Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across $7$ domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.

We show that the popular $k$-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have deleterious implications for human-centric applications such as resource allocation. We present a fair $k$-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's heuristic for $k$-means, inheriting its simplicity, efficiency, and stability. In comparison with standard Lloyd's, we find that on benchmark data sets, Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output $k$-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever $k$-means is currently used.

Amazon has created a new AI called the Distance Assistant to help its fulfillment facility employees keep a safe distance from one another during the ongoing coronavirus pandemic. Using a time-of-flight sensor similar to the depth-sensing cameras you'll find on modern smartphones like the Galaxy S20, the assistant measures the distance between employees. The AI component is there to help it differentiate people from the background. What the AI sees is then displayed on a 50-inch screen for workers to glance at as they pass high-traffic areas. The final piece the puzzle is an augmented reality overlay. In a kind of magic-mirror like way, employees will see a green or red circle around them on the display.

Stitching a patient back together after surgery is a vital but monotonous task for medics, often requiring them to repeat the same simple movements over and over hundreds of times. But thanks to a collaborative effort between Intel and the University of California, Berkeley, tomorrow's surgeons could offload that grunt work to robots -- like a macro, but for automated suturing. The UC Berkeley team, led by Dr. Ajay Tanwani, has developed a semi-supervised AI deep-learning system, dubbed Motion2Vec. This system is designed to watch publically surgical videos performed by actual doctors, break down the medic's movements when suturing (needle insertion, extraction and hand-off) and then mimic them with a high degree of accuracy. "There's a lot of appeal in learning from visual observations, compared to traditional interfaces for learning in a static way or learning from [mimicking] trajectories, because of the huge amount of information content available in existing videos," Tanwani told Engadget.

Artificial intelligence (AI) leads as Verdict lists the top five terms tweeted on IoT in May 2020, based on data from GlobalData's Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. According to an article shared by Ronald van Loon, a top technology influencer, the world in 2045 will be one where humans will communicate their intent directly and instantly to machines according to the Pentagon. The article further noted that humans will control gadgets with their brains, and will communicate using neural activity alone. Additionally, artificial intelligence will control passenger planes right from take-off to landing, the report suggested.

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As governments, businesses and organizations, and workers figure out how to operate in the new normal brought on by COVID-19, technology, big data, and artificial intelligence are playing an important role. Some governments are deploying contact-tracing technologies, including app-based tracking and facial recognition, to identify those who may be at risk of infection and to keep others at a distance. To increase workplace safety and create a sense of security among staff, many organizations may follow the lead of those governments and launch contact-tracing capabilities in the office. Technologies that protect workplace safety will be instrumental in helping employees feel secure enough to go back to the office -- and back to a semblance of normalcy. According to PwC's CFO Pulse, 41 percent of surveyed chief financial officers consider the pandemic's effects on their workforce to be a top-three concern.

Climate change impact studies are subject to numerous uncertainties and assumptions. One of the main sources of uncertainty arises from the interpretation of climate model projections. Probabilistic procedures based on multimodel ensembles have been suggested in the literature to quantify this source of uncertainty. However, the interpretation of multimodel ensembles remains challenging. Several ... [Show full abstract] assumptions are often required in the uncertainty quantification of climate model projections.

When science fiction writer Isaac Asimov introduced the Three Laws of Robotics to the world in 1942, practical robotic applications such as industrial pneumatic arms, all-transistor calculators and even the term "artificial intelligence" itself were all still a decade or two in the future. Asimov's laws boil down to three simple maxims: protect humans; obey humans; if it doesn't violate rule one or two, protect itself. Seems simple and sensible enough, yet the limits and internal tensions of these basic laws have inspired writers to dream up a wide range of science fiction dystopias, from 2001 to Blade Runner to the Terminator. And let's not forget to add Asimov's own collection of stories, I, Robot, which features the Three Laws, to the list. For business leaders, ushering in an AI-driven global calamity isn't a top-of-mind concern, but even avoiding smaller risks can be a major challenge.

In the future, machines will replace humans in jobs. This is not controversial: it's what machines have done since well before the start of the industrial revolution. Petrol pump attendants were replaced by automated pumps, secretaries were replaced by Microsoft Office. This is what economists call the substitutive effect of automation: humans are substituted in jobs by machines. From time to time, fears have been expressed that humans would run out of jobs entirely. I first wrote about this concern back in 1980, and like many other people at the time, I under-estimated the resilience of what economists call the complementary effect of automation.