Kaggle Learn bills itself as "Faster Data Science Education," a free repository of micro-courses covering an array of "[p]ractical data skills you can apply immediately." As I'm sure you are well aware, there are all sorts of free and low-cost data science education alternatives available via numerous online platforms. So why am I feeling it necessary to write about another data science learning resource? As I plan to embark on a fresh fall learning initiative -- once Those Lazy-Hazy-Crazy Days of Summer are out of my system -- I wanted to first find some concise review material for concepts I have previously learned and skills I have already acquired but which may have gone a bit rusty on me. To be clear, Kaggle Learn does not bill its micro-courses specifically as review material; however, I am so far finding that they fit this requirement for me rather well (though, admittedly, I'm still early in the process).

"There's no simple answer," said Stuart Russell, a computer scientist at UC Berkeley, an adjunct professor of neurological surgery at UC San Francisco and the author of a forthcoming book, "Human Compatible: Artificial Intelligence and the Problem of Control." "But in the long run nearly all current jobs will go away, so we need fairly radical policy changes to prepare for a very different future economy. In his book, Russell writes, "One rapidly emerging picture is that of an economy where far fewer people work because work is unnecessary." That's either a very frightening or a tantalizing prospect, depending very much on whether and how much you (and/or society) think people ought to have to work and how society is going to put a price on human labor. There will be less work in manufacturing, less work in call centers, less work driving trucks, and more work in health care and home care and construction. MIT Technology Review tried to track all the different reports on the effect ...

The criteria for identifying technical systems with artificial intelligence (AI) as a specific type of subject are described. The process of making AI machines more complicated is interpreted as the process of making a technosubject. The evolution of AI is considered to be a form of technical evolution stimulating the evolution of Homo sapiens. The co-evolution of human beings and technosubjects has two probable vectors of the development. The first is the complete substitution of man by technosubjects resulting in the emergence of a new form of sociality -- technosociety.

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning methods to identify conceptual entities. We associate source code entities to these natural language concepts using word embedding and clustering techniques. Practical naming conventions for methods and functions tend to reflect the concept(s) they implement. We take advantage of this specificity by presenting a novel process for joint clustering text concepts that combines word-embeddings, nonlinear dimensionality reduction, and clustering techniques to assist in understanding, organizing, and comparing software in the open science ecosystem. With our pipeline, we aim to assist scientists in building on existing models in their discipline when making novel models for new phenomena. By combining source code and conceptual information, our knowledge graph enhances corpus-wide understanding of scientific literature.

Most of existing manifold learning methods rely on Mean Squared Error (MSE) or $\ell_2$ norm. However, for the problem of image quality assessment, these are not promising measure. In this paper, we introduce the concept of an image structure manifold which captures image structure features and discriminates image distortions. We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold. The LLISE is inspired by Locally Linear Embedding (LLE) but uses SSIM rather than MSE. This paper builds a bridge between manifold learning and image fidelity assessment and it can open a new area for future investigations.

Microsoft Research India has announced the launch of a center for Societal impact through Cloud and Artificial Intelligence (SCAI). Part of the Microsoft Research (MSR) Lab in Bengaluru, SCAI will focus on creating and nurturing projects and transitioning them from lab to scale for real-world impact. "There are so many opportunities to leverage recent advances in cloud computing and AI technologies to address long-term societal challenges spanning multiple sectors and realms, including health and wellness, education, transportation, and agriculture," said Eric Horvitz, Technical Fellow and Director at Microsoft Research. SCAI will engage with NGOs, academicians, and startups through external collaborations; graduate and undergraduate students through the SCAI Fellow program in collaboration; and actively seek collaborators through calls for proposals. To start with, Microsoft is currently working with four organizations – Respirer Living Sciences for a project focusing on urban air pollution, NIMHANS for a project on mental health, Pratham Books for assisted translation system which enables children to read storybooks in multiple languages, and Voicedeck Technologies for Learn2Earn, a program which reinforces education and rewards learning through financial incentives.

After six months of competition (and a few last-minute submissions), we are happy to announce the conclusion and winners of the Obstacle Tower Challenge. We want to thank all of the participants for both rounds and congratulate Alex Nichol, the Compscience.org We are also excited to share that we have open-sourced Obstacle Tower for the research community to extend for their own needs. We started this challenge in February as a way to help foster research in the AI community, by providing a challenging new benchmark of agent performance built in Unity, which we called Obstacle Tower. The Obstacle Tower was developed to be difficult for current machine learning algorithms to solve, and push the boundaries of what was possible in the field by focusing on procedural generation. Key to that was only allowing participants access to one hundred instances of the Obstacle Tower, and evaluating their trained agents on a set of unique procedurally generated towers they had never seen before.

Violeta Misheva works as a data scientist at ABN AMRO bank in Amsterdam. Before her current role, she completed a PhD degree in applied microeconomics from Erasmus School of Economics, after which she worked as a data science consultant. She is passionate about AI for good, fair and unbiased machine learning and is an advocate for diversity in the tech world. She enjoys sharing her data science knowledge with others, that's why part-time she conducts workshops with students, has designed a course for the DataCamp, and regularly attends and presents at conferences and other events.

The report highlights the determined vendor overview of the market along with the summary of the leading market players. The growth of every segment of the market is also predicted in the global research report over the estimated period. Furthermore, the market evaluation in terms of value and volume (US$ mn and thousand units) consists of data from across all six regions of the globe including North America, Asia Pacific, South America, Middle East & Africa, and Europe.