I became Editor-in-Chief of Communications of the ACM (CACM) to make the magazine again the forum where the computer science community shares ("communicates") its most important results. Whether you compute with bits or qubits, write software or proofs, develop algorithms or neural networks, teach or take classes, work in industry or academia, live in the U.S. or elsewhere, believe tech is the way forward or not, CACM should be the place to share your best work with our broad, diverse, and international community. Early in my career, CACM played this role. Everyone in the field read the magazine, and the CS community shared its most important results there. To get a sense, look at the 1983 CACM 25th Anniversary issue (https://dl.acm.org/toc/cacm/1983/26/1), which reprinted articles from the magazine's early years.

In a press release on Tuesday, Governor Kathy Hochul announced that the University at Albany will become the home of a new artificial intelligence supercomputing initiative. The $200 million project will turn the building which was formerly Albany High School into an engineering college capable of housing a supercomputer that can reach a quintillion computations per second. It would be the first university-based supercomputer capable of reaching that kind of production. In the press release, Governor Hochul said "My administration is steadfast in its commitment to transform SUNY into a globally renowned, 21st century education leader. This funding will help drive economic revenue by attracting companies to New York's emerging advanced research centers, creating jobs and strengthening communities for decades to come."

I was recently a guest speaker at the Sales Leadership Conference organized by Dr. Karen Peesker, Co-Founder of the Sales Leadership Institute, a department at the Toronto Metropolitan University (formally Ryerson University) in Toronto, Canada. The conference was hosted by the Ted Rogers School of Management at Toronto Metropolitan University (Formerly Ryerson University) in collaboration with HEC Montreal and Ivey, funded by SSHRC, IT World Canada, Microsoft, DHL, Rogers, RBC, CPSA, and other community leaders. The conference goals were to bring university professors, students, industry leaders, and academicians to share their learning programs, identify gaps and requirements to advance the sales profession and most importantly, tackle a vision for the future of sales. The strongest theme of the conference was the business imperative for advancing digital literacy, data literacy and ensuring that technology was firmly embedded in all sales learning programs. Digital literacy is best defined as an individual's ability to find, evaluate, and clearly communicate information and knowledge through using diverse digital platforms.

A report from a Government advisory group has found that net job losses are not expected as a result of the adoption of Artificial Intelligence (AI). The Expert Group on Future Skills Needs said that many jobs will change as certain tasks are taken over by AI and that the technology has the potential to bring substantial productivity increases. It comes amid concerns that automation will lead to job displacement and unemployment as machines take over jobs once done by humans. Minister for Trade Promotion, Digital and Company Regulation Robert Troy has welcomed the publication of the report. It highlights the need for everyone, regardless of whether they work in tech or not, to have some level of knowledge and understanding of AI. "The report finds that AI is not likely to bring about a net loss of jobs, but it will replace certain tasks within many jobs over time," Mr Troy said.

Mobile health apps are revolutionizing the healthcare ecosystem by improving communication, efficiency, and quality of service. In low- and middle-income countries, they also play a unique role as a source of information about health outcomes and behaviors of patients and healthcare workers, while providing a suitable channel to deliver both personalized and collective policy interventions. We propose a framework to study user engagement with mobile health, focusing on healthcare workers and digital health apps designed to support them in resource-poor settings. The behavioral logs produced by these apps can be transformed into daily time series characterizing each user's activity. We use probabilistic and survival analysis to build multiple personalized measures of meaningful engagement, which could serve to tailor content and digital interventions suiting each health worker's specific needs. Special attention is given to the problem of detecting churn, understood as a marker of complete disengagement. We discuss the application of our methods to the Indian and Ethiopian users of the Safe Delivery App, a capacity-building tool for skilled birth attendants. This work represents an important step towards a full characterization of user engagement in mobile health applications, which can significantly enhance the abilities of health workers and, ultimately, save lives.

In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous surveys and interviews with experts (researchers and practitioners) who have published their research at top-tier educational conferences in the last year, we conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice, that lead the community to have a clear understanding on the main questions raising doubts in this topic. Based on these findings, we highlighted directions that will facilitate the ongoing research towards fairer AI for education.

By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you've completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization.

It's hard to think of an industry that hasn't been disrupted by artificial intelligence and machine learning (AI/ML).

Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purify the feature, we take into account two types of additional features from different local views to enrich the feature representation. The proposed multi-view features are carefully integrated into our cluster contrast learning to leverage more discriminative cues that the global feature easily ignored and biased. To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme. Specifically, we first train a teacher model from noisy pseudo labels, and then use the teacher model to guide the learning of our student model. In our setting, the student model could converge fast with the supervision of the teacher model thus reduce the interference of noisy labels as the teacher model greatly suffered. After carefully handling the noise and bias in the feature learning, our purification modules are proven to be very effective for unsupervised person re-identification. Extensive experiments on three popular person re-identification datasets demonstrate the superiority of our method. Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and 94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting. The code will be released.

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly.