Learn the fundamentals of Microsoft's Machine Learning and Artificial Intelligence services and how to build applications using them. Develop practical job skills that you can start using straight away. Courses provided free in partnership with Pluralsight. Overview Using Microsoft Cognitive Services in your applications Let's begin with this short video that explains the Microsoft Cognitive Services and shows a sample application. Explain to a friend what Cognitive Services are and give an example of how you can use them in an application. Introduction and Setup Install and configure Visual Studio To get started, you'll need Visual Studio to create and run your Cognitive Services application. Open Visual Studio and create a simple application.

Private information can be easily extracted from neural networks trained on sensitive data, according to new research. A paper released on arXiv last week by a team of researchers from the University of California, Berkeley, National University of Singapore, and Google Brain reveals just how vulnerable deep learning is to information leakage. The researchers labelled the problem "unintended memorization" and explained it happens if miscreants can access to the model's code and apply a variety of search algorithms. That's not an unrealistic scenario considering the code for many models are available online. And it means that text messages, location histories, emails or medical data can be leaked.

Cost reduction by AI seems to be an important driver to counter the ever increasing cost of healthcare. However the opportunity to improve the effectiveness of healthcare by AI driven diagnostics and treatment plans is much bigger. Opportunities range from drug design to patient diagnosis and to create personalized treatment plans (almost on DNA level). Just as an example, Infervision uses Deep Learning techniques on patient data derived from X-Ray, CT, MRI, text descriptions of symptoms and diagnostic reports, to construct automatic diagnostic recommendations2. Another example is the use of Deep Learning to classify skin cancer.

Subscribe and review: Apple, Spotify, Soundcloud, Audioboom, Mixcloud & Acast and join the discussion on Facebook, Twitter & email us as podcast@theguardian.com Technology companies seem to have a bad reputation at the moment. Whether through honest mistakes or more intentional oversights, the likes of Apple, Facebook, Google and Twitter have created distrust among consumers. But as technology develops, and as we hand over more control to artificial intelligence and machines, it becomes difficult for developers to foresee the negative consequences or side-effects that might arise. In October 2017, the AI company DeepMind, a subsidiary of Google, created an ethics group made up of employees and external experts called DeepMind Ethics & Society. But are these groups any more than a PR strategy? And how can we train technology students to preempt an ethical disaster before they enter the workforce?

If you've ever worked on a project for deep learning for NLP, you'll know how painful and tedious all the preprocessing is. Torchtext is a library that makes all the above processing much easier. In this post, I'll demonstrate how torchtext can be used to build and train a text classifier from scratch.To make this tutorial realistic, I'm going to use a small sample of data from this Kaggle competition. The data and code are available in my GitHub repo, so feel free to clone it and follow along. Or, if you just want to see the minimal working example, feel free to skip the rest of this tutorial and just read the notebook.

Last year's reports that some apparently well-funded Russian entities used Facebook to systemically disseminate fake news and seed widespread dissent during the 2016 U.S. elections is not only worrisome because of the damage that is being done to the democratic process, the impact on the credibility of social media and the overall trust in the Internet as a medium. It also shows how Facebook has – intentionally or inadvertently but lucratively, either way – built a gigantic manipulation machine that is run by algorithms (or dare we say'artificial intelligence'), not by people, and, crucially, not for people. Facebook – a company now valued at half a Trillion USD – has now emerged as the world's first AI-run media platform; as'asocial media'. Its purpose is no longer its users i.e. to'be human', rather, FB's purpose is itself. The tool has truly become the purpose (read McLuhan!), and we, the users, just exist to feed it – convenient fodder for the deep-learning algorithms whose only'mission' is to have us stay on the site or use the app as long as possible, or otherwise become addicted to the pleasure-trap that Facebook has so cleverly perfected.

Researchers at Elon Musk's startup, OpenAI, think they have discovered the most efficient way to train artificial neural networks: have them compete against each other. For more videos, subscribe to Mashable News: http://on.mash.to/SubscribeNews Give us a follow: Facebook: https://www.facebook.com/mashable/

Articles, Tutorials and Talks How to build a deep learning model in 15 minutes An open source framework for configuring, building, deploying and maintaining deep learning models in Python. How To Train an Object Detection Classifier Using TensorFlow 1.5 (GPU) on Windows 10 This tutorial shows you how to train your own object detector for multiple objects using Google's TensorFlow Object Detection API on Windows. The questions are of 4 levels of difficulties with L1 being the easiest to L4 being the hardest. How to write a Discord bot in Python In this tutorial, you'll learn to create a simple Discord bot with Python. In case you don't know what Discord is, it is a Slack-like service aimed at gamers essentially.

Listings will be in reverse chronological order (like a CV). N.B.: This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most.

As Instacart has grown, we've learned a few things the hard way. We're open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers. To address these issues we're standardizing our machine learning in Lore. At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production. If you want a super quick demo that serves predictions with no context, you can clone my_app from github. Skip to the Outline if you want the full tour.