It was presented by Cognizant's Center For The Future of Work and Duke Corporate Education. I had no idea how much it would impact my future life decisions! Watch this video (15 mins) to experience what I experienced. In an age of automation, algorithms and AI, many begin to seek the answer to the question, "What do I do when machines do everything?" It was very interesting to hear different speakers' points of view and witness how they occasionally disagreed with one another on stage.

Early in November, a Japanese delegation visited the Indian Institute of Technology, Guwahati campus. The team of five were looking for students with artificial intelligence and deep learning skills from India's premier technology schools for companies in Japan. They had visited IIT-Bombay before flying to the east. In an hour-long meeting, the visitors listed their requirements before K Mohanty, head of career development at IIT-Guwahati and the convenor of the All IITs Placement Committee. "They are willing to come to the placements only if we can provide them with people trained with AI skills. On confirmation, they will hire 15-20 people next year from Guwahati and an equal number from IIT-Bombay," he told FactorDaily.

A large number of E-Commerce and tech companies rely on real time training and predictions for their products. Google predicts real time click-through rates for their ads. This is used as an input to their auction mechanism, apart from a bid from the advertiser to decide which ads to show to the user. Stackoverflow uses real time predictions to automatically tag a question with the correct programming language so that they reach the right asker. An election management team might want to predict real time sentiment using Twitter to assess the impact of their campaign.

Some courts in the U.S., particularly in states from California to New Jersey, use crime-predicting algorithms to determine if a defendant is likely to commit another crime in the future. While the software helps judges decide who gets bail, who goes to jail and who can walk away free, it appears the technology isn't very reliable and opens doors to a more unfair justice system. Dartmouth College researchers Julia Dressel and Hany Farid tackled the issue with the so-called risk assessment algorithms in a paper published in Science Advances. The study examined one popular risk-assessment algorithm, called Compas, and pointed out how the software's recidivism predictions are no different from the answers random people give to online surveys. Farid, who teaches computer science at Dartmouth, and Dressel, who majored in computer science and gender studies at the same school, used Amazon Mechanical Turk in the study.

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.