No, an intelligent robot isn't ready to take over (most) legal jobs. But machine learning has the potential to transform many legal services.

RAVN Systems, leading experts in Artificial Intelligence, Search and Knowledge Management solutions, announced today the launch of a RAVN ACE powered Robot for LPP (Legal Professional Privilege) review, allowing clients to automate the review of determining if material is subject to LPP. The LPP Robot uses state of the art AI (Artificial Intelligence) techniques to automatically read through vast document collections, such as case material in litigation, as well as other document types, to determine whether individual items are subject to LPP. The accuracy levels achieved by the Robot making these determinations now surpass traditional manual efforts and is several orders of magnitude faster. The solution exploits supervised iterative Machine Learning models inside the RAVN ACE (Applied Cognitive Engine) platform, meaning the Robot will become more accurate over time. It is a technological leap from legacy predictive coding methods that sometimes are used today.

Welcome back for another edition of Under the Decision Tree. This week we had everything from machine learning being applied to cucumber farming, to major tech companies joining up to tackle the ethics of machine learning. Please send any suggestions to: Decision Tree We would love to hear from you.

Alphabet Inc's Google has renamed its business-to-business cloud computing brand and enhanced some enterprise applications using artificial intelligence, the company's latest gambit to better compete with Amazon.com and Microsoft Corp. in the lucrative cloud business. Discussing the rebranded Google Cloud, Diane Greene, senior vice president of Google's enterprise business, said the company has made good progress courting customers and improving its technology. Cloud computing uses remote internet servers to store, manage and process data, and Google offers a range of apps like word processing and email, as well as the ability to host data and offer resources for developers. The new name replaces the Google for Work brand. "We are closing the gap incredibly fast" with competitors, Greene, a former CEO of VMware who joined Google last year to ramp up its cloud business, told experts and journalists at an event.

For all their brilliance, our phones still have as much emotional intelligence as glue. Yet, as electronics become ever more important in our lives, it may make sense to start teaching them to be more aware of our feelings. Early glimpses of such efforts were afoot at a gathering of artificial-intelligence software developers, academics and researchers this week in Manhattan, where several talks focused on finding ways to add emotion into our robots, voice assistants and chatbots. "People are building these very intimate relationships with these companions, but right now these companions have no empathy," Rana el Kaliouby, CEO of emotional-recognition tech firm Affectiva, said onstage Tuesday at the inaugural O'Reilly Artificial Intelligence Conference. Teaching robots about emotion illustrates both the promise and huge challenges involved in developing AI tools. Artificial intelligence, which lets machines mimic human learning and problem solving, is already used to improve Google searches and scan Facebook photos for faces.

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.