Students code software at desktops, while others assemble odd machines with wires and multi-colored boxes. Earning a spot at this elite university isn't easy; UC-Berkeley accepted a mere 14.8 percent of applicants for the class of 2020. So this young crew will likely be tomorrow's tech leaders and pioneers. Despite all the promise, it appears that BRETT is struggling. BRETT is a robot, and he – or she, or it – is attempting to place a small wooden block into a small hole. Again and again, BRETT swings his arm over the opening, attempts to place the block, but fumbles. Just can't make it fit. However, as robots go, BRETT has a huge advantage: he can learn. Every time BRETT swings his arm and fails, he calculates what went wrong. In essence he's doing what we humans do: he's failing, and in response he's deciding how to improve the next effort. I stand watching for about 15 minutes, and finally BRETT succeeds – a lengthy period given the simple task. But the astounding point is that the robot really did learn.

A robotic strawberry picker built by AgroBot, a Spanish company. It's being tested in California as hiring laborers becomes increasingly difficult. But for one small corner, agricultural technology, it represents an opportunity. Farmers have been facing an increasingly tight labor market for years. The immigrant workforce that has long picked and packed the nation's fruits and vegetables move to better jobs as soon as they can, replaced by new immigrants.

Fail fast, fail often

With the U.S. election finally in the rear-view mirror, the marketing community can get back to normal, or at least to debating what is normal now. After a period of rapid technological changes--connecting cars, homes, and humans--insiders agree 2017 will be a year of adapting to many of the recent trends. "We are between two worlds of legacy thinking and the new reality. Those two worlds don't mesh," said David Cohen, president of Magna North America. Part of that middle ground we predicted here--such as the mainstreaming of virtual reality and debates about transparency, fraud, and ad blocking--have come true even faster than expected. Others, such as "social shopping" and marketers having to pay consumers for their data, have met resistance.

You don't have to be a gambler to appreciate the complexities of the card game Texas Hold'Em. It involves a strategy that needs to evolve based on the players around the table, it takes a certain amount of intuition, and it doesn't require the player to win every hand. Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold'Em tournament played out over 20 days. If you have even the slightest understanding of how to write code, you would realize that it is impossible to actually code a software program to do that with such "imperfect information". The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before.

Do you remember the over-the-top political ad that featured a man representing Rep. Paul Ryan (R-Wisconsin) throwing an elderly woman off a cliff? Well that spot, which took a not so subtle swipe at Ryan's Medicare plan, may have met its match. A newly released ad, also targeted at seniors, aims to portray autonomous cars as four-wheeled killing machines. The ad, which was created by the PAC (political action committee) the Committee to Protect Florida, criticizes Florida State Senate candidate Jeff Brandes (R) because he sponsored legislation to allow driverless cars in Florida. In order to drive the point home, the commercial features a voice over by an elderly woman who seems frightened of autonomous technology.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

A week after Donald Trump's election, a thirty-year-old cognitive scientist named Maya Shankar purchased a plane ticket to Flint, Michigan. Shankar held one of the more unorthodox jobs in the Obama White House, running the Social and Behavioral Sciences Team, also known as the President's "nudge unit." When she launched the team, in early 2014, it felt, Shankar recalls, "like a startup in my parents' basement"--no budget, no mandate, no bona-fide employees. Within two years, the small group of scientists had become a staff of dozens--including an agricultural economist, an industrial psychologist, and "human-centered designers"--working with more than twenty federal agencies on seventy projects, from fixing gaps in veterans' health care to relieving student debt. Usually, the initiatives had, at their core, one question: Could the growing body of knowledge about the quirks of the human brain be used to improve public policy? For months, Shankar had been thinking about how to bring behavioral science to bear on the problems in Flint, where a crisis stemming from lead contamination of the drinking water had stretched on for almost two years. She wondered if lessons from the beleaguered city could inform the Administration's approach to the broader threat posed by lead across America--in pipes, in paint, in dust, and in soil. "Flint is not the only place poisoning kids," Shankar said. In recent years, behavioral science has become a voguish field. In 2002, the Israeli psychologist Daniel Kahneman won a Nobel Prize in Economic Sciences for his work with a colleague, Amos Tversky, exploring the peculiarities of human decision-making in the face of uncertainty. A basic premise of the discipline they'd helped to create was that people's cognition is bias-prone, and susceptible to the cognitive equivalent of optical illusions. As a result, small tweaks of presentation or circumstance could make a major difference: if a judge rendered a decision about granting parole just before a meal, the inmate's odds for a favorable outcome dipped to near zero; just after the judge ate, the chances rose to around sixty-five per cent. Grocers had learned that they could sell double the amount of soup if they placed a sign above their cans reading "limit of 12 per person." But, for all the field's potential, its advances seemed mostly to have served the private sector. A prominent exception was the "nudge," a notion advanced by the legal scholar Cass R. Sunstein, now at Harvard Law School, and the University of Chicago behavioral economist Richard Thaler, in their 2008 best-seller "Nudge: Improving Decisions About Health, Wealth, and Happiness."

These nightmarish images offer a unique and mesmerizing insight into how computers see the world - and what happens when a mind-bogglingly complex system of artificial intelligence is let off its leash. To discern and process the billions of photos that pass through its site, Google engineers designed ingenious tools known as artificial neural networks, or'ANNs'. Google's ANNs are programmed, through an endless stream of similar photos, to recognise objects within images by their distinctive characteristics. For example, the ANN will be taught to recognise a fork by processing millions of pictures of forks - eventually understanding that it has a handle and two to four tines. Now Google is offering the public these networks' codes - allowing people to upload photos and mutate them into terrifying and wonderful versions of the original.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.