It makes sense: the opportunities for growth are enormous, as is the competition. Apple continues to leverage its partnership with IBM to muscle its way in. Facebook will reportedly soon launch its business-focused Facebook at Work. And Microsoft still reigns, thanks to its entrenched Office apps. To increase its enterprise stake, Google's leveraging one of its key strengths: "G Suite," as Apps for Work are now called, is infused with a bushel of new machine-intelligence perks.

Since its inception in the late 50s, the Defense Advance Research Projects Agency (DARPA), part of the US Department of Defense, has funded multiple research and development projects to come up with sophisticated technology for the military. Their current goal is to invest in IoT and artificial intelligence to keep "warfighters informed as they never have been before," DARPA said. By investing in IoT and artificial intelligence, DARPA wants to build better weapons and spying mechanisms to improve war tactics and performance. The new sensors and electromagnetic systems will allow the US military to hack into enemy devices and communication systems to collect and analyze data. As GPS can occasionally prove unreliable, DARPA has also been looking into developing new tracking technology, superior and more reliable than current GPS.

Convergent TechFocus is a blog look at technology developments: IT, Audio Visual, Unified Communications and Collaboration (UCC), video conferencing, cloud and software-defined, mobility/BYOD, Internet of Things, cybersecurity and more. This blog focus is on Microsoft Ignite, which took place this week in Atlanta, GA. Two major technology categories discussed here: Skype for Business UC and videoconferencing solutions, and Artificial Intelligence (AI) as an emerging trend in enterprise business. Microsoft Ignite kicked off on Monday with exceptional keynotes throughout the day. In his opening Keynote, Scott Guthrie, Executive Vice President, Microsoft focused on how technology enables digital transformation and demonstrated new technologies.

Lots of work is going into the apparently simple, but in fact complex, field of turning things off. As soon as you give any machine a goal, even as innocent as making daisy chains, you give it the subsidiary goal of staying alive, because it cannot make daisy chains if it has been turned off. So any reasonably intelligent system will seek a method to disable the off button. It is the hardest technical challenge AI fans face. But there is no answer yet.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Industry analysts such as Gartner and IDC predict that algorithms – or artificial intelligence (AI) – are the future of business. For lawyers, that will mean creating legal algorithms to automate at least the routine parts of their practices. Starting perhaps with those tasks that are frequently undertaken and are less legally complex. Over time, the complexity will increase as intelligent software becomes more capable. Either way, what we are seeing is the intersection between technologies capable of creating legal algorithms and an Internet capable of distributing services on-line.

Cognitive business took a bold leap forward in New York City the week of 26 September 2016. At two events on Manhattan's west side, IBM led customers, partners and industry at large in an exploration of how to put machine learning, artificial intelligence (AI), and big data analytics to work. At the extremely well-attended IBM DataFirst Launch Event at Hudson Mercantile, the chief news was the announcement of Project DataWorks. This new, cloud-based offering provides a self-service environment for teams of data scientists, data engineers and other professionals to collaboratively develop, iterate and deploy sophisticated AI, cognitive computing, machine learning and other advanced analytics. Check out what Dinesh Nirmal, vice president, IBM Analytics, had to say about DataWorks in action during the Strata Hadoop World 2016 conference.

If you need an Uber, Amazon Alexa has your back. Unfortunately if what you need is a conversation buddy, the personal assistant isn't quite up to the task. In an effort to bolster Alexa's social intelligence, Amazon is putting 2.5 million into a new university competition to design and build "socialbots" for the platform. Engineers will use the Alexa Skills Kit to build the artificial socialites. As part of the competition, the Washington Post, owned by Amazon's Jeff Bezos, will also be making its corpus available to participants as a tool to train the bots.

Following up on CEO Satya Nadella's big talk about artificial intelligence, Microsoft today announced the creation of the AI and Research Group, gathering more than 5,000 computer scientists and engineers. The group will include the engineering teams for the Cortana personal assistant and Bing search engine. To lead the group, Microsoft has chosen 20-year company veteran Harry Shum, whose previous experience includes Bing and Microsoft Research. The AI and Research Group will work on products, but it will also conduct basic research. AI was a major point in Nadella's keynote this week at Microsoft Ignite, the company's annual IT conference.

NVIDIA is the inventor of the GPU, the computer device that creates interactive graphics on laptops, workstations, mobile devices, notebooks, and personal computers. It is Tesla's supplier of choice for the components that power its 17? touchscreen in the Model S. NVIDIA is a global company with interests in many emerging computer technologies, one of which is autonomous driving systems. A team of NVIDIA engineers working out of a former Bell Labs office in New Jersey decided to use deep learning to teach an automobile how to drive. As it happens, Bell Labs is where the earliest research into deep learning -- sometimes called artificial intelligence or AI -- took place. The project was called DAVE2, in honor of DARPA Autonomous Vehicle (DAVE) program.