IBM researchers, Tayfun Gokmen and Yurii Vlasov, unveiled a paper in which they invented the concept for a new chip called a Resistive Processing Unit (RPU) that can accelerate Deep Neural Networks training by up to 30,000x compared to conventional CPUs. A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers that can be trained in an unsupervised or supervised way, resulting in machine learning (or artificial intelligence) that can "learn" on its own. This is similar to what Google's AlphaGo AI has been using to learn playing Go. One, called the "policy network," would calculate which move has the highest chance of helping the AI win the game, and another one, called the "value network," would estimate how far it needs to predict the outcome of a move before it has a high enough chance to win in a localized battle. Many machine learning researchers have begun focusing on deep neural networks because of their promising potential.

IBM researchers, Tayfun Gokmen and Yurii Vlasov, unveiled a paper in which they invented the concept for a new chip called a Resistive Processing Unit (RPU) that can accelerate Deep Neural Networks training by up to 30,000x compared to conventional CPUs. A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers that can be trained in an unsupervised or supervised way, resulting in machine learning (or artificial intelligence) that can "learn" on its own. This is similar to what Google's AlphaGo AI has been using to learn playing Go. AlphaGo used a combination of a search-tree algorithm and two deep neural networks with multiple layers of millions of neuron-like connections. One, called the "policy network," would calculate which move has the highest chance of helping the AI win the game, and another one, called the "value network," would estimate how far it needs to predict the outcome of a move before it has a high enough chance to win in a localized battle.

News of Google's self-driving car getting in a minor collision with a bus has been all over the internet recently. At the same time, I also came across this article in Forbes magazine, "Machine Learning Needs a Human-In-The-Loop." Both topics raise questions about the boundaries of autonomous operations.I started to consider this in relation to my own experience in the world of IT automation. The fact is that while automation is now deeply embedded in most manufacturing processes, IT has been comparatively slow in routinely applying automation technology to large areas of IT and security operations. Why is that the case?

Every week, CIO Journal offers a glimpse into the mind of the CEO, whose view of technology is shaped by stories in management journals, General interest magazines and, of course, in-flight publications. AI may undermine big-company advantages. Machine learning – software that can improve itself without human intervention – may mean trouble for big companies that depend on their heft to outmaneuver smaller upstarts, writes Howard Yu for the Harvard Business Review. And for a sneak preview of where the world is headed, one need not look further than the success story of AlphaGo, an artificial intelligence that beat a champion of the ancient game of Go, something that was previously thought to be impossible. "It is easy to imagine a world where self-taught algorithms will play a much bigger role in coordinating economic transactions; AlphaGo simply shows us what is possible in the near future.

Reports in the press today that just 24 hours after release, Microsoft closed down Tay, it's new artificially intelligent software chatbot. In its first day, Tay was seen tweeting anti-Semitic rants. See Microsoft Muzzles Artificially Intelligent Chatbot. I guess the real question here is this: Is the design of Tay at fault, or is this more a criticism of the design of us and our current society? I think that might be outside the scope of this blog.

Google is reportedly working on a competitor to the Amazon Echo, Amazon's voice-controlled personal assistant. According to The Information, Google is working on a'secret project' which would create a rival to the Echo, currently the leading device of its kind on the market. The Amazon Echo is the home of the digital assistant Alexa, who responds to a'wake word' and can help out around the house by creating to-do lists, playing music, setting alarms, providing weather information and doing internet searches. The Echo hasn't been released in the UK yet, but it's generated a lot of buzz in the US, and has proved successful enough that Amazon is releasing a new version, the Echo Dot, which connects to users' existing speakers. Google-owned company Nest, the creators of smart home devices like the Nest Thermostat, reportedly wanted to be part of the project, but were apparently pushed away by Google itself.

Amazon's Alexa can do a lot, from powering your home to ordering a pizza, but the 180 price tag may not be worth it to some. Luckily the e-commerce giant has published a do-it-yourself guide for running the voice service in a homemade device that won't break the bank. All you need is a Raspberry Pi, a USB sound card, an external speaker and a push button, which should cost 60 total. Luckily the e-commerce giant has published a do-it-yourself guide for running the voice service in a homemade device that won't break the bank. A new Pi Model 2, a micro SD card to load the software on, a mini mic and an Ethernet cable.

Google has announced that it will embed machine learning into its cloud to help developers build smarter applications with their data. Cloud Machine Learning will take the technology currently used for Google Now, Google Photos and voice recognition in Google Search, allowing scientists and developers to build machine learning models using the company's open-source TensorFlow software library. Fausto Ibarra, director of product manager for Google Cloud Platform, said: "Hundreds of different big data and analytics products and services fight for your attention as it's one of the most fertile areas of innovation in our industry. "This is an area where Google Cloud Platform has invested almost two decades of engineering, and today we're announcing some of the latest results of that work." The tool will be compatible with multiple data formats, and will be fully integrated with Cloud Platform products including Google Cloud Dataflow, Google BigQuery, Google Cloud Dataproc, Google Cloud Storage, and Google Cloud Datalab.

For any cloud to be taken seriously, it has to meet an ever rising bar of features. Machine learning seems to be on that list, as all the major cloud providers now feature it. But how they go about doing it is another story. Aside from the "curated API vs. open-ended algorithm marketplace" models, there are the "everything and then some vs. just enough" variants. Here's how the four big cloud providers -- IBM, Microsoft, Google, and Amazon -- stack up next to each other in machine learning.

A man walks at the Samsung Electronics' headquarters in Seoul January 7, 2015. Samsung wants to use some of its US 61 billion (RM246 billion) in cash and equivalents to help it morph into more of a software-driven company, executive vice president Rhee In Jong said in an interview. The South Korean consumer-electronics giant also is spending more to develop its own services because the global market for gadgets is saturated and can't be counted on for significant revenue growth, he said. "We are actively looking for M&A targets of all sorts in the software area," said Rhee, who runs the mobile division's research-and-development business. "We are open to all possibilities, including artificial intelligence.