Driverless buses in Estonia have had a number of close calls, including ignoring a speeding police car's emergency lights. The vehicles were introduced in the capital Tallinn in recent weeks as part of the Baltic state's presidency of the European Union. Eyewitnesses have reported a few near misses since, including ignoring a red light, but no'major incidents'. Driverless buses in Estonia (pictured) have had a number of close calls, including ignoring a speeding police car's emergency lights Tallinn is the first city in which driverless buses will come in direct contact with live traffic, according to reports from state broadcaster ERR. A number of similar trial schemes have already been tested around the world.

I am a great admirer of both Mark Zuckerberg and Elon Musk. That is one reason why the social media debate last week between them concerning artificial intelligence, a subject also near and dear, caused such dissonance. How could they disagree on such an important subject? This blog will lay out the "great debate." It is far from a private argument between Elon and Mark. It is a debate that percolates throughout scientific and technological communities concerned with AI.

WASHINGTON – The United States is weighing additional support to the Philippine military to fight an Islamist insurgency in the south, a U.S. defense official said Tuesday. Discussions are "pretty advanced" and would see the U.S. provide increased surveillance drone capabilities and training for local forces, the official said. The drones could hypothetically be used to conduct strikes, the official added, although that would only be for self-defense to protect U.S. or partner forces and would not signal another front in America's drone wars. "It's not necessarily what those (drones) are there for. Those are there for ISR and support," the official said, using an acronym for intelligence, surveillance and reconnaissance.

It might be considered a platitude, but people are always looking for new ways to break away from the monotonous beat of everyday normalcy – either temporarily or permanently. According to a 2013 report on drug abuse by the United States government, 9.4 percent (around 24.6 million people) of individuals age 12 or old noted that they had recreationally used a drug within the past month. This tendency to seek life-changing experiences is true whether it concerns things like the countercultural movements of the 1970s – which infamously involved controversial music and use of illicit drugs – or the technological experiences today. Most people are fascinated with those experiences that allow them to escape crushing boredom and constancy of regular life. That's why the prospect of virtual realities and the possibilities of automation afforded by artificial intelligence are so exciting. Here are some of the biggest changes related to these two fields that are quickly arriving with the technological advents of modern society.

One company in the US state of Colorado has a new lesson for artificial intelligence. And it could help us end the drudgery of sorting our trash.

Bengaluru: Last year, for the first time ever, an international beauty contest was judged by machines. Thousands of people from across the world submitted their photos to Beauty.AI, hoping that their faces would be selected by an advanced algorithm free of human biases, in the process accurately defining what constitutes human beauty. In preparation, the algorithm had studied hundreds of images of past beauty contests, training itself to recognize human beauty based on the winners. But what was supposed to be a breakthrough moment that would showcase the potential of modern self-learning, artificially intelligent algorithms rapidly turned into an embarrassment for the creators of Beauty.AI, as the algorithm picked the winners solely on the basis of skin colour. "The algorithm made a fairly non-trivial correlation between skin colour and beauty. A classic example of bias creeping into an algorithm," says Nisheeth K. Vishnoi, an associate professor at the School of Computer and Communication Sciences at Switzerland-based École Polytechnique Fédérale de Lausanne (EPFL).

Max Tegmark had it right when he wrote, "Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before -- as long as we manage to keep the technology beneficial." The year 2016 saw artificial intelligence (AI) reach new heights and 2017 has even more exciting news in store for us. While many have faith in the developing technology, the hype and publicity surrounding AI is often negative. Is AI's role in our future really a matter of concern? Tremendous and steep has been the growth graph of AI.

Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.

Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC) methods are commonly used in learning graphical models. However, MCMC cannot be directly applied in learning topic models because of the quasi-ergodicity problem caused by multimodal distribution of topics. In this paper, we develop an embarrassingly parallel MCMC algorithm for sLDA. Our algorithm works by switching the order of sampled topics combination and labeling variable prediction in sLDA, in which it overcomes the quasi-ergodicity problem because high-dimension topics that follow a multimodal distribution are projected into one-dimension document labels that follow a unimodal distribution. Our empirical experiments confirm that the out-of-sample prediction performance using our embarrassingly parallel algorithm is comparable to non-parallel sLDA while the computation time is significantly reduced.

The Trump administration released a draft drone bill as they struggle to keep up with drone technology. Drones are a hot technology item during the holiday season. The Pentagon gave 130-plus U.S. military bases across the country permission to shoot down private and commercial drones that qualify as threats. That's according to the Military Times, which reported that a Pentagon spokesman on Monday said bases may also disable, track and seize such drones in addition to destroying them. The bases "retain the right of self-defense," said Navy Capt. Just how a base responds to threatening drones "will depend on specific circumstances," he said.