For prospective candidates, there is plenty to pick from, and more is coming: two-thirds of firms expect the demand for AI skills in their organization to increase in the next 12 months. If you're considering a career change, it might be a good time to start looking for a good coding course. While many industries remain severely affected by the consequences of the COVID-19 crisis, there is one sector that is actively recruiting: jobs in AI are booming, and the trend is showing no sign of abating. A new report carried out by research agency Ipsos Mori into the current state of the UK's AI labor market found that close to 110,500 job opening were posted in the past year for roles related to AI and data science. That's more than double the number of vacancies registered in 2014, and a 16% increase from 2019, marking the highest year to date for AI jobs posted on the market.

I will recommend this class to any one looking towards Data Science" "This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost." "This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning. The codes used is practical and useful. I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"

Logical reasoning, which is closely related to human cognition, is of vital importance in human's understanding of texts. Recent years have witnessed increasing attentions on machine's logical reasoning abilities. However, previous studies commonly apply ad-hoc methods to model pre-defined relation patterns, such as linking named entities, which only considers global knowledge components that are related to commonsense, without local perception of complete facts or events. Such methodology is obviously insufficient to deal with complicated logical structures. Therefore, we argue that the natural logic units would be the group of backbone constituents of the sentence such as the subject-verb-object formed "facts", covering both global and local knowledge pieces that are necessary as the basis for logical reasoning. Beyond building the ad-hoc graphs, we propose a more general and convenient fact-driven approach to construct a supergraph on top of our newly defined fact units, and enhance the supergraph with further explicit guidance of local question and option interactions. Experiments on two challenging logical reasoning benchmark datasets, ReClor and LogiQA, show that our proposed model, \textsc{Focal Reasoner}, outperforms the baseline models dramatically. It can also be smoothly applied to other downstream tasks such as MuTual, a dialogue reasoning dataset, achieving competitive results.

Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain. In this paper, we introduce a novel hierarchical consistency regularized mean teacher framework for 3D left atrium segmentation. In each iteration, the student model is optimized by multi-scale deep supervision and hierarchical consistency regularization, concurrently. Extensive experiments have shown that our method achieves competitive performance as compared with full annotation, outperforming other stateof-the-art semi-supervised segmentation methods.

Artificial Intelligence solutions are slowly making a profound impact on our lives. It is soon becoming a mainstream technology. Whether it is automatic parking systems, smart sensors, and personal assistance by virtual assistants, Artificial intelligence is causing digital disruption in different industries. It is making its presence felt in the education industry changing traditional and conventional teaching methods. The academic world is getting more high tech with convenient and personalized teaching experience thanks to the numerous applications of Artificial Intelligence in education.

In the future Cdr Ramsey believes submarines will be equipped with the ability to launch their own autonomous vessels. The next generation of surface warships entering service are already planned to feature mission-bays for launching uncrewed vessels. And autonomous submarines can be deployed by transport aircraft, giving the batteries a rest and the XLUUV a global reach.

I walked into my adviser's office, overflowing with frustration and confusion about the advice I had received at a recent career development workshop. It reiterated what I had heard so many times before: I should follow my dream, and if I didn't yet know what that was, I should live with career uncertainty until I figured it out. But as an international student working in the United States, taking time to explore wasn't an option for me. After listening to me rant, my adviser calmly looked across his desk. He told me that instead of focusing on finding a dream job, I should think about what I am good at and what makes me happy at least 80% of the time. This advice surprised me at first, but it ended up being exactly what I needed to hear. > “I should think about what I am good at and what makes me happy at least 80% of the time.” I had spent the previous 22 years following my childhood dream—becoming a professor of marine biology. However, in grad school I saw how applying for grants is a constant source of worry for many professors. I realized I did not want to be responsible for the salaries of my hypothetical lab members. About 4 years into the program, I decided I did not want to pursue a career in research after all. I began to attend career panels, which all followed a worryingly similar template. I would walk into the room with other excited graduate students and collect my free cookies and coffee, confident that the panelists would have the magical answers I needed. Instead, they would talk—again—about following their dreams. The message: I just needed to find a new dream. It would mean taking time off from work to self-reflect and discover a new path. But I couldn't stay in the country without a visa. For most academic researchers, obtaining a university-sponsored visa is relatively straightforward. But outside of academia, it is infinitely more complex, requiring a company that has a job opening and is willing to foot the bill for a work visa. As well-meaning as the panelists were, they fell silent when I brought up this dilemma. I felt totally lost. Finally, I went to my adviser for help. We hadn't talked much about my career plans over the years, but I felt I needed a new perspective from someone who knew me well. When he offered his advice, I was taken aback at first. What happened to “if you love what you do, you'll never work a day in your life”? My adviser assured me there is seldom such a job. Every job has its ugly bits. But as long as you're happy most of the time, you can struggle through the parts you don't like. He also said it was important to find a job I was good at, especially because my visa applications required me to make the case that I would benefit the country. I was relieved to finally have helpful, practical advice. But I discovered that finding overlap between what I like and what I'm good at was not easy. I love scuba diving, but the physical demands are a challenge for me. I'm good at teaching, as evidenced by my friends nagging me to teach them chemistry and microbiology during my high school and undergraduate years and getting rave reviews from my students when I was a teaching assistant, but I don't like repeating the same content every year. Through my teaching experience, however, I also learned that I love telling stories about science. Maybe science communication would offer the overlap I was looking for. To test the waters, during my “spare time” in grad school I started a blog about the history of scientific discoveries. I found that I loved the freedom to choose what to write about, and I never encountered a challenge I didn't enjoy. As for whether I was any good at it, the signs were promising. My writing got noticed, eventually by people at my institution, and I was given opportunities to write press releases and stories for the university's news bureau. After 3 years of writing, I was offered a position as a science writer. It's nothing like my childhood dream. But I am happy—more than 80% of the time.

Charles Darwin's The Descent of Man was published in 1871. Ever since, it has been the foundation stone of human evolutionary studies. Richerson et al. reviewed how modern studies of human biological and cultural evolution reflect the ideas in Darwin's work. They emphasize how cooperation, social learning, and cumulative culture in the ancestors of modern humans were key to our evolution and were enhanced during the environmental upheavals of the Pleistocene. The evolutionary perspective has come to permeate not just human biology but also the social sciences, vindicating Darwin's insights. Science , aba3776, this issue p. [eaba3776][1] ### BACKGROUND Charles Darwin’s The Descent of Man , published on 24 February 1871, laid the grounds for scientific studies into human origins and evolution. We look at the advances in our understanding of these processes through the lenses of modern speciation theory. Applying this theory to specific cases requires one to identify and understand the nature of (i) the ancestor and various preexisting adaptations and traits that it possessed that allowed or simplified the speciation process, (ii) evolutionary forces responsible for major differences between the emergent species and its close relatives, and (iii) the most salient adaptations characteristic of the new species and its evolutionary history (such as genetic, morphological, behavioral, spatial, and temporal). ### ADVANCES Modern research shows that we share many developmental, physiological, morphological, cognitive, and psychological characteristics as well as about 96% of our DNA with the anthropoid apes. We now know that since our last common ancestor with the other apes 6 million to 8 million years ago, human evolution followed the path common for other species with diversification into closely related species and some subsequent hybridization between them. Since Darwin, a long series of unbridgeable gaps have been proposed between humans and other animals. They focused on tool-making, cultural learning and imitation, empathy, prosociality and cooperation, planning and foresight, episodic memory, metacognition, and theory of mind. However, new insights from neurobiology, genetics, primatology, and behavioral biology only reinforce Darwin’s view that most differences between humans and higher animals are “of degree and not of kind.” What makes us different is that our ancestors evolved greatly enhanced abilities for (and reliance on) cooperation, social learning, and cumulative culture—traits emphasized already by Darwin. Cooperation allowed for environmental risk buffering, cost reduction, and the access to new resources and benefits through the “economy of scale.” Learning and cumulative culture allowed for the accumulation and rapid spread of beneficial innovations between individuals and groups. The enhanced abilities to learn from and cooperate with others became a universal tool, removing the need to evolve specific biological organs for specific environmental challenges. These human traits likely evolved as a response to increasing high-frequency climate changes on the millennial and submillennial scales during the Pleistocene. Once the abilities for cumulative culture and extended cooperation were in place, a suite of subsequent evolutionary changes became possible and likely unavoidable. In particular, human social systems evolved to support mothers through the recruitment of males and nonreproductive females. The most distinctive feature of our species, language, appeared arguably driven by selection for simplifying cooperation. Reliance on social learning and conformity led to the emergence of new factors constraining and driving human behavior, such as morality, social norms, and social institutions. These forces often act against the immediate biological or material interests of individuals, promoting instead the interests of the society as a whole or of its powerful segments. Continuous engagement in cooperation has led to the evolution of strong coalitionary psychology, which can bring us together whenever we perceive that our identity group faces outside threats. Coalitionary psychology also has an undesirable byproduct: often negative or even hostile reaction to others who differ from us in their looks, behaviors, beliefs, caste, or class. ### OUTLOOK Our society faces challenges, including climate change; various types of inequality; economic crises; political, cultural, and religious conflicts; and pandemics. Similar challenges have repeatedly arisen and were dealt with in the past with varying success. What makes the current situation different is not only the scale of societal threats but also that modern science can provide guidance on how to respond to them. Adequately answering these challenges requires understanding humans’ social behavior and the roles of cooperation, social learning, and culture for human decision-making. Evolutionary perspective is already helping to synthesize the contributions of social sciences, including anthropology, psychology, economics, political science, and history. The impact of Descent on the social sciences and on the development and implementation of different policies by practitioners and policymakers to improve our society will only grow. ![Figure][2] Depictions of organic evolution versus cultural evolution. (Left) Organic evolution and (right) cultural evolution, as depicted in Alfred L. Kroeber’s 1923 textbook Anthropology: Cultural Patterns and Processes . Biological inheritance is rigid from parents to offspring in eukaryotes, and species mostly do not exchange genes. Culture is potentially acquired from anyone in a person’s social network, and ideas spread rather readily from culture to culture. IMAGE: N. CARY/ SCIENCE Charles Darwin’s The Descent of Man , published 150 years ago, laid the grounds for scientific studies into human origins and evolution. Three of his insights have been reinforced by modern science. The first is that we share many characteristics (genetic, developmental, physiological, morphological, cognitive, and psychological) with our closest relatives, the anthropoid apes. The second is that humans have a talent for high-level cooperation reinforced by morality and social norms. The third is that we have greatly expanded the social learning capacity that we see already in other primates. Darwin’s emphasis on the role of culture deserves special attention because during an increasingly unstable Pleistocene environment, cultural accumulation allowed changes in life history; increased cognition; and the appearance of language, social norms, and institutions. [1]: /lookup/doi/10.1126/science.aba3776 [2]: pending:yes

In this episode we have PhD students Yongsu Ahn and Alex Cabrera to talk about two separate data visualization systems they developed to help people analyze machine learning models in terms of potential biases they may have. The systems are called FairSight and FairVis and have slightly different goals. FairSight focuses on models that generate rankings (e.g., in school admissions) and FairVis more on comparison of fairness metrics. With them we explore the world of "machine bias" trying to understand what it is and how visualization can play a role in its detection and mitigation. That's why you don't have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal.

The direction of AI development is not preordained. It can be altered to increase human productivity, create jobs and shared prosperity, and protect and bolster democratic freedoms--if we modify our approach. The direction of AI development is not preordained. It can be altered to increase human productivity, create jobs and shared prosperity, and protect and bolster democratic freedoms--if we modify our approach. Artificial Intelligence (AI) is not likely to make humans redundant. Nor will it create superintelligence anytime soon. But like it or not, AI technologies and intelligent systems will make huge advances in the next two decades--revolutionizing medicine, entertainment, and transport; transforming jobs and markets; enabling many new products and tools; and vastly increasing the amount of information that governments and companies have about individuals. Should we cherish and look forward to these developments, or fear them? Current AI research is too narrowly focused on making advances in a limited set of domains and pays insufficient attention to its disruptive effects on the very fabric of society. There are reasons to be concerned. Current AI research is too narrowly focused on making advances in a limited set of domains and pays insufficient attention to its disruptive effects on the very fabric of society. If AI technology continues to develop along its current path, it is likely to create social upheaval for at least two reasons. For one, AI will affect the future of jobs. Our current trajectory automates work to an excessive degree while refusing to invest in human productivity; further advances will displace workers and fail to create new opportunities (and, in the process, miss out on AI's full potential to enhance productivity). For another, AI may undermine democracy and individual freedoms. Each of these directions is alarming, and the two together are ominous. Shared prosperity and democratic political participation do not just critically reinforce each other: they are the two backbones of our modern society.