Matthew Berggren on making electronics accessible

The O’Reilly Hardware Podcast: Better ways to design electronics.

Subscribe to the O’Reilly Hardware Podcast for insight and analysis about the Internet of Things and the worlds of hardware, software, and manufacturing.

350px-Product_piracy_of_consumer_electronics

In our new episode of the Hardware Podcast, David Cranor and I talk with Matthew Berggren, who at the time the interview was conducted last December was senior director of product at Supplyframe. (Berggren is now director of Autodesk Circuits at Autodesk.)

Our discussion focuses on the need for abstracted modules and better metadata in electronics. Berggren gets to the root of it here:

There are 30 software developers for every hardware engineer in the world. That’s not only a tremendous bottleneck, but if you accept the premise that the next generation of products are going to be some hybrid of hardware and software—and really, hardware is the means to interact with the real world, and I want to write software applications that will interact with the real world—then there is this massive blue ocean out there that should present tremendous opportunity to semiconductor manufacturers, or anyone else who wants to get into that space.

Read more…

Comment

Chrissie Brodigan on user research at GitHub

The O’Reilly Design Podcast: Product development, user research, and identifying blindspots.

Subscribe to the O’Reilly Design Podcast, our podcast exploring how experience design—and experience designers—are shaping business, the Internet of Things, and other domains.

350px-18602171094_1fad7b03c9_k

In this week’s Design Podcast episode, I sit down with Chrissie Brodigan, manager of user experience research at GitHub. Brodigan will be be speaking at OReilly’s inaugural Design Conference. In this episode, we talk about user research and product development at Github, and the blindspots in product development and organizational development.

Here are a few highlights from our conversation:

Our internal philosophy around research is about when we make our design decisions, we come up with hypotheses about how that design change will impact behavior as well as user experience. We may need to add a particular control to the workflow, but if it has a negative consequence on the overall experience of our users, we may decide that that’s not the right decision for us. Even if it’s helpful in one area, it causes unhappiness in another. We measure impact with controlled experiments, which a lot of people would refer to as ‘AB testing.’ We do do some variance testing, which is short term, but we also do longitudinal analysis, which is to study a cohort over a longer period of time. Internally, we’re always asking ourselves ‘Why?’

Read more…

Comment: 1

Roger Chen on hardware and robotics startups

The O’Reilly Hardware Podcast: Hardware from the venture capitalist’s point of view.

Subscribe to the O’Reilly Hardware Podcast for insight and analysis about the Internet of Things and the worlds of hardware, software, and manufacturing.

350px-8358087911_31b466f062_k

In this new episode of the Hardware Podcast, David Cranor and I talk with Roger Chen, formerly a principal at O’Reilly AlphaTech Ventures, O’Reilly Media’s sister VC firm.

Discussion points:

  • Chen’s perspective as an investor on companies that are creating 3D robotics, drones, and satellites
  • The Maker movement’s impact on the hardware startups
  • Etsy’s influence on the new hardware movement
  • Trends in robotics, and the outlook for robotics startups

Read more…

Comment
Four short links: 20 January 2016

Four short links: 20 January 2016

Rules-Based Distributed Code, Open Source Face Recognition, Simulation w/Emoji, and Berkeley's AI Materials

  1. 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.
  2. OpenFace — open source face recognition software using deep neural networks.
  3. Simulating the World in Emoji — fun simulation environment in the browser.
  4. Berkeley’s Intro-to-AI MaterialsWe 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.
Comment

Is 2016 the year you let robots manage your money?

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.

350px-Merchants'_Exchange,_Wall_Street,_New_York_City

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.

Read more…

Comment: 1

Mark Burgess on a CS narrative, orders of magnitude, and approaching biological scale

The O'Reilly Radar Podcast: "In Search of Certainty," Promise Theory, and scaling the computational net.

Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

MRSA,_Ingestion_by_Neutrophil

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.

Read more…

Comment