SoftBank Robotics Corp. ("SoftBank Robotics") and SoftBank Corp. ("SoftBank") today announced that its humanoid robot'Pepper' will support Google's Android, and that presales of models for developers will begin from July 2016. Ahead of the presales launch, starting today SoftBank Robotics will offer a beta version of'Pepper SDK for Android Studio', a software development kit that enables the development of RoboApps on the Android platform. By making Pepper compatible with Android, Android application developers will be able to utilize their existing knowledge and technologies to develop RoboApps for Pepper. With the high number of Android developers around the world, the possibilities for Pepper RoboApp developers will greatly increase with Android support. SoftBank Robotics will continue to provide its SDK, 'Choregraphe'.

Google this week hosted its annual I/O developers conference, announcing new products that highlight the tech giant's focus on artificial intelligence. Google Translate Product Lead Barak Turovsky lent some content in a post to the company's corporate blog: "The assistant is conversational – an ongoing two-way dialogue between you and Google that understands your world and helps you get things done. It makes it easy to buy movie tickets while on the go, to find that perfect restaurant for your family to grab a quick bite before the movie starts, and then help you navigate to the theater. Turovsky said the company views the current technological landscape as at a "seminal moment. Many of these advances have been thanks to machine learning and artificial intelligence – specifically, areas like natural language processing, voice recognition and translation – and they have helped us build an increasingly useful and assistive experience for users.

Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google IO developer conference highlighted progress in key areas such as Android and especially Artificial Intelligence (AI). AI is going main stream, becoming an essential part of platform operating systems and cloud services. As the creator of AlphaGo, the first AI to defeat a human Go player, Google is arguably out in front in AI services. Leveraging AI to confer competitive advantage for its various platforms is the centerpiece of Google's business strategy. My title for this article was meant to be more tongue-in-cheek than alarmist.

In the arms race between Silicon Valley giants to develop faster and more complex artificial intelligence capabilities, Google has a secret weapon: It's developing its own chips. At a conference for developers on Wednesday, chief executive Sundar Pichai said the tech giant had designed the chip, which the company says it's been using for over a year, specifically to improve its deep neural network. These networks are the brains that "learn" over time to to power features such as Gmail's "Smart Reply," and the ability to tag people in photos and search by voice. The chips were also in place when Google's AlphaGo computer program beat Go champion Lee Sedol in March, although the company didn't announce it at the time. As companies have increasingly focused on building tools that use machine learning as a backbone, they've also branched out into creating their own chips instead of purchasing them from major vendors, such as Nvidia.

A year ago, when Google began to unwind Google, it felt like a positive sign for the company's underperforming social efforts. After sinking years into building a product overstuffed with photos, communication tools, link-sharing, and discussions, Google began to shrink them into more manageable tools. The results were largely positive. Google Photos has become a monster with 200 million monthly users, the company said during its I/O keynote. And communities evolved into a more modern take on message boards, emerging last week as a new mobile app called Spaces.

Google has unveiled a custom chip created specifically to facilitate projects related to machine learning and artificial intelligence. The company is calling the chip a Tensor Processing Unit, or TPU, in reference to the fact that it has been tailored to work with its TensorFlow machine learning API. A blog post published by hardware engineer Norm Jouppi reveals that the TPU started its life as a "stealthy project" several years ago. The technology has now been implemented in Google's data centers for more than a year, and it appears that the chips offer distinct advantages over standard processors. Google claims that its TPUs offer "better-optimized performance per watt for machine learning."

I expect we'll see AI start to affect a wide variety of domains, including "obvious" ones that have never really worked before such as robotics. Here are some interesting domains which seem quite close but may not be on people's radar:

Computers are pretty good at stocking shelves and operating cars, but are not so good at writing poetry. Scientists in a Dartmouth College competition reached that conclusion after designing artificial intelligence algorithms that could produce sonnets. Judges compared the results with poems written by humans to see if they could tell the difference. In every instance, the judges were able to find the sonnet produced by a computer program. The competition was a variation of the "Turing Test," named for British computer scientist Alan Turing, who in 1950 proposed an experiment to determine if a computer could have humanlike intelligence.

How is the Google Fellowship in Deep Learning?

Hi folks, this meetup will be held on Thursday 19th May at Sainsbury's HQ at 33 Holborn, London EC1N 2HT. If you are interested in speaking at an upcoming event, please contact me on Twitter @YannCluchey. In this talk we'll describe some of the data characteristics which make anomaly detection for real world problems challenging and describe some of the techniques we use at Prelert for anomaly detection. As the complexity of IT systems and the quantity of data people gather increases, proactively managing the health and security of these systems requires increasingly sophisticated monitoring tools. Rule based approaches are either becoming unmanageable or in need of augmentation, and the complexity and scale of the data pose significant challenges.