Four years ago, Google was faced with a conundrum: if all its users hit its voice recognition services for three minutes a day, the company would need to double the number of data centers just to handle all of the requests to the machine learning system powering those services. Rather than buy a bunch of new real estate and servers just for that purpose, the company embarked on a journey to create dedicated hardware for running machine- learning applications like voice recognition. The result was the Tensor Processing Unit (TPU), a chip that is designed to accelerate the inference stage of deep neural networks. Google published a paper on Wednesday laying out the performance gains the company saw over comparable CPUs and GPUs, both in terms of raw power and the performance per watt of power consumed. A TPU was on average 15 to 30 times faster at the machine learning inference tasks tested than a comparable server-class Intel Haswell CPU or Nvidia K80 GPU.

If you heard about AlphaGo's latest exploits last week -- crushing the world's best Go player and confirming that artificial intelligence had mastered the ancient Chinese board game -- you may have heard the news delivered in doomsday terms. There was a certain melancholy to Ke Jie's capitulation, to be sure. The 19-year-old Chinese prodigy declared he would never lose to an AI following AlphaGo's earthshaking victory over Lee Se-dol last year. To see him onstage last week, nearly bent double over the Go board and fidgeting with his hair, was to see a man comprehensively put in his place. But focusing on that would miss the point.

Want to implement machine learning algorithms from scratch? A recent KDnuggets poll asked "Which methods/algorithms you used in the past 12 months for an actual Data Science-related application?" with results found here. The results are analyzed by industry employment sector and region, but the main take away for the uninitiated is that there are a wide array of algorithms covered. And let's be clear: this is not a complete representation of available machine learning algorithms, but rather a subset of the most-used algorithms (as per our readers). There are lots of machine learning algorithms in existence today.

San Francisco: Jensen Huang, co-founder, president and chief executive officer of Santa Clara-based Nvidia Corp., says that the rapid adoption of artificial intelligence (AI) technologies such as machine learning, deep learning, natural language processing and computer vision augur well for the growth prospects of his company. His confidence stems from the fact that Nvidia designs the chips that can deliver the extra computing power that clients need in an algorithm-driven world, which is increasingly using these AI technologies to make business sense of the voluminous data that users generate and thus gain a competitive edge. These chips, called graphics processing units (GPUs), helped Nvidia fuel the growth of the personal computer gaming market almost two decades back. Huang hopes the increasing use of GPUs for AI will help his company repeat the success. Huang argues that even when you increase the number of central processing unit transistors in a computer, they result in a small increase in application performance, whereas GPUs, which are specifically designed to handle multiple tasks simultaneously, make them more suitable for high-performance computing tasks.

The goal of Azure Container Services (ACS) is to provide a container hosting environment by using popular open-source tools and technologies. Like all software, deploying machine learning (ML) models can be tricky due to the plethora of libraries used and their dependencies. In this tutorial, we will demonstrate how to deploy a pre-trained deep learning model using ACS. ACS enables the user to configure, construct and manage a cluster of virtual machines preconfigured to run containerized applications. Once the cluster is setup, DC/OS is used for scheduling and orchestration.

Deep Learning is the new buzzword. Big Data is last decades news (so please stop saying it!). Deep Learning is the new biggest thing. It will save the world… or at least the cheerleaders. Sorry, while I will always remain cynical when it comes to buzz-pop branding of analytics, I will admit that Deep Learning IS the real deal. It was when I started doing it in 1988… and today it has a nice shiny buzzword of its own!

When the rat sees object A, it must lick the nozzle on the left to get a drop of sweet juice; when it sees object B, the juice will be in the right nozzle. But the objects are presented in various orientations, so the rat has to mentally rotate each shape on display and decide if it matches A or B. Interspersed with training sessions are imaging sessions, for which the rats are taken down the hall to another lab where a bulky microscope is draped in black cloth, looking like an old-fashioned photographer's setup. Here, the team uses a two-photon excitation microscope to examine the animal's visual cortex while it's looking at a screen displaying the now-familiar objects A and B, again in various orientations. The microscope records flashes of fluorescence when its laser hits active neurons, and the 3D video shows patterns that resemble green fireflies winking on and off in a summer night. Cox is keen to see how those patterns change as the animal becomes expert at its task.

Over the last three years, the field of NLP has gone through a huge revolution thanks to deep learning. The leader of this revolution has been the recurrent neural network and particularly its manifestation as an LSTM. Concurrently the field of computer vision has been reshaped by convolutional neural networks. This post explores what we "text people" can learn from our friends who are doing vision. To set the stage and agree on a vocabulary, I'd like to introduce a few of the more common tasks in NLP.

Only 4 years ago, the 2012 Harvard Business Review article by Thomas Davenport and DJ Patil proclaimed Data Scientist: The Sexiest Job of the 21st Century But here is what may be coming ... Data Scientist: "I thought I had the sexiest job of the 21st century" This cartoon was ably drawn by Jon Carter. The cartoon was inspired by a recent post by the same Thomas Davenport and Julia Kirby: Six Very Clear Signs That Your Job Is Due To Be Automated, which among criteria for jobs likely to be automated included: It Involves Answering Data-Dependent Questions It Involves Quantitative Analysis It Involves The Creation Of Data-Based Narratives all criteria describing the job of a Data Scientist! Here are more KDnuggets posts on Data Science automation Contest Winner: Winning the AutoML Challenge with Auto-sklearn Contest 2nd Place: Automating Data Science Data Science Automation: Debunking Misconceptions - Aug 2, 2016. I've Been Replaced by an Analytics Robot and KDnuggets tags Automated Data Science, Here is KDnuggets Big Data, Data Mining, and Data Science Cartoon page Related: Cartoon: Facebook data science experiments and Cats Cartoon: When Automation Goes Too Far The Secret to a Perfect Data Science Interview Cartoon: Citizen Data Scientist At Work Data Scientist Valentine's Day Collection Cartoon: Deeper Deep Learning More Data Science Humor and Cartoons Cartoon: Surprise Data Science Recommendations Cartoon: 2nd place in a Data Science contest Cartoon: It all started with the iPhone answering my email Cartoon: KDnuggets Addiction Cartoon: Big Data in Retirement Cartoon: Big Data and the dog question Cartoon: Where humans are still ahead of Deep Learning Cartoon: Data Scientist Mother Cartoon: A solution for Data Scientists allergies caused by Big Data I've Been Replaced by an Analytics Robot I've Been Replaced by an Analytics Robot Cartoon: Facebook data science experiments and Cats Cartoon: When Automation Goes Too Far The Secret to a Perfect Data Science Interview Cartoon: Citizen Data Scientist At Work Data Scientist Valentine's Day Collection Cartoon: Deeper Deep Learning More Data Science Humor and Cartoons Cartoon: Surprise Data Science Recommendations Cartoon: 2nd place in a Data Science contest Cartoon: It all started with the iPhone answering my email Cartoon: KDnuggets Addiction Cartoon: Big Data in Retirement Cartoon: Big Data and the dog question Cartoon: Where humans are still ahead of Deep Learning Cartoon: Data Scientist Mother Cartoon: A solution for Data Scientists allergies caused by Big Data

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").