June 22, 2015

The one which offers 10 answers to the question: ‘What purpose does this biochip serve?’
The State Road Safety Inspection has long abandoned their hopes of the impossibility to fake beacons, “flags”, licenses and badges (no hologram would protect from that, really). That’s why, once a vehicle is pulled over by the police, an officer checks the driver’s license and a vehicle certificate against their database to find out whether the piece of plastic is legitimate (and whether the bearer is a good guy). How would the entire procedure look with a biochip in play? The officer presents the reader through the windshield, I touch it with my hand – and that’s it.

Big data log analysis thrives on machine learning
Clearly, automation is key to finding insights within log data, especially as it all scales into big data territory. Automation can ensure that data collection, analytical processing, and rule- and event-driven responses to what the data reveals are executed as rapidly as the data flows. Key enablers for scalable log-analysis automation include machine-data integration middleware, business rules management systems, semantic analysis, stream computing platforms, and machine-learning algorithms. Among these, machine learning is the key for automating and scaling distillation of insights from log data. But machine learning is not a one-size-fits-all approach to log-data analysis.

How a grocery delivery service became a red hot robotics company
"The ultimate aim is for humans to end up relying on collaborative robots because they have become an active participant in their daily tasks," says Dr Graham Deacon, Robotics Research Team Leader at Ocado Technology. "In essence, the SecondHands robot will know what to do, when to do it and how to do it in a manner that a human can depend on." To get a sense of what these collaborative robot helpers will be doing, imagine an Ocado warehouse. Conveyor belts zip colorful baskets to and fro along diverging paths, placing them in front of an army of human workers who pack them full of groceries. The warehouse is full of machinery, and all of it requires careful and constant maintenance.

Decision Boundaries for Deep Learning and other Machine Learning classifiers
With using {h2o} on R, in principle we can implement “Deep Belief Net”, that is the original version of Deep Learning. I know it’s already not the state-of-the-art style of Deep Learning, but it must be helpful for understanding how Deep Learning works on actual datasets. Please remember a previous post of this blog that argues about how decision boundaries tell us how each classifier works in terms of overfitting or generalization, if you already read this blog. It’s much simple how to tell which overfits or well gets generalized with the given dataset generated by 4 sets of fixed 2D normal distribution. My points are: 1) if decision boundaries look well smoothed, they’re well generalized, 2) if they look too complicated, they’re overfitting, because underlying true distributions can be clearly divided into 4 quadrants with 2 perpendicular axes.

The Advantages Of An Agile Company Culture
The real change comes from the company culture. Is the company still a command-and-control type of environment? Agile is about quickly adapting to change, and not being afraid to fail. As a leader, you need to create the type of environment where failure is not only accepted, but actively encouraged. Agile is more about how your team approaches problems, not the tools used to solve them. In an agile environment, employees are expected to communicate frequently, because internal feedback is important to improving the team. The constant learning and iterative nature of agile means that you need to embrace failure and allow that learning to occur.

Can We Design Trust Between Humans and Artificial Intelligence?
What is it that makes getting on a plane or a bus driven by a complete stranger something people don’t even think twice about, while the idea of getting into a driverless vehicle causes anxiety? Part of this is that we generally perceive other people to be reasonably competent drivers—something that machines can probably manage—but there is more to it than that. We understand why people behave the way they do on an intuitive level, and feel like we can predict how they will behave. We don’t have this empathy for current smart systems.

Who Will Own the Robots?
It is notoriously hard to determine the factors that go into job creation and earnings, and it is particularly difficult to isolate the specific impact of technology from that of, say, globalization, economic growth, access to education, and tax policies. But advances in technology offer one plausible, albeit partial, explanation for the decline of the middle class. A prevailing view among economists is that many people simply don’t have the training and education required for the increasing number of well-paying jobs requiring sophisticated technology skills. At the same time, software and digital technologies have displaced many types of jobs involving routine tasks such as those in accounting, payroll, and clerical work, forcing many of those workers to take more poorly paid positions or simply abandon the workforce.

A Manifesto for Creating Extraordinary Teams
Well, there's a name for that state of mind, it's called "flow" and a good friend of mine, Dr. Judy Glick-Smith, has been studying it for years. She recently wrote an article about it that captures perfectly what flow is all about and how to create teams that sustain a flow-state. I'm borrowing heavily from it here because it is a manifesto that I believe every leader should know by heart. Yes, I'm looking at you! ... Creativity and innovation are the inevitable results of unfettered team-flow. If all of these components are in place, each individual in the organization becomes a leader. Change is integrated into the fabric of the culture. Your people will embrace change, because they are creating it on a moment-by-moment basis.

Do the mobile developers your hire thoroughly understand the internet of things
When you hire mobile app developers to create modern apps working on such multiple devices, they should have their concept clear regarding IoT and its user experiences as well as intricacies involved in it. For mobile app designers, HCI are taking place in variety of contexts due to mobility involved in case of mobile devices. Designers have to deal with different resolutions and scale designs accordingly. They have to address resolutions of tiny top of wearable smart watches at one end, go to smartphones, tablets, desktops, and on the TV user interfaces.

DockerCon 2015: Game On
The bug that is being put in our ear is that enterprises are worried about security. Well, yeah, enterprises are always worried about security, but that’s not the point. While on the one hand, Docker does not present a conventional “attack surface” for the typical malicious user, it also does not present a conventional platform for the typical security vendor or security service. All security now, whether containerized or virtualized or on Facebook’s bare metal servers, is no longer a matter of hardening endpoints, but rather of maintaining the desired state of connections in the network. At this moment, even after a few years of rapid development, we don’t really know what a containerized network will look like, once the architectural debates get settled.

Quote for the day:

“No great manager or leader ever fell from heaven, its learned not inherited.” -- Tom Northup

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