Model distillation was originally designed to distill knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. We investigate model distillation for another goal -- transparency -- investigating if fully-connected neural networks can be distilled into models that are transparent or interpretable in some sense. Our teacher models are multilayer perceptrons, and we try two types of student models: (1) tree-based generalized additive models (GA2Ms), a type of boosted, short tree (2) gradient boosted trees (GBTs). More transparent student models are forthcoming. Our results are not yet conclusive. GA2Ms show some promise for distilling binary classification teachers, but not yet regression. GBTs are not "directly" interpretable but may be promising for regression teachers. GA2M models may provide a computationally viable alternative to additive decomposition methods for global function approximation.

Flying cars have always been a goalpost of the future, but last year companies like Toyota, Airbus, DeLorean and Volvo's parent company invested in or announced plans to get their own units flying soon. If you wanted to get in on the ground floor of tomorrow's transportation, you might try joining the first class of'flying car engineers' in a new nanodegree program at Udacity fronted by Sebastian Thrun, the former leader of Google's self-driving car program. Thrun has quite a pedigree as a founder of Udacity himself along with the Kitty Hawk prototype flying'car,' but the rest of the course's instructors are likewise impressive. They include MIT professor Nicholas Roy, founder of the Alphabet-backed Project Wing whose drones air-delivered burritos to Australians last October; Aerospace professor at University of Toronto Angela Schoellig; And lastly the founder of Kiva Systems (now Amazon Robotics), Raffaello D'Andrea. The course itself aims to educate engineers on both robotics and aerospace concepts to understand particular demands of'flying cars.'

Halo announced today the worldwide release of HaloBoost, Halo's proprietary demand forecasting engine that leverages proven Machine Learning algorithms. HaloBoost combines Machine Learning methods to improve forecast accuracy over time, a high-speed modeling workflow to improve analyst productivity and knowledge discovery, and a simple, scalable method to introduce external factors like pricing, promotion, social media, and weather predictors. "Manufacturers, Distributors, and Retailers have been seeking tools that can provide simplification in the forecasting process to improve accuracy and throughput, and we've responded by introducing our most powerful modeling engine, HaloBoost . Traditional approaches are limited in their ability to maximize forecast accuracy without significant analyst effort across broad and sparse data dimensions such as regions, points-of-sale, and SKU-level granular forecasts. Our proprietary modeling workflow effectively uses the computer to simulate a large team of forecast experts, working in real-time, to find the best result across a broad range of forecast scenarios," said Bill Panak, Ph.D. Vice President of Data Sciences, Halo.

It's that time of year where every summer music festival announces its lineup with a poster filled with band names. It's a tried and true formula, and one that is ripe to be made fun of with a little humor and an artificially intelligent neural network trained on a data set of thousands of band names. Announcing your 2018 COACHELLA LINEUP, generated by a neural network trained on thousands of band names: https://t.co/EskuBWOdfy All these computer-generated names are good, but some of them are more than good. We've separated those out, and recognized them in the following three categories: Digg is what the internet is talking about, right now. It's also the website you are currently on.

Machine Learning is a hot topic these days, as can be seen from search trends. It was the success of Deepmind and AlphaGo in 2016 that really brought machine learning to the attention of the wider community and the world at large. Yet it's a success that followed a long preamble that includes recent advances in three key areas: hardware, particularly GPUs (ideally suited to the vector and matrix based mathematics usually required in machine learning); data, due to the accessibility of larger and larger datasets; and algorithms and techniques, as deep learning research breakthroughs like those described in Krizhevsky, Sutskever and Hinton's landmark paper began to demonstrate best-of-breed results on benchmark challenges. So it's not just hype, and as IT engineers it's worth our while to gain better understanding of it. But the field can seem rather daunting to a newcomer due to all the math, statistics and algorithms involved.

Emerging anxieties pertaining to the rapid advancement and sophistication of artificial intelligence appear to be on a collision course with historic models of human exceptionality and individuality. Yet it is not just objective, technical sophistication in the development of AI that seems to cause this angst. It is also the linguistic treatment of machine "intelligence." But what is really at stake? Are we truly concerned that we will be surpassed in our capacities as human beings?

Massive open online courses (MOOCs) were supposed to bring a revolution in education. But they haven't lived up to the expectations. We have been putting educators in front of cameras and shooting video -- just as the first TV shows did with radio stars, microphone in hand. This is not to say the millions of hours of online content are not valuable; the limits lie in the ability of the underlying technology to customise the material to the individual and to coach. That is about to change, though, through the use of virtual reality, artificial intelligence and sensors.

The code has been copied to your clipboard. Machines equipped with artificial intelligence (AI) have performed better than human beings in a high-level test of reading comprehension. Two natural language processing tools received higher test scores than humans in recent exams. One of the tools is a product of the American software maker Microsoft. The other was created by the Chinese online seller Alibaba Group.

In 2016, training expenditure in the US exceeded $70.6 billion--yet experts say that 90% of new skills are lost within a year if they aren't reinforced. Doing the rough math, by not reinforcing training, organizations run the risk of wasting up to $63.54 billion a year. If you're in charge of employee development--whether in HR, a Business Unit, or Sales Enablement--employee training is a big investment that can sometimes feel like a high-stakes gamble, especially when it comes to soft skills training. You might wonder, "Am I choosing the right solution? Will they adopt the skills? Will my investment turn results? Can I confidently report a viable ROI?"

One simple way to describe Machine Learning is letting artificially intelligent machines pick up information by themselves. It's a bit like leaving a person alone with a set of Lego: give them the bricks and come back later to see what they come up with. Using machine learning techniques with AI opens up technology to a whole new host of possibilities. These have the potential to disrupt entire industries in ever changing and exciting ways. Let's take a look at some of the industries which are being radically transformed by machine learning technology right now The modern classroom is definitely receptive to new technology.