Unlike computer programs that rigidly follow rules written by humans, both machine learning and deep learning algorithms can look at a dataset, learn …

When you hear the word "cyborg," scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgs--humans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the body--are already among us.

We are facing a future with great uncertainty and tremendous promise, and the best we can do is to confront it with a combination of heart and mind, of common sense and rigorous science. In the realm of AI, what this means is, we need to do our best to guide the AI minds we are creating to embody the values we cherish: love, compassion, creativity, and respect. Currently, most AI development occurs under the aegis of military organizations or large corporations oriented heavily toward advertising and marketing. Put crudely, an awful lot of AI today is about "spying, brainwashing, or killing." This has the potential to lead to various civilization-scale failure modes involving the intersection of geopolitics, AI, cyberterrorism, and so forth.

To understand the benefits that artificial intelligence can bring to the world of human medicine, consider the case of Ayako Yamashita, a 60-year-old Japanese woman, whose condition befuddled doctors in 2015. Yamashita was thought to be suffering from acute myeloid leukemia. But after several unsuccessful treatment attempts, her doctors decided to search for another answer to her condition. They turned to IBM's Watson, an AI system capable of analyzing vast amounts of data. The computer reviewed nearly 20 million previously-published oncological research studies and cross-referenced data points. Watson's analysis suggested the woman had a rare form of leukemia not detected through conventional methods.

The next time you sit down to watch a movie, the algorithm behind your streaming service might recommend a blockbuster that was written by AI, performed by robots, and animated and rendered by a deep learning algorithm. An AI algorithm may have even read the script and suggested the studio buy the rights. It's easy to think that technology like algorithms and robots will make the film industry go the way of the factory worker and the customer service rep, and argue that artistic filmmaking is in its death throes. Just like computers made it so animators didn't have to draw every frame by hand, advanced algorithms can automatically render advanced visual effects. In both cases, the animator didn't lose their job.

In a field of sugar beet in Switzerland, a solar-powered robot that looks like a table on wheels scans the rows of crops with its camera, identifies weeds and zaps them with jets of blue liquid from its mechanical tentacles. Undergoing final tests before the liquid is replaced with weedkiller, the Swiss robot is one of new breed of AI weeders that investors say could disrupt the $100 billion pesticides and seeds industry by reducing the need for universal herbicides and the genetically modified (GM) crops that tolerate them. Dominated by companies such as Bayer, DowDuPont, BASF and Syngenta, the industry is bracing for the impact of digital agricultural technology and some firms are already adapting their business models. Herbicide sales are worth $26 billion a year and account for 46 percent of pesticides revenue overall while 90 percent of GM seeds have some herbicide tolerance built in, according to market researcher Phillips McDougall. The company said it is close to signing a financing round with investors and is due to go on the market by early 2019.

Five new papers from Uber in San Francisco, California, demonstrate the power of so-called neuroevolution to play video games, solve mazes, and even make a simulated robot walk. Neuroevolution [is] a process of mutating and selecting the best neural networks. At Uber, such applications might include driving autonomous cars, setting customer prices, or routing vehicles to passengers. But the team, part of a broad research effort, had no specific uses in mind when doing the work. In part, they merely wanted to challenge what Jeff Clune, another Uber co-author, calls "the modern darlings" of machine learning: algorithms that use something called "gradient descent," The most novel Uber paper uses a completely different approach that tries many solutions at once.