Last November, Google opened up its in-house machine learning software TensorFlow, making the program that powers its translation services and photo analytics (among many other things) open-source and free to download. Now, the company is giving TensorFlow the machine learning equivalent of smart pills, releasing a distributed version of the software that allows it to run across multiple machines -- up to hundreds at a time. This sounds like an obvious way to improve TensorFlow, and, well, it is. Machine learning software only gets to be clever by analyzing large amounts of data -- looking for common properties and trends like facial features in photographs, for example. Letting TensorFlow run these sorts of operations on networks of computers simultaneously rather than individual machines means users can make smarter systems, and improve them faster.

Machine Learning, which is a process to predict future patterns and incidents based on the models created out of past data, is definitely the most important part of the success of the Internet of Things in the enterprise and consumer space. The main reason is that without machine learning the entire backbone of the Internet of Things - event acquisition, event processing, event storage and event reporting - is merely a live display of events happening elsewhere and will not provide any value to its consumers. Think of a smart monitor in an oil well that monitors various climatic conditions and other factors that can cause a failure; unless the monitor is able to predict of a failure and corrects itself the usage of such solution is quite limited. MLPaaS - Azure Vs AWS In that context, Machine Learning Platform as a Service (MLPaaS) has been a major component of the major cloud platforms. Both Azure and AWS have equivalent services, the below thoughts are comparison of major building blocks of a machine learning service and how the respective cloud providers handle them.

Salesforce's acquisition of an artificial intelligence startup this week will ultimately help customers wring more value from the data they've been collecting for years, according to Salesforce president and chief operating officer Keith Block. The business software giant bought MetaMind, a Palo Alto, Calif.-based company that sells natural language processing, computer vision, and database prediction tools, for an undisclosed amount. Computers running the appropriate software can use MetaMind's products to sift through reams of data and adapt its actions going forward based on what it learns. Late last year, Salesforce bought MinHash to bring AI to its marketing services; Microsoft msft bought fellow AI company SwiftKey; Intel intc bought Saffron AI; and IBM ibm simply can't stop talking about Watson, its cognitive computing (IBM-speak for AI) service. Additionally, Amazon is said to have bought Orbeus, although it hasn't publicly confirmed the acquisition.

Toyota toyof will expand the footprint of its artificial intelligence and robotics research center by adding a third facility in Ann Arbor, Mich. Gill Pratt, CEO of the Toyota Research Institute, made the announcement on Thursday during his keynote speech at Nvidia's GPU Technology Conference in San Jose. The Ann Arbor facility will be located near the University of Michigan, where it will fund research in artificial intelligence, robotics, and materials science. Last year, the world's largest automaker said it would invest 1 billion over the next five years in a research center for artificial intelligence to be based in Palo Alto, Calif. The institute aims to bridge the gap between research in AI and robotics in order to bring this technology to market.

While voice-recognition-powered virtual assistants like Siri, Cortana and Amazon's Echo continue to get more useful and reliable on the march to platform-dom, there is a parallel wave of development underway that has captured the public eye, where machine learning, artificial intelligence and natural language processing are getting corralled for more narrowly purposed means -- by… Read More

The impending western Sydney Zoo plans to use drones, robots, augmented reality, and whatever innovative technology that an upcoming hackathon at Western Sydney University will dream up. The hackathon is expected to attract more than academics and scientists, but also start-ups, business and industry professionals, and engineers. The partnership between the new zoo and the university could disrupt any current norm of what a zoo experience entails. Using Levi Stadium, the home of the 49ers, deep in the heart of Silicon Valley as the high-tech model, Don Wright, the manager of the university's Launch Pad program, believes that a visit to the new zoo should be "one of the most technologically advanced wildlife experiences in the world." First, they are considering the overall user experience from the moment visitors arrive at the zoo. "There is lots of focus on reducing queues, getting food to people faster, and using real-time data through the whole facility to deliver a better experience."

A "new" Rembrandt portrait is actually the creation of a 3-D printer -- and a statistical analysis of 346 paintings by the Dutch master. "The Next Rembrandt," as it's been dubbed, was the brainchild of Bas Korsten, creative director at the advertising firm J. Walter Thompson in Amsterdam. Bas Korsten, executive creative director of the J. Walter Thompson Amsterdam agency, stands with the painting at its unveiling Tuesday in Amsterdam. Bas Korsten, executive creative director of the J. Walter Thompson Amsterdam agency, stands with the painting at its unveiling Tuesday in Amsterdam.

Google's free open source framework TensorFlow is about to get more powerful. Last year Google opened TensorFlow to the entire world. This meant that any individual, company, or organization could build their own AI applications using the same software that Google does to fuel everything from photo recognition to automated email replies. But there was a catch. While Google stretches its platform across thousands of computer servers, the version it released to the public could run only on a single machine.

I was reflecting on the demonstrations of Deep Mind against Go champion Lee Se-dol, along with Watson at CeBIT and other artificial intelligence (AI) developments. It soon becomes apparent that we are evolving rapidly to a state where data learning through data analytics will be the battleground. In fact, it's clear that the battle over data is already won by those who have data architectures fit for AI. If you don't recognise this chart, please read the series of blogs I wrote in 2014 about such things. In this chart, I outlined the structure of a bank as front, middle, and back office (or retailer, processor, manufacturer), and that this structure is being attacked by technologies.

Something that I think should be fully available and would be of tremendous benefit is the use of machine learning tools to help medical diagnosis. It's quite feasible to imagine that a good fraction of the medical knowledge of which symptoms are related to which diseases. Based on this we should never be in the situation of a doctor missing' a diagnosis. Indeed, it's quite conceivable that the entire medical history of an individual should be signficantly able to help form a diagnosis.