- Washers and Screws (YouTube) — this chap is making his own clock from scratch, and here he is making his own washers and screws. Sometimes another person’s obsession can be calming. (via Greg Sadetsky)
- ROScon 2015 Recap with Videos (Robohub) — Shuttleworth suggests that robotics developers really need two things at this point: a robust Internet of Things infrastructure, followed by the addition of dynamic mobility that robots represent. However, software is a much more realistic business proposition for a robotics startup, especially if you leverage open source to create a developer community around your product and let others innovate through what you’ve built.
- Getting Deep Speech to Work in Mandarin (Baidu SVAIL) — TIL that some of the preprocessing traditionally used in speech-to-text systems throws away pitch information necessary to decode tonal languages like Mandarin. Deep Speech doesn’t use specialized features like MFCCs. We train directly from the spectrogram of the input audio signal. The spectrogram is a fairly general representation of an audio signal. The neural network is able to learn directly which information is relevant from the input, so we didn’t need to change anything about the features to move from English speech recognition to Mandarin speech recognition. Their model works better than humans at decoding short text such as queries.
- Sequencing Genomes of All Known Kakapo — TIL there’s a project to sequence genomes of 10,000 bird species and that there’s this crowdfunded science project to sequence the kakapo genome. There are only 125 left, and conservationists expect to use the sequenced genomes to ensure rare genes are preserved. Every genome in this species could be sequenced … I’m boggling. (via Duke)
The O’Reilly Data Show podcast: Vasant Dhar on the race to build “big data machines” in financial investing.
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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.