Contrary to what Silicon Valley would like you to believe, the rise of social computing--using computers to connect people and enable them to interact, collaborate, and communicate--not only happened long before the personal computer era even got underway, but it happened far from California. The PLATO computer system, launched in 1960 at the University of Illinois at Urbana-Champaign, was designed to be a platform for online learning, but by the early 1970s it had evolved into something much more, thanks to a growing, enthusiastic user community, many of whom were high school and college students. Within one 12-month stretch between 1973–74, PLATO's users created online message forums, chat rooms, graphical multiplayer games, instant messaging, email, and even early forms of emoji. Why have these early innovations--which disrupt the generally accepted computer history timeline--been largely forgotten? The fact is, they were not forgotten.

Denise Garcia knows that her students sometimes cheat, but the situation she unearthed in February seemed different. A math teacher in West Hartford, Connecticut, Garcia had accidentally included an advanced equation in a problem set for her AP Calculus class. Yet somehow a handful of students in the 15-person class solved it correctly. Those students had also shown their work, defeating the traditional litmus test for sussing out cheating in STEM classrooms. Garcia was perplexed, until she remembered a conversation from a few years earlier.

Every new year brings new expectations and hopes for technology markets. In the case of machine learning, the space is moving so fast that is hard to differentiate hype from reality. Many of the ground-breaking advancements in machine learning or artificial intelligence(AI) research are simply unpractical to apply in real world solutions and many of the basic artifacts needed to streamline the adoption of machine learning technologies in real world scenarios are still missing. At Invector Labs, we spend a lot of time building large scale machine learning solutions. As a result, we are in constant exposure to different machine learning technology stacks as well as the latest research from academic institutions.

The demand for skills in artificial intelligence (AI) and specifically machine learning has been growing exponentially over the past five years, as businesses from online entertainment to eCommerce scramble for new ways to utilize data to improve customer experience and realize new features. Simplilearn recently appointed Mike Tamir, Ph.D., as the Advisor for Simplilearn's Artificial Intelligence and Machine Learning curricula. He has been instrumental in developing the course structure and incorporating advanced programs on AI Engineering, Machine Learning and Deep Learning with TensorFlow. Dr. Tamir is ranked number one globally as an influencer for Machine Learning and AI by Onalytica and currently serves as Head of Data Science at Uber ATG (self-driving vehicles) and is a lecturer for the University of California, Berkeley - iSchool Data Science Master's Program. Recently, Simplilearn spoke with Mike Tamir about his insights, predictions, and recommendations about machine learning and how both businesses and career-seekers could prepare themselves for the future.

Artificial Intelligence and Machine Learning have been the most discussed amongst other emerging technologies of 2018, adding the extra zing to the coffee breaks of technocrats. With global leaders like Amazon, Google and Microsoft ramping up resources for research in these fields, the trend is definitely not going to dip down anytime soon. In fact, Google is offering free online training to enhance knowledge and build capabilities. Analysts believe, 2019 is going to be the year for business enterprises who have been waiting to finally get on board to witness a plethora of advancements for their industry. What are the technology disruptions we expect next year?

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.

Machine Learning has become one of the booming career options that are highly sought after in the job market. That is why there are many institutes offering Machine Learning courses in Delhi. Companies are striving hard to make the most out of emerging technologies. The most wonderful thing about machine learning is its unlimited applicability. Hardly there is any field today where machine learning does not apply. In simple terms, deep learning is known as a subset of machine learning.

IBM and Coursera are giving away subscriptions worth up to $1 million (U.S.) to help address the lack of skilled data scientists the world is currently facing. Similar to a scholarship, eligibility is independent on social status, location, gender and race. So in the spirit of having some fun – and to ensure highly motivated individuals (with a minimum set of prerequisite skills) are able to benefit most – we've created a technical challenge that focuses your learning and allows you to demonstrate your expertise…but please don't worry, it's not too hard! In this challenge, we address data scientists wanting to get more practical experience. Come and learn the latest advancements in AI and deep learning, understand scalability, and get one month free subscription on Coursera for the most relevant courses.

This post reviews my experiences in 2018. I welcomed the year in the gorgeous beaches of Goa and am now ending it in the wilderness of South Africa. Joining NVIDIA: I joined NVIDIA in September and started a new research group on core AI/ML. I am hiring at full pace and have started many new projects. Honor of being the youngest named chair professor at Caltech: I was one of the six faculty members that Caltech recognized during the 2017-18 academic year.

The world's AI superpower is at it again. This time they are using AI technology to gain an upper hand in the fight against truancy. Many Chinese schools were fed up with students leaving school without permission or skipping class. So they decided to do the only thing they believed would work. They created so called "smart" uniforms to keep students in line.