High-quality, low-cost online courses could be used to shift schools away from being "exam factories" and help students keep pace with the threat of automation, according to a new report by the Institute of Directors. The report argues that the internet allows schools to be more flexible and adapt learning towards "a future in which more and more work is taken over by robots or computers". Related: Welcome to the robot-based workforce: will your job become automated too? "The cost savings, convenience and flexibility that online learning offers has the potential to revolutionise education provision, but only if businesses and the education sector work together to capitalise on the potential of computer-based teaching applications to support employees in their pursuit of lifelong learning," the report said. Last year the CBI's director general also called for GCSEs to be scrapped and A-levels to be augmented by vocational courses. The report also calls for new tax incentives to encourage people to return to education, and to make it easier for employers to invest in their staff.

Kernel mean embeddings have recently attracted the attention of the machine learning community. They map measures $\mu$ from some set $M$ to functions in a reproducing kernel Hilbert space (RKHS) with kernel $k$. The RKHS distance of two mapped measures is a semi-metric $d_k$ over $M$. We study three questions. (I) For a given kernel, what sets $M$ can be embedded? (II) When is the embedding injective over $M$ (in which case $d_k$ is a metric)? (III) How does the $d_k$-induced topology compare to other topologies on $M$? The existing machine learning literature has addressed these questions in cases where $M$ is (a subset of) the finite regular Borel measures. We unify, improve and generalise those results. Our approach naturally leads to continuous and possibly even injective embeddings of (Schwartz-) distributions, i.e., generalised measures, but the reader is free to focus on measures only. In particular, we systemise and extend various (partly known) equivalences between different notions of universal, characteristic and strictly positive definite kernels, and show that on an underlying locally compact Hausdorff space, $d_k$ metrises the weak convergence of probability measures if and only if $k$ is continuous and characteristic.

We consider the problem of learning a non-negative linear classifier with a $1$-norm of at most $k$, and a fixed threshold, under the hinge-loss. This problem generalizes the problem of learning a $k$-monotone disjunction. We prove that we can learn efficiently in this setting, at a rate which is linear in both $k$ and the size of the threshold, and that this is the best possible rate. We provide an efficient online learning algorithm that achieves the optimal rate, and show that in the batch case, empirical risk minimization achieves this rate as well. The rates we show are tighter than the uniform convergence rate, which grows with $k^2$.

Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model. This gives us a flexible formalism for multi-view learning where the latent variables can be used both for exploratory purposes and for learning representations that enable efficient inference for ambiguous estimation tasks. Learning is performed in a Bayesian manner through the formulation of a variational compression scheme which gives a rigorous lower bound on the log likelihood. Our Bayesian framework provides strong regularization during training, allowing the structure of the latent space to be determined efficiently and automatically. We demonstrate this by producing the first (to our knowledge) published results of learning from dozens of views, even when data is scarce.

Now it's time for us to see how deep learning really works! In case you missed the previous part and is now wondering how deep learning has anything to do with you, go check it out! In this part, we'll show you all the basic concepts you need to get started with deep learning. Machine learning problems are typically where you want a computer to answer some questions without being explicitly programmed. For example, the question can be something like "What's the price of my 1800 sqft apartment in Seattle?", or "Is this news article telling the truth?"

The visually informed embedding of word (VIEW) is a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW can be applied to transferring multimodal background knowledge to NLP algorithms, i.e. VIEW can be concatenated to word2vec embedding to improve the encoding of spatial background knowledge. WIEW was evaluated in Spatial Role Labeling (SpRL) algorithms (which recognize spatial relations between objects mentioned in the text) using the Task 3 of SemEval-2013 benchmark data set, SpaceEval.

One of the first key takeaways from Vanderbilt Law School's conference on Thursday about artificial intelligence is that the term doesn't carry much value in the scientific community. "A.I. is whatever we can't do this year," David Lewis, a speaker who holds a PhD in computer science, said in between panel sessions. Lewis estimated we're currently experiencing the second or third wave of "A.I. hype," in which everyone uses the term to describe their technology. That's happened before, he said, and then it went out of style as a marketing buzzword. "By 2020, it'll have a negative connotation again," he predicted.

Let's say you're scrolling through your favorite social media app and you come across a series of pictures of a man in a tuxedo and a woman in a long white dress. An automated image captioning system might describe that scene as "a picture of a man and a woman," or maybe even "a bride and a groom." But a person might look at the pictures and think, "Wow, my friends got married! As image captioning tools get increasingly good at correctly recognizing the objects in an image, a group of researchers is taking the technology one step further. They are working on a system that can automatically describe a series of images in the same kind of way that a human would, by focusing not just on the items in the picture but also what's happening and how it might make a person feel. "Captioning is about taking concrete objects and putting them together in a literal description," said Margaret Mitchell, a Microsoft researcher who is leading the research project. "What I've been calling visual ...

Google is expanding its free software to now include self-learning smart computers. TensorFlow, the company bringing this software to Google users, will allow for anyone with access to computer software to create their own smart computer from scratch that can program itself. Users can customize the settings to specify what programs they want the computer to learn, and it takes off from there. Learned skills can range anywhere from drawing and talking to recognizing pictures. Making these programs available to programmers aids the next frontier for many tech vendors, as "machine-learning tech" is allowing them to better integrate services into their apps.

Actor and writer Wil Wheaton wants to "add an A to the STEM acronym and make it STEAM." He'll be speaking at the USA Science and Engineering Festival April 16-17 in Washington about why he thinks the arts should be represented in the acronym commonly used when referring to the science, technology, engineering and math fields. Wheaton, 43, best known for his role as Wesley Crusher on "Star Trek: The Next Generation" in the 1980s and '90s and more recently as a fictionalized version of himself on "The Big Bang Theory," says that he has always been fascinated by science and technology, and has made it a goal of his to ensure that kids get the encouragement they need to pursue those fields. Wheaton spoke with U.S. News by phone about why he got involved in the festival, how science fiction and fact have shaped his life and career and why he thinks it should be "science, technology, engineering, arts and math." How did you get involved with the USA Science and Engineering Festival?