Visual environments are structured, consisting of distinct objects or entities. These entities have properties -- both visible and latent -- that determine the manner in which they interact with one another. To partition images into entities, deep-learning researchers have proposed structural inductive biases such as slot-based architectures. To model interactions among entities, equivariant graph neural nets (GNNs) are used, but these are not particularly well suited to the task for two reasons. First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be. Second, GNNs do not factorize knowledge about interactions in an entity-conditional manner. As an alternative, we take inspiration from cognitive science and resurrect a classic approach, production systems, which consist of a set of rule templates that are applied by binding placeholder variables in the rules to specific entities. Rules are scored on their match to entities, and the best fitting rules are applied to update entity properties. In a series of experiments, we demonstrate that this architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information. This disentangling of knowledge achieves robust future-state prediction in rich visual environments, outperforming state-of-the-art methods using GNNs, and allows for the extrapolation from simple (few object) environments to more complex environments.

From being not much more but a catchy phrase to one of the most promising and powerful technologies, AI has come a long way. While communication and digital service providers start to implement ever more solutions based on artificial intelligence and machine learning, the technology is growing stronger by the minute. The predictions, in fact, are that without AI-driven systems, telecoms won't be able to survive in a highly competitive, digital-first market. So, what is the state of AI implementation in telcos? What solutions are the most popular at the moment, and which are expected to surge in the coming months?

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).

In this report, we investigate (element-based) inconsistency measures for multisets of business rule bases. Currently, related works allow to assess individual rule bases, however, as companies might encounter thousands of such instances daily, studying not only individual rule bases separately, but rather also their interrelations becomes necessary, especially in regard to determining suitable re-modelling strategies. We therefore present an approach to induce multiset-measures from arbitrary (traditional) inconsistency measures, propose new rationality postulates for a multiset use-case, and investigate the complexity of various aspects regarding multi-rule base inconsistency measurement.

Machine learning (ML) is to train a machine so that it can make decisions for us. This can be achieved by expert system or machine learning. Expert system is a computer system that emulates the decision-making ability of a human expert. Expert system are also known as Rule Based Systems. It emulates how a human makes a decision.

… are ensuring that our team in India is future-ready and is constantly leveraging newer technologies including machine learning, artificial intelligence, …

An important step in the development of value alignment (VA) systems in artificial intelligence (AI) is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids committing “naturalistic fallacy,” which is an attempt to derive “ought” from “is,” and it provides a more adequate form of ethical reasoning when the fallacy is not committed. Using quantified modal logic, we precisely formulate principles derived from deontological ethics and show how they imply particular “test propositions” for any given action plan in an AI rule base. The action plan is ethical only if the test proposition is empirically true, a judgment that is made on the basis of empirical VA. This permits empirical VA to integrate seamlessly with independently justified ethical principles. This article is part of the special track on AI and Society.

The Fourth Industrial Revolution is upon us. Human evolution has entered a new phase on the back of breathtaking technology advances. Professor Klaus Schwab, in his seminal book, The Fourth Industrial Revolution, talks about the blurring of the lines between the physical, digital and biological spheres. The Fourth Industrial Revolution deals with how technologies like artificial intelligence, machine learning and the internet of things change the way we live and interact with the world and each other. Terms like AI and ML are thrown around a lot and are sometimes used alternatively.

The first area most people think of with fraud is finance. That extends past scammers and includes a wide range of attacks including banking and trades. There has been much discussion on how artificial intelligence (AI) is being used to address wider areas of fraud, such as in pharmaceutical prescription fraud. Last year saw a phenomenal growth in the use of online marketplaces and delivery services. The growth of fraud in those areas also increased.

Describing a decision-making system as an "algorithm" is often a way to deflect accountability for human decisions. For many, the term implies a set of rules based objectively on empirical evidence or data. It also suggests a system that is highly complex--perhaps so complex that a human would struggle to understand its inner workings or anticipate its behavior when deployed. But is this characterization accurate? For example, in late December Stanford Medical Center's misallocation of covid-19 vaccines was blamed on a distribution "algorithm" that favored high-ranking administrators over frontline doctors. The hospital claimed to have consulted with ethicists to design its "very complex algorithm," which a representative said "clearly didn't work right," as MIT Technology Review reported at the time.