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. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Martine Rothblatt, futurist and founder of Sirius XM, says by the time her biotech company's genetically modified transplant organs are in use, drones will likely deliver them. Rothblatt gave her view of the future at The Washington Post's Transformers conference Wednesday. Her United Therapeutics company, which has offices is Silver Spring, Md. and Research Triangle Park, N.C., is raising pigs with genome modifications its researchers hope will improve the animals' organs for transplant recipients. Pigs organs, because of their size and function, make good transplant material, but often the patient is trading their current disease for "a chronic organ rejection kind-of-disease that ultimately takes the life of many, if not most, people who receive transplants," she said. The company hopes to begin trials on organ transplants from genetically-modified pigs by the end of the decade, with regulatory approval ten years from now, Rothblatt said.

However, Google too seems to acknowledge, "great software shines brightest with great hardware underneath". The aim, the search giant says, was to see what they could accomplish with custom accelerators for machine learning applications. The TensorFlow-tailored TPU, which has been running inside the firm's data centers for more than a year now, delivered "an order of magnitude better-optimized performance per watt for machine learning". If that doesn't sound impressive, to put that in perspective Google said that the improvement is roughly equivalent to a technology fast-forwarding an approximate seven years into the future, or three generations of Moore's Law. Ultimately, it all comes down to crazy optimization.

Do we finally have real competition in the deep learning hardware space?

Pepper the robot participates in a Japanese ribbon-cutting ceremony earlier this year. Its manufacturer, SoftBank Robotics, is opening new offices in San Francisco and releasing a development kit for Android programmers. Japan-based SoftBank Robotics announced Wednesday at Google I/O, the company's annual developer's conference, that it is opening a new Pepper-focused outpost in San Francisco and unveiling an Android SDK, or software development kit, in the hopes of enticing programmers to write code for the robot. "Pepper is ultimately an unfinished product, and we just wanted to incentivize developers to expand the ways in which people can engage with a humanoid robot," says Steve Carlin, vice president of SoftBank Robotics Americas, which has an existing office in Boston. Asked if SoftBank will roll out at SDK for iOS developers, Carlin says he wouldn't rule anything out but "for the moment Android is the pervasive language."

I recently wrote a guide on recurrent networks in TensorFlow. That covered the basics but often we want to learn on sequences of variable lengths, possibly even within the same batch of training examples. In this post, I will explain how to use variable length sequences in TensorFlow and what implications they have on your model. Since TensorFlow unfolds our recurrent network for a given number of steps, we can only feed sequences of that shape to the network. We also want the input to have a fixed size so that we can represent a training batch as a single tensor of shape batch_size x max_length x frame_size.

The predictive powers of computers will work nicely in cases where reality does not change dramatically. However, it will fail in any case where there are dramatic, unpredictable, changes in the future. The authoritative science journal Nature announced recently that a computer designed by Google's DeepMind defeated a human master in the ancient Chinese board game, "Go." This impressive achievement once again raised the expectations for a predicted future in which computers will have artificial intelligence, with major media outlets worldwide touting this anticipated future. One of the major questions raised in response to DeepMind's achievement is what are the outer limits, if any, of intelligent machines?

This repo contains a curated list of Python tutorials for Data Science, NLP and Machine Learning.

In a new paper, researchers from CSAIL and the machine-learning start-up PatternEx have demonstrated an artificial-intelligence platform called "AI²" that can predict 85% of cyber-attacks, by continuously incorporating input from human experts. To predict attacks, AI² combs through data and detects suspicious activity by clustering the data into meaningful patterns using unsupervised machine-learning. It then presents this activity to human analysts who confirm which events are actual attacks, and incorporates that feedback into its models for the next set of data. Check out all the Circuit Playground Episodes! Our new kid's show and subscribe!

Google used its I/O 2016 event to talk up its work in areas including machine learning and virtual reality, with head Sundar Pichai stating that "we are pushing ourselves really hard so that Google is evolving, and staying ahead of our users". A significant amount of time was dedicated to the growing importance of voice-driven services, with the executive stating that 20 per cent of queries from US mobile users are already made in this way. "Given how differently users are engaging with us, we want to push ourselves and deliver rich information in the context of mobile," he said. Driving this is Google Assistant, which it described as "conversational", and more like a context-aware two way dialogue. This, it said, is enabled by its natural language processing technology – "our ability to do conversational understanding is far ahead of what other assistants can do".