Werner Vogels, CTO of Amazon, announced yesterday that the MXNet deep learning framework would become "[Amazon's] deep learning framework of choice." Choosing MXNet might come as a surprise to some, given the number of other frameworks -- TensorFlow, Theano, Torch, or Caffe, to name a few -- with far broader name recognition. Amazon claims it chose MXNet because it scales and runs better than almost anything else out there, but other motives may also be at work, too. MXNet caught InfoWorld's attention earlier this year as one of the Open Source Rookies of the Year 2016. Among its notable attributes are its compact size and cross-platform portability, both of which Vogels praised: "The core library (with all dependencies) fits into a single C source file and can be compiled for both Android and iOS."

Serkan Piantino, a longtime Facebook engineer who helped to create its artificial intelligence research lab, has left the social networking firm to start a company focused on making it easier for developers to access the best AI processing hardware. His new startup, called Top 1 Networks, will offer customers access to the latest Nvidia graphics processing units (or GPUs) as a cloud service, much like Amazon and others offer cloud computing services. Nvidia's GPUs have taken off in deep learning, a flavor of AI where the computer teaches itself how to do specific tasks. Major cloud service providers (Amazon, Microsoft, Google) already offer access to GPUs in their data centers, but Piantino said it's usually older hardware. "These cloud providers are pretty far behind," said Piantino, who spoke to FORBES for the first time about his new company over the phone.

Last week was an exciting one with some rumors about Apple's AAPL iPhone manufacturing in the U.S., Cisco's CSCO earnings report, Intel's INTC first AI Day plus lots more. In June this year, Apple requested its iPhone assemblers Pegatron and Foxconn to evaluate the feasibility of making the devices in the U.S. While Pegatron refused outright on cost considerations, Foxconn has decided to do the job. The study is likely to show that the cost increase, skill mismatch and supply chain problems (the supply chain is largely in Asia) would make this cost prohibitive. This would raise iPhone prices for consumers/squeeze Apple's margins.

What is the difference between AI, Machine Learning, NLP, and Deep Learning? AI (Artificial intelligence) is a subfield of computer science that was created in the 1960s, and it was/is concerned with solving tasks that are easy for humans but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic and includes all kinds of tasks such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. NLP (Natural language processing) is simply the part of AI that has to do with language (usually written). Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g.

As artificial intelligence advances, the goal for modern tech companies is to build AI software that thinks for itself without human intervention. Towards that end, Amazon Web Services just picked MXNet, as its favored deep-learning framework to facilitate that work, according to a blog post Tuesday by Amazon chief technology officer Werner Vogels. Deep learning, as detailed in Fortune earlier this year, is a subset of AI that involves the use of software known as neural networks. Within this realm, software learns by churning through vast reams of data with the help of algorithms--not human programmers--to sort it out. Vogels said AWS will provide software code, documentation, and invest in the development of MXnet and the ecosystem of companies supporting it.

A new artificial intelligence tool created by Google and Oxford University researchers could significantly improve the success of lip-reading and understanding for the hearing impaired. In a recently released paper on the work, the pair explained how the Google DeepMind-powered system was able to correctly interpret more words than a trained human expert. The tool is called Watch, Listen, Attend and Spell (WLAS), and the paper describes it as a "network that learns to transcribe videos of mouth motion to characters." Using videos from the BBC, the team trained the system with a dataset of more than 100,000 natural sentences. While similar attempts in the past have focused on a narrow set of words, the report said, Google and Oxford wanted to address lip reading through "unconstrained natural language sentences, and in the wild videos." The professional human lip reader whom the researchers compared the results against had roughly 10 years of experience in the field and had deciphered videos for the royal wedding and in court for trials as well, the report said.

It is a massive trial-and-error process. From time to time, you will hear about a new fantastic piece of computer science. For example, right now deep learning is the hot new thing. Some years ago, people were very excited about MapReduce. As an ecosystem changes, some tools become less likely to be useful while others gain dominance in common use cases.

Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy. It was hard to foresee then, but artificial intelligence is now permeating our daily lives. Since then, IBM has expanded the Watson brand to a cognitive computing package with hardware and software used to diagnose diseases, explore for oil and gas, run scientific computing models, and allow cars to drive autonomously. The company has now announced new AI hardware and software packages. The original Watson used advanced algorithms and natural language interfaces to find and narrate answers.

Go master Cho Chikun triumphed Wednesday in his final game against DeepZenGo, a Japanese artificial intelligence system, to win the three-game series 2-1. Cho, 60, has won 74 titles in his long career, the largest number in Japan. He defeated DeepZenGo with the 167th move in the third game of the series, which was played on even terms with no handicaps. DeepZenGo uses deep learning and other advanced technologies. It is being developed with support mainly from Dwango Co., a video-sharing website operator, and the University of Tokyo. "I felt as if I was playing with a human, because (DeepZenGo) has both strong and weak points," Cho said after the final game.

DeepMind Health's inaugural collaboration with the U.K.'s National Health Service (NHS), initially focused on building an app for helping early detection of Acute Kidney Injury (AKI), was relaunched earlier today -- under a new information-sharing agreement with the Royal Free NHS Trust, and a broader scope for the collaboration. Under the arrangement, patient identifiable data (PID, aka people's medical records) continues to be shared across a wide range of data types for some 1.6 million individuals who are being treated or have been treated at the Royal Free's three London hospitals (five years of historical in-patient data is also made accessible under the arrangement). The types of data being shared under ISA 1 and 2 (aka the legal contracts that set out how the data can be used) are described as "similar" by DeepMind -- and a spokesman confirmed that patient data shared under the original arrangement has therefore not been deleted (given that they view it as a continuation of the same arrangement). The relevant section of ISA 2, detailing the data types being shared, can be found at the bottom of this post. There are some notable additions to the project at this point -- such as a plan to create a technical audit infrastructure to track and log individual access to patient data, and an explicit commitment in the ISA that Google will not use the PID for any other purpose, nor combine it with other data, nor sell data to third parties.