Huyen, aka Huyen Chip, ranked fifth in the annual Top Voices list released this week by the U.S. professional networking site. It compiles the list by examining all sharing activity on its platform from October 1, 2019 through September 30, 2020, and using a combination of quantitative and qualitative signals including engagement (comments, reactions and shares), follower growth and posting cadence. It said: "Having worked at prominent tech companies including Netflix and NVIDIA, Huyen joined the AI startup Snorkel last December. A Stanford graduate, Huyen turned to LinkedIn to find reviewers for the course she'll start teaching there in January next year, Machine Learning Systems Design." Before coming to the U.S., Chip helped launch Vietnam's second most popular web browser, Coc Coc.
Facebook says that it will expand an online course in deep learning to more students to help improve the diversity of its AI division. After a successful pilot program at Georgia Tech, the company will roll out this graduate-level course in deep learning to more colleges across 2021. The focus will be on offering the system to universities that serve large numbers of Black and Latinx students. It's hoped that, by improving the diversity of the people building these systems, some of the more odious biases will be weeded out. This is part of a broader program to encourage people to enter the computer science field even if their undergraduate training is in another area.
"…In many organizations, the human resource department is responsible for many strategic tasks from managing the hiring to [the] termination of employee[s], for example monitoring of employees' at all the levels, handling payroll, managing employee[s'] benefits and so on. To make this work easier[,] organizations across the world are investing in HR automation [to] [carry] out the best human capital decision[s]…" I know what you're thinking: "…my company's board of directors is too visually impaired to consider what kind of impact these new-flanged capabilities will have on the company to actually consider them-- let alone implement them…" but you would be wrong to think this way; because the change is not only already happening, but it is accelerating. While it is true that some companies have not fully considered implementing a complete, top-to-bottom HR automation strategy -- largely because such a thing is still too abstract a problem and a not-so-clear-opportunity right now -- news like Amazon's drive to automate hiring and onboarding for its hourly warehouse workers will not stay secret for long. Do not kid yourselves, while corporate boards are not known for being bastions of innovation and forward-thinking, they know it's possible -- even if they are unable to see its affect on the corporation's current business -- at least, not yet, anyway.
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The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.
The demand for people with knowledge and skills in artificial intelligence (AI) and machine learning (ML) hugely outstrips the supply. This means that learning and gaining qualifications in these subjects can be a great way to enhance your career prospects. However, not everyone has the spare time and money to spend years studying for a degree or other formal qualifications. Today, with the wealth of freely available educational content online, it may not be necessary. There are so many courses, tutorials, and guides available online that it is perfectly possible to gain a thorough grounding in these subjects without paying a penny.
It is sad that we are affected by Coronavirus outbreak which has led to schools/colleges being closed, engineering/mba internships being cancelled and job joining being postponed. But, on a positive side we can use this time of crisis to invest in ourselves to hone our skills and prepare for a better tomorrow. Having spent 6 years in the industry as a software developer/product manager and with a decade of experience in Python programming, I want to give back to the community in this time of crisis. I will share with you lectures, notebooks, have youtube/hangout live training workshops to teach you skills which can help you in the industry.
For those who want to understand how Artificial Intelligence is transforming financial services i.e. AI in Finance, learn from those who are building the future of finance in the biggest banks, tech companies and fast-growing startups: http://www.cfte.education/aifinance It is designed around 18 modules of video lectures, reading assignments and assessment quizzes. Learners can interact with other participants through an online forum, and receive weekly emails with additional content. Once enrolled in the course, participants join a global community of finance professionals, technologists and entrepreneurs interested in AI.
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We see the growth of people analytics at first-hand at Insight222, where we are now working with over 60 global organisations to help them put people analytics at the centre of business. In tandem we have also created a digital learning academy with myHRfuture to upskill HR in digital and analytics. For the last six years I have collated and published a collection of the'best' articles of the preceding 12 months – see 2014, 2015, 2016 2017 and 2018, and following are my choices for the 50 best articles of 2019. Those who have read the previous annual collections may note that the number of articles that make the cut has steadily risen. This is partly down to my inability to prune down to 30 or 20 - although it was hard enough to get it down to 50! Mainly though this recognises the increased number, variety and quality of people analytics and data-driven HR material now being published, which is another indicator of progress in the field. I hope that the articles selected will act as a venerable resource library for those working, researching or interested in the people analytics space. That is certainly the intention. I have arranged the 50 articles into twelve topics: i) Driving business value, ii) the future of work, iii) the future of the HR function, iv) ethics and trust, v) employee experience, vi) strategic workforce planning, vii) ONA, viii) diversity and inclusion, ix) organisational culture, perspectives and case studies from people analytics leaders, x) retention, xi) assessment and xii) getting started, as well as highlighting a few of my own articles from 2019 at the end. I hope you enjoy the articles selected, and if you do, please subscribe to my weekly Digital HR Leaders newsletter. Ultimately, people analytics should be about creating value – for leaders, for managers and for the workforce. So, where better to start than with seven articles that collectively provide insights on how to create value and/or give examples of where organisations have created value from people analytics.