An organization is only as good as its people, and therefore, effective human resource management is vital for the success of a business. This is the reason why millions upon millions of dollars are poured into HR efforts each and every year. Everyone wants the best piece of the talent cake. However, nothing comes without a struggle, and with the changing job market, alongside a widening skills gap, employers are starting to call for better and more effective ways to attract, acquire, and retain top talent. Artificial intelligence (commonly dubbed as AI) is here to answer that calling.

In medicine, diseases can be detected at a much earlier stage, and we can support the elderly to live a more independent life, simply by identifying deviations from their usual behaviour and body movements. The UK Government recently announced that AI could help the National Health Service predict those in an early stage of cancer, to ultimately prevent thousands of cancer-related deaths by 2033. The algorithms will examine medical records, habits and genetic information pooled from health charities, the NHS and AI. Virtual nurses could transform patient care, being available round the clock to answer questions, monitor patients and provide quick answers. Beyond healthcare, AI could inform a better allocation of resources in energy, logistics and transport, as well as support the digital advertising industry with more efficient marketing.

Artificial intelligence is your new insurance claims agent. For years, insurance companies have used "InsurTech" AI to underwrite risk. But until recently, the use of AI in claims handling was only theoretical. The advent of AI claims handling creates new risks for policyholders, but it also creates new opportunities for resourceful policyholders to uncover bad faith and encourage insurers to live up to their side of the insurance contract.Most readers are familiar with Lemonade, the InsurTech start-up that boasts a three-second AI claims review process. However, as noted in a Law360 article last year, Lemonade deferred any potential claim denials for human review, so the prospect of AI bad faith is still untested.

Machine learning algorithms process vast quantities of data and spot correlations, trends and anomalies, at levels far beyond even the brightest human mind. But as human intelligence relies on accurate information, so too do machines. Algorithms need training data to learn from. This training data is created, selected, collated and annotated by humans. And therein lies the problem.

On Monday, Apple is expected to announce something altogether different from its typical new products that consumers can touch and feel: A subscription service that in some ways resembles Amazon Prime and will include Apple's own films and television shows with Hollywood stars including Reese Witherspoon, Jennifer Aniston and Steven Spielberg. Usually, when Apple holds big, splashy press events, it unveils a new product like the iPhone or the MacBook. For years, Apple's business has centered around the iPhone, but sales of that once-revolutionary but now commonplace device have slowed. And the entire world of computers has been flipped on its head. Apple has been diversifying beyond hardware, selling iCloud storage to its customers, a $10 a month music streaming service and movies and television shows through iTunes.

In the first days after a fleet of 25 delivery robots descended on George Mason University's campus in January, school officials could only speculate about the machines' long-term impact. The Igloo cooler-sized robots from the Bay Area start-up Starship Technologies -- which were designed to deliver food on demand across campus -- appeared to elicit curious glances and numerous photos, but not much else. It was clear, officials said at the time, that more time and more data would be necessary to understand whether the robots would actually change the campus culture or become a forgettable novelty. Today, some of that data emerged for the first time. In the two months since the robots arrived at the Fairfax, Va.-based school, an extra 1,500 breakfast orders have been delivered autonomously, according to Starship Technologies and Sodexo, a company that manages food services for GMU on contract and works closely with the robots.

The success of Jordan Peele's 2017 film, "Get Out," bought him time, he said, in a recent interview with Le Monde--for his new film, "Us," he had twice as many shoot days. The expanded time frame allowed him to produce a work of expanded ambition: "Us" bounces back and forth between 1986 and the present day, and its action, compared to "Get Out," has a vast range--geographical, dramatic, and intellectual. The movie's imaginative spectrum is enormous, four-dimensionally so: it delves deep into a literal underground world that lends the hallucinatory concept of the "sunken place" from "Get Out" a physical embodiment. And it captures the transformative, radical power of a political conscience, of an idea long held in secret, as it ripens and develops over decades' worth of time. "Us" is nothing short of a colossal achievement.

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization.

The Turing Machine is the paradigmatic case of computing machines, but there are others, such as Artificial Neural Networks, Table Computing, Relational-Indeterminate computing and diverse forms of analogical computing, each of which based on a particular underlying intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and generalizing Newell's hierarchy, which includes the knowledge level at the top and the symbol level immediately below it. In this re-interpretation the knowledge level consists of human knowledge and the symbol level is generalized into a new level that here is called The Mode of Computing. Each computing paradigm uses a particular mode, and a central question for Cognition is what is the mode of natural computing. The mode of computing provides a novel perspective on the phenomena of computing, the representational and non-representational views of cognition, and consciousness.

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. We consider an alternative approach that instead addresses game design for a better player experience by training human-like game agents. Specifically, we study the problem of training game agents in service of the development processes of the game developers that design, build, and operate modern games. We highlight some of the ways in which we think intelligent agents can assist game developers to understand their games, and even to build them. Our early results using the proposed agent framework mark a few steps toward addressing the unique challenges that game developers face.