Billie Eilish with nominations in the top four categories, according to CBS News. Taylor Larkin, a data scientist with the company, used the DataRobot enterprise AI platform to predict the winners for song and record of the year.

If you've got coin on who'll win at the 62nd annual Grammys this Sunday, predictive tips are available, and it's not insider trading, it's artificial intelligence (AI). Last year, Boston-based data science company DataRobot successfully chose 2019's song of the year, Childish Gambino's "This is America." This year, DataRobot hopes to revisit that win. Some of the top nominees are Lizzo, who has eight nominations, Lil Nas X with six nominations, and Billie Eilish with nominations in the top four categories, according to CBS News. Taylor Larkin, a data scientist with the company, used the DataRobot enterprise AI platform to predict the winners for song and record of the year.

If you've got coin on who'll win at the 62nd annual Grammys this Sunday, predictive tips are available, and it's not insider trading, it's artificial intelligence (AI). Last year, Boston-based data science company DataRobot successfully chose 2019's song of the year, Childish Gambino's "This is America." This year, DataRobot hopes to revisit that win. Some of the top nominees are Lizzo, who has eight nominations, Lil Nas X with six nominations, and Billie Eilish with nominations in the top four categories, according to CBS News. Taylor Larkin, a data scientist with the company, used the DataRobot enterprise AI platform to predict the winners for song and record of the year.

The field of probabilistic numerics (PN), loosely speaking, attempts to provide a statistical treatment of the errors and/or approximations that are made en route to the output of a deterministic numerical method, e.g. the approximation of an integral by quadrature, or the discretised solution of an ordinary or partial differential equation. This decade has seen a surge of activity in this field. In comparison with historical developments that can be traced back over more than a hundred years, the most recent developments are particularly interesting because they have been characterised by simultaneous input from multiple scientific disciplines: mathematics, statistics, machine learning, and computer science. The field has, therefore, advanced on a broad front, with contributions ranging from the building of overarching generaltheory to practical implementations in specific problems of interest. Over the same period of time, and because of increased interaction among researchers coming from different communities, the extent to which these developments were -- or were not -- presaged by twentieth-century researchers has also come to be better appreciated. Thus, the time appears to be ripe for an update of the 2014 Tübingen Manifesto on probabilistic numerics[Hennig, 2014, Osborne, 2014d,c,b,a] and the position paper[Hennig et al., 2015] to take account of the developments between 2014 and 2019, an improved awareness of the history of this field, and a clearer sense of its future directions. In this article, we aim to summarise some of the history of probabilistic perspectives on numerics (Section 2), to place more recent developments into context (Section 3), and to articulate a vision for future research in, and use of, probabilistic numerics (Section 4).

Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund. Many of the world's largest hedge funds already rely on powerful computing infrastructure and quantitative methods--whether that's high-frequency trading, incorporating machine learning, or applying data science--to make trades.

Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund. Many of the world's largest hedge funds already rely on powerful computing infrastructure and quantitative methods--whether that's high-frequency trading, incorporating machine learning, or applying data science--to make trades.