Are you ready for robots swapping out high-density servers immersed in fluid? TMGcore believes the market is indeed ready for its OTTO data center platform, which is launching at the SC19 conference in Denver. The two-year old company has been refining its technology in a Dallas-area data center, test-driving its two-phase immersion cooling with bitcoin mining hardware, and developing custom servers, a series of micro-data center enclosures, and a robotic system to replace servers. "We've redefined what a data center looks like," said John-David Enright, the CEO of TMGcore, which is partnering with Vertiv, Jabil, Corgan, 3M and Dell Technologies to bring its offering to the data center market. TMGcore says the OTTO platform offers extreme density and efficiency that can radically reduce the space and cost to deploy IT infrastructure. The OTTO system is perhaps the most ambitious effort yet to create an autonomous data center that can be managed by software and robotics.

Go is one of the most complex abstract strategy games which involves surrounding more territory than your opponent to win the game. Lee Sedol is the world champion of the Go game and has bagged #2 position in international titles. In March 2016, Lee Sedol competed against Google's AI-based AlphaGo program and lost four out of five matches to the AI. Sedol was shocked after the defeat and said, "I don't know how to start or what to say today, but I think I would have to express my apologies first. I do apologize for not being able to satisfy a lot of people's expectations. I kind of felt powerless."

We all must have heard the word "Artificial Intelligence" somewhere before in this rapidly growing technological world. So, according to computer science, artificial intelligence is sometimes called machine intelligence established by different machines, in contrast to the natural intelligence displayed by humans' skill set. According to NASA scientists, the machines are becoming capable of doing any sort of work and humans are becoming lazy at the same time. Artificial Intelligence was first introduced back in 1956 by the scientists of America and since then, AI has experienced several changes from having thought of not doing anything to have thought of doing anything. The solar storm is the next thing on which NASA scientists are working with the help of Artificial Intelligence. It is a flash of increased brightness on the sun, observed near the Earth's surface due to which the layer ozone is depleting.

Instead of cutting marketing budgets, Diageo decides to invest in marketing but to remove inefficient expenditures, and thus improve profits by shifting money investments. Two years ago, the company launched the platform Catalyst, whose main goal is to provide instant data to marketeers and help them create investment-worthy strategies. According to Diageo's global marketing effectiveness director, Adam Ben-Yousef, they have "achieved a more profound shift at the highest level in the belief in marketing investment. Marketing is no longer the first spend to be cut when a market wants to deliver its annual plan." It does that by solving issues and helping companies reach customer satisfaction.

Ronald Schmelzer is Managing Partner & Principal Analyst at AI Focused Research and Advisory firm Cognilytica (http://cognilytica.com), a leading analyst firm focused on application and use of artificial intelligence (AI) in both the public and private sectors. He is also co-host of the popular AI Today podcast, a top AI related podcast that highlights various AI use cases for both the public and private sector as well as interviews guest experts on AI related topics. He is a sought-after expert in AI, Machine Learning, Enterprise Architecture, venture capital, startup and entrepreneurial ecosystems, and more. Ron received a B.S. degree in Computer Science and Engineering from Massachusetts Institute of Technology (MIT) and MBA from Johns Hopkins University.

Maybe every paper abstract should have a mandatory field of what the limitations of the proposed approach are. That way some of the science miscommunications and hypes could maybe be avoided. The media is often tempted to report each tiny new advance in a field, be it AI or nanotechnology, as a great triumph that will soon fundamentally alter our world. Occasionally, of course, new discoveries are underreported. The transistor did not make huge waves when it was first introduced, and few people initially appreciated the full potential of the Internet.

Construction worker Delmer Joel Ramirez Palma was deported Friday, the US Immigration and Customs Enforcement agency confirmed. Ramirez survived the partial collapse of the New Orleans Hard Rock hotel in October. He had also been cooperating with a federal safety investigation into the incident.

--This study refers to a reverse question answering (reverse QA) procedure, in which machines proactively raise questions and humans supply answers. This procedure exists in many real human-machine interaction applications. A crucial problem in human-machine interaction is answer understanding. Existing solutions rely on mandatory option term selection to avoid automatic answer understanding. However, these solutions lead to unnatural human-computer interaction and harm user experience. T o this end, this study proposed a novel deep answer understanding network, called AntNet, for reverse QA. The network consists of three new modules, namely, skeleton extraction for questions, relevance-aware representation of answers, and multi-hop based fusion. As answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing (NLP) deep models. The effectiveness of the three new modules is also verified. UTOMA TIC question answering (QA) is a crucial component in many human-machine interaction systems, such as intelligent customer service, as it can provide a natural way for humans to acquire information [1]. Therefore, QA has received increasing attention in academic research and industry communities in recent years [2]. Questions are solely raised by humans, and answers are then returned by machines in the conventional QA scenario. How to select the best matched answer is the key problem in this setting [3].

It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.