Which kinds of knowledge workers are at high risk of job loss thanks to smart machines? Usually we don't love getting that question, because the answer isn't the simple one interviewers are seeking. Many jobs include tasks that can and will be automated, but by the same token, almost all jobs have major elements that -- for the foreseeable future -- won't be possible for computers to handle. Our advice therefore can't boil down to a clear "avoid careers in a, b, and c" or "apply for jobs x, y, or z." And yet, we have to admit that there are some knowledge-work jobs that will simply succumb to the rise of the robots.

DeepMind, the artificial intelligence research lab acquired by Google for a reported 400 million in 2014, has called on the UK government to consider funding degree courses that focus on machine learning, which is a subfield of AI. The company -- cofounded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011 -- said the government needs to support the next generation of machine learning experts if it wants the UK to cement its position as a world leader in AI. Writing in evidence submitted to a parliamentary inquiry into robotics and AI last month, DeepMind said: "The government should consider funding for machine learning masters and PhD programmes at British universities, to encourage more research in the field and nurture the next generation of scientists who will help preserve the UK's preeminent position." The company added: "This funding could also include direct support for modules within programmes that train machine learning researchers in the ethics of data science and increasingly autonomous decision-making, to ensure that the pursuit of beneficial outcomes is embedded in the science of machine learning at every level." Machine learning masters degrees and PhDs can cost individuals upwards of 10,000 at the top universities.

FILE - In this July 2, 2015, file photo, Peter Koerber, a pilot and air tactical officer with the U.S. Forest Service, talks about the hazards of flying drones over wildfire areas during a news conference in Redding, Calif. The U.S. Department of the Interior says it's working with drone makers and mapping companies to create a system allowing smartphones to quickly update no-fly zones at wildfires.

Robots in Europe may soon be classified as "electronic persons" if the European Union adopts a recently submitted proposal. Owners of these robots would be liable to paying social security on each robot in an unprecedented step meant to address the rising presence of robotic workers in the EU. The proposal calls for "the creation of a European Agency for robotics and artificial intelligence in order to provide the technical, ethical and regulatory expertise." Robots are being used in exponentially greater numbers in factories and also taking on tasks ranging from surgery to manufacturing and even personal care. Robots are becoming so ubiquitous that there are growing fears over unemployment, wealth inequality and alienation.

The Trolley Problem is an ethical brainteaser that's been entertaining philosophers since it was posed by Philippa Foot in 1967: A runaway train will slaughter five innocents tied to its track unless you pull a lever to switch it to a siding on which one man, also innocent and unawares, is standing. Pull the lever, you save the five, but kill the one: what is the ethical course of action? The problem has run many variants over time, including ones in which you have to choose between a trolley killing five innocents or personally shoving a man who is fat enough to stop the train (but not to survive the impact) into its path; a variant in which the fat man is the villain who tied the innocents to the track in the first place, and so on. Now it's found a fresh life in the debate over autonomous vehicles. The new variant goes like this: your self-driving car realizes that it can either divert itself in a way that will kill you and save, say, a busload of children; or it can plow on and save you, but the kids all die.

The system consists of a mountable hardware band that scans up to 200 meters in every direction along with software that automatically avoids detected obstacles. If objects larger than a meter enter that radius, the radar system will detect it and automatically move the drone to avoid a collision. Arbe Robotics claims that its solution only uses 5 percent battery life throughout the flight. Obviously, this is for a civilian solution detecting slow-moving obstacles. DARPA has been innovating a collision avoidance system for drones to dodge speedy things like small aircraft and other drones.

Despite apparently being up for sale, Alphabet's Boston Dynamics has revealed its latest robot creation. The robotics firm is best known for Atlas, its 5 foot 9 humanoid robot, and spot, a four legged'dog robot'. Now, a new mini version of spot with a strange extendable neck has been shown off helping around the house. The robor has an extenable neck and is shown helping around the house in a new video from Alphabet's secretive firm. 'SpotMini is a new smaller version of the Spot robot, weighing 55 lbs dripping wet (65 lbs if you include its arm.)' the secretive firm said.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas."

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.