Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith & Culbertson, 2020). Multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human result. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith and Culbertson (2020)'s findings that highlight the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.

Protenus was founded in 2014 by Nick Culbertson, CEO, and Robert Lord, Chief Strategy Officer. The two met while attending the Johns Hopkins School of Medicine during the rise of the electronic medical record. They saw firsthand the new slate of serious security and privacy concerns brought in its wake. Both had analytical backgrounds, as Lord designed and managed analytical systems for a highly successful hedge fund, while Culbertson served eight years in the U.S. Army working in human intelligence and completed his service as a highly-decorated Special Forces operator, as a Green Beret. Culbertson also has experience in biomedical research participating in a variety of studies, including synthetic biology, cellular engineering and clinical outcomes.

As artificial intelligence and machine learning continue to evolve, they're starting to have real-world impacts on how physicians do their jobs. But some docs are also skeptical if not outright scared that AI might be coming for their jobs. That's the wrong way to look at it, said cardiologist Anthony Chang, MD, chief intelligence and innovation officer at Children's Hospital of Orange County, who's excited about what he says is an "amazing paradigm shift in medicine." It can perhaps be equally exciting and disconcerting seeing IBM's Watson winning on Jeopardy! With computerized smarts like that, it's no wonder that many physicians, particularly radiologists, harbor concerns that their hard-earned wisdom could eventually be replaced by artificial intelligence.

Humans lives their lives trapped in a glass cage of perception. You can only see a limited range of visible light, you can only taste a limited range of tastes, you can only hear a limited range of sounds. But machines can kind of leapfrog over the limitations of natural selection. By creating advanced robots, humans have invented a new kind of being, one that can theoretically sense a far greater range of stimuli. Which is presenting roboticists with some fascinating challenges, not only in creating artificial senses of touch and taste, but in figuring out what robots should ignore in a human world.

This leveling off surprised Daniel Culbertson, economist at the Indeed Hiring Lab and author of the report, who expected job-seeker interest to remain strong, given the amount of opportunity in the high-paying field. Is it fair to say there's an AI talent shortage? "I wouldn't use these data [sets] to conclusively say there is a shortage of AI talent," Culbertson said. "What I can say is the leveling off could be due to the fact that AI is such a burgeoning and high-skilled field. To Forrester analyst Brandon Purcell, the correlation is obvious.

Demand for workers with artificial intelligence (AI) skills has rapidly increased in the past 18 months, according to a new report from job search site Indeed. However, job seeker interest in these roles has leveled off, suggesting that competition for tech talent in this area is going to heat up quickly. While many fear that AI will soon replace jobs, at this phase in the technology's development, it is still creating positions, as companies need highly-skilled workers to develop and maintain a wide range of applications. Employer demand for AI-related roles has more than doubled over the past three years, the report found, and the number of AI- related job postings as a share of all job postings is up 119%. Meanwhile, job seeker interest is much higher than it was three years ago, but hit a plateau in 2017, Indeed found.

As Canada's tech ecosystem continues to see a rise in AI and machine learning startups -- and as the Canadian government and institutions allocate more funding towards AI research -- there's potential for Canada to become a global leader in the space. The Indeed.com report reflects a study that looked at the share of job postings and searches (per one million postings and searches) using the terms "artificial intelligence" and "machine learning" between June 2015 and June 2017. Specifically, the report looked at which cities are creating jobs in AI, and what types of AI and machine learning jobs are high in demand. "Rapid advancements in the technology behind artificial intelligence have had significant impacts on the labour market." "Rapid advancements in the technology behind artificial intelligence have had significant impacts on the labour market," said Daniel Culbertson, an economist at Indeed.com.

About half of millennials looking for work are interested in jobs that carry a risk of automation, a new study suggests. The findings indicate the youngest and most educated generation in the American workforce isn't necessarily more robot-proof than older workers, who tend to be portrayed as the primary victims of automation. "Millennials show a considerable amount of interest in occupations that face a threat of automation," said Daniel Culbertson, an economist at the Indeed Hiring Lab, the research institute attached to the international job site, and the author of the report. "That gets lost when people talk about millennials being so highly educated and more interested in tech roles." A college degree doesn't protect against robot rivals because even well-paid, highly skilled jobs could shrink or vanish in the near future, he said. Recent graduates who land high salaries aren't impervious if their job is characterized by repetitive tasks and decisions.

This work draws its inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads into a consistent principled functorial theory of clustering. Those three are the overlapping clustering of Jardine and Sibson, the functorial approach of Carlsson and Mémoli to partition-based clustering, and the Isbell/Dress school's study of injective envelopes. Carlsson and Mémoli introduce the idea of viewing clustering methods as functors from a category of metric spaces to a category of clusters, with functoriality subsuming many desirable properties. Our first series of results extends their theory of functorial clustering schemes to methods that allow overlapping clusters in the spirit of Jardine and Sibson. This obviates some of the unpleasant effects of chaining that occur, for example with single-linkage clustering. We prove an equivalence between these general overlapping clustering functors and projections of weight spaces to what we term clustering domains, by focusing on the order structure determined by the morphisms. As a specific application of this machinery, we are able to prove that there are no functorial projections to cut metrics, or even to tree metrics. Finally, although we focus less on the construction of clustering methods (clustering domains) derived from injective envelopes, we lay out some preliminary results, that hopefully will give a feel for how the third leg of the stool comes into play.

We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is naturally constrained to refine single-linkage clusters and be refined by maximal-linkage clusters. We work in the context of metric spaces with non-expansive maps, which is appropriate for modeling data processing which does not increase information content.