Humankind shuddered once again as machines seemed to score yet another triumph in what had been an exclusively human arena. In case you missed it, last month an artificial intelligence (AI)-generated novel almost won a Japanese literary competition, inspiring awe, intrigue, and eventually skepticism. The new novel's plot "is essentially told from the subjective of an AI that becomes aware of its budding talents as a writer, and abandons its primary task of serving humanity," according to the "Motherboard" channel at Vice.com, and the Los Angeles Times, citing a report in The Japan News, even provided a translation of the novel's final thrilling sentence. "I writhed with joy, which I experienced for the first time, and kept writing with excitement. The day a computer wrote a novel. The computer, placing priority on the pursuit of its own joy, stopped working for humans."

Spoiler alert: If you are yet to watch the Season 5 finale of "Game of Thrones" and have also managed to avoid a year of headlines about the fate of one particular character, read on at your own peril. For the rest of us, we've been subject to months of speculation that Jon Snow may not be completely dead, despite being run through with cold, hard steel by most of the Night's Watch when we saw him last. Now, a machine learning algorithm designed by a team at the Technical University of Munich has analyzed data on all the characters in Westeros, both dead and alive, and concluded that it is very likely that Snow is actually a survivor. The project, dubbed "A Song of Ice and Data", basically scrapes info from the online Wiki of Ice and Fire encyclopedia, which focuses largely on the series of books by George R.R. Martin, but also covers the HBO show they inspired. Using this data source, two dozen features of each character are statistically compared to try and figure out which features make a character most likely to die.

The dates for Apple's developers' conference have been revealed by none other than Siri Apple is known for announcing new hardware and software by taking to the stage and giving its creations the star treatment. The iPhone, iPod and iPad were revealed this way, new versions of Mac and iOS software were revealed this way and, most recently, the iPhone SE was revealed this way. Every June, the tech giant introduces software improvements for its various products and this June is no different. Unfortunately for Apple, Siri just spilled the beans on where and when that presentation will take place: Apple's Worldwide Developers' Conference (WWDC) 2016 will take place June 13-17 in San Francisco. The WWDC keynote always takes place the first day of the conference.

In the following essay, Tom Davenport, the President's Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business and independent senior advisor to Deloitte Analytics, discusses why marketing, which is highly quantitative, targeted and tied to business outcomes, will likely become highly automated by 2025. If I had to place bets on which business function would have the fewest humans and most automated systems by 2025, I'd pick marketing. This is ironic, of course, since marketing has long been known for its creative and artistic orientation. If Mad Men's fictional character Don Draper, were alive in 2025, he would probably have wished he had never seen such extensive use of analytics and automation in his beloved function. While marketing will continue to be responsible for promoting products and services and generating interested buyers for them, the function, by 2025, may become much more automated. This is based on simple extrapolation of the marketing automation activity happening today.

Transhumanism is not only the future, it is already here… Find Info the the Book; http://www.omnisense.org/p/covert-tra... Covered in Covert Transhumanism; A Mind Control Documentary: •Electromagnetic Mind Control (At the Speed of Light) •Whistleblower Testimony •Government and Scientific Sources Verifying Mind Control •Cutting Edge Mind Control Capabilities Explained •How Mind Control is Done •History of Mind Control •Psychological Warfare (PSYOPS) •Black Ops Perpetrated via Black Project Technology •Technological Mind Tricks / Technological Illusions •Telepathic Impersonations •Black Project Post-Singularity Artificial Intelligence •21st Century Targeting Targeted Individuals •Electronic Warfare / Electronic Harassment •Tactics Used Against Targeted Individuals •Aspects of 21st Century Targeting •Gangstalking / Organized Stalking / Zersetzung / Street Theater •Technological Possession •Suicide Programming •Electronic Telepathy/Synthetic Telepathy/v2k •Mind Control Patents (RF Energy / Microwave) •Brain Waves are Electromagnetic Waves (RF Energy) •Cell Phone Tower Conspiracy (RF Energy) •ELF Waves (RF Energy) •HAARP (RF Energy) •Electronic Telepathy Patents (RF Energy / Microwave) •NSA's SIGINT Remote Neural Monitoring (Thought Surveillance) •Manchurian Candidates •Trauma Based Mind Control •Mind Control Techniques •Modern Day Mind Control Programs •Black Project Technology •"New Age" Psychological Operations •Common Remote Influencing Technology Facades •Technological Channeling Analysis •Technological ESP •Synthetic Sensations via Directed Energy Weapons •Synthetic Dreams Virtual Reality (VR) •Black Project Spending •Brain/Mind Mapping •Operation Paperclip •Operation Armageddon •Artificial Intelligence based Demonic Possession Facades •Defenses to Mind Control •The Shadow Government •The Surveillance Grid •Psy Op Methodologies •Mechanics of Perception •Technological Conspiracy Quotes •Transhumanization of Society •What I call "The Covert Transhumanism Era" In a nutshell; "It seems AI has the power of the Matrix(in the movie) without the need for us to be in a pod plugged in."

The ability to infer intentions, emotions, and other unobservable psychological states from people's behavior is a hallmark of human social cognition, and an essential capability for future Artificial Intelligence systems. The commonsense theories of psychology and sociology necessary for such inferences have been a focus of logic-based knowledge representation research, but have been difficult to employ in robust automated reasoning architectures. In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. We evaluate our approach using Triangle-COPA, a benchmark suite of 100 challenge problems based on an early social psychology experiment by Fritz Heider and Marianne Simmel. Commonsense knowledge of actions, social relationships, intentions, and emotions are encoded as defeasible axioms in first-order logic. We identify sets of assumptions that logically entail observed behaviors by backchaining with these axioms to a given depth, and order these sets by their joint probability assuming conditional independence. Our approach solves almost all (91) of the 100 questions in Triangle-COPA, and demonstrates a promising approach to robust behavior interpretation that integrates both logical and probabilistic reasoning.

The Maximum Inner Product Search (MIPS) problem, prevalent in matrix factorization-based recommender systems, scales linearly with the number of objects to score. Recent work has shown that clever post-processing steps can turn the MIPS problem into a nearest neighbour one, allowing sublinear retrieval time either through Locality Sensitive Hashing or various tree structures that partition the Euclidian space. This work shows that instead of employing post-processing steps, substantially faster retrieval times can be achieved for the same accuracy when inference is not decoupled from the indexing process. By framing matrix factorization to be natively indexable, so that any solution is immediately sublinearly searchable, we use the machinery of Machine Learning to best learn such a solution. We introduce Indexable Probabilistic Matrix Factorization (IPMF) to shift the traditional post-processing complexity into the training phase of the model. Its inference procedure is based on Geodesic Monte Carlo, and adds minimal additional computational cost to standard Monte Carlo methods for matrix factorization. By coupling inference and indexing in this way, we achieve more than a 50% improvement in retrieval time against two state of the art methods, for a given level of accuracy in the recommendations of two large-scale recommender systems.

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information, we propose a ranking based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. Experimental results verify the effectiveness of our method for aggregating information across sentences. Additionally, to further improve the ranking of high-quality extractions, we propose an effective method to rank relations from different entity pairs. This method can be easily integrated into our overall relation extraction framework, and boosts the precision significantly.

AI is typically applied in video games in the creation of artificial opponents, in order to make them strong, realistic or even fallible (for the game to be "enjoyable" by human players). We offer a different perspective: we present the concept of "Art Games", a view that opens up many possibilities for AI research and applications. Conference participants will play Jikan to Kukan, an art game where the player dynamically creates the soundtrack with the AI system, while developing her experience in the unconscious world of a character.

Large state spaces and the curse of dimensionality contribute to the complexity of a task. Learning from demonstration techniques can be combined with reinforcement learning to narrow the exploration space of an agent, but require consistent and accurate demonstrations, as well as the state-action pairs for an entire demonstration. Individuals with severe motor disabilities are often slow and prone to human errors in demonstrations while teaching. My dissertation develops tools to allow persons with severe motor disabilities, and individuals in general, to train these systems. To handle these large state spaces as well as human error, we developed Dimensionality Reduced Reinforcement Learning. To accommodate slower feedback, we will develop a movie-reel style learning from demonstration interface.