With high-volume transactional data, historical insights, accessible computer power and new analytic tools, few industries are better suited for using artificial intelligence and machine learning than banking. Yet, few organizations have fully leveraged the many ways machine learning can improve back office operations or the consumer experience. Subscribe to The Financial Brand via email for FREE!Financial services organizations realize they have the potential to apply advanced analytics for both internal and external benefits since they have large data sets and experience with analytical tools. From payment services to everyday banking, insight is captured that can make machine learning more powerful. The good news is that banks and credit unions state that they are going to apply the data at their disposal to improve the customer experience, first and foremost.

In the two months since Richard Watson strapped 200 remote-control-sized transmitters around his cows' necks, an artificial-intelligence system named Ida has pinged his phone with helpful alerts: when his cows are chewing the cud, when they're feeling sick, when they're ready for insemination. "There may be 10 animals out there that have a real problem, but could you pick them?" he said one morning, standing among a grazing herd of dairy cattle wearing what he calls "cow Fitbits." But on the neighboring pastures here in rural Georgia, other farmers say they aren't that impressed. When a cow's in heat, they know she'll start getting mounted by her bovine sisters, so they smear paint on the cows' backsides and then just look for the incriminating smudge. "I can spot a cow across a room that don't feel great just by looking in her eyes," said Mark Rodgers, a fourth-generation dairy farmer in Dearing, Georgia, whose dad still drives a tractor at 82. "The good Lord said, 'This is what you can do.' I can't draw, paint or anything else, but I can watch cows."

We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks. Our main hypothesis is that constraining the Lipschitz constant of a networks will have a regularising effect. To this end, we provide a simple technique for computing the Lipschitz constant of a feed forward neural network composed of commonly used layer types. This technique is then utilised to formulate training a Lipschitz continuous neural network as a constrained optimisation problem, which can be easily solved using projected stochastic gradient methods. Our evaluation study shows that, in isolation, our method performs comparatively to state-of-the-art regularisation techniques. Moreover, when combined with existing approaches to regularising neural networks the performance gains are cumulative.

Free users of Spotify could soon benefit from premium account features thanks to an updated version of the app. Sources say the update is designed to make the service easier to use in a bid to boost subscribers after launching on the stock market last week. Mobile users with free plans will be able to access playlists faster and have greater control over how they listen to music on playlists, sources say. Free users of Spotify will soon benefit from premium account features, thanks to an updated version of the app, sources say. At the moment, the free plan prevents users from selecting tracks within a playlist.

N-Star generated jet of material shot outward at nearly speed of light.

You can create skimmers with the formula syntax from rlang! You can now control the object name output for topojson_write, and there's now an analog of geojson_sp for sf (geojson_sf) We accept community contributed packages via our onboarding system - an open software review system, sorta like scholarly paper review, but way better. We'll highlight newly onboarded packages here. A huge thanks to our reviewers, who do a lot of work reviewing (see the blog post on our review system), and the authors of the packages! If you want to be a reviewer fill out this short form, and we'll ping you when there's a submission that fits in your area of expertise.

As technology continues to advance, the gap between science fiction and reality becomes smaller and smaller. Just like something straight out of a post-apocalyptic horror film, scientists are now concerned that an Artificial Intelligence robot army that is currently being developed by a top South Korean university could potentially wipe out all of humanity. In February of this year, KAIST University allegedly launched a brand new AI weapons lab, which has thus far led dozens of researchers to believe that the technology being developed there will "have the potential to be weapons of terror." One of these researchers is Toby Walsh, a professor at the University of New South Wales in Sydney, who argued in an open letter: "If developed, autonomous weapons will… permit war to be fought faster and at a scale great than ever before. They will have the potential to be weapons of terror."

We propose Rademacher complexity bounds for multi-class classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing κ predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold stands for clustering consistency. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the κ predominant classes of the identified clusters. The resulting data-dependent generalization error bound involves the margin distribution of the classifier, the stability of the clustering technique used in the first step and Rademacher complexity terms corresponding to partially labeled training data. Our theoretical result exhibit convergence rates extending those proposed in the literature for the binary case, and experimental results on different multi-class classification problems show empirical evidence that supports the theory.

With IBM, Google, and Microsoft pouring funding into the research of quantum computing, it's really starting to look like we are going to see the benefits in the next 5 - 10 years. Google may be just weeks from announcing they reached the quantum supremacy milestone and IBM may not be far behind either. Today, I wanted to share my thoughts on how quantum computing may affect cryptography as we know it. Effects on cryptography When we talk about the basic cryptography used for things like TLS when you access your bank's website, the premise behind securing your data is surprisingly simple. The certificate uses a public key which is really just a large number that's the result of multiplying two prime numbers together.