The team behind Roborace has taken a big step towards introducing a fully driverless racing competition. At a press conference in Barcelona, chief executive Denis Sverdlov and chief designer Daniel Simon revealed the final design for its track-ready "Robocar." We've seen images of the vehicle before, but they were merely renders, a hint of what the company was working on. The unveiling of a real car, all curves and carbon fibre, is our best evidence yet that the futuristic motorsport will actually happen. The complete Robocar is 4.5 meters long and 2 meters wide, considerably larger than a Formula 1 racer.

Designers get to have a lot of fun with self-driving cars. After all, things get wild when the human inside doesn't have to drive, or even look at the road, anymore. But when you take the human out of the car altogether, the design department can fully let loose. "We want people to see this like a Tron, or an Oblivion, or a Star Wars spaceship," says Justin Cooke, chief marketing officer of Roborace. Roborace, if you haven't figured it out, is the company starting the world's first motorsports serious for driverless cars.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Unless you've been on a sabbatical deep in the rainforests of Peru, you've probably heard about Artificial Intelligence (AI). But if you still relate it to all things science fiction and robotic, it's time to look further.

Multimodal clustering is an unsupervised technique for mining interesting patterns in $n$-adic binary relations or $n$-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OA-biclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding $n$-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.

NEW YORK Artificial intelligence has made history by beating humans in poker for the first time, the last remaining game in which humans had managed to maintain the upper hand. Libratus, an AI built by Carnegie Mellon University racked up over $1.7 million worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended on Tuesday in Philadelphia. While machines have beaten humans over the last two decade in chess, checkers, and most recently in the ancient game of Go, Libratus' victory is significant because poker is an imperfect information game -- similar to the real world where not all problems are laid out and the difficulty in figuring out human behavior is one of the main reasons why it was considered immune to machines. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Tuomas Sandholm, professor of computer science at CMU who created Libratus with a Ph.D student Noam Brown said on Wednesday. The victory prompted inquiries from companies all over the world seeking to use Libratus' algorithm for problem solving.

What would it look like to ask a deep learning AI system to watch every political television advertisement of the 2016 presidential campaign season for two months and describe what it sees? That was the question I asked last February when I collaborated with the Internet Archive to take all 267 political ads they had identified (which had aired a collective 72,807 times as monitored by the Archive) and ran them frame-by-frame through Google's Cloud Vision API, producing what is likely the first large-scale application of production deep learning algorithms to describe the visual narratives of political advertising on television. Now, what if we took this same approach and instead of examining television, we looked at a quarter billion news photographs compiled from online news outlets in nearly every country of the world over the course of 2016? What would AI see in that vast archive of the visual narratives of the world's media? Google's Cloud Vision API is a commercial cloud service that accepts as input any arbitrary photograph and uses deep learning algorithms to catalog a wealth of data about each image, including a list of objects and activities it depicts, recognizable logos, OCR text recognition in almost 80 languages, levels of violence, an estimate of visual sentiment and even the precise location on earth the image appears to depict.

Industry-leading toy company Mattel (NASDAQ:MAT) had a rough 2016. Its problems, however, started much earlier. Mattel's iconic Barbie has fallen out of fashion, and in September of 2014, the company also lost the licensing for the lucrative Walt Disney Princess line to rival Hasbro after being the sole beneficiary for nearly 20 years. After that stumble, Mattel's performance has suffered -- the shoes left by the Disney darlings have simply been too big for Barbie to fill. As a result, investors have been left wondering if Mattel's best days are behind it.

The award-winning show inspired by a singular graphic memoir. Plus: SCI-Arc in Mexico City, Oscar-nominated films that emerged from important plays, and a longtime curator leaves the downtown gallery he helped establish. I'm Carolina A. Miranda, staff writer for the Los Angeles, and I'm in your inbox with a weekly digest of everything culture: On March 7, Los Angeles will head to the polls to vote on a development measure that could affect the profile of the city. Measure S (formerly known as the Neighborhood Integrity Initiative) seeks to put a two-year moratorium on development projects that require an amendment to the city's general plan, among other factors. Times architecture critic Christopher Hawthorne parses the measure and its backers, whose roots lie in anti-growth initiatives from the 1980s -- and whose vision of Los Angeles seems to lie squarely in the 1960s.

The ocean is essential to the livelihoods and food security of billions of people around the globe. Shipping, tourism, transport, fisheries, oil and gas, renewable energy all depend on the sea. Two years ago, economists put a dollar value on what our oceans are worth and came up with $24 trillion. If it were a country, the sea would be the seventh-largest economy on the planet. "When you look at the blue economy, it has an asset value of $24 trillion and that's delivering something between $4-500bn each year in terms of the dividend to humanity," says Professor Ove Hoegh-Guldberg, director of the Global Change Institute.

The brain is capable of massively parallel information processing while consuming only 1–100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy ( 10 pJ for 103 μm2 devices), displays 500 distinct, non-volatile conductance states within a 1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.