- Social Intelligence in Mario Bros (YouTube) — collaborative agents built by cognitive AI researchers … they have drives, communicate, learn from each other, and solve problems. Oh, and the agents are Mario, Luigi, Yoshi, and Toad within a Super Mario Brothers clone. No code or papers about it on the research group’s website yet, just a YouTube video and a press release on the university’s website, so appropriately adjust your priors for imminent world destruction at the hands of a rampaging super-AI. (via gizmag)
- How we Monitor and Run ElasticSearch at Scale (SignalFx) — sweet detail on metrics, dashboards, and alerting.
- Simple Anomaly Detection for Weekly Patterns — Rule-based heuristics do not scale and do not adapt easily, especially if we have thousands of alarms to set up. Some statistical approach is needed that is generic enough to handle many different metric behaviours.
- How to Design a Robotics Experiment (Robohub) — although there are many good experimental scientists in the robotic community, there has not been uniformly good experimental work and reporting within the community as a whole. This has advice such as “the five components of a well-designed experiment.”
The O’Reilly Data Show podcast: Vasant Dhar on the race to build “big data machines” in financial investing.
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
In this episode of the O’Reilly Data Show, I sat down with Vasant Dhar, a professor at the Stern School of Business and Center for Data Science at NYU, founder of SCT Capital Management, and editor-in-chief of the Big Data Journal (full disclosure: I’m a member of the editorial board). We talked about the early days of AI and data mining, and recent applications of data science to financial investing and other domains.
Dhar’s first steps in applying machine learning to finance
I joke with people, I say, ‘When I first started looking at finance, the only thing I knew was that prices go up and down.’ It was only when I actually went to Morgan Stanley and took time off from academia that I learned about finance and financial markets. … What I really did in that initial experiment is I took all the trades, I appended them with information about the state of the market at the time, and then I cranked it through a genetic algorithm and a tree induction algorithm. … When I took it to the meeting, it generated a lot of really interesting discussion. … Of course, it took several months before we actually finally found the reasons for why I was observing what I was observing.