Are you building a robot that's supposed to autonomously navigate in a useful way? Cool, that means you'll be needing a LIDAR system, then. For better or worse, it's usually just that straightforward: LIDAR is arguably the best sensor we have right now for reliable navigation, localization, and obstacle avoidance for ground robots. In terms of relatively low-cost sensors, sonar is poor resolution and short range; structured light and time-of-flight sensors are short range and don't work well outdoors; and camera-based vision systems aren't robust enough for reliable navigation. The "relatively low cost" bit is the problem: LIDARs are pricey, and an "affordable" 2D unit, with a range of 10 meters or less, can cost you over US 1,000.

Imagine you could use a standard 3D printer to create your next robotic assistant. Just snap in a motor and battery, and it's ready to go. That's precisely the scenario made possible by a new 3D printing technique developed at MIT. Liquids have long been a challenge for 3D printing, and they're necessary for hydraulic devices like moving robots. On Wednesday, researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) announced what they call the first-ever technique for 3D printing robots that can print solid and liquid materials at the same time. That means it's possible to print dynamic robots in a single step, using a commercially available 3D printer.

No one's quite sure on just when people will be able to summon a self-driving car and go wherever they need to go. Too many variables--how the technology advances, how regulations are developed, what consumer acceptance looks like--remain for anyone to say, but that's not keeping Volvo from offering a date: 2020. The Swedes synonymous with safety want to eliminate all traffic deaths and serious injuries in their cars by the end of the decade. Because airbags, automatic braking and other active tech can only do so much, Volvo plans to use automated driving to do it. Next year, it plans to see how that tech works in the real world when it puts 100 customers in robocars for an extended beta test in Gothenburg, Sweden.

Andrej Karpathy is holding a classroom full of Stanford grad students and undergrads rapt with his description of the pros and cons of different kinds of algorithms used in training a neural network to recognize objects in an image. Suddenly, from the middle of the room, the distinctive artificial voice of Apple's Siri pipes up: "I'm not sure what you said." Siri, probably activated accidentally, draws big laughs. In this room, where students are deep into the intricacies of learning how to make software that better understands humans and our data, the error message is a reminder of the technology's exploding real-world applications. As a consequence, the students in Karpathy's class are likely to graduate into a favorable job market.

Volvo is about to embark on one of the greatest adventures in the history of self-driving cars... at leat, if everything goes according to plan. It's planning an experiment in China (reportedly the country's biggest to date) that will have as many as 100 autonomous vehicles driving on public streets in regular traffic conditions. Real-life tests aren't completely novel -- just ask Google. However, this would both offer a rare chance at a large-scale test and give Volvo a toehold in China's increasingly hot self-driving car space.

Maluuba, a deep learning company which raised a 9 million series A in January, has opened a research and development lab with a focus on developing proprietary algorithms to solve language problems. The research lab will be led by Maluuba's CTO, Kaheer Suleman, and will be staffed by 13 deep learning research scientists. Maluuba has partnered with Yoshua Bengio, a machine learning and neural computation expert from the Montreal Institute for Learning Algorithms (MILA) and a Canada Research Chair in statistical learning algorithms. Maluuba has also partnered with reinforcement learning expert Richard Sutton, a principal investigator from the Alberta Innovates Centre for Machine Learning and an Association for the Advancement of Artificial Intelligence Fellow. "For a computer to understand humans speaking in natural language and respond appropriately, it needs to capture and represent a large amount of knowledge that is not just words, but also common sense and context about the topic being discussed by the human," said Sam Pasupalak, co-founder and CEO of Maluuba.

Are we headed for a future of robot lawyers, online judges, mechanical mediators, cyborg litigators and android attorneys? Reading some recent media headlines, you would think so. Last year in Dublin, a conference warned that lawyers must'cut fees or face a future led by robots'. In fact, headlines have been predicting this onslaught of the machines ever since IBM's Watson (a question-answering supercomputer) beat two human Jeopardy! But it's not just the press – many practitioners see the same future.

To test whether his invention is indistinguishable from a human being, he helicopters in a young engineer to see if he falls in love with the robot. Today, making machines and humans indistinguishable from each other is no longer science fiction--it's good business. In fact, a wave of startups are part of a new trend that promises to radically simplify our lives by making it harder to determine whether we're communicating with a person or computer code. In my last post I discussed how I use some of these services and in this post, I'll go deeper into what this trend is all about. I'll look into how pairing new technologies with human assistants will result in tremendous new products, which promise to enhance our lives -- that is, until the robots completely take over and destroy us all.

Take a three year-old to the zoo, and she intuitively knows that the long-necked creature nibbling leaves is the same thing as the giraffe in her picture book. That superficially easy feat is in reality quite sophisticated. The cartoon drawing is a frozen silhouette of simple lines, while the living animal is awash in color, texture, movement and light. It can contort into different shapes and looks different from every angle. Humans excel at this kind of task.

Toronto's Meta, an AI that maps and monitors the universe of scientific information, has launched Meta Science, a free AI-enabled site that assists scientific researchers in identifying which biomed papers are relevant among the almost 4,000 published daily. Meta Science uses the power of AI to identify concepts, people and relationships buried within the world's 26 million research papers. "As a cancer researcher, I've experienced first-hand the realities of information overload and the need to separate noise from signal", said Sam Molyneux, CEO of Meta. "As research output continues to grow, AI can help researchers shorten the path to knowledge at major bottlenecks where human efforts alone can no longer scale." Meta accomplishes this through a simple and familiar user experience that lets scientists create libraries and news feeds on specific or general topics.