- Experience with Rules-Based Programming for Distributed Concurrent Fault-Tolerant Code (A Paper a Day) — To demonstrate applicability outside of the RAMCloud system, the team also re-wrote the Hadoop Map-Reduce job scheduler (which uses a traditional event-based state machine approach) using rules. The original code has three state machines containing 34 states with 163 different transitions, about 2,250 lines of code in total. The rules-based re-implementation required 19 rules in 3 tasks with a total of 117 lines of code and comments. Rules-based systems are powerful and underused.
- OpenFace — open source face recognition software using deep neural networks.
- Simulating the World in Emoji — fun simulation environment in the browser.
- Berkeley’s Intro-to-AI Materials — We designed these projects with three goals in mind. The projects allow students to visualize the results of the techniques they implement. They also contain code examples and clear directions, but do not force students to wade through undue amounts of scaffolding. Finally, Pac-Man provides a challenging problem environment that demands creative solutions; real-world AI problems are challenging, and Pac-Man is, too.
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.
The O'Reilly Radar Podcast: "In Search of Certainty," Promise Theory, and scaling the computational net.
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In this week’s Radar Podcast episode, Aneel Lakhani, director of marketing at SignalFx, chats with Mark Burgess, professor emeritus of network and system administration, former founder and CTO of CFEngine, and now an independent technologist and researcher. They talk about the new edition of Burgess’ book, In Search of Certainty, Promise Theory and how promises are a kind of service model, and ways of applying promise-oriented thinking to networks.
Here are a few highlights from their chat:
We tend to separate our narrative about computer science from the narrative of physics and biology and these other sciences. Many of the ideas of course, all of the ideas, that computers are based on originate in these other sciences. I felt it was important to weave computer science into that historical narrative and write the kind of book that I loved to read when I was a teenager, a popular science book explaining ideas, and popularizing some of those ideas, and weaving a story around it to hopefully create a wider understanding.
I think one of the things that struck me as I was writing [In Search of Certainty], is it all goes back to scales. This is a very physicist point of view. When you measure the world, when you observe the world, when you characterize it even, you need a sense of something to measure it by. … I started the book explaining how scales affect the way we describe systems in physics. By scale, I mean the order of magnitude. … The descriptions of systems are often qualitatively different with these different scales. … Part of my work over the years has been trying to find out how we could invent the measuring scale for semantics. This is how so-called Promise Theory came about. I think this notion of scale and how we apply it to systems is hugely important.
You’re always trying to find the balance between the forces of destruction and the forces of repair.
The O’Reilly Hardware Podcast: The merging worlds of software, hardware, and biology.
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In this new episode of the Hardware Podcast—which features our first discussion focusing specifically on synthetic biology—David Cranor and I talk with Charles Fracchia, an IBM Fellow at the MIT Media Lab and founder of the synthetic biology company BioBright.
- The blurring of the lines between biology, software development, hardware engineering, and electrical engineering
- BioBright’s efforts to create hardware and software tools to reinvent the way biology is done in a lab
- The most prominent market forces in biology today (especially healthcare)
- How experiments conducted using Arduino or Raspberry Pi devices are impacting synthetic biology
- Pembient’s synthetic rhino horns
The O’Reilly Design Podcast: Managing, mentoring, and recruiting designers.
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In this week’s Design Podcast episode, I sit down with Wesley Yun, director of user experience on the hardware side at GoPro. Yun will be be speaking at O’Reilly’s inaugural Design Conference. In this episode, we talk about managing and recruiting designers at GoPro, Designer Fund’s Bridge Guild, and mentoring the next generation of designers.
Here are a few highlights from our conversation:
Managing is humbling. My job isn’t to tell my designers what to do. My job is to hire the best designers I know how to, or I can hire at the organization that’s right for them and then create this space and the opportunity for them to do the best work of their life. That, to me, is what a good manager does. I very rarely tell my designers what to do. I help them frame problems. I help them sell ideas. I help streamline their thoughts.
[When recruiting], I look for things that are very unique, not something that you can see on a page or a resume. There are people who just bring a sense of joy and happiness and collaboration and trust; it’s nothing specific that you can ever point out.