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Artificial Intelligence: 101 Things You Must Know Today About Our Future: Lasse Rouhiainen: 9781982048808: Amazon.com: Books

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Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society in general can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses. Lasse has been a speaker at renowned seminars such as Mobile World Capital and TEDx, and has worked with top brands and institutions such as Michelin, Össur and the European Union Intellectual Property Office.


Flagging suspicious healthcare claims with Amazon SageMaker Amazon Web Services

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The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually--3% of the nation's $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses for consumers, as well as reduced benefits or coverage. Labeling a claim as fraudulent could require a complex and detailed investigation. This post demonstrates how to train an Amazon SageMaker model to flag anomalous post-payment Medicare inpatient claims and target them for further investigation on suspicion of fraud. The solution doesn't need labeled data; it uses unsupervised machine learning (ML) to create a model to flag suspicious claims. This solution uses Amazon SageMaker, which provides developer and data scientists with the ability to build, train, and deploy ML models.


AI Superpowers: China, Silicon Valley, and the New World Order: Kai-Fu Lee: 9781328606099: Amazon.com: Books

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Acoustically engineered to produce exceptional frequency response for an enhanced listening experience. Sweat proof, portable and lightweight headset can stay in your ears comfortably. Allowing you to control the volume, answer or end calls, control the playback of music and video with click of button and without taking your phone out.


Efficient Second-Order Online Kernel Learning with Adaptive Embedding

Neural Information Processing Systems

Online kernel learning (OKL) is a flexible framework to approach prediction problems, since the large approximation space provided by reproducing kernel Hilbert spaces can contain an accurate function for the problem. Nonetheless, optimizing over this space is computationally expensive. Not only first order methods accumulate $\O(\sqrt{T})$ more loss than the optimal function, but the curse of kernelization results in a $\O(t)$ per step complexity. Second-order methods get closer to the optimum much faster, suffering only $\O(\log(T))$ regret, but second-order updates are even more expensive, with a $\O(t 2)$ per-step cost. Existing approximate OKL methods try to reduce this complexity either by limiting the Support Vectors (SV) introduced in the predictor, or by avoiding the kernelization process altogether using embedding.


Efficient Second Order Online Learning by Sketching

Neural Information Processing Systems

We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches. Papers published at the Neural Information Processing Systems Conference.


AWS CEO Andy Jassy On Channel Conflict, Competition And AI

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"There's this folklore mythology around if Amazon launches a business in a certain area, it means that all the other businesses in those areas are not going to be as successful," Jassy said at the Goldman Sachs Technology and Internet Conference in San Francisco yesterday. "I just haven't seen it." There are only two significant industries that Amazon has "disrupted," according to Jassy: retail with Amazon.com, and technology infrastructure with AWS. His remarks come as federal and state regulators are conducting antitrust probes to determine whether Amazon and other technology giants stifle competition and innovation. "In both cases, they were models that were pretty antiquated, and customers weren't so happy with those models, and somebody was going to end up reinventing them," Jassy said.


Amazon Personalize can now use 10X more item attributes to improve relevance of recommendations Amazon Web Services

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Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with no prior machine learning experience. AWS is pleased to announce that Amazon Personalize now supports ten times more item attributes for modeling in Personalize. Previously, you could use up to five item attributes while building an ML model in Amazon Personalize. This limit is now 50 attributes. You can now use more information about your items, for example, category, brand, price, duration, size, author, year of release etc., to increase the relevance of recommendations.


Build a unique Brand Voice with Amazon Polly Amazon Web Services

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AWS is pleased to announce a new feature in Amazon Polly called Brand Voice, a capability in which you can work with the Amazon Polly team of AI research scientists and linguists to build an exclusive, high-quality, Neural Text-to-Speech (NTTS) voice that represents your brand's persona. Brand Voice allows you to differentiate your brand by incorporating a unique vocal identity into your products and services. Amazon Polly has been working with Kentucky Fried Chicken (KFC) Canada and National Australia Bank (NAB) to create two unique Brand Voices, using the same deep learning technology that powers the voice of Alexa. The Amazon Polly team has built a voice for KFC Canada in a Southern US English accent for the iconic Colonel Sanders to voice KFC's latest Alexa skill. The voice-activated skill available through any Alexa-enabled Amazon device allows KFC lovers in Canada to chat all things chicken with Colonel Sanders himself, including re-ordering their favorite KFC.


Amazon's most popular Echo is now even smarter

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TL;DR: The Echo Dot (3rd Gen) smart speaker is on sale for £29.99 on Amazon, saving you 40% on list price. This isn't a typical time for deals on Amazon devices, but the online retail giant has thrown a curveball by putting its entire range on sale. This sort of thing usually only happens on Prime Day and Black Friday, but we've been treated to an early taste of what's to come with discounted e-readers, smart speakers, video doorbells, tablets, and more. One of the standout deals from this selection is the discounted Echo Dot. This popular smart speaker is down to just £29.99 on Amazon, saving you 40% on list price.


Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition: Giuseppe Bonaccorso: 9781838820299: Amazon.com: Books

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Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.