Thursday, October 31, 2013

Some favorite crowd-funded projects

There is a lot going on with crowd-funding these days. Over the past year or so, it has become huge. One might venture to say that it is a significant source of innovation in science, technology and art. Obviously there will be projects that cannot be crowd-funded; it is difficult to imagine a successfully crowd-funded Mars mission or cancer cure. But the space of feasible new projects is vast, and what follow are a few of my favorite crowd-funded projects.

But first, there is a recent noteworthy development from the SEC: previously allowed only to hand out rewards, crowd-funding campaigns will soon be able to award equity too. Equity had thus far been only for accredited investors, people with buckets of spare money in their garages and garden sheds. The possibility of investing in a successful venture rather than simply receiving a toy and a good feeling might make the already-fascinating crowd-funding scene a much more interesting place. It could play an important role in economic recovery.


The Oculus Rift is a virtual-reality headset representing an enormous improvement in performance-to-price ratio. The head tracking is smooth and the graphics are good. This is one of the first crowd-funded projects I heard about, and the first one I contributed to. For $300, I got a headset with a very entertaining demo, and if I get up the energy I will do something myself with science education.

By getting in early and having a huge success, the Oculus Rift set a precedent for big splashy projects, and probably helped Kickstarter as much as Kickstarter helped Oculus.


CastAR is another virtual reality gadget, this time a pair of glasses that project an image onto a retroreflective surface in front of the user. One big innovation here is that the virtual reality can be mixed with actual reality, for instance using game pieces or other objects. Also, because the user is looking at things some distance away, eye strain is reduced. The head-tracking on CastAR follows both rotation and translation where the Oculus Rift only follows rotation.


This is a Bluetooth-enabled Arduino board. Arduino is a cheap easy-to-use controller board for hobbyist projects and art installations. With Bluetooth, whatever you're building can connect to a phone or tablet.


The Espruino is another Arduino-based controller board. What's unique is that it is designed to operate with a language called JavaScript, which has been used in web browsers for a long time but has slowly been gaining momentum as a hardware control language.


This is an instructional program to teach yourself Mandarin. There are flashcards and animations to learn the written characters, and audio materials to learn the spoken language.


If you miss the pre-J-J-Abrams Star Trek franchise, this is for you. This movie brings back Walter Koenig (Chekhov from the original series) with several actors from Star Trek: Voyager. It is set ten years after Voyager's return to human space, and politics and hilarity ensue.


Another big success story, the Pebble can now be purchased for $150 at Best Buy. It connects to your phone and can run Android apps on a very small screen. It has a magnetometer (compass), a three-axixs accelerometer, Bluetooth, ambient light sensors, a 144x168-pixel screen, and a week of battery life between charges. It connects via Bluetooth to your phone so the phone can stay in your pocket most of the time.

My long list below includes some projects that were already funded and have gained significant fame, like the Oculus Rift virtual reality headset, or the Pebble smartwatch now available at Best Buy.

Random projects

Phone and tablet

Electronics and computers

Robots and Flying Things


Maker stuff

Here's an interesting list of crowd-funding resources:

The shortened URL for this post is

Friday, October 25, 2013

Bar Camp Boston 2013 talk on automation of science

This is an outline for a talk I gave at Bar Camp Boston 8 on the automation of science. It's a topic I've blogged and spoken about before. The shortened URL for this post is

In 2004, a robot named Adam became the first machine in history to discover new scientific knowledge independently of its human creators. Without human guidance, Adam can create hypotheses to explain observations, design experiments to test those hypotheses, run the experiments using laboratory robotics, interpret the experimental results, and repeat the cycle to generate new knowledge. The principal investigator on the Adam project was Ross King, now at Manchester University, who published a paper on the automation of science (PDF) in 2009. Some of his other publications: 1, 2, 3.

Adam works in a very limited domain, in nearly complete isolation. There is plenty of laboratory automation but (apart from Adam) we don't yet have meaningful computer participation in the theoretical aspect of scientific work. A worldwide scientific collaboration of human and computer theoreticians working with human and computer experimentalists could advance science and medicine and solve human problems faster.

The first step is to formulate a linked language of science that machines can understand. Publish papers in formats like RDF/Turtle or JSON or JSON-LD or YAML. Link scientific literature to existing semantic networks (DBpedia, Freebase, Google Knowledge Graph,, etc). Create schemas for scientific domains and for the scientific method (hypotheses, predictions, experiments, data). Provide tutorials, tools and incentives to encourage researchers to publish machine-tractable papers. Create a distributed graph or database of these papers, in the role of scientific journals, accessible to people and machines everywhere. Maybe use Stackoverflow as a model for peer review.

Begin with very limited scientific domains (high school physics, high school chemistry) to avoid the full complexity and political wrangling of the professional scientific community in the initial stages. As this stuff approaches readiness for professional work, deploy it first in the domain of computer science and other scientific domains where it can hope to avoid overwhelming resistance.

Machine learning algorithms (clustering, classification, regression) can find patterns in data and help to identify useful abstractions. Supervised learning algorithms can provide tools of collaboration between people and computers.

The computational chemistry folks have a cool little program called Babel which translates between a large number of different file formats for representing molecular structures. It does this with a rich internal representation of structures, and pluggable read and write modules for each file format. At some point, something like this for different file formats of scientific literature might become useful, and might help to build consensus among different approaches.

A treasure trove would be available in linked patient data. In the United States this is problematic because of the privacy restrictions associated with HIPAA regulation. In countries like Iceland and Norway which have universal health care, there would be no equivalent of HIPAA, and those would be good places to initiate a Linked Patient Data project.

Thursday, October 17, 2013

The first neon sign I've ever wanted to own

This sign appears in the Cambridge UK office of Autonomy Corporation. I want one. I need to talk to the people who make neon signs. There are a few online threads (1, 2) where people express curiosity about this sign.

This equation is Bayes' Law. Thomas Bayes (1701-1761) proposed it as a way to update one's beliefs based on new information. I saw this picture on a blog post by Allen Downey, author of Think Bayes, and whom I recently had the pleasure of meeting briefly at a Boston Python meetup. Very interesting guy, also well versed in digital signal processing, another interest shared with myself. Before the other night, I probably hadn't heard the word "cepstrum" in almost twenty years.

Allen's blog is a cornucopia of delicious problems involving Bayes' Law and other statistical delights that I learned to appreciate while taking 6.432, an MIT course on detection and estimation that I'm afraid may have been retired. The online course materials they once posted for it have been taken down.

But imagine my satisfaction upon looking over Think Bayes and realizing that it is the missing textbook for that course! I haven't checked to see that it covers every little thing that was in 6.432, but it definitely covers the most important ideas. At a quick glance, I don't see much about vectors as random variables, but I think he's rightly more concerned with getting the ideas out there without the intimidation of extra mathematical complexity.