Present customer experience is "all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured" says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it's much easier to find another company with similar offerings than spending time complaining or calling about a problem.

And I am talking Season 3. Or Amazon's hit, The Handmaid's Tale? Do you just binge and veg out or are you like me, and see how easily we could, and are, slipping into these worlds? After watching shows like this I often find myself reflecting back on George Orwell's 1984. It proves more eerily prophetic with each passing year. This Season, I fear, the writers of Westworld are almost scripting our future lives. You may not have caught it, but it is all in there.

Also, besides StoryBreak, Uru offers other APIs which are interesting and useful for understanding and analyzing videos.

How GPT-3, your smartphone and Augmented Reality can disrupt a dinosaur industry. The earliest photographic studios made use of painters' lighting techniques to create portraits. In my country, generations of Indians would assemble under the studio lights to get that perfect family portrait. We have come a staggering distance since then. Today, these photo studios that were responsible for many families and their portraits, have all but disappeared.

Artificial intelligence and its applications have made a significant impact on nearly every industry. Defined as a technique enabling machines to mimic human behaviour, brands are using AI to automate processes at an increasing rate. We see this at many points of brand interaction – site suggestions on our search engine, lane assistance in passenger vehicles, and app troubleshooting, to name a few. It has been around for almost 50 years, learning constantly, almost on a daily basis. As we evolve and become more efficient, and artificial intelligence learns to better emulate human intelligence, businesses benefit from increased process and operational efficiencies.

Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characteristics. A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature representing the data samples to explain how the model generates the specific output. In this paper, we propose a Model-agnostic Effective Efficient Direct (MEED) IFS framework for model interpretation, mitigating concerns about sanity, combinatorial shortcuts, model identifiability, and information transmission. Also, we focus on the following setting: using selected features to directly predict the output of the given model, which serves as a primary evaluation metric for model-interpretation methods. Apart from the features, we involve the output of the given model as an additional input to learn an explainer based on more accurate information. To learn the explainer, besides fidelity, we propose an Adversarial Infidelity Learning (AIL) mechanism to boost the explanation learning by screening relatively unimportant features. Through theoretical and experimental analysis, we show that our AIL mechanism can help learn the desired conditional distribution between selected features and targets. Moreover, we extend our framework by integrating efficient interpretation methods as proper priors to provide a warm start. Comprehensive empirical evaluation results are provided by quantitative metrics and human evaluation to demonstrate the effectiveness and superiority of our proposed method. Our code is publicly available online at https://github.com/langlrsw/MEED.

Create a pipeline to remove stop-words,perform tokenization and padding. In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. Note: This course works best for learners who are based in the North America region.

After almost six years on the market, smart speakers now come in a variety of sizes, shapes, capabilities and prices. Whether you want a cheap speaker to keep the kids entertained, one that doubles as a digital photo frame or one that sounds so good you'll want to yell "turn it up to 11", here's a quick guide to the best on the market. Google's Assistant is the best voice system on the market. It has better understanding than rivals, an enormous range of knowledge and – importantly – the ability to choose between male and female voices, even on a user-by-user basis as Google can distinguish between the individuals giving instructions. The Nest Mini is the second generation of Google's smallest and cheapest smart speaker.

Participants were prompted to review a queue of news stories and share 12 true news for social media users. To engage participants to review news articles and their explanations, users had to select at least one article that represents the news headline for each news story they chose to share. They could always skip to the next news story (as many times as needed) if they were not familiar with the topic. The choice of the sharing task and ability to skip unfamiliar topics (unlike work that assumes participants are familiar with a short curated list of news stories e.g., [horne2019rating, nguyen2018believe]) improves the fake news detection task by allowing participants to interact and examine the AI/XAI assistant rather than focusing on news analysis. Participants also had the chance to flag news stories as fake if they found headlines to be fake; however, these were not counted toward the required number of shared stories needed for task completion.

Artificial intelligence and machine learning can take on simple tasks in healthcare so people can focus on collaboration and work on a higher …