Ethics has always been one of the most important subjects among scientists and philosophers. There are a lot of AI researchers discussed about the matter of AI Ethics, some have exaggerated ideas about the invasion of robots that ruin humanity and human life in future and some others have been more optimistic. There is no doubt that A.I has changed our life and how we operate in manufacturing, Industry 4.0, online security, healthcare and so on. Artificial Intelligence, machine learning and machine intelligence have had a great impact on invention of new technology within software design, quantum search engine and IT applications. As the prospects of real-time recognition for security purposes grows bolder, it has also engendered a lot of controversies. With this innovation currently being used on experimentally in actual operations by police of England and China, it becomes imperative to give a comprehensive analysis of the innovation.

The Vatican Secret Archives comprise 600 collections of texts spanning 12 centuries, most of which are nearly impossible to access. The Atlantic reports that a team of scientists is hoping to change that with help from some high school students and artificial intelligence software. In Codice Ratio is a new research project dedicated to analyzing the vast majority of Vatican manuscripts that have never been digitized. When other libraries wish to make a digital archive of their inventory, they often use optical-character-recognition (OCR) software. Such programs can be trained to recognize the letters in a certain alphabet, pick them out of hard-copy manuscripts, and convert them to searchable text.

India is changing and so is our technology eco-system. The eco-system which was in terms of technology innovation a laggard is now running a marathon at a speed which perhaps is putting other global tech startup eco-systems to rethink their India strategy. The credit of this must also go to Niti Ayog s vision and setting aside a fund of Rs. 200 crore for future technology companies from India. Under Atal Innovation Mission, startups can raise upto Rs. 10 crore that fits the criteria set up by the Niti Ayog.

Westworld watchers, we knew this moment was coming. The second season's third episode, "Virtù e Fortuna," opens not in Westworld but in an India-themed park. Where Westworld is an emblem of the colonization of Native American land, this park represents Britain's takeover of the subcontinent, and the racial-social hierarchy is clearly encoded: Women in saris and men in turbans--the hosts--walk amidst people dressed in turn-of-the-20th-century British garb. A white man, Nicholas (Neil Jackson), approaches a woman seated at a lawn table and flirts with her. But she's a seasoned guest, and she's done having flings with hosts--she wants to know that he's a real human with real desire, not a fleshbot programmed to seduce her. She announces that she'll have to shoot him to know for sure.

The journal Frontiers in Human Neuroscience have published a paper about how artificial intelligence can help predict your personality from your eye movements. Sabrina Hoppe, Tobias Loetscher, Stephanie Morey and Andreas Bulling are the authors and their affiliations are University of Stuttgart, University of South Australia, Flinders University, and the Max Planck Institute for Informatics. The title says it all: "Eye Movements During Everyday Behavior Predict Personality Traits." Notice their use of the word "Everyday" because this is important. Their exploration is not labs-based but in the real world.

We present a model for exact recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations. The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable -- where the identity of entities does not matter -- it automatically uses a representation that abstracts from ordering (achieving an exponential reduction in complexity) -- and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry.

Knowledge graphs (KGs) model facts about the world, they consist of nodes (entities such as companies and people) that are connected by edges (relations such as founderOf). Facts encoded in KGs are frequently used by search applications to augment result pages. When presenting a KG fact to the user, providing other facts that are pertinent to that main fact can enrich the user experience and support exploratory information needs. KG fact contextualization is the task of augmenting a given KG fact with additional and useful KG facts. The task is challenging because of the large size of KGs, discovering other relevant facts even in a small neighborhood of the given fact results in an enormous amount of candidates. We introduce a neural fact contextualization method (NFCM) to address the KG fact contextualization task. NFCM first generates a set of candidate facts in the neighborhood of a given fact and then ranks the candidate facts using a supervised learning to rank model. The ranking model combines features that we automatically learn from data and that represent the query-candidate facts with a set of hand-crafted features we devised or adjusted for this task. In order to obtain the annotations required to train the learning to rank model at scale, we generate training data automatically using distant supervision on a large entity-tagged text corpus. We show that ranking functions learned on this data are effective at contextualizing KG facts. Evaluation using human assessors shows that it significantly outperforms several competitive baselines.

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.

Preference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.

Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcher's intelligence and the speed of automatic devices by virtue of development of the intelligent system "Artificial dispatcher" on the basis of deep machine learning technology. For realization of the system "Artificial dispatcher" in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The "gain" for "Artificial dispatcher" will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions.