Machine learning, which helps computers do things like understand complex voice commands and improve image search capabilities, can be taxing on traditional hardware. Google should know – over 100 of its products and features use this technology to run and improve themselves constantly. The company has revealed that over the past few years, it quietly developed its own custom processor for such tasks. The Tensor Processing Unit (TPU) is built expressly for running TensorFlow, Google's in-house machine learning system that it open-sourced last year. Our biggest ever edition of TNW Conference is fast approaching!

Artificial Intelligence (AI) techniques have been used extensively for understanding how systems and humans interact. Such techniques can be applied in the context of cyber security to enable a better understanding of how human beings interact with cyber systems. Resilience in cyber security systems can be augmented using these techniques, including Machine Learning, Natural Language Processing and Game theory. AI does provide us with the tools that enable greater cyber threat intelligence, as we try to stay one step ahead of the criminals. However, AI itself poses a number of challenges not least around the ethical question of using "human-like" machines.

If there's one thing that I've learned in the few short years that I've been a fully-fledged scientist, it's that time is one of the most valuable commodities that you can give a researcher. In all its myriad forms, time is invaluable to the scientific process – time to develop ideas, time to write grants. The time that you need to run an experiment. Critically, the time that participants are willing to give you in the pursuit of knowledge. It's a precious thing, for everyone involved.

Self-driving cars are expected to usher in a new era of mobility, safety and convenience. The problem, say transportation researchers, is that people will use them too much. Experts foresee robot cars chauffeuring children to school, dance class and baseball practice. The disabled and elderly will have new mobility. Commuters will be able to work, sleep, eat or watch movies on the way to the office.

I wanted to put some of my ideas together, so as usual I'm writing a blog post. These are all pretty basic ideas but maybe they are helpful to people who are new to thinking about machine learning! When explaining what machine learning is, I'm giving the example of predicting the country someone lives in from their first name. So John might be American and Johannes might be German. In this case, it's really easy to imagine what data you might want to do a good job at this -- just get the first names and current countries of every person in the world!

The SPS IPC Drives event--held every November in Nuremberg, Germany--disappointed some attendees this past fall by not showing many steps forward for Industrial Internet of Things (IIoT) technologies. Yes, the marketing noise was loud, but little was actually being delivered. I am proud that Hilscher stood out from the crowd with its new portfolio of IIoT products. With connectivity being our core business, it was clear to us as we initially approached the IIoT concept that some sort of hardware (e.g., gateways) would be needed. After all, IIoT is just another "gateway" challenge, right?

Google unveiled a brand new self-driving car prototype on Tuesday; the first company to build a car with no a steering wheel, accelerator or brake pedal. The car's arrival marks the next stage in Google's self-driving car project, which was born from the Darpa Grand Challenges for robotic vehicles in the early 2000s. Google kickstarted its own self-driving car project in 2008, and it has been rumbling on ever since, first with modified Toyota Prius and then with customised Lexus SUVs, which took the car's existing sensors, such as the cruise-control cameras, and added a spinning laser scanner on the top. It is the first truly driverless electric car prototype built by Google to test the next stage of its five-year-old self-driving car project. It looks like a cross between a Smart car and a Nissan Micra, with two seats and room enough for a small amount of luggage.

Applications of spatial data analysis are endless, cutting across many disciplines. With the advent of various sensors today – space, airborne, terrestrial alike, spatial analysis is becoming paramount in order to turn these data into valuable information. However, processing massive amount of data is a challenge as it requires complex procedures and multiple tools. The session shall highlight the latest development in big data analytics as well as some exemplary applications from different domains.

Pepper is finally coming to America. SoftBank said today that its chatty humanoid robot, unveiled with great fanfare by the company's founder and CEO Masayoshi Son two years ago, is expected to debut in the North American market later this year. SoftBank also announced that a new developer portal is now available to anyone interested in creating applications for the robot. And tomorrow at Google I/O, SoftBank engineers will take the stage, along with Pepper, to introduce a tool that they hope will entice more developers to build apps for the robot: an Android SDK. "We're so excited to see what the development community can bring on to our platform," Steve Carlin, vice president of marketing and business development for SoftBank Robotics America, told IEEE Spectrum, adding that "ultimately what is going to really power Pepper is the creativity of this community."

Using text mining and machine learning based on the SAP HANA platform, the initiative aims to help companies review job descriptions, performance reviews and similar people processes for potential bias and suggest changes to encourage equity. The announcement was made at the 28th annual SAPPHIRE NOW conference. These new capabilities will complement existing SAP SuccessFactors offerings that already help address inequity. Analytics and reports focused on diversity and inclusion are available to help organizations identify and track where biases exist in talent acquisition and management processes -- recruiting, compensation, succession and the like -- coupled with guidance on actions to take to address those biases. SAP is also exploring applications for mentoring programs that will help people from historically disadvantaged groups more effectively navigate and develop their careers, as well as tools for balancing family and work that will integrate elements of benefits, scheduling and management into a single process.