A scholarship programme seeking to nurture talent in technological research in Africa's public universities has been launched. The three-year programme aims to meet the rising demand for expertise in responsible artificial intelligence (AI) and machine learning (ML) in the continent. While machine learning encompasses the study of computer algorithms and use of data, artificial intelligence involves the simulation of human intelligence by machines, usually computer system. The scholarship programme will support selected scholars to undertake PhD research in AI and ML in African universities, and early career academics to strengthen their research and development capacities in the two areas. Murang'a County to give dairy firm to farmers Sacco What Matiang'i didn't reveal on deployment of police officers The initiative, dubbed the A14D Africa scholarship, is implemented by the African Centre for Technology Studies (ACTS) based in Kenya in partnership with Kwame Nkrumah University in Ghana, University of Linkoping, Sweden, University Cheikh Anta Diop de Dakar, Senegal, University of California, Human Sciences Research Council and Institute for Humanities in Africa based in South Africa and the University of Eduardo Mondlane, Mozambique.

With the popularity and success of our first Introduction to Embedded Machine Learning course, we decided to launch another! We listened to feedback from students, engineers, and industry leaders about which areas in tinyML were most interesting and useful. One topic stood above the rest: vision. Shawn Hymel returns as the main instructor, and we teamed up with OpenMV, Seeed Studio, and the tinyML Foundation to create a new course: Computer Vision with Embedded Machine Learning. The course covers important concepts in computer vision, including how digital images are constructed, stored, and manipulated.

Netflix is reinventing entertainment from end to end. We are revolutionizing how shows and movies are produced, pushing technological boundaries to efficiently deliver streaming video at massive scale over the internet, and continuously improving the personalization of how entertainment is presented to our more than 200 million members around the globe. Applied Machine Learning Research at Netflix improves various aspects of our business, including personalization algorithms, search, systems optimization, content valuation, tooling for artists, and streaming video optimization. Great applied research also requires great Machine Learning infrastructure, another large area of emphasis at Netflix. In 2022, Netflix Research will be hosting a small number of summer Machine Learning internships.

As digital transformation gains traction, organizations are exploring sophisticated technologies such as artificial intelligence (AI) and machine language (ML) to improve and expedite critical functions. However, rural America remains unequipped to realize its AI transformation dreams. This equates to approximately 37 million people -- roughly 15% of the nation's total population and a potential contribution of $41.3 billion to the annual GDP --that faces a lack of access to high-quality technical education, infrastructure and talent. In my last article in this series, I outlined the need to teach AI in rural and underserved America. Now, I'll lay out what kind of training we should bring to the underserved in America -- particularly in high-paying technical fields such as AI -- to introduce economic opportunities. What follows is a guide on offering various training programs for students, businesses and government officials with the goal of solving the AI talent and training dilemma that has long faced rural America.

We examine the long-run behavior of multi-agent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we show that the induced sequence of play (a) converges to Nash equilibrium in time-varying games that stabilize in the long run to a strictly monotone limit; and (b) it stays asymptotically close to the evolving equilibrium of the sequence of stage games (assuming they are strongly monotone). Our results apply to both gradient-based and payoff-based feedback - i.e., the "bandit feedback" case where players only get to observe the payoffs of their chosen actions.

Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Different from traditional vision tasks, VEA is inherently more challenging since it involves a much higher level of complexity and ambiguity in human cognitive process. Most of the existing methods adopt deep learning techniques to extract general features from the whole image, disregarding the specific features evoked by various emotional stimuli. Inspired by the \textit{Stimuli-Organism-Response (S-O-R)} emotion model in psychological theory, we proposed a stimuli-aware VEA method consisting of three stages, namely stimuli selection (S), feature extraction (O) and emotion prediction (R). First, specific emotional stimuli (i.e., color, object, face) are selected from images by employing the off-the-shelf tools. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Then, we design three specific networks, i.e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously. Finally, benefiting from the inherent structure of Mikel's wheel, we design a novel hierarchical cross-entropy loss to distinguish hard false examples from easy ones in an emotion-specific manner. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets. Ablation study and visualizations further prove the validity and interpretability of our method.

The wide application of smart devices enables the availability of multimodal data, which can be utilized in many tasks. In the field of multimodal sentiment analysis (MSA), most previous works focus on exploring intra- and inter-modal interactions. However, training a network with cross-modal information (language, visual, audio) is still challenging due to the modality gap, and existing methods still cannot ensure to sufficiently learn intra-/inter-modal dynamics. Besides, while learning dynamics within each sample draws great attention, the learning of inter-class relationships is neglected. Moreover, the size of datasets limits the generalization ability of existing methods. To address the afore-mentioned issues, we propose a novel framework HyCon for hybrid contrastive learning of tri-modal representation. Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning (that is why we call it hybrid contrastive learning), with which the model can fully explore cross-modal interactions, preserve inter-class relationships and reduce the modality gap. Besides, a refinement term is devised to prevent the model falling into a sub-optimal solution. Moreover, HyCon can naturally generate a large amount of training pairs for better generalization and reduce the negative effect of limited datasets. Extensive experiments on public datasets demonstrate that our proposed method outperforms existing works.

Rachel earned her math PhD at Duke University. She is a popular writer and keynote speaker, on topics of data ethics, AI accessibility, and bias in machine learning. Her writing has been read by nearly a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 9x.

According to the US Bureau of Labour Statistics, "Employment of computer and information technology occupations is projected to grow 12% from 2018 to 2028, much faster than the average for all occupations. These occupations are projected to add about 546,200 new jobs." We are at the cusp of the Fourth Industrial Revolution, where the physical, digital and biological worlds are merging in unprecedented forms and scale. Yet, despite the number of STEM jobs flourishing, less than a third (29.3%) of those employed in scientific research and development across the world in 2016 are women. Eurostat found that in 2020, of almost 73 million persons employed in science and technology in the EU, aged from 15 to 74, nearly 37.5 million were women (51.3%) and 35.5 million men (48.7%).

Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights. The Data Science field is growing so rapidly, and revolutionizing so many industries. Data Science has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.