Over the last few weeks, I have taken a deep dive into the world of Artificial Intelligence (AI). It's a space that requires continuous learning and the blogs that I have written are just a pin prick in the vast opportunities that this space presents. In thinking about intelligence and our future, my mind starts to work through the different models that surround us – from business models; to educational systems; and to seeing how natural systems are changing and adapting. Google defines intelligence as "the ability to acquire and apply knowledge and skills". A broad definition and I am sure it will be challenged on a few levels.
No matter whether you were a straight-A student at university or more a student of beer pong, it's extremely unlikely that your positive memories of college took place in an examination hall. Beyond being generally miserable, exams exacerbate anxiety and other mental health issues, and do a poor job of assessing skills like critical thinking and creativity. Time-pressured tests are used as the key filter for several prestigious professions and universities and, some argue, for no apparent good reason. Given this sad state of affairs, it should be positive to see supervised exams and tests fall slowly out of vogue. Headmasters and professors have urged that more flexible, less time-pressured assessments like essays and written assignments should replace exams.
The report on the Global Deep Learning Software Market offers complete data on the Deep Learning Software market. Components, for example, main players, analysis, size, situation of the business, SWOT analysis, and best patterns in the market are included in the report. In addition to this, the report sports numbers, tables, and charts that offer a clear viewpoint of the Deep Learning Software market. The top Players/Vendors Artelnics, Bright Computing, BAIR, Intel, Cognex, IBM, Keras, Microsoft, VLFeat, NIVIDA, PaddlePaddle, Torch, SignalBox, Wolfram of the global Deep Learning Software market are further covered in the report. The latest data has been presented in the study on the revenue numbers, product details, and sales of the major firms.
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.
Telstra's independent venture capital arm has shown its intention to expand into the artificial intelligence data market following a $US100m (145m AUD) capital raising for San Francisco company Trifacta. Trifacta employs machine-learning technology to deduce a greater depth of insights from the increasing level of data migrating to cloud-based storage. Australia's largest venture capital fund, Telstra Ventures Fund No 2, led the investment, joined in the round by the likes of Energy Impact Partners, NTT Docomo, BMW Ventures and ABN AMRO. Telstra Venture joins a long and credible list of existing investors from Accel Partners, Greylock Partners, Ignition Partners and Google. "The share register for Trifacta is very impressive. It is great to have so many experienced and impressive co-investors in this deal. That is a really massive plus for us," Mr Koertge said.
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."
A team of scientists is now applying the power of artificial intelligence (AI) and high-performance supercomputers to accelerate efforts to analyze the increasingly massive datasets produced by ongoing and future cosmological surveys. In a new study, researchers from NCSA and Argonne have developed a novel combination of deep learning methods to provide a highly accurate approach to classifying hundreds of millions of unlabeled galaxies. The team's findings were published in Physics Letters B. "The NCSA Gravity Group initiated, and continues to spearhead, the use of deep learning at scale for gravitational wave astrophysics. We have expanded our research portfolio to address a computational grand challenge in cosmology, innovating the use of several deep learning methods in combination with high-performance computing (HPC)," said Eliu Huerta, NCSA Gravity Group Lead. "Our work also showcases how the interoperability of NSF and DOE supercomputing resources can be used to accelerate science."