In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a field study leading to the ultimate deployment of the system at a university.

Artificial Intelligence (AI), machine learning and the internet of things (IoT) lead emerging technology conversations across the world. Companies recognise that these technologies are ready to be used to drive real business benefits. The Asia Pacific and Japan (APJ) region is set to pick up the pace on these two fronts. According to a recent cloud survey by MIT Technology Review Custom and VMware, more than 70% of non-users of AI in APJ said their organisations will adopt the technology within five years. IDC forecasted global IoT spending to surpass $1 trillion USD in 2020, with APJ leading the way.

Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Embeddings (VASE). Based on the Minimum Description Length principle, VASE automatically detects shifts in the data distribution and allocates spare representational capacity to new knowledge, while simultaneously protecting previously learnt representations from catastrophic forgetting. Our approach encourages the learnt representations to be disentangled, which imparts a number of desirable properties: VASE can deal sensibly with ambiguous inputs, it can enhance its own representations through imagination-based exploration, and most importantly, it exhibits semantically meaningful sharing of latents between different datasets. Compared to baselines with entangled representations, our approach is able to reason beyond surface-level statistics and perform semantically meaningful cross-domain inference.

I have come across a couple of resources about dimensionality reduction techniques. This topic is definitively one of the most interesting ones, and it is great to think that there are algorithms able to reduce the number of features by choosing the most important ones that still represent the entire dataset. One of the advantages pointed out by authors is that these algorithms can improve the results of a classification task. In this post, I am going to verify this statement using a Principal Component Analysis ( PCA) to try to improve the classification performance of a neural network over a dataset. Does PCA really improve classification outcome?

Computational tools to support this through proof checking, automatic theorem proving, and computer algebra are well-established, though they require formal, computationally explicit, content as input. However, the existing mathematical literature, particularly informal mathematical dialogues, and expository texts, is opaque to such systems, which cannot currently handle the variety of activities typically involved in producing such knowledge and proofs, such as, for example, exposition and argument that concerns making conjectures, forming concepts, and discussing examples and counterexamples. Our goal is to bridge this gap through devising an expressive modelling language that is closely related to the way mathematics is actually done. Our approach to modelling such content is inspired by the general-purpose argument modelling formalism Inference Anchoring Theory (IAT), introduced by Reed and Budzynska (2010). As its name suggests, IAT anchors logical inferences in discourse. IAT has been applied to mediation (Janier and Reed, 2017), debates (Budzynska et al, 2014b), and to paradoxes in ethotic argumentation (Budzynska, 2013), along with other real-world dialogues (Budzynska et al, 2013).

"Machines that can change society, and have been much dreamed of, are now here in the shape of networked computers and robots, fed by data whose figures far exceed the human imagination, and increasingly autonomous artificial intelligence." Artificial intelligence (AI) has little to do with human intelligence, even if it may seem that way. You could sum it up by saying that it merely resembles human intelligence. After all, even the most complex processes can be reproduced on machines using AI – not just in production halls, but increasingly in offices too. This is what differentiates AI from conventional automation and rationalization.

At this stage, the practitioner might be satisfied with the analysis and be ready to build a final production-ready model. Clearly decision trees are performing best, but is there a (statistically) significant difference between the different implementations? Is it possible to improve performance further? There might be more than one dataset (from different stores/sites) that needs to be considered. In such situations, it is a good idea to perform a more principled experiment to answer these questions.

By 2025 the aim is for 3.5 million tons more of "permanent, quantifiable greenhouse gas reductions." That would be lot of cuts--akin to the tailpipe output of 2.6 million passenger cars. One of the best spots to reduce emissions is right in BP's oil and gas fields. BP figures that half of its fugitive methane emissions--a fancy way of saying natural gas leaking out of pumps and pipes--come from its operations in the Lower 48. And a good portion of those happen in mature fields like the one near Wamsutter, in the Great Divide Basin of Wyoming.

Many researchers have co-opted powerful graphics processing units, or GPUs, to run climate models and other scientific programs, while tech and financial giants use large banks of these processors to train machine-learning algorithms. They all have video-game players to thank for the emergence of these workhorse processors: It was gamers who stoked the original demand for chips that could do the massive amounts of parallel number crunching required to produce rich graphics quickly enough to keep up with fast-paced action. By 1995, movies like Pixar's Toy Story, the first full-length digitally animated movie, had demonstrated the potential of high-quality computer animation. But gamers drove the technology in a very specific direction. Pixar had created Toy Story's graphics by slowly rendering each frame individually and then stitching it all together.

Companies are currently spending big on artificial intelligence and machine learning initiatives to the tune of $12 billion, but estimates put that figure as high as $57.6 billion by 2021, according to the International Data Corporation (IDC). With such massive shifts, the focus is usually on what we might lose, but it shouldn't be. A recent report on the future of work from the McKinsey Global Institute suggests that while only about 5% of jobs can be completely eliminated by automation, the rise of AI requires workers to beef up both technical and soft skills in order to stay competitive. What's seldom discussed is how AI can revolutionize our jobs. It's now possible to pinpoint peak productivity for a single day, improve communication in meetings (even before people ever work together face to face), or even teach you to be a better leader, all thanks to AI platforms.