The Intel RealSense cameras have been gaining in popularity for the past few years for use as a 3D camera and for visual odometry. I had the chance to hear a presentation from Daniel Piro about using the Intel RealSense cameras generally and for SLAM (Simultaneous Localization and Mapping). The following post is based on his talk. Depth information is important since that gives us the information needed to understand shapes, sizes, and distance. This lets us (or a robot) know how far it is from items to avoid running into things and to plan path around obstacles in the image field of view.

Jellyfish are about 95% water, making them some of the most diaphanous, delicate animals on the planet. But the remaining 5% of them have yielded important scientific discoveries, like green fluorescent protein (GFP) that is now used extensively by scientists to study gene expression, and life-cycle reversal that could hold the keys to combating aging. Jellyfish may very well harbor other, potentially life-changing secrets, but the difficulty of collecting them has severely limited the study of such "forgotten fauna." The sampling tools available to marine biologists on remotely operated vehicles (ROVs) were largely developed for the marine oil and gas industries, and are much better-suited to grasping and manipulating rocks and heavy equipment than jellies, often shredding them to pieces in attempts to capture them. Now, a new technology developed by researchers at Harvard's Wyss Institute for Biologically Inspired Engineering, John A. Paulson School of Engineering and Applied Sciences (SEAS), and Baruch College at CUNY offers a novel solution to that problem in the form of an ultra-soft, underwater gripper that uses hydraulic pressure to gently but firmly wrap its fettuccini-like fingers around a single jellyfish, then release it without causing harm.

Using AI to recruit and retain employees is hugely advantageous in terms of establishing a holistic and cost-effective process, but it's vital that HR directors should retain full control of implementing systems so that bias and non-compliance do not creep in, writes Dr Alan Bourne. In our experience, if used responsibly, AI and machine learning can help organisations find more best-fit employees, eliminate bias and make the whole recruitment journey more efficient and better for the candidate. We are using it with clients to improve their organisational agility and to create fair and robust development processes. In the future we see it not only supporting a wide range of assessment functions, but also ensuring a better and more effective employee journey, increasing the ability of organisations to be agile and adaptable. However, one of the key issues HR directors face, which other sector practitioners are better used to dealing with, is linking technology and data integration.

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.

Linear algebra is an important subject not only to mathematicians but also to an ever widening range of disciplines.

"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."

This simple introduction to matrix theory offers a refreshing perspective on the subject. Using a basic concept that leads to a simple formula for the power of a matrix, we see how it can solve time series, Markov chains, linear regression, data reduction, principal components analysis (PCA) and other machine learning problems. These problems are usually solved with more advanced matrix calculus, including eigenvalues, diagonalization, generalized inverse matrices, and other types of matrix normalization. Our approach is more intuitive and thus appealing to professionals who do not have a strong mathematical background, or who have forgotten what they learned in math textbooks. It will also appeal to physicists and engineers.

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You'll learn how to program--a useful skill by itself--but you'll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you've learned.

All industries face similar challenges as they seek to extract information from forms, documents, and visual artifacts - and most agree that is costly, time consuming and prone to errors with manual data entry. In this session, you will learn how to use machine learning on a scalable cloud-based platform to efficiently analyze documents - and use the knowledge hiding within - to improve decision-making at your company. Iron Mountain will show how they have been able to ingest nearly every type of imaged data from a wide variety of origins, both on-premise and in the cloud, to capture, process, analyze and then store data integrated into a complete visual search interface to enable their customers to unlock insights from their documents.

Technology is becoming an increasingly important investment for the fast food chain, especially since it can help improve drive-thru times and labor costs, two areas the company has been working to improve. McDonald's previously said it was testing voice-activated drive-thrus and must have liked the results to pursue an acquisition. It is also testing automated deep-fryers that cut down on labor in the kitchen. With this latest acquisition, McDonald's is securing its place as a tech leader within the fast food space. It previously bought Dynamic Yield for $300 million earlier this year and has since deployed the company's decision technology at the drive-thru at 8,000 restaurants in the U.S. and plans to reach just about all drive-thrus in the U.S. and Australia by the end of the year.