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.

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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.

The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the literature is full of methods with different explanations. We provide a categorization of explanation methods based on the type of explanation returned. We present the most recent and widely used explainers, and we show a visual comparison among explanations and a quantitative benchmarking.

The 1980s saw the evolution of computing as it transitioned from industry to homes through the boom of personal computers. In the field of data management, the Relational Database industry was developing rapidly (Oracle, Sybase, IBM, among others). Object-oriented abstractions were developed as a new form of representational independence. The Internet changed the way people communicated and exchanged information.

It is argued that a broad class of AGI-relevant algorithms can be expressed in a common formal framework, via specifying Galois connections linking search and optimization processes on directed metagraphs whose edge targets are labeled with probabilistic dependent types, and then showing these connections are fulfilled by processes involving metagraph chronomorphisms. Examples are drawn from the core cognitive algorithms used in the OpenCog AGI framework: Probabilistic logical inference, evolutionary program learning, pattern mining, agglomerative clustering, pattern mining and nonlinear-dynamical attention allocation. The analysis presented involves representing these cognitive algorithms as recursive discrete decision processes involving optimizing functions defined over metagraphs, in which the key decisions involve sampling from probability distributions over metagraphs and enacting sets of combinatory operations on selected sub-metagraphs. The mutual associativity of the combinatory operations involved in a cognitive process is shown to often play a key role in enabling the decomposition of the process into folding and unfolding operations; a conclusion that has some practical implications for the particulars of cognitive processes, e.g. militating toward use of reversible logic and reversible program execution. It is also observed that where this mutual associativity holds, there is an alignment between the hierarchy of subgoals used in recursive decision process execution and a hierarchy of subpatterns definable in terms of formal pattern theory.

Applications for self-regulated teaching are very popular (e.g., with Duolingo estimates of 100M downloads from Google Play at the time of writing). One of the central challenges for research on intelligent user interfaces is to identify algorithmic principles that can pick the best interventions for reliably improving human learning toward stated objectives in light of realistically obtainable data on the user. The computational problem we study is how, when given some learning materials, we can organize them into lessons and reviews such that, over time, human learning is maximized with respect to a set learning objective. Predicting the effects of teaching interventions on human learning is challenging, however. Firstly, the state of user memory is both latent (that is, not directly observable) and non-stationary (that is, evolving over time, on account of such effects as loss of activation and interference), and an intervention that is ideal for one user may be a poor choice for another user -- there are large individual-to-individual differences in forgetting and recall.