Businesses are constantly looking for ways to reduce their overhead cost and at the same time increase their revenue. One way of doing this has been cutting down on traditional advertising and using a tiny fraction of the marketing budget to advertise on social media. Another viable option has been to reduce the human workforce and replacing it with AI. Now, what happens when these two- AI and social media- combine? This article sheds more light on the AI and social media interaction, and how AI has impacted social media marketing.

This episode is the first of a two-part series focusing on an important question: what are some of the ways digital health startups are commercializing their offerings -- in other words what are the strategies they are using to make money? The first installment in this series focuses on artificial intelligence in healthcare. In a previous podcast episode we provided a framework outlining the levels of evidence AI in health firms are using to prove their solutions are effective. You'll learn how health AI companies use each of these levels of evidence to support and enhance their commercialization strategies, including how data can be used to gain competitive advantage. The digital health maven dot edu podcast is designed to deepen your understanding (and ability to leverage) trends, technologies and innovations that are re-shaping global health.

In Alex Garland's Ex Machina, Ava (Alicia Vikander) is the creation of CEO-genius–madman Nathan (Oscar Isaac), a reclusive inventor who invites a young programmer Caleb (Domhnall Gleeson) to take part in a "Turing Test" to see if his lovely invention can pass as a human being. Like its predecessors, 2001: A Space Odyssey or The Terminator franchise, Ex Machina posits the notion that a highly functioning computer system may not be necessarily benevolent to mankind. But unlike those that came before it, the new film suggests humans can't be trusted much, either. Sloan Science and Film spoke with Dr. David J. Freedman, an Associate Professor of Neurobiology at the University of Chicago and member of the Center for Integrative Neuroscience and Neuroengineering Research, about how brains and machines learn and process data, if computers can attain consciousness, and, if they could, what the implications might be. Sloan Science and Film: Can you explain your specific area of research?

The film studio A24 that created "Ex Machina" has teamed up with AI-powered personal assistant service x.ai so that the robotic main character "Ava." The robot can be cc'd at ava@x.ai when scheduling meetings. Brian Heater has worked at a number of tech pubs, including Engadget, Laptop, and PCMag (where he served as Senior Editor). Most recently, he was as the Managing Editor of TechTimes.com. His writing has appeared in Spin, Wired, Playboy, Entertainment Weekly, The Onion, Boing Boing, Publishers Weekly, The Daily Beast and various other publications.

The clash reflects American anxiety about China's rise as a potential competitor in telecommunications and other technology. Trump wants Beijing to roll back initiatives like "Made in China 2025," which calls for the state-led creation of global competitors in such fields as robotics and artificial intelligence. American officials worry those might erode U.S. industrial leadership.

Abstract--In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. T o speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie). Melanie is the first approach able to transfer knowledge between multiple data streaming sources in non-stationary environments. It creates several sub-classifiers to learn different aspects from different source and target concepts over time. The sub-classifiers that match the current target concept well are identified, and used to compose an ensemble for predicting examples from the target concept. We evaluate Melanie on several synthetic data streams containing different types of concept drift and on real world data streams. The results indicate that Melanie can deal with a variety drifts and improve predictive performance over existing data stream learning algorithms by making use of multiple sources. Index Terms --concept drift, non-stationary environment, multi-sources, transfer learning. I NTRODUCTION Many real world applications produce data in a streaming fashion, i.e., as a sequence of observations that arrive over time. Examples include prediction of customer behaviour, credit card approval, fraud detection, software effort estimation, software defect prediction, etc. A challenge in data stream mining is how to describe a given target probability distribution accurately without knowing the whole data stream beforehand.

Ultrasound (US) imaging is based on the time-reversal principle, in which individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented as a delay-and-sum (DAS) beamformer, the image quality quickly degrades as the number of measurement channels decreases. To address this problem, various types of adaptive beamforming techniques have been proposed using predefined models of the signals. However, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate. Here, we demonstrate for the first time that a single universal deep beamformer trained using a purely data-driven way can generate significantly improved images over widely varying aperture and channel subsampling patterns. In particular, we design an end-to-end deep learning framework that can directly process sub-sampled RF data acquired at different subsampling rate and detector configuration to generate high quality ultrasound images using a single beamformer. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

Artificial intelligence is fast becoming an essential ingredient for an array of solutions across all industries – which promises to transform every aspect of our lives. While intelligence similar to (or perhaps even surpassing) that of humans may emerge in the not-too-distant future, it's clear from our collective everyday experience we are not there yet. We are, however, firmly on the path. And there are steps we can take to create the brighter future we desire. This is especially important when it comes to the media and publishing industry.

I just completed my Machine Learning course by Andrew N G. for our graduation project we are planning to build a good chatbot (I browsed thought the sub I was unable find an answer).