With each passing year, parents are getting more worried about how their children will fare once it's time to take that step from school to the workforce. They have good reason to fret. Some 17 million Americans under age 30--about one third of the under-30 population--are saddled with student debt. Many are worried about their career prospects despite having invested--heavily, in some cases--in education. The cost of college is being hotly debated.
Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies.
Udemy, the largest online learning source, just published its Udemy for Business 2020 Workplace Learning Trends Report: The Skills of the Future (48 pp., PDF, opt-in). As Forbes noticed, the report claims that it is now key "to prepare workforces for the future of work in an AI-enabled world." The report states that "In the world of finance, investment funds managed by AI and computers account for 35% of America's stock market today," citing a recent article in The Economist, The rise of the financial machines. For their part, in the report, Udemy notes that AI is reshaping the world of work. The organization notes that 65% of the leaders cited that AI and robotics are an important or very important issue in human capital.
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.
Automated essay scoring (AES) is a broadly used application of machine learning, with a long history of real-world use that impacts high-stakes decision-making for students. However, defensibility arguments in this space have typically been rooted in hand-crafted features and psychometrics research, which are a poor fit for recent advances in AI research and more formative classroom use of the technology. This paper proposes a framework for evaluating automated essay scoring models trained with more modern algorithms, used in a classroom setting; that framework is then applied to evaluate an existing product, Turnitin Revision Assistant.