We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

Dictionary learning and component analysis models are fundamental in learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.). The model complexity is encoded by means of specific structure, such as sparsity, low-rankness, or nonnegativity. Unfortunately, approaches like K-SVD - that learn dictionaries for sparse coding via Singular Value Decomposition (SVD) - are hard to scale to high-volume and high-dimensional visual data, and fragile in the presence of outliers. Conversely, robust component analysis methods such as the Robust Principle Component Analysis (RPCA) are able to recover low-complexity (e.g., low-rank) representations from data corrupted with noise of unknown magnitude and support, but do not provide a dictionary that respects the structure of the data (e.g., images), and also involve expensive computations. In this paper, we propose a novel Kronecker-decomposable component analysis model, coined as Robust Kronecker Component Analysis (RKCA), that combines ideas from sparse dictionary learning and robust component analysis. RKCA has several appealing properties, including robustness to gross corruption; it can be used for low-rank modeling, and leverages separability to solve significantly smaller problems. We design an efficient learning algorithm by drawing links with a restricted form of tensor factorization, and analyze its optimality and low-rankness properties. The effectiveness of the proposed approach is demonstrated on real-world applications, namely background subtraction and image denoising and completion, by performing a thorough comparison with the current state of the art.

Staying on top of recent work is an important part of being a good researcher, but this can be quite difficult. Thousands of new papers are published every year at the main ML and NLP conferences, not to mention all the specialised workshops and everything that shows up on ArXiv. Going through all of them, even just to find the papers that you want to read in more depth, can be very time-consuming. In this post, I have summarised 50 papers. After going through a paper, if I had the chance, I would write down a few notes and summarise the work in a couple of sentences. These are not meant as reviews – I'm not commenting on whether I think the paper is good or not. But I do try to present the crux of the paper as bluntly as possible, without unnecessary sales tactics. Hopefully this can give you the general idea of 50 papers, in roughly 20 minutes of reading time. The papers are not selected or ordered based on any criteria. It is not a list of the best papers I have read, more like a random sample.

The field of robotics is growing at an incredible rate, and demand for software engineers with the right skills far exceeds the current supply. This makes this an ideal time to enter this field, and this groundbreaking program represents a unique opportunity to develop these in-demand skills. Expert instructors, personalized project reviews, and exclusive hiring opportunities are hallmarks of this program, and in collaboration with the NVIDIA Deep Learning Institute--one of the most exciting and innovative companies in the world--we have built an unrivalled curriculum that offers the most cutting-edge learning experience currently available. You will graduate from this program having completed several hands-on robotics projects in simulation that will serve as portfolio pieces demonstrating the skills you've acquired. This will enable you to pursue a rewarding career in the robotics field.

Computers are built to process data, but there's a particular form of information so rich and dense in meaning that it's beyond the full comprehension of even the most advanced AI. It's also one that you and I process intuitively and deal in every day: language. Understanding the written and spoken word is a big an important challenge for computer scientists. This month, a small milestone was passed when a pair of teams from Microsoft and Alibaba independently created AI programs that can outperform humans in a reading comprehension test. As you might expect, this news resulted in a flurry of coverage.

At the Adecco Group, we are building innovative tailor-made apprenticeship programs that link youth, educators and employers in countries where our role as employer allows for such a solution. As an example, our Youth Employment Solutions (YES!) program in North America has introduced 2,500 students and educators in Kentucky to the merits of work- based training. We have secured permanent employment for 93% of participants in their chosen field and created a pool of skilled candidates through work-based training in the most sought-after industries, including healthcare, welding, IT, supply-chain management, business administration and engineering. Building on this best-practice and through continued partnerships with further States and companies, we pledged to facilitate 10,000 work-based learning opportunities in the US, with an emphasis on apprenticeships, by 2020.

Somewhere, buried in one of tens of millions of cell samples, could lie the next great breakthrough in disease prevention or cure. But one of the great barriers to finding it could be the need for human eyes to evaluate a corresponding mountain of cell images, one by one. In an era when terabytes of data can be analyzed in just a few days, the opportunity to enhance automation of biomedical analysis could help researchers achieve breakthroughs faster in the treatment of almost every disease--from cancer, diabetes and rare disorders to the common cold. To spur this automation, Booz Allen Hamilton (NYSE: BAH) and Kaggle today launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA DGX Station, a personal AI supercomputer that delivers the computing capacity of 400 CPUs in a desktop workstation.

This is the incredible moment a bubble transformed into a delicate snow globe, before freezing completely and shattering. Mesmerising footage shows a bubble balanced on a straw that quickly crystallises in sub-zero temperatures, creating a stunning swirl of delicate icy flakes. The clip captures ice crystals dancing along the fragile surface of the bubble before it collapses in on itself, all in just 14 seconds. Drone footage shows final touches being put on Apple's new campus Larry Nassar's victims confront him during his sentencing This is the incredible moment a bubble transformed into a delicate snow globe, before freezing completely and shattering. Soap bubbles are formed from three individual layers, a thin layer of water molecules between two layers of a water-based solution containing the salts of a fatty acid.

Artificial intelligence laboratories have been cropping up with increasing frequency on campuses in recent years. By and large, though, these labs have been located in computer science or electrical engineering buildings, providing a space for researchers and graduate students to develop computer algorithms that can learn or exhibit intelligent behavior. The University of Rhode Island is taking a very different approach with its new AI lab, which may be the first in the U.S. to be located in a university library. For URI, the library location is key, as officials hope that by putting the lab in a shared central place, they can bring awareness of AI to a wider swath of the university's faculty and student body. "When you have an AI lab in a specific college, the impression is that access is only for students of that college," said Karim Boughida, dean of libraries at the University of Rhode Island.

Artificial intelligence (AI) from Alibaba and Microsoft beat the human score in a Stanford reading comprehension test, the companies announced separately on Monday. The Stanford Question Answering Dataset (SQuAD) uses a set of questions and answers about Wikipedia articles, according to our sister site CNET. Microsoft scored 82.65 and Alibaba's score was 82.44, both good for first place, but barely beat the human score of 82.304. The results, however slim the margin, suggest AI may be able to match or outperform humans in certain tasks. As the field develops, this margin will most likely increase, potentially allowing AI to be smart enough to take over certain jobs--possibly even high-level ones--and let humans focus on others.