This paper examines five key questions surrounding computer generated art. Driven by the recent public auction of a work of "AI Art" we selectively summarise many decades of research and commentary around topics of autonomy, authenticity, authorship and intention in computer generated art, and use this research to answer contemporary questions often asked about art made by computers that concern these topics. We additionally reflect on whether current techniques in deep learning and Generative Adversarial Networks significantly change the answers provided by many decades of prior research.

Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics. Recently, several classes of BAFs have been captured as instances of bipolar Assumption-Based Argumentation, a class of Assumption-Based Argumentation (ABA). In this paper, we establish the complexity of bipolar ABA, and consequently of several classes of BAFs. In addition to the standard five complexity problems, we analyse the rarely-addressed extension enumeration problem too. We also advance backtracking-driven algorithms for enumerating extensions of bipolar ABA frameworks, and consequently of BAFs under several interpretations. We prove soundness and completeness of our algorithms, describe their implementation and provide a scalability evaluation. We thus contribute to the study of the as yet uninvestigated complexity problems of (variously interpreted) BAFs as well as of bipolar ABA, and provide the lacking implementations thereof.

Despite what Donald Trump would have you believe, the biggest factor when it comes to American employment is automation, not job theft by Mexico or China or other foreign countries that the president says "you've never even heard of." Although as John Oliver points out, Trump is the same person who reportedly pronounced Nepal and Bhutan as nipple and button, so the list of countries he's never heard of might be higher than average. Elsewhere in the segment, Oliver stopped listing fake countries long enough to explain in detail how machines are replacing jobs in some fields and how that can actually a good thing (unless you want to kill a lumberjack). He also broke the news to some kids who will probably grow up to do jobs that don't already exist, like "crypto-baker" or "snail rehydrater." Good thing that unlike "mermaid doctor," the job of "culture blogger" will never be replaced by BEEP BOOP ERROR 404.

Epilepsy affects millions of people in the U.S. (approximately three million in 2015, according to Healthline). It's commonly diagnosed by interpretation of electroencephalograms, or EEGs -- measurements of the brain's electrical activity taken from the scalp. But the signals tend to be quite long. This makes them challenging to interpret. Researchers at Edith Cowan University in Australia and Pabna University of Science and Technology in Bangladesh propose a solution in a newly published preprint paper on Arxiv.org

Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been proposed, each with its own trade-offs. We propose a hybrid network which combines the strengths of current neural- and tree-based learning techniques in conjunction with ternary quantization, and show a detailed analysis of the associated model design space. Using this hybrid model we obtained a 11.1% reduction in the number of computations, a 52.2% reduction in the model size, and a 30.6% reduction in the overall memory footprint over a state-of-the-art keyword-spotting neural network, with negligible loss in accuracy.

This paper introduces, philosophically and to a degree formally, the novel concept of learning $\textit{ex nihilo}$, intended (obviously) to be analogous to the concept of creation $\textit{ex nihilo}$. Learning $\textit{ex nihilo}$ is an agent's learning "from nothing," by the suitable employment of schemata for deductive and inductive reasoning. This reasoning must be in machine-verifiable accord with a formal proof/argument theory in a $\textit{cognitive calculus}$ (i.e., roughly, an intensional higher-order multi-operator quantified logic), and this reasoning is applied to percepts received by the agent, in the context of both some prior knowledge, and some prior and current interests. Learning $\textit{ex nihilo}$ is a challenge to contemporary forms of ML, indeed a severe one, but the challenge is offered in the spirt of seeking to stimulate attempts, on the part of non-logicist ML researchers and engineers, to collaborate with those in possession of learning-$\textit{ex nihilo}$ frameworks, and eventually attempts to integrate directly with such frameworks at the implementation level. Such integration will require, among other things, the symbiotic interoperation of state-of-the-art automated reasoners and high-expressivity planners, with statistical/connectionist ML technology.

SpaceX's new crew capsule arrived at the International Space Station on Sunday, acing its second milestone in just over a day. No one was aboard the Dragon capsule launched Saturday on its first test flight, only an instrumented dummy. But the three station astronauts had front-row seats as the sleek, white vessel neatly docked and became the first American-made, designed-for-crew spacecraft to pull up in eight years. TV cameras on Dragon as well as the space station provided stunning views of one another throughout the rendezvous. If the six-day demo goes well, SpaceX could launch two astronauts this summer under NASA's commercial crew program.

Are you consciously or unconsciously aware of the fact that Artificial Intelligence is omnipresent? It is finely weaved in our day to day routine from phones to computer and tablets, every device embraces the technology in our surrounding. Even the rising trending craze for Netflix is also a gift of Artificial Intelligence, for sure. Therefore, it would not be an element surprise if we monitor the involvement of the Indian government in this sector. The Government of India is set to regulate Artificial Intelligence with a transparent set of guidelines for the procedure to develop and implement the AI technology, as confirmed by Minister of Commerce & Industry and Civil Aviation Suresh Prabhu.

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperparameters that determine measures of simulation discrepancy crucially balance inference accuracy and sample efficiency, yet are difficult to tune. In this paper, we present kernel embedding likelihood-free inference (KELFI), a holistic framework that automatically learns model hyperparameters to improve inference accuracy given limited simulation budget. By leveraging likelihood smoothness with conditional mean embeddings, we nonparametrically approximate likelihoods and posteriors as surrogate densities and sample from closed-form posterior mean embeddings, whose hyperparameters are learned under its approximate marginal likelihood. Our modular framework demonstrates improved accuracy and efficiency on challenging inference problems in ecology.

As data continues to pile up, enterprises that maintain flexible approaches to managing and mining that data are the ones most likely to achieve competitive success, according to Gartner, which recently released its top 10 analytics technologies and trends for 2019. The Global Datashere currently measures 33 zettabytes, according to a recent IDC report, and is predicted to grow to 175 zettabytes by 2025. Navigating this data deluge is no simple matter, as the volume and velocity exceeds the capabilities of existing data analytics rigs running atop legacy architectures. "The size, complexity, distributed nature of data, speed of action, and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down," explains Donald Feinberg, vice president and distinguished analyst at Gartner. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change."