Very interesting talk by Bret Victor on the power and effectiveness of organizing your work around a guiding principle. Victor's principle is "creators need an immediate connection to what they create" and he shows some really cool ways he's exploring that idea.
(via waxy)
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Continue reading Ubuntu's full desktop OS coming to multi-core Android devices
Ubuntu's full desktop OS coming to multi-core Android devices originally appeared on Engadget on Tue, 21 Feb 2012 13:11:00 EDT. Please see our terms for use of feeds.
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But is it mature? And will businesses have access to the right people with the skill sets necessary to master this new aspect of information management? After having spent five years as a senior engineer at Sybase, another six years as a development director at Informix, and over three years managing DB2 development for IBM, Anjul Bhambhri is arguably one of the most skilled plain data architects in the business. In September 2010, IBM promoted her to the new post of Vice President for Big Data and Streams. In an interview with ReadWriteWeb, we asked Bhambhri whether the big data tools developed in so short a time are mature enough to be used by IT workers everywhere, or whether they will truly require a scientist to master.
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Anjul Bhambhri, VP for Big Data, IBM: You're absolutely right that what we've seen in just this last year around big data, what people want to do with it, the possibilities, the use cases that we've heard from customers, have all been completely mind-blowing. It's reaching this level of... I wouldn't say maturity, but obviously everybody's at a different point in this whole curve of big data. There are clearly people who are still trying to understand, what is big data versus small data? But definitely with social media... it's clear that people do understand that there's a lot of unstructured data, and they cannot just stick with deterministic data that has been residing in warehouses, maybe in IBMs or Oracles. People do recognize that unstructured data needs to be brought into their information management platforms. Otherwise, they're not getting the complete view of the different data points that they should be looking at to make decisions.
From that standpoint, what has been happening in the open source community has been fabulous. The Apache Hadoop project has really gone to that next level of being able to pre-process a lot of this noisy, unstructured data in a cost-effective manner. It made it possible for people to start using commodity hardware. This is a very important step for sure, that you can now pre-process and analyze this data - what we call the discovery phase, where you can observe things about the data. [But] they cannot just take the open source and be able to observe and discover data. So there is a certain set of consumability or tooling that is needed for data scientists to be able to observe and discover, what is the data telling them? That tooling also has not reached a level of maturity, so that these people's jobs become easier.
Next, of course, there is more analysis and correlation of this data that has to be done across all the data in or outside the enterprise. There's the aspect of integrating, bringing it in, and not looking at data in a silo. I think the integration of data is going to take a different meaning, and more advances have to be done there. This integration has to be very seamless, so that when questions are asked, all the right pieces of information have to be pulled from the five, six, ten, however many repositories there are in the enterprise, in the right context.
Scott Fulton, ReadWriteWeb: I know you've mentioned online recently that the data scientist is the Job of the Future. There might be some enterprises that might be scared by the concept - that they have to hire a scientist, somebody at that level, just to be able to make sense of what the analytics are telling her about big data. If the tools mature to the next level, as you suggest they do, shouldn't understanding how the data works not necessarily be the task of scientists, but maybe more what we would call "skilled artisans?"
Anjul Bhambhri: In terms of observing and discovering and analyzing, yes, a lot of us have come to some agreement that you need a role like a data scientist. Now, people are going to be emerging [from college] with pretty much the same kinds of education that they were getting in the last three to five years. So it's not like the whole of education is going to change here.
It's going to be very critical that the tooling we build actually helps people with the educational background that they might have in mathematics or statistics, computer science, modeling, analytics. If they've taken those kinds of courses, then they have a good foundation. But the tools themselves have to help them become good data scientists. And it'll happen over a period of time. They at least have to have that mindset that there are new sources of data that they have to look at. They have to understand as data scientists that they will probably have to cause some shifts inside the organization, maybe across the culture of the organization. That attitude will need to be there. [But] without the tools, it will be very difficult even for a smart data scientist to do this on their own, because the volumes here are really large.
It's not that this analysis or observation has to be done once and it's over. It's an ongoing thing - it has to be done every day. The demands on these scientists are going to be huge, and my point of view is that, even if we get the best and the brightest, they will need the right set of tools to examine this data.
Scott Fulton: So you perceive a future where, just like a graphic artist must adopt Photoshop as a skill, and a mathematician might have to adopt Mathematica or Wolfram Alpha as a skill - where the tool itself becomes a skill - there will be a field for big data analysis, where whatever tool emerges from that field becomes the skill.
Anjul Bhambhri: Yes, absolutely. The tools will have to help build those skills, because the skills cannot be built alone by getting more people educated in mathematics, for example. Vendors like us... will leverage the people who are actually coming out with these degrees to help build the right tools, because this is not just about the visualization. A lot of the magic we want that hasn't happened yet, has to happen behind the scenes. We don't want to expose this to the domain experts, because they have to be able to focus on asking the questions, on exploring, on doing the what-if kind of analysis, extracting the right kinds of information from these silos or from these disparate data sources. They can't get bogged down by the algorithms behind the scenes.
Scott Fulton, ReadWriteWeb: The greatest number of big data use cases in the last six to twelve months for the enterprise is in the marketing department. They're gleaning the textual value coming out of the streams and streams of text generated by social media, and trying to find out, "Are they saying something positive about our product? Are they having a positive experience with our product? Is this a like or a dislike? Are they talking about our presidential candidate the way we want them to?" And a lot of the people asking these questions have degrees in business or marketing, but not mathematics or computer science.
Anjul Bhambhri, VP for Big Data, IBM: Exactly. I would state that our goal is for those people to become the data scientists. Because if we are talking about observing and discovering, they are the subject matter experts. They will know. They see a pattern, they will identify it.
Just as you said, these may be marketing officers. They are not just going to be the CEOs of the company. They could be the Chief Marketing Officers. I would claim, if we can make the CMO a data scientist, then we have achieved what we needed to, and that's when the potential of big data will actually be realized to get the business outcome that we think can come out of tapping into what's hidden in this big data.
Scott Fulton: But doesn't that require a substantial rethinking of the educational platform that we're presenting to people who aspire to be CMOs now? I'm thinking of college entrance exams and processes where your future is plotted on a graph, and where they may say, "You're not particularly skilled in such things as biology, science, mathematics. But you know what you could be good at? You'd be good at marketing!" It's like we take people away from math and put them into marketing, and we push that as the future. You would have these two worlds be fused, and you see a future in doing so. That's going to take a lot of work, won't it?
At the same time, the individual may spend hours on something like social media, and they can go on for hours expressing what their point of view or sentiment is on certain products. This marketing thing is one example. If they don't tap into that, either the business is going to suffer, or they'll personally suffer, but somebody's going to suffer. And the consumer will suffer too, because nobody's listening to them.
California may be the 800-pound gorilla in the automotive legislation world, but their neighbor Nevada seems to be taking the initiative when it comes to self-driving cars. They’ve adopted a number of regulations into law, and are pushing the state as a legal testing-ground for companies preparing such vehicles. These changes were telegraphed last summer, when the state legalized driverless cars to begin with. Now they’re hammering out the details.
There’s money involved, of course: a $1m to $3m bond must be purchased if you want to test your robocar in the state, depending on the nature of the project. And the laws will have to change, naturally, once the vehicles go from science project to highway reality. But these early calls seem reasonable.
The new regulations are partly aimed at making testing safe and legitimate, and partly at actually accommodating driverless vehicles on the road. For instance, information from testing must be shared with the state, along with the purpose of the experimentation and information about the cars. This information will be useful to urban planners and the like, who may want to immunize their cities against future robocar problems.
Two people must be in the cars at all times for now, and there must be a sort of “black box” device that records data in case of a crash. Robocars will have red license plates, and later, when certified, green. There will likely have to be some kind of national standard for identifying an autonomous vehicle.
And there are also allowances for the human element: a person will be considered the “operator” of a vehicle whether they are physically present or not. How this will work when the cars are completely autonomous and cause a wreck anyway isn’t clear. But it does also inform an interesting (and probably correct) decision also included in the regulations: for now, you can’t use an autonomous vehicle as a designated driver. Just because you aren’t turning the steering wheel doesn’t mean you’re not “operating” the vehicle. Drinkers be warned.
You will, however, be able to call or text on your mobile while in a robocar. That particular decision was carved out of the earlier law banning texting while driving. So text away or watch a movie, but remember that you’re still in charge of the vehicle.
Doodle Defense game uses Kinect to turn white boards into displays, dry-erase markers into weapons originally appeared on Engadget on Thu, 16 Feb 2012 23:06:00 EDT. Please see our terms for use of feeds.
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Classic Driver: The car will make its public debut at the forthcoming Kuwait Concours d’Elegance (15-18 February) and will be on centre-stage at the company’s Geneva Motor Show stand in March; first deliveries are scheduled for autumn 2012
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Source: Classic Driver
The clever designers at Chicago’s Tanagram adapted code developed to recognize those little black-and-white augmented-reality markers (“fiduciary markers”) for target acquisition on a DARPA-funded robot that can autonomously deliver humanitarian aid and other supplies to 20′ square markers unrolled on the ground.
What’s great about fiducial marker tracking technology is that it is pre-built to handle the marker being viewed at ANY angle (including nearly flat) in the crappiest of lighting conditions. A marker does not have to be smack in front of the camera to be recognized. Another added bonus is that it is easy to embed a two-dimensional barcode within the marker.
Their system can recognize 64 distinct markers, which opens up the possibility, for instance, of delivering particular types of supplies to particular places in a refugee camp. [Thanks, Joseph!]
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