Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall. In this paper, we develop a subset selection algorithm that is differentiable and discrete, which can be trained on supervised data and can model complex dependencies between elements in a straightforward and comprehensible way. This is of particular interest in natural language processing tasks such as fact extraction, fact verification, and question answering where the proposed optimization scheme can be used for evidence retrieval.

It's the stuff of horror films: an intruder in your house, impossible to find but undeniably somewhere, watching you at your most private moments. Or perhaps it's the plot of a thriller, where you are recruited into international crime without even knowing it, at the behest of smart criminals. If the worst fears about the prevalence of weakly secured smart home gadgets materialise, those terrifying situations could become all too real. As we fill our homes with internet-enabled and smart devices, we are opening ourselves up to attacks that exploit houses themselves – and we might not even realise they are happening. Everything from washing machines to baby monitors is being hooked up to the internet by companies convinced that features such as remote control and artificial intelligence will make our lives easier and safer.

Apple boss Tim Cook said that being gay is "God's greatest gift to me" as he spoke of his pride at being the first CEO of a major company to come out. He revealed he made the decision to be open about his sexual orientation four years ago after hearing from children who were being bullied and abused and thinking of suicide. "I am a private person and so I kept me to my small circle, and I started thinking that's a selfish thing to do," he told CNN. Apple boss says data is being'weaponised with military efficiency' Tim Cook responds after Apple becomes first trillion-dollar company Apple boss Tim Cook denies Facebook privacy scandal ties Apple boss Tim Cook on the importance of kids learning to code Apple boss says data is being'weaponised with military efficiency' "I needed to be bigger than that, I need to do something for them and show them that you can be gay and still go on and do some big jobs in life. "I did not do it for other CEOs to come out.

Increasingly, digital technology is eroding the assumptions and conditions that have underpinned democracy for centuries. By now, fake news and polarization are familiar subjects to those interested in democracy's health. Just last week Facebook announced that it was doubling its'security and community' staff to 20,000. But in the future, we'll have to grapple with the much more significant idea of AI Democracy, asking which decisions could and should be taken by powerful digital systems, and whether such systems might better represent the people than the politicians we send to Congress. It's a prospect which holds possible glories but also terrible risks.

It began with a series of thought provoking topics; eliminating noise; driving faster data; successful data science by becoming boring. Eliminating Noise and AI for Compliance Amber Case of the MIT Media Lab talked about the noise, and the abundance of alerts that surround all of us. In our hotels.... High frequency content can boost salt and sweet flavors on airplanes (which is good) and on road trips (which gets you to eat more snacks). Her principles for sound design in products include: (i) Use better hardware that have better frequency ranges, (ii) Embed more information into sound (iii) Remove mechanical noises (example: Blendtec blenders) (iv) a lower level sound conveying information like Star Wars' R2D2 (v) allow us to convert sound to other modalities when required. Dinesh Nirmal of IBM starts with the question, "What is this guy smoking"?

If you want to build your blog audience, you're going to have to get smarter with your content. One of the biggest challenges that bloggers and content marketers face is writing content that's optimized for search engines, yet will also appeal to people. According to Copyblogger, SEO is the most misunderstood topic online. But, SEO content isn't complicated, once you understand that people come first, before search algorithms. SEO firms make their money understanding these simple concepts. Thriving in your online business means that you must go beyond simply "writing content." Your content needs to accomplish two goals: first, appeal to the end-user (customers, clients, prospects, readers, etc.) and second, solve a particular problem. But, how do you create content that meets those goals? How do you create content that ranks well with Google and also persuades people? Don't worry if you can't afford an expensive SEO copywriter. You can do this following simple rules. And, that's what you're going to learn in this article. We all know what happens when you type a search query into a search engine and hit "enter": You get a list of search results that are relevant to your search term. Those results pages appear as a result of search engine optimization (SEO).

Understanding Human reasoning has been a focal point of a lot of AI research in the past decade. The article is an extremely well researched attempt to bring out the journey.

Google brings video to the talking speaker category with the new Google Home Hub. USA TODAY's Jefferson Graham explains why the device has potential. When it comes to cutting the cord, the satellite companies are really being hit the hardest. That shouldn't come as a surprise to anyone who's looked at their pricing. Which I did for all of you this week.

ABSTRACT In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals. Index Terms-- Source separation, speaker recognition, spectrogram masking, speech recognition 1. INTRODUCTION Recent advances in speech recognition have led to performance improvement in challenging scenarios such as noisy and far-field conditions. However, speech recognition systems still perform poorly when the speaker of interest is recorded in crowded environments, i.e., with interfering speakers in the foreground or background. One way to deal with this issue is to first apply a speech separation system on the noisy audio in order to separate the voices from different speakers.

Recently I read the paper: Auto-Encoding Variational Bayes*. Kingma, D. P. & Welling, M. (2013)*, which introduces variational autoencoders a type of generative model which finds application from generating faces to even music! Would love to get any feedback on it. One shortcoming of VAEs I found is that you can't tell what feature each latent dimension controls. There has been research done on this problem and Beta-VAEs seem to address this problem.