Legislation passed by the U.S. Senate could pave the way for the commercial deployment of drones in the national airspace, besides addressing safety issues by, for example, providing for a pilot that would find ways to lock down errant drones if they are close to airports. The new rules in the Federal Aviation Administration Reauthorization Act of 2016, passed Tuesday by a vote of 95-3, reflect the opportunities seen in the country for the use of drones both for commercial and other applications such as in emergencies. They also highlight privacy and safety concerns about the reckless use of consumer drones by hobbyists. Referring to an object, believed to be a drone, hitting a British Airways plane landing in Heathrow airport on Sunday, Senator Bill Nelson, a Democrat from Florida, said that if a drone is sucked into a jet engine, it could certainly render the engine inoperable and might start an explosion. The current bill proposes a pilot program to develop and test technologies to intercept or shut down drones when they are near airports.

Imagine yourself one hundred years from now. Yes, you're still alive, breathing through genetically engineered pig lungs, and having dinner at your favorite restaurant. A robot waiter rolls up to refill your glass of wine, which is equipped with a sensor that allows the restaurant to automatically deduct 10 from your Bitcoin account. Your companion, who doesn't actually speak the same language as you, is saying how much she loves her salad, which comes from a farm that uses precision agriculture techniques to boost productivity by effectively dividing fields into one-inch square plots that each receive customized fertilizer mixes based on their specific conditions. You understand her perfectly thanks to a small device in your ear that instantly translates her words and perfectly mimics her voice.

Of course we have all heard about machine learning and recommendation engines in big business ecommerce. For quite some time, massive ecommerce businesses like Netflix, Amazon, and Ebay have been leveraging the power of data science to improve customer service and boost sales. Where once this technology was cost-prohibitive to all but the major players, recently things have changed. Thanks to multi-channel ecommerce platforms like Shopify, and the developers who are building custom machine learning add-ons, now mom and pop online businesses get the chance to infuse their operations with the power of data science. In this article I introduce how machine learning algorithms work to produce recommendation systems for small business ecommerce.

Unless you've been living under a rock, 'chatbots' have been all the rave in the retailing world. Well, before they became the now famously short-named'chatbots', they were known as Chatterbots. Coined by Michael Mauldin in 1994 to explain simply a programme that, for all intensive purpose, chats. Chatterbots are programmed to have conversations with another party in either text or audio. Basically, bots are artificial intelligence that do the most menial of tasks, like organising your calendar, reading emails and/or having a simple conversations to convey a programmed message.

Scientists at Massachusetts Institute of Technology (MIT) claim they have created an AI that can detect 85% of cyber attacks -- albeit with the help of humans. The "AI2" algorithm, developed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and machine learning startup PatternEx, can reportedly detect cyber attacks three times more effectively than today's current systems. AI2 has been tested on 3.6 billion pieces of data, known as "log lines," which were created over a three month period by millions of people. In order to predict attacks, AI2 scans sets of data and identifies suspicious activity. It does this by clustering the data into meaningful patterns using unsupervised machine-learning, according to MIT.

As part of the BBC's Intelligent Machines season, Google's Eric Schmidt has penned an exclusive article on how he sees artificial intelligence developing, why it is experiencing such a renaissance and where it will go next. Until recently, AI seemed firmly stuck in the realm of science fiction. The term "artificial intelligence" was coined 60 years ago - on August 31 1955, John McCarthy proposed a "summer research project" to work out how to create thinking machines. It's turned out to take a bit longer than one summer. We're now entering the seventh decade, and just starting to see real progress.

Even just a generation ago, the concept of a handheld device able to talk back to you was a fantasy, but it's now something that even toddlers are used to. Besides all the fanfare, there are some very real-world changes AIs are making, especially in the job market. While they offer us new opportunities for innovation, they take away some of our most grounded and trusted security nets.Is artificial intelligence a step in the right direction, or the beginning of a dystopia? Here are the pros and cons of AI's influence on the job market: While AIs take away some jobs, as we've all heard, they also create them in the process. With the creation of the self-driving car, the need for taxi drivers will decrease; however, the need for those who can design such cars, gather data on their effectiveness, and create interactive systems to customize the user's experience are suddenly in demand.

We are fast moving from the app era to the era of the intelligent agent. By definition, these agents complete entire jobs by themselves, which means they must learn to understand us and our objectives. Therein lies the technical challenge--for humans often don't say what they mean. Worse, we believe that we're being clear when our communications are riddled with ambiguity. Amy's job is to schedule meetings.

Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.

In a series of videos posted on Kellogg Insight, David Ferrucci, the lead scientist behind IBM's Watson computer, sits down with Kellogg School of Management professor Brian Uzzi to discuss how machine learning and artificial intelligence will become central to the future of business. The discussion took place at the Kellogg School's first Computational Social Science Summit.