When used correctly, they can help us unpick some of the mysteries of our biology, and our mortality. Be honest: Have you ever looked up someone from your childhood on social media with the sole intention of seeing how they've aged? One of my colleagues, who shall remain nameless, certainly has. He recently shared a photo of a former classmate. "Can you believe we're the same age?" he asked, with a hint of glee in his voice. A relative also delights in this pastime. "Wow, she looks like an old woman," she'll say when looking at a picture of someone she has known since childhood. The years certainly are kinder to some of us than others. But wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging, under the hood. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging (such as elevated cholesterol or markers of inflammation), might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Longevity clinics offer a mix of services that largely cater to the wealthy.

Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to identify regimes in time series data, and one-dimensional asset returns in particular. In this paper, we begin by studying in detail the behaviour of the Wasserstein k-means clustering algorithm applied to synthetic one-dimensional time series data. We study the dynamics of the algorithm and investigate how varying different hyperparameters impacts the performance of the clustering algorithm for different random initialisations. We compute simple metrics that we find are useful in identifying high-quality clusterings. Then, we extend the technique of Wasserstein k-means clustering to multidimensional time series data by approximating the multidimensional Wasserstein distance as a sliced Wasserstein distance, resulting in a method we call `sliced Wasserstein k-means (sWk-means) clustering'. We apply the sWk-means clustering method to the problem of automated regime detection in multidimensional time series data, using synthetic data to demonstrate the validity of the approach. Finally, we show that the sWk-means method is effective in identifying distinct market regimes in real multidimensional financial time series, using publicly available foreign exchange spot rate data as a case study. We conclude with remarks about some limitations of our approach and potential complementary or alternative approaches.

It can seem like "help wanted" signs are everywhere. But at the same time, unemployment in Arizona is the lowest it has been for more than a decade. This week, The Buzz focuses on the future of work in a tight labor market. "We're seeing a strong labor market but more people looking for jobs, maybe taking their time to look for that perfect job or that perfect industry they want to work in," said Jennifer Pullen, an economist at the University of Arizona's Eller College of Management. And while the city remains one of the more affordable places to buy a home for someone earning the median family income, wages have not increased at the same rate as home prices, she said.

My current blog is epistem.ink. This one is here just for archival purposes. It surprises me that when people think of "software that brings about the singularity" they think of text models, or of RL agents. To me, this seems counter-intuitive, and the fact that most people researching ML are interested in subjects like vision and language is flabbergasting. For one, because getting anywhere productive in these fields is really hard, for another, because their usefulness seems relatively minimal.

It's the ultimate unanswerable question we all face: When will I die? If we knew, would we live differently? So far, science has been no more accurate at predicting life span than a $10 fortune teller. But that's starting to change. The measures being developed will never get good enough to forecast an exact date or time of death, but insurance companies are already finding them useful, as are hospitals and palliative care teams.

When a group of about 40 players first tested out a live game called the Machine Learning President at a private event in San Francisco this February, they were unaware that the game would end up memorialized in the pages of The New Yorker. But during a ski vacation in March, the Republican mega-donor Rebekah Mercer gathered her friends together to play several rounds of the game, which pits special interest groups, political candidates, and activist organizations against each other in a simulated presidential election, aided by cash and artificial intelligence. A lawyer for Mercer told The New Yorker that she owned a copy of the Machine Learning President but had not created it and that it did not reflect her family's views. It's not hard to draw comparisons between the rules of the game, with its reliance on big cash and tech capabilities, and the actions of the Mercer-backed Cambridge Analytica during the 2016 U.S. presidential election. But, as Mercer's lawyer stated, she had nothing to do with creating the game--in fact, it was conceptualized by one of her vocal critics.