Hundreds of schoolchildren have been drafted in to make Amazon's Alexa devices in China as part of a controversial and often illegal attempt to meet production targets, documents seen by the Guardian reveal. Interviews with workers and leaked documents from Amazon's supplier Foxconn show that many of the children have been required to work nights and overtime to produce the smart-speaker devices, in breach of Chinese labour laws. According to the documents, the teenagers – drafted in from schools and technical colleges in and around the central southern city of Hengyang – are classified as "interns", and their teachers are paid by the factory to accompany them. Teachers are asked to encourage uncooperative pupils to accept overtime work on top of regular shifts. Some of the pupils making Amazon's Alexa-enabled Echo and Echo Dot devices along with Kindles have been required to work for more than two months to supplement staffing levels at the factory during peak production periods, researchers found.

In MEAP, you read a book chapter-by-chapter while it's being written and get the final book as soon as it's finished. Save big on Manning books and liveVideo courses with our exclusive Tech in a Box bundles! Each bundle is carefully curated to enhance your skills in a key subject area. Deep learning is exploding, driving everything from autonomous vehicles to real-time computer vision and speech recognition. New languages and new approaches to programming are always emerging.

"Market Overview of the Report 2025: The report on global Machine Learning in Education Market offers high-quality, proven, and wide-ranging …

Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such as nonparametric regression. In this paper, we show that variational inference for sparse deep learning retains the same generalization properties than exact Bayesian inference. In particular, we highlight the connection between estimation and approximation theories via the classical bias-variance trade-off and show that it leads to near-minimax rates of convergence for H\"older smooth functions. Additionally, we show that the model selection framework over the neural network architecture via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence.

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. The method has two parts. The first part uses subject-matter knowledge and interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for the statistical significance of the effect modifiers while strongly controlling familywise error rate. We conducted this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities, and found evidence of heterogeneity in older men who prefer English and don't self-identify as Asian and younger individuals who have at most a high school diploma or GED and prefer English.

Qualitative relationships describe how increasing or decreasing one property (e.g. altitude) affects another (e.g. temperature). They are an important aspect of natural language question answering and are crucial for building chatbots or voice agents where one may enquire about qualitative relationships. Recently a dataset about question answering involving qualitative relationships has been proposed, and a few approaches to answer such questions have been explored, in the heart of which lies a semantic parser that converts the natural language input to a suitable logical form. A problem with existing semantic parsers is that they try to directly convert the input sentences to a logical form. Since the output language varies with each application, it forces the semantic parser to learn almost everything from scratch. In this paper, we show that instead of using a semantic parser to produce the logical form, if we apply the generate-validate framework i.e. generate a natural language description of the logical form and validate if the natural language description is followed from the input text, we get a better scope for transfer learning and our method outperforms the state-of-the-art by a large margin of 7.93%.

Object oriented programming with classes Understanding the power of object oriented programming using abstract data types Defining abstract data types using classes Writing class member and static functions Understanding the class and object structure Exploiting Python's dynamic class and object behaviour

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. But, for universities, the most conspicuous aspect of the announcement may well have been their omission from it.

I am so thrilled to welcome you to the absolutely awesome world of data science. It is an interesting subject, sometimes difficult, sometimes a struggle but always hugely rewarding at the end of your work. While data science is not as tough as, say, quantum mechanics, it is not high-school algebra either. It requires knowledge of Statistics, some Mathematics (Linear Algebra, Multivariable Calculus, Vector Algebra, and of course Discrete Mathematics), Operations Research (Linear and Non-Linear Optimization and some more topics including Markov Processes), Python, R, Tableau, and basic analytical and logical programming skills. If you are studying the Data Science course at Dimensionless Technologies, you are in the right place.

As data breaches, misuse of personal information and the spread of disinformation erode the public's trust in Silicon Valley, it can be all too easy to become cynical about technology's impact on the world. But there are still plenty of reasons to be optimistic about tech's role in society moving forward. Below, TIME speaks to 10 innovators, founders, investors and even athletes who remain upbeat about technology's influence despite the many challenges facing the industry today. Moustapha Cisse left Senegal a decade ago to study artificial intelligence, and now he believes the technology can change Africa for the better. Cisse, 34, is leading Google's AI research center in Accra, Ghana, the company's first such venture in Africa. "I built my team here around people who are really committed to make a difference in people's lives," Cisse tells TIME. "[They] bring a fresh perspective in the field by looking at the problems that we have in Africa." Growing up, no one would have expected Cisse to be heading up a multi-billion dollar corporation's research initiative.