Almost every second of Betty Li's school life is monitored. The 22-year-old student at a university in northwestern China must get through face scanners to enter her dormitory and register attendance, while cameras above the blackboards in her classrooms keep an eye on the students' attentiveness. Like many other educational institutions across the country, the university in Xian, Shaanxi province, deployed AI-powered gates and facial recognition cameras several years ago as a part of the "smart campuses" campaign promoted by the Ministry of Education. Some schools are even exploring ways to use artificial intelligence to analyse the behaviour of teachers and students. The universities are at the forefront of a national effort to lead the world in emerging technologies and move China's economy up the value chain.
Last week, researchers at the Allen Institute for Artificial Intelligence demonstrated in a new paper that an AI they'd designed could ace an eighth-grade multiple-choice science test with more than 90 percent correct answers -- and do quite well on a 12th-grade science test, too, with more than 80 percent correct answers. The system, called Aristo, took the New York Regents Science Exam (a standardized test for students across New York State), with a few limitations: it didn't have to solve the problems that involved looking at diagrams. Nonetheless, the researchers tested the program on different versions of the test as well as on tests from different years and found that its performance was pretty consistent: It's an A student. Aristo demonstrates how quickly AI is advancing. As recently as 2016, the paper's authors note, no one in the field could manage to score as well as 60 percent on a similar eighth-grade science exam.
Reviewed by Douglas Farenick, University of Regina Undergraduate mathematics textbooks are not what they used to be, and Gilbert Strang's superb new edition of Introduction to Linear Algebra is an example of everything that a modern textbook could possibly be, and more. First, let us consider the book itself. As with his classic Linear Algebra and its Applications (Academic Press) from forty years ago, Strang's new edition of Introduction to Linear Algebra keeps one eye on the theory, the other on applications, and has thestated goal of "opening linear algebra to the world" (Preface, page x).Aimed at the serious undergraduate student - though not just thoseundergraduates who fill the lecture halls of MIT, Strang's homeinstitution - the writing is engaging and personal, and the presentation is exceptionally clear and informative (even seasoned instructors maybenefit from Strang's insights). The first six chapters offer atraditional first course that covers vector algebra and geometry,systems of linear equations, vector spaces and subspaces, orthogonality, determinants, and eigenvalues and eigenvectors. The next three chapters are devoted to the singular value decomposition, lineartransformations, and complex numbers and complex matrices, followed bychapters that address a wide range of contemporary applications andcomputational issues. The book concludes with a brief but cogenttreatment of linear statistical analysis. I would like to stress that there is arichness to the material that goes beyond most texts at this level.Included are guides to websites and to OpenCourseWare, which I shallcomment upon later in this review.
"I have several books on data science and R, as well as other similar subjects and programming languages, in my personal library. However, this book is a great blend of important data science topics and R programming that will make it a great reference for anyone working in this important and immensely popular area. I highly recommend this book for college students learning what it takes to start their career in data science or even current professionals wanting to make a career change or who just want to know more about the subject (and do some R programming as well)." "Due to the self-contained introduction to many of the features of R and RStudio, Graham J. Williams The Essentials of Data Science, Knowledge Discovery Using R would make an excellent recommended or supplementary text for a course that plans to use the rattle package. This book would also serve as a great resource for those with an interest in data science who would like a hands-on approach to learning R and gettting a flavor for a handful of topics within data science."
What this will mean, in the short term, is that AI will become significantly more capable, in less time due to dramatically faster prototyping and larger scale training. In addition, there will be a growth in practical applications of AI because the new paradigm of training at the edge avoids the huge upfront costs of centralized training in the cloud. Millions more developers can now participate in advancing AI solutions. Because training can be coordinated between devices using the IoT (Internet of Things), the cloud infrastructure will have a diminished role. One of the early applications of AI in the construction industry is for training workers and improving their skills.
Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will predict the sales prices in the Housing data set using LinearRegression one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will create Employee Attrition Prediction Project using Decision Tree Classification algorithm one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
In this Data science Machine Learning project, we will create Telecom Customer Churn Prediction Project using Classification Model Logistic Regression, Naive Bayes and One-vs-Rest classifier few of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
Three decades and hundreds of millions of lines of computer code after graduating from the Milwaukee School of Engineering, NVIDIA's Dwight Diercks returned today to celebrate a donation that will put his alma mater at the forefront of AI undergraduate education. Diercks, who grew up the son of a mailman, working on his family's pig farm in Red Wing, Minnesota, came to NVIDIA as its 22nd employee. Today, he oversees a team of some 5,000 software engineers around the world who ship tens of millions of lines of code each month that help accelerate the world's computing. Diercks' $34 million gift, the largest from an alum in MSOE's 116-year history, is the keystone in the school's efforts to infuse its engineering program with artificial intelligence. Two years ago, MSOE became one of the very few programs, together with Carnegie Mellon, to offer a computer science degree focused on AI.
Following the announcement by the Central Board of Secondary Education (CBSE) that it will soon begin to offer Artificial Intelligence (AI) as an elective to students from classes 9 to 12, it has now been revealed that American IT major IBM will be preparing the curriculum for the same, reports The Times of India. Members of IBM India, along with individuals from its global team, will collaborate with subject experts to prepare the curriculum; IBM will launch a pilot project covering 1,000 schools across a number of cities as a trial for the curriculum. As part of the pilot, school principals will firstly be made aware of AI technology, after which foundational lessons regarding the same will given to teachers. The curriculum will consist of a five-stage training process for students, and through it students will participate in research-informed readings, ideation and innovation workshops, as well as hands-on projects. Commenting on the effort, vice-president of India Software Labs, IBM, stated,"Unlike creating a curriculum for professionals, introducing the subject to schoolchildren is different. We have a chance to create a course to give the best effect by using a design-based approach to problem-solving with AI. The programme intends to develop a professional learning community for teachers in partnership with the industry. We will ensure it provides collaborative global project opportunities for students and recognise learning achievements through badging and accreditation."