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Artificial Intelligence Is Now In Residing In Your Pocket

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Each TPU has four chips that delivers 180 trillion of floating points performance per second, if this was not enough Google combined 64 of these TPUs together using patented high speed network to create machine learning supercomputer called TPU pod. Remember, Google's real innovation has been on hardware patents in high end cloud computing, chips, servers, networking for its own data centers. Google has been unsuccessful in social media space, but is now using machine learning to help users share photos, even suggesting whom to share it with. Google has search data, complete email conversation data, photos, and location data.


Smarter Advertising with Artificial Intelligence

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Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours. For example, in May 2016 a millennial taskforce at McCann Japan developed the world's first artificial intelligence creative director, AI-CD ß. For instance, Mondelez asked a real life creative director to develop the creative direction for AI-CD ß's ad and to explain the product's benefits.


NVIDIA and Microsoft Boost AI Cloud Computing with Launch of Industry-Standard Hyperscale GPU Accelerator

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Providing hyperscale data centers with a fast, flexible path for AI, the new HGX-1 hyperscale GPU accelerator is an open-source design released in conjunction with Microsoft's Project Olympus. It will enable cloud-service providers to easily adopt NVIDIA GPUs to meet surging demand for AI computing." NVIDIA Joins Open Compute Project NVIDIA is joining the Open Compute Project to help drive AI and innovation in the data center. Certain statements in this press release including, but not limited to, statements as to: the performance, impact and benefits of the HGX-1 hyperscale GPU accelerator; and NVIDIA joining the Open Compute Project are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations.


After Uber, Snapchat's boom & tech ethics #103

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What if algorithms can spot suicide risks before the people around you can? In a soon to be published paper, a researcher claims to be able to spot suicide risks with 80% accuracy up to two years in advance. Similar peer-reviewed work has applied machine learning to long-term observation of physical symptoms to improve early identification of suicide risk. Even Facebook has developed an algorithm that can spot suicide risk based on your status updates.


Applications of AI for Competitive Advantage Cortex Blog

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Plus, there is no shortage of social media management tools that import data into a centralized dashboard. While Ana Gotter was speaking specifically about Instagram in 3 Instagram Analytics Tools for Marketers, "tracking engagement will help you serve quality content that keeps you at the top," also applies to Facebook and most other networks. As Dominique Jackson explains in 9 Ways Social Media Measurement Can Improve Your Marketing Strategy, "social media measurement enables businesses to make better decisions. As technology continues to advance, leading organizations will adopt AI marketing software to enhance their social media measurement, gain competitive intelligence and optimize content performance.


How to Measure Content Performance in the Age of AI

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Do you feel like artificial intelligence has nothing to do with social media marketing or content performance? Measuring content performance basically serves as the barometer of success for all the photos and videos you send out into the world to represent your brand. This is a whole new ball game: the visual touchpoints are multiple and extend far beyond social media. Artificial intelligence, machine learning, sorcery of the future, whatever you want to call it.


5 Ways Artificial Intelligence Will Impact Your Marketing in 2017

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What if we told you that we could create something the world has never seen before by combining the creative, data-driven mind (you guys) with artificial intelligence, visual search, and machine learning? In a world where there are billions of visual touchpoints with customers -- social marketing, e-commerce, advertising, PR, brand design initiatives -- you need to get those interactions right. Thousands of photo and video decisions are made every day in brand design, e-commerce, advertising, strategy, digital marketing, social, and sales. Today's smartest marketers will jump on Creative AI, leaving others in a trail of sparkling metallic dust.


5 Ways Artificial Intelligence Will Impact Your Marketing in 2017

#artificialintelligence

What if we told you that we could create something the world has never seen before by combining the creative, data-driven mind (you guys) with artificial intelligence, visual search, and machine learning? In a world where there are billions of visual touchpoints with customers -- social marketing, e-commerce, advertising, PR, brand design initiatives -- you need to get those interactions right. Thousands of photo and video decisions are made every day in brand design, e-commerce, advertising, strategy, digital marketing, social, and sales. Today's smartest marketers will jump on Creative AI, leaving others in a trail of sparkling metallic dust.


"Printing Money" with Operational Machine Learning

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However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. It includes machine learning models to customize offers, an open-source solution for run-time decisioning, and a scoring service to match customers and offers. In order to help create these capabilities, the company created both a Chief Data Officer and a Chief Loyalty and Analytics Officer within the marketing function. Building these capabilities on top of a big data stack (including data lake storage and data transformation capabilities) is transformational in terms of the availability of information to support the decision, the performance of the decision request, and the performance of the learning service.


Sentiment Analysis of Movie Reviews (3): doc2vec

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Fig.1: Distributed Memory Model of Paragraph Vectors (PV-DM) (from: Distributed Representations of Sentences and Documents) With distributed bag-of-words (PV-DBOW), there even aren't any word vectors, there's just a paragraph vector trained to predict the context: Fig.2: Distributed Bag of Words (PV-DBOW) (from: Distributed Representations of Sentences and Documents) Like word2vec, doc2vec in Python is provided by the gensim library. I've trained 3 models, with parameter settings as in the above-mentioned doc2vec tutorial: 2 distributed memory models (with word & paragraph vectors averaged or concatenated, respectively), and one distributed bag-of-words model. These are the words found most similar to awesome (note: the model we're asking this question isn't the one that performed best with Logistic Regression (PV-DBOW), as distributed bag-of-words doesn't train word vectors, – this is instead obtained from the best-performing PV-DMM model): So, what we see is very similar to the output of word2vec – including the inclusion of awful. Same for what's judged similar to awful: To sum up – for now – we've explored how three models: bag-of-words, word2vec, and doc2vec – perform on sentiment analysis of IMDB movie reviews, in combination with different classifiers the most successful of which was logistic regression.