When you finally get your chance to ride in an autonomous Uber, you may find yourself clambering into a Mercedes-Benz. That's because Daimler, which owns Mercedes, announced today that it will use Uber's immense network to deploy its own robocars. Last year, it struck up with Volvo, which built Uber's autonomous tech into a fleet of XC90 SUVs for testing in Pittsburgh and San Francisco (until the California DMV kicked it out). But the Daimler deal marks a new approach for Uber. Until now, its public plan called for stuffing its homegrown tech into vehicles built by an established manufacturer like Volvo, then deploying it.

So far the names of 34 of the 100 speakers are listed on the conference website. The name that is likely to be the most familiar to IProgrammer readers is that of Geoffrey Hinton, now a Google VP and the person who we consider as the principal pioneer of deep neural networks. Yoshua Bengio, head of MILA (Montreal Institute for Learning Algorithms) which was a recent recipient of extended Google funding is another many will recognize. If our recent //No Comment coverage of the cleverhans adversarial learning library project attracted your attention all three co-authors of the paper are among the speakers. At the moment one third of the speakers at the conference are women and it is to be hoped this balance is maintained as more names are revealed.

The dirty little secret is out about artificial intelligence. This one is more insidious. Data scientists, AI experts and others have long suspected it would be a problem. But it's only within the last couple of years, as AI or some version of machine learning has become nearly ubiquitous in our lives, that the issue has come to the forefront. Name an -ism, and more likely than not, the results produced by our machines have a bias in one or more ways.

As Friday night became Saturday morning, Dong Kim sounded defeated. Kim is a high-stakes poker player who specializes in no-limit Texas Hold'Em. The 28-year-old Korean-American typically matches wits with other top players on high-stakes internet sites or at the big Las Vegas casinos. But this month, he's in Pittsburgh, playing poker against an artificially intelligent machine designed by two computer scientists at Carnegie Mellon. No computer has ever beaten the top players at no-limit Texas Hold'Em, a particularly complex game of cards that serves as the main event at the World Series of Poker. Nearly two years ago, Kim was among the players who defeated an earlier incarnation of the AI at the same casino.

Distributed denial of service protection firm Radware Ltd. has acquired cloud-based security firm Seculert for an undisclosed sum. Founded in 2012, Seculert offers a software-as-a-service security platform that aims to fill gaps left by legacy perimeter defense and breach detection systems. It claims to protect enterprises from advanced threats by focusing on malicious outbound network traffic. The company's platform combines big data analytics, machine learning technology and behavioral analysis to provide visibility on the final two stages of the malware kill chain. Seculert's Javelin service is claimed to be the first inside-out attack simulation and remediation service that allows enterprises to determine how well their secure web gateway, next-generation firewalls or proxy would do at preventing real world malicious malware attacks from succeeding in communicating with their perpetrator's command and control servers.

Syngenta and the AI for Good Foundation have partnered to launch the Syngenta AI Challenge, a new international competition focused on leveraging Artificial Intelligence (AI) tools for use in seed breeding. The competition is accepting submissions from applicants who are ready to put their programming skills to the test for the chance to win $7,500. "This new competition will give entrants the chance to use their talents to take on the extraordinary complexity of seed genetic data," said Joseph Byrum, Ph.D., MBA, PMP and senior R&D strategic marketing executive with Syngenta. "In the face of a rising global population, we need to grow plants that can adapt and thrive in changing conditions – especially as vital resources like water and land are finite. The Syngenta AI Challenge is about creating models that can help solve this puzzle and ensure world food security."

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities' states. These entities' states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.

A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typically used performance measures such as the receiver operating characteristic curve, or the precision-recall curve obtained on such data can be corrected with the knowledge of class priors; i.e., the proportions of the positive and negative examples in the unlabeled data. We extend the results to a noisy setting where some of the examples labeled positive are in fact negative and show that the correction also requires the knowledge of the proportion of noisy examples in the labeled positives. Using state-of-the-art algorithms to estimate the positive class prior and the proportion of noise, we experimentally evaluate two correction approaches and demonstrate their efficacy on real-life data.