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.

Forget the CPU, GPU, and FPGA, Google says its Tensor Processing Unit, or TPU, advances machine learning capability by a factor of three generations. "TPUs deliver an order of magnitude higher performance per watt than all commercially available GPUs and FPGA," said Google CEO Sundar Pichai during the company's I/O developer conference on Wednesday. TPUs have been a closely guarded secret of Google, but Pichai said the chips powered the AlphaGo computer that beat Lee Sedol, the world champion in the incredibly complicated game called Go. Pichai didn't go into details of the Tensor Processing Unit but the company did disclose a little more information in a blog posted on the same day as Pichai's revelation. "We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law)," the blog said.

Back in 1950, computer scientist, codebreaker, and war hero Alan Turing introduced the world to a very simple premise: If a robot can engage in a text-based conversation with a person and fool that person into believing it is human at least 30 percent of the time, surely we could agree that the robot is a "thinking" machine. Turing's goal was to force people to think more creatively about computer interaction, but he inadvertently ended up creating the test that robot intelligence developers and commentators have relied on for years. They're focused on more substantive metrics. Fundamentally, the problem with the Turing Test is that it's poorly defined therefore facilitates hype (i.e. that fake teaching assistant in Georgia) rather than offering easily duplicated results. Beyond that, one can argue that it measures human weakness, not artificial strength.

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r 0.90 with R2 0.80 and MAE 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r 0.91 with R2 0.82 and MAE 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes.