It sounds like the punchline from a joke. A law firm has hired the world's first artificially intelligent lawyer. According to Futurism, the lawyer has been named Ross. Ross was built on IBM's cognitive computer, Watson. IBM describes Watson as "a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data."

Ever find yourself in an endless game of being put on hold and pushing numbers to try to reach a certain department on a customer service call? Ever find yourself throwing your hands up in frustration and shouting, "WHY CAN'T I JUST TALK TO A REAL PERSON?!" This sentiment is coloring the way we use the internet. As messaging apps make their way onto more mobile devices and brands follow marketing trends with their own tools like Facebook Messenger for Business, searching for terms and clicking through multiple menus to find what we're looking for feels increasingly passé every day. It's hard to know which came first--advanced chatbot technology or the mounting use of chat-based apps--but it's clear that we're at an inflection point. What role will chatbots have in serving consumers, and how does their adoption affect brands' current methods of reaching people?

Nvidia's (NVDA) AI technology with Facebook (FB), Alphabet (GOOGL) and Microsoft (MSFT) is powering "superhuman" levels of inference, or artificial intelligence, Nvidia CEO Jen-Hsun Huang said Thursday, after the Tesla Motors (TSLA) partner blasted Q1 views on record deep-learning sales. "The truth is that nobody really knows how big this deep-learning market is going to be," Huang said on the company's earnings conference call. He referred specifically to gains that customer Microsoft was making with AI and deep learning. "The work that recently was done at Microsoft Research, they've achieved superhuman levels of inferencing … of image recognition and voice recognition that's really kind of hard to imagine," he said, "and these networks are now huge." On the stock market today, Nvidia stock rocketed 15% and hit an all-time high of 41.

An unclassified 2016 Department of Defense (DoD) document, the Human Systems Roadmap Review, reveals that the US military plans to create artificially intelligent (AI) autonomous weapon systems, which will use predictive social media analytics to make decisions on lethal force with minimal human involvement. Despite official insistence that humans will retain a "meaningful" degree of control over autonomous weapon systems, this and other Pentagon documents dated from 2015 to 2016 confirm that US military planners are already developing technologies designed to enable swarms of "self-aware" interconnected robots to design and execute kill operations against robot-selected targets. More alarmingly, the documents show that the DoD believes that within just fifteen years, it will be feasible for mission planning, target selection and the deployment of lethal force to be delegated entirely to autonomous weapon systems in air, land and sea. The Pentagon expects AI threat assessments for these autonomous operations to be derived from massive data sets including blogs, websites, and multimedia posts on social media platforms like Twitter, Facebook and Instagram. The raft of Pentagon documentation flatly contradicts Deputy Defense Secretary Robert Work's denial that the DoD is planning to develop killer robots.

Messaging is the new battleground among powerful tech companies. Facebook, Snapchat and Microsoft are racing to build ever more powerful chat apps, based on artificial intelligence, virtual assistants and bots. Some industry insiders predict that messaging will become the next big computing platform, providing a new way for consumers to do everything from shop online to reading the news. As Google kicks off its annual developer conference near San Francisco next week, a growing number of users, software developers and analysts think Google needs to get on the ball and make some messaging-based announcements. The company's Hangouts chat app was an early success when it was introduced years ago, but appears to have languished recently. Here's why Google can't afford to ignore the messaging threat: Everyone's talking about how chat is the next big thing.

IS BIG DATA TAKING US CLOSER TO THE DEEPER QUESTIONS IN ARTIFICIAL INTELLIGENCE? What I'm worried about and what I'm thinking about these days is if we're really making progress in AI. I'm also interested in the same kind of question in neuroscience, which is that we feel like we're making progress, but are we? There's huge progress in AI, or at least huge interest in AI--a bigger interest than there's ever been in my lifetime. I've been interested in AI since I was a little kid trying to program computers to play chess, and do natural language databases, and things like that, though not very well. I've watched the field and there have been ups and downs. There were a couple of AI winters where people stopped paying attention to AI altogether. People who were doing AI stopped saying that they were in the field of AI. They say, "Yes, I do artificial intelligence," where two years ago they would have said, "I do statistics." Even though there's a lot of hype about AI and a lot of money being invested in AI, I feel like the field is headed in the wrong direction. There's been a local maximum where there's a lot of low-hanging fruit right now in a particular direction, which is mainly deep learning and big data. People are very excited about the big data and what it's giving them right now, but I'm not sure it's taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world. The big data paradigm is great in certain scenarios. One of the most impressive advances is in speech recognition. You can now dictate into your phone and it will transcribe most of what you say right most of the time. That doesn't mean it understands what you're saying. Each new update of Siri adds a new feature. First, you could ask about movie times, then sports, and so forth. The natural language understanding is coming along slowly. You wouldn't be able to dictate this conversation into Siri and expect it to come out with anything whatsoever.

Back in October 2014, Taylor Swift opened an account on Line, the messaging app that is massively popular in Asia. The account currently doesn't do much. If you communicate with it, you can hear a funny voice message from Swift, for instance. Paul McCartney has one, too. Burberry and Selfridges also have accounts on WeChat and Line.

Machine learning has invaded and challenged traditional methods in unimaginable ways in the recent past. Continuing in the same vein, today we look at some of the innovative ways machine learning is addressing alcohol drinking related issues. Be it drunken outburst on social media or drunken driving, new age algorithms are preparing to help you save both your life as well as your reputation. Detecting outbursts on social media More than harmful, drunk tweeting or social media updates are regretful. And a group of researchers at the University of Rochester have been building a machine learning tool just to address this issue.

Accenture has acquired OPS Rules, a boutique analytics consulting company that specializes in the application of data science to create supply chain and operations analytics solutions. When the acquisition is completed, Accenture will add new operations analytics professionals to its team to apply machine learning and optimization techniques to develop analytics approaches for clients across many industries. Founded in 2012, OPS Rules has offices in Waltham, Mass., and Richardson, Texas. OPS Rules is led by David Simchi-Levi, a professor of engineering systems at the Massachusetts Institute of Technology (MIT) and renowned supply chain and operations analytics expert. Simchi-Levi and his team will join Accenture Analytics, part of Accenture Digital, and will also be a part of Accenture's Data Science Center of Excellence, an innovation team that focuses on solving immediate and complex client problems through advanced analytics approaches, including machine learning, deep learning, text analytics and more.

Artificial Intelligence has been prominent in tech news recently, and was a hot topic at SXSW. The technology has massive potential for use in customer communication, yet will it ever be able to completely replace the need for a human element? Facebook has already launched itself into the AI arena by introducing chatbots to its messenger app. Facebook Messenger users will now be able to chat to select partners and check on the status of their orders, receiving replies from AI, rather than an actual customer service agent. The question is, is this a feasible solution for the majority of brands at the moment?