Algorithms are increasingly being used to make ethical decisions. Perhaps the best example of this is a high-tech take on the ethical dilemma known as the trolley problem: if a self-driving car cannot stop itself from killing one of two pedestrians, how should the car's control software choose who live and who dies? In reality, this conundrum isn't a very realistic depiction of how self-driving cars behave. But many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs. Assessment tools currently used in the criminal justice system must consider risks to society against harms to individual defendants; autonomous weapons will need to weigh the lives of soldiers against those of civilians. The problem is, algorithms were never designed to handle such tough choices.

Over the past week, the #2009vs2019 meme challenge, alternately known as the #10yearchallenge and #HowHardDidAgeHitYou, has become the latest social media trend ripe for think piece fodder. While the challenge inspired a host of discussions about social media narcissism and gendered norms, author and consultant Kate O'Neill put her own spin on the meme in a tweet raising the privacy implications of posting age-separated photos of oneself on Facebook. The post generated enough buzz and discussion on Twitter that O'Neill expanded it into an article in Wired, in which she argued that Facebook or another data-hungry entity could exploit the meme to train facial recognition algorithms to better handle age-related characteristics and age progression predictions. She noted that the clear labeling of the year in which the pictures were taken, along with the volume of pictures explicitly age-separated by a set amount of time, could be quite valuable to a company like Facebook. "In other words, thanks to this meme, there's now a very large data set of carefully curated photos of people from roughly 10 years ago and now," O'Neill wrote.

It gets knocked down, but it gets up again. This dog-like robot learns to explore all the ways to stand up after falling over – or being shoved, as they often are during testing – one of the toughest tests for four-legged robots to pass. ANYmal is about the size of a large dog, standing 70 centimetres high and weighing 35 kilograms. Its has 12 moving parts that must be coordinated to walk, run or right the robot after it falls over. Modelling all those points and the positions they could potentially take in various landscapes and at different speeds would take weeks for a human to input, says Jemin Hwangbo at ETH Zurich in Switzerland, who led the study.

In 2017, a poker bot called Libratus made headlines when it roundly defeated four top human players at no-limit Texas Hold'Em. Now, Libratus's technology is being adapted to take on opponents of a different kind--in service of the US military. Libratus--Latin for balanced--was created by researchers from Carnegie Mellon University to test ideas for automated decision-making based on game theory. Early last year, the professor who led the project, Tuomas Sandholm, founded a startup called Strategy Robot to adapt his lab's game-playing technology for government use, such as in wargames and simulations used to explore military strategy and planning. Late in August, public records show, the company received a two-year contract of up to $10 million with the US Army.

Much of recent machine learning has focused on deep learning, in which neural network weights are trained through variants of stochastic gradient descent. An alternative approach comes from the field of neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, inspired by the fact that natural brains themselves are the products of an evolutionary process. Neuroevolution enables important capabilities that are typically unavailable to gradient-based approaches, including learning neural network building blocks (for example activation functions), hyperparameters, architectures and even the algorithms for learning themselves. Neuroevolution also differs from deep learning (and deep reinforcement learning) by maintaining a population of solutions during search, enabling extreme exploration and massive parallelization. Finally, because neuroevolution research has (until recently) developed largely in isolation from gradient-based neural network research, it has developed many unique and effective techniques that should be effective in other machine learning areas too.

Federal regulators have announced plans to allow drone operators to fly their unmanned aerial vehicles over populated areas and at night. A Wing Hummingbird drone from Project Wing arrives and sets down its package at a delivery location in Blacksburg, Va., last year. Federal regulators have announced plans to allow drone operators to fly their unmanned aerial vehicles over populated areas and at night. A Wing Hummingbird drone from Project Wing arrives and sets down its package at a delivery location in Blacksburg, Va., last year. Package delivery by drone is one small step closer to reality today.

As we see artificial intelligence impacting the real world, it's no longer a niche computer science, technical field. Policymakers, business leaders, educators, social scientists--they all need to take part and guide the future of A.I. Also, as a technical field, A.I. thoroughly lacks diversity. It lacks women and underrepresented minorities. We're committed to diversity, especially starting with high school students. It's unthinkable that such an important technology that will influence humanity has such an imbalance in terms of the representation of people taking part. A.I. doesn't belong to a niche group of people.

Artificial intelligence (AI) is a term that is now widely used (and abused), loosely defined and mostly misunderstood. Much the same might be said of, say, quantum physics. But there is one important difference, for whereas quantum phenomena are not likely to have much of a direct impact on the lives of most people, one particular manifestation of AI – machine-learning – is already having a measurable impact on most of us. The tech giants that own and control the technology have plans to exponentially increase that impact and to that end have crafted a distinctive narrative. Crudely summarised, it goes like this: "While there may be odd glitches and the occasional regrettable downside on the way to a glorious future, on balance AI will be good for humanity. Oh – and by the way – its progress is unstoppable, so don't worry your silly little heads fretting about it because we take ethics very seriously."

Austrian mathematician Kurt Gödel is known for his'incompleteness' theorems.Credit: Alfred Eisenstaedt/ LIFE Picture Coll./Getty A team of researchers has stumbled on a question that is mathematically unanswerable because it is linked to logical paradoxes discovered by Austrian mathematician Kurt Gödel in the 1930s that can't be solved using standard mathematics. The mathematicians, who were working on a machine-learning problem, show that the question of'learnability' -- whether an algorithm can extract a pattern from limited data -- is linked to a paradox known as the continuum hypothesis. Gödel showed that the statement cannot be proved either true or false using standard mathematical language. The latest result appeared on 7 January in Nature Machine Intelligence1.

GANs, or generative adversarial networks, are the social-media starlet of AI algorithms. They are responsible for creating the first AI painting ever sold at an art auction and for superimposing celebrity faces on the bodies of porn stars.