Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model. One of the most popular regularization algorithms is to impose L-2 penalty on the model parameters resulting in the decay of parameters, called weight-decay, and the decay rate is generally constant to all the model parameters in the course of optimization. In contrast to the previous approach based on the constant rate of weight-decay, we propose to consider the residual that measures dissimilarity between the current state of model and observations in the determination of the weight-decay for each parameter in an adaptive way, called adaptive weight-decay (AdaDecay) where the gradient norms are normalized within each layer and the degree of regularization for each parameter is determined in proportional to the magnitude of its gradient using the sigmoid function. We empirically demonstrate the effectiveness of AdaDecay in comparison to the state-of-the-art optimization algorithms using popular benchmark datasets: MNIST, Fashion-MNIST, and CIFAR-10 with conventional neural network models ranging from shallow to deep. The quantitative evaluation of our proposed algorithm indicates that AdaDecay improves generalization leading to better accuracy across all the datasets and models.

Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing local changes in word embedding models trained on newspapers with divergent ideological backgrounds. We demonstrate clear differences in gender bias and changes within and between newspapers over time. In relation to themes such as sexuality and leisure, we see the bias moving toward women, whereas, generally, the bias shifts in the direction of men, despite growing female employment number and feminist movements. Even though Dutch society became less stratified ideologically (depillarization), we found an increasing divergence in gender bias between religious and social-democratic on the one hand and liberal newspapers on the other. Methodologically, this paper illustrates how word embeddings can be used to examine historical language change. Future work will investigate how fine-tuning deep contextualized embedding models, such as ELMO, might be used for similar tasks with greater contextual information.

We introduce a semantics for epistemic logic exploiting a belief base abstraction. Differently from existing Kripke-style semantics for epistemic logic in which the notions of possible world and epistemic alternative are primitive, in the proposed semantics they are non-primitive but are defined from the concept of belief base. We show that this semantics allows us to define the universal epistemic model in a simpler and more compact way than existing inductive constructions of it. We provide (i) a number of semantic equivalence results for both the basic epistemic language with "individual belief" operators and its extension by the notion of "only believing", and (ii) a lower bound complexity result for epistemic logic model checking relative to the universal epistemic model.

Judgment aggregation (JA) is concerned with aggregating sets of binary truth valuations assigned to logically related issues [27, 19]. Various collective decision making problems in artificial intelligence can be modelled as JA problems, e.g., problems of constructing agreements, such as finding a collective goal in multi-agent systems [36, 2]. In agreement reaching problems each agent in a group is a source of judgments and also typically affected by the collective choice resulting from the aggregation of individual judgments. For example, I am a citizen voting on a referendum that decided not to impose global warming curbing methods, but I am also a citizen that has to live with the consequences of that collective decision.

Tremendous research effort has been dedicated over the years to thoroughly investigate non-monotonic reasoning. With the abundance of non-monotonic logical formalisms, a unified theory that enables comparing the different approaches is much called for. In this paper, we present an algebraic graded logic we refer to as LogAG capable of encompassing a wide variety of non-monotonic formalisms. We build on Lin and Shoham's argument systems first developed to formalize non-monotonic commonsense reasoning. We show how to encode argument systems as LogAG theories, and prove that LogAG captures the notion of belief spaces in argument systems. Since argument systems capture default logic, autoepistemic logic, the principle of negation as failure, and circumscription, our results show that LogAG captures the before-mentioned non-monotonic logical formalisms as well. Previous results show that LogAG subsumes possibilistic logic and any non-monotonic inference relation satisfying Makinson's rationality postulates. In this way, LogAG provides a powerful unified framework for non-monotonicity.

AutoX, the Hong Kong and San Jose, Calif.-based autonomous vehicle technology company, is pushing past its grocery delivery roots and into the AV supplier and robotaxi business. AutoX has partnered with NEVS -- the Swedish holding company and electric vehicle manufacturer that bought Saab's assets out of bankruptcy -- to deploy a robotaxi pilot service in Europe by the end of 2020. Under the exclusive partnership, AutoX will integrate its autonomous drive technology into a next-generation electric vehicle inspired by NEVS's "InMotion" concept that was shown at CES Asia in 2017. This next-generation vehicle is being developed by NEVS in Trollhättan, Sweden. Testing of the autonomous NEVs vehicles will begin in the third quarter of 2019.

The "Planet Earth Report" connects you to headline news on the science, technology, discoveries, people and events changing our planet and the future of the human species. We have a new global tally of the insect apocalypse. "Scary Known Unknown" –A Vast Hidden Asteroid Population Close to Sun Elizabeth Warren wants to ban the US from using nuclear weapons first –This 12-word bill could change how we use nuclear weapons. Bill Gates tweeted out a chart and sparked a huge debate about global poverty–Has global poverty declined dramatically? Intelligent Machines –Trump has a plan to keep America first in artificial intelligence.

AI has got an unbelievable momentum in the past couple of years. The current intelligent frameworks have the capability of managing a lot of data and simplifying complicated calculations very fast. In any case, these are not the sentient machines. AI developers are trying to build up this feature in the future. And in the coming years, the AI framework will reach and surpass the performance of humans in solving different tasks.

Not only is it possible to have a fairly realistic holographic replica of yourself, but Microsoft has just shown that it is also possible to have that same replica speak in different languages, too. According to The Verge, on Wednesday, July 17, Microsoft provided a demo of this latest innovation during its keynote speech at the Microsoft Inspire partner conference in Las Vegas. Tom Warren of The Verge posted a video clip on YouTube of Microsoft's demonstration of the hologram's language translation capabilities. Microsoft's demonstration of the technology included Azure executive Julia White, a HoloLens 2 headset, and White's hologram. White's hologram began as a small green outline of a hologram that White could hold in her hand, but as soon as she uttered two simple words, "render keynote," the small hologram grew into a fully rendered, human-sized replica of White and immediately began delivering the keynote speech in Japanese, in a voice that still matched White's.

Cambridge Analytica may have become the byword for a scandal, but it's not entirely clear that anyone knows exactly what that scandal is. It's more like toxic word association: "Facebook", "data", "harvested", "weaponised", "Trump" and, in this country, most controversially, "Brexit". It was a media firestorm that's yet to be extinguished, a year on from whistleblower Christopher Wylie's revelations in the Observer and the New York Times about how the company acquired the personal data of tens of millions of Facebook users in order to target them in political campaigns. This week sees the release of The Great Hack, a Netflix documentary that is the first feature-length attempt to gather all the strands of the affair into some sort of narrative – though it is one contested even by those appearing in the film. "This is not about one company," Julian Wheatland, the ex-chief operating officer of Cambridge Analytica, claims at one point. "This technology is going on unabated and will continue to go on unabated.[…] There was always going to be a Cambridge Analytica. It just sucks to me that it's Cambridge Analytica."