On Theory of Model-Agnostic Meta-Learning Algorithms

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Based on a joint work with Aryan Mokhtari, UT Austin, and Asu Ozdaglar, MIT. Imagine sitting in your autonomous car, going for a vacation. Your vehicle should follow the directions provided by the navigation app, and also use multiple sensors to monitor other vehicles, road signs, street light, etc. As a result, during the course of your journey, your car might need to take actions within a few seconds, such as turning or stopping. The question is how should your vehicle be programmed to be able to adapt to the new tasks within a short amount of time and limited data.


TensorFlow.js Image Classification Made Easy

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TensorFlow.js Image Classification Made Easy In this video you're going to discover an easy way how to train a convolutional neural network for image classification and use the created TensorFlow.js image classifier afterwards to score x-ray images locally in your web browser. TensorFlow.JS is a great machine learning javascript-based framework to run your machine learning models locally in the web browser as well as on your server using node.js. But defining your model structure and training it, is way more complex than just using a trained model. Azure Custom Vision - one of the various Cognitive Services - offers you an easy way to avoid this hassle. The dataset I used (Pulmonary Chest X-Ray Abnormalities), can be downloaded here: https://www.kaggle.com/kmader/pulmona... Subscribe for more free data analytics videos: https://www.youtube.com/saschadittman...


Going meta: learning algorithms and the self-supervised machine with Dr. Philip Bachman - Microsoft Research

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Phil Bachman: …and it's not always clear what the distinction between the methods are. But supervised learning is sort of what's had the most immediate success and what's driving a lot of the deep learning power technologies that are being used for doing things like speech recognition in phones or doing automated question answering for chat bots and stuff like that. So supervised learning refers to kind of a subset of the techniques that people apply when they have access to a large amount of data and they have a specific type of action that they want a model to perform when it processes that data. And what they do is, they get a person to go and label all the data and say, okay, well this is the input to the model at this point in time. And given this input, this is what the model should output.


Overcoming Obstacles to Machine Learning Adoption - insideBIGDATA

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Our friends over a H2O.ai have sponsored a new Business Impact Brief from 451 Research – "Overcoming Obstacles to Machine Learning Adoption." The brief highlights the organizational barriers to machine learning adoption from 451 Research's Voice of the Enterprise: AI and Machine Learning 2H 2018 survey, asking the question: "What are your organization's most significant barriers to using machine learning?" After many fits and starts, the era of enterprise machine learning has finally arrived. According to the 451 Research survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes.


A Sobering Message About the Future at AI's Biggest Party

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More than 13,000 artificial intelligence mavens flocked to Vancouver this week for the world's leading academic AI conference, NeurIPS. The venue included a maze of colorful corporate booths aiming to lure recruits for projects like software that plays doctor. Google handed out free luggage scales and socks depicting the colorful bikes employees ride on its campus, while IBM offered hats emblazoned with "I A ." Tuesday night, Google and Uber hosted well-lubricated, over-subscribed parties. At a bleary 8:30 the next morning, one of Google's top researchers gave a keynote with a sobering message about AI's future. Blaise Aguera y Arcas praised the revolutionary technique known as deep learning that has seen teams like his get phones to recognize faces and voices.


14 data scientists you should follow on Twitter TechBeacon

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The application of artificial intelligence (AI) and machine learning to business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. To help you keep up, TechBeacon assembled this list of leading data scientists to follow on Twitter. Plus: Get the 2019 Forrester Wave for ESM.


Machine Learning Team Lead ai-jobs.net

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Elastic is a search company with a simple goal: to solve the world's data problems with products that delight and inspire. As the creators of the Elastic Stack, we help thousands of organizations including Cisco, eBay, Grab, Goldman Sachs, ING, Microsoft, NASA, The New York Times, Wikipedia, and many more use Elastic to power mission-critical systems. From stock quotes to Twitter streams, Apache logs to WordPress blogs, our products are extending what's possible with data, delivering on the promise that good things come from connecting the dots. We have a distributed team of Elasticians across 30 countries (and counting), and our diverse open source community spans over 100 countries. We are looking for a Senior Engineering Manager to join the machine learning team.


How To Transform The Customer Journey With AI And Big Data

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Providing excellent customer service needs to be a priority for every business. Think of your customer service department as the personality of your brand. It is also the reason why people will choose your products and services on a repeated basis. It is quite simple really, with no customers, there is no company. The path that consumers travel on to make a purchase, is called the "customer journey."


12 Everyday Applications Of Artificial Intelligence Many People Aren't Aware Of

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By now, almost everyone knows a little bit about artificial intelligence, but most people aren't tech experts, and many may not be aware of just how big an impact AI has. The truth is most consumers interact with technology incorporating AI every day. From the searches we perform in Google to the advertisements we see on social media, AI is an ever-present feature of our lives. To help nonspecialists grasp the degree to which AI has been woven into the fabric of modern society, 12 experts from Forbes Technology Council detail some applications of AI that many may not be aware of. Calling customer service used to be as exciting as seeing a dentist.


Perfect Deepfake Tech Could Arrive Sooner Than Expected

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Professor Hao Li used to think it could take two to three years for the perfection of deepfake videos to make copycats indistinguishable from reality. But now, the associate professor of computer science at the University of Southern California, says this technology could be perfected in as soon as six to 12 months. Deepfakes are realistic manipulated videos that can, for example, make it look a person said or did something they didn't. "The best possible algorithm will not be able to distinguish," he says of the difference between a perfect deepfake and real videos. Li says he's changed his mind because developments in computer graphics and artificial intelligence are accelerating the development of deepfake applications.