Washington (AFP) - If it has to make a choice, will your autonomous car kill you or pedestrians on the street? The looming arrival of self-driving vehicles is likely to vastly reduce traffic fatalities, but also poses difficult moral dilemmas, researchers said in a study Thursday. Autonomous driving systems will require programmers to develop algorithms to make critical decisions that are based more on ethics than technology, according to the study published in the journal Science. "Figuring out how to build ethical autonomous machines is one of the thorniest challenges in artificial intelligence today," said the study by Jean-Francois Bonnefon of the Toulouse School of Economics, Azim Shariff of the University of Oregon and Iyad Rahwan of the Massachusetts Institute of Technology. "For the time being, there seems to be no easy way to design algorithms that would reconcile moral values and personal self-interest -- let alone account for different cultures with various moral attitudes regarding life-life tradeoffs -- but public opinion and social pressure may very well shift as this conversation progresses."

Researchers have developed a series of AI-based systems that can interpret pathology images and identify the presence and absence of metastatic cancer. The AI systems could lead to new and improved diagnostic methods and treatment. A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School in Boston have teamed up to develop new diagnostic methods based on artificial intelligence (AI). Humayun Irshad, PhD research fellow at Harvard Medical School and one of the lead authors on the research, says that their group is using all kinds of different computational methods to improve diagnostic techniques. "We are developing robust and efficient computational methods to improve diagnostic and prognostic assessment of pathological samples," Irshad says.

Does the self-driving car protect its passengers at all costs with no regard for the lives of others, or should the car instead put its passengers in harm's way to avoid a higher number of casualties that could result from a collision with pedestrians or other motorists? So if there is just one passenger aboard a car, and the lives of 10 pedestrians are at stake, the survey participants were perfectly fine with a self-driving car "killing" its passenger to save many more lives in return. MIT Media Lab has developed a complimentary online "Moral Machine" game that allows you to walk through a number of scenarios of who lives and who dies when a self-driving vehicle has to make a tough call. With this all being said, we can't help but recall a rather poignant scene from the movie I, Robot, in which Detective Spooner (played by Will Smith) recalls a story of how a robot saved his life in a car crash.

While the AI present in today's experimental self-driving cars can navigate city streets, change lanes, avoid accidents and are for the most part fairly competent "drivers", what happens when it comes to an "us versus them" scenario? What if a self-driving car is presented with no-win situation -- no matter what the outcome of a collision, someone will likely die? Does the self-driving car protect its passengers at all costs with no regard for the lives of others, or should the car instead put its passengers in harm's way to avoid a higher number of casualties that could result from a collision with pedestrians or other motorists? That's the subject of a new study published in Science, entitled, "The Social Dilemma of Autonomous Vehicles." The survey results showed that people overwhelmingly decided that self-driving cars should take a "utilitarian approach" in which casualties are minimized, even it means that passengers within the car must have their lives sacrificed for the greater good.

Massachusetts … of the robotics institute at Carnegie Mellon University in Pittsburgh, who was not involved in the MIT study, called it "an important work." "Some argue that prediction is a central part of (artificial) intelligence," Hebert …… Read More MIT's artificial intelligence passes key Turing test An MIT algorithm has managed to produce sounds able to fool human listeners and beat Turing's sound test for artificial intelligence. Researchers from the Massachusetts Institute of Technology are using Alan Turing's tests, developed in the 1950's … According to a new paper, researchers at the University of Georgia and the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory have developed a piece of software capable of turning almost any smartphone into … Massachusetts … of the robotics institute at Carnegie Mellon University in Pittsburgh, who was not involved in the MIT study, called it "an important work." "Some argue that prediction is a central part of (artificial) intelligence," Hebert … Researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT) demonstrated an algorithm that was able to view silent video clips and accurately predict realistic sounds that might appear.

Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come with an at least quadratic computational cost in the number of observations, which can be prohibitive in many applications. Arguably, it is exactly in such large-scale datasets that capturing any type of dependence is of interest, so striking a favourable tradeoff between computational efficiency and test performance for kernel independence tests would have a direct impact on their applicability in practice. In this contribution, we provide an extensive study of the use of large-scale kernel approximations in the context of independence testing, contrasting block-based, Nystrom and random Fourier feature approaches. Through a variety of synthetic data experiments, it is demonstrated that our novel large scale methods give comparable performance with existing methods whilst using significantly less computation time and memory.

This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks. Since psychological attribute datasets typically suffer from small sample sizes, we apply transfer learning with two base models to avoid overfitting. These models were trained on an age and gender prediction task, respectively. Using a novel explanation method we extract heatmaps that highlight the parts of the image most responsible for the prediction. We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task. Finally, we observe that the multiclass model is more feature rich than its binary counterpart. The experimental evaluation is performed on the 2222 images from the 10k US faces dataset containing psychological attribute labels as well as on a subset of KDEF images.

We consider a group of Bayesian agents who try to estimate a state of the world $\theta$ through interaction on a social network. Each agent $v$ initially receives a private measurement of $\theta$: a number $S_v$ picked from a Gaussian distribution with mean $\theta$ and standard deviation one. Then, in each discrete time iteration, each reveals its estimate of $\theta$ to its neighbors, and, observing its neighbors' actions, updates its belief using Bayes' Law. This process aggregates information efficiently, in the sense that all the agents converge to the belief that they would have, had they access to all the private measurements. We show that this process is computationally efficient, so that each agent's calculation can be easily carried out. We also show that on any graph the process converges after at most $2N \cdot D$ steps, where $N$ is the number of agents and $D$ is the diameter of the network. Finally, we show that on trees and on distance transitive-graphs the process converges after $D$ steps, and that it preserves privacy, so that agents learn very little about the private signal of most other agents, despite the efficient aggregation of information. Our results extend those in an unpublished manuscript of the first and last authors.

I'm sure I'm not going be the first or last to make the association between the software name "Quixote" and the legendary story of The Ingenious Gentleman Don Quixote of La Mancha by Miguel de Cervantes Saavedra. The main character of the story is first driven by his wild fantasies that originates from all the romantic stories he read. Essentially, the character has no connection to reality and sets out on a journey where the final result is death, not just of the character, but also a metaphorical death of chivalry. In an ONR release, Marc Steinberg, the program manager says "For years, researchers have debated how to teach robots to act in ways that are appropriate, non-intrusive, and trustworthy," There-in lies the rub. "One important question is how to explain complex concepts such as policies, values, or ethics to robots. Humans are really good at using narrative stories to make sense of the world and communicate to other people. This could one day be an effective way to interact with robots."

Registration has just opened up for an all new US 5 million XPrize, this time focusing on getting humans collaborating better with artificial intelligence to solve major global issues. Unlike previous competitions, this XPrize, sponsored by IBM's Watson division, doesn't feature a set of pre-determined goals, but instead challenges teams to come up with their own. You might be familiar with XPrize from its ongoing Google Lunar effort, which is seeing small teams from around the world compete to successfully land a robot on the Moon. It's a seriously ambitious project, and one that has seen rivals team up in the hope of winning out against the competition. This new project is totally different to the Lunar XPrize, but it's no less ambitious.