The first thought is about open sourcing technologies. I have already written on this trend, which is quite unusual at a first look if you think about it, but my thinking around open source has been highly stimulated by the talk given by Wes McKinney -- for who doesn't know who he is, well, he is definitely not a random guy but is THE open source guy (creator of pandas and author of Python for Data Analysis). The open source model is quite hard to be reconciled with the traditional SaaS model, especially in the financial sector. However, we are observing many firms providing cutting-edge technologies and algorithms for free. While in some cases there is a specific business motivation behind it (e.g., Google releasing Tensorflow to avoid conflict of interests with their cloud offering), the decision of open sourcing (part of) the technology actually represents an emerging trend.

TORONTO – When we see artificial intelligence (AI) in fiction, it usually encompasses the AI functioning just like a human. That's called'strong AI' and well, we aren't there yet. There are two types of AI: strong AI, the aforementioned AI that function as a human would; and weak AI, the type of AI we see today. For example: robotics in a manufacturing plant that function autonomously to complete one task is an example of weak AI. "That's where we are [with weak AI], but I think we are trending towards strong AI," said Wayne Thompson, the chief data scientist at SAS, at the analytics vendor's event in Toronto. "We are trending towards what I consider modern machine learning."

A new website that predicts congressional bills' success foresaw the Affordable Care Act replacement bill would be shelved instead of passed – awarding it only a 15 percent chance of being enacted. Users can look up any pending bill on PredictGov or find predictions through its partner, legislation tracker GovTrack, which now includes a "prognosis" line in its overview of each bill. Vanderbilt University law Professor J.B. Ruhl is a co-founder in bill forecasting site PredictGov. PredictGov, which uses big data and artificial intelligence to reach its conclusions, is the invention of Vanderbilt University law Professor J.B. Ruhl; computer scientist and doctoral candidate John Nay, and their team. It pulls from decades of congressional data plus hundreds of variables, including the bill's sponsor, amendments, economic trends and political shifts.

University teaching is under the microscope as institutions brace themselves for the first Teaching Excellence Framework, which will accord them gold, silver and bronze status. The biggest developments in university teaching are being driven by technology. The old techniques of talk and chalk are being challenged by lecture capture, flipped learning and decision-making based on data analysis. But technology can have worrying consequences. One (unnamed) university was recently brought under attack by its smart devices – a network including vending machines and light sensors was hacked, wreaking havoc with internet speeds across campus. And then there are the concerns about privacy raised by such developments.

Prediction has always been a curious topic in life due to a key attribute – the extreme human desire to know what is coming next. Let's ponder over our thoughts to answer a simple question – "Where is prediction most relevant in your life today?" Predictions are central to every aspect of our life, whether we realize it or not. During school days, it was predicting what we would love to do in the future to choose a career path, checking the weather today to determine how should I dress, evaluating inventory numbers for the next day, to less important predictions made daily during our interactions with other people – like doing time management and getting into classes for a student, to dining, socializing, etc. A prediction or forecast, is a statement about the future.

Artificial intelligence is one of the most exciting scientific developments in human history. Only a decade ago, AI was a far-fetched fairy tale. Advancements in deep learning have propelled AI enthusiasts' wildest dreams into reality. In fact, artificial intelligence is already a part of our everyday lives, whether it be in the form of your email app that learns what messages you frequently trash or the digital assistant tracking your speech patterns. AI is more than a passing fad or unfortunate bubble.

I think that is the main reason for getting acknowledgement from the jury.

Summary: For those of you traditional data scientist who are interested in AI but still haven't given it a deep dive, here's a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI). We and others have written quite a bit about the various types of data science that make up AI. Still I hear many folks asking about AI as if it were a single entity. AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.

The artificial intelligence (AI) revolution has begun, and it's going to take all of us -- government, businesses and employees -- to steer through the resulting workforce disruption. More than just helping our kids avoid jobs that machines will take over in the future -- from driving trucks and reading X-rays to picking stocks and balancing the books -- we need to look at ways of retraining the millions of adults who will be displaced by machines and get them back doing meaningful, relevant work. Change is happening so fast that waves of today's professionals -- educated, established, many in mid-career -- may see their jobs swept away with the technological tide. These white-collar workers with six-figure lifestyles, long the backbone of the knowledge economy, face what the World Economic Forum calls the fourth industrial revolution. What happens when their jobs disappear?

Computer vision is ready for its next big test: seeing in 3D. The ImageNet Challenge, which has boosted the development of image-recognition algorithms, will be replaced by a new competition next year that aims to help robots see the world in all its depth. Since 2010, researchers have trained image recognition algorithms on the ImageNet database, a go-to set of more than 14 million images hand-labelled with information about the objects they depict. The algorithms learn to classify the objects in the photos into different categories, such as house, steak or Alsatian. Almost all computer vision systems are trained like this before being fine-tuned on a more specific set of images for different tasks.