Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system’s models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world

Last week, experts from the AI world came together for the Artificial Intelligence Conference at San Francisco to discuss insights, opportunities, challenges and trends related to the rapidly expanding field of AI. The conference included hands-on trainings, tutorials, startup showcase (which was won by PipelineAI), keynotes, sessions, expo, and social events. Rana el Kaliouby, Co-founder and CEO, Affectiva gave the opening keynote on "The inevitable merger of IQ and EQ in technology". She shared her bold vision of making machines "emotion-aware" through understanding human emotions, gestures, conversations, facial expressions and tone of voice. As we design AI products and services, we need to embed in them not just high IQ but also high EQ, because emotions are a big part of human life.

Machine learning may sound like an overwhelmingly complicated concept rather than a data-driven method to extract insights that drive future business decisions. To fully utilize machine learning, we first need to understand the benefits to our organization, and the techniques to create models based on questions we need to answer. In this webinar series, we will show you how to easily and automatically apply complex algorithms to data in real world applications.

Text data must be encoded as numbers to be used as input or output for machine learning and deep learning models. The Keras deep learning library provides some basic tools to help you prepare your text data. In this tutorial, you will discover how you can use Keras to prepare your text data. How to Prepare Text Data for Deep Learning with Keras Photo by ActiveSteve, some rights reserved. A good first step when working with text is to split it into words.

About this course: You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.

In today's tutorial, I'll demonstrate how you can configure your macOS system for deep learning using Python, TensorFlow, and Keras. This tutorial is the final part of a series on configuring your development environment for deep learning. I created these tutorials to accompany my new book, Deep Learning for Computer Vision with Python; however, you can use these instructions to configure your system regardless if you bought my book or not. In case you're on the wrong page (or you don't have macOS), take a look at the other deep learning development environment tutorials in this series: To learn how to configure macOS for deep learning and computer vision with Python, just keep reading. As you get acclimated in the deep learning domain, you'll want to perform many experiments to hone your skills and even to solve real-world problems.

Text data requires special preparation before you can start using it for predictive modeling. The text must be parsed to remove words, called tokenization. Then the words need to be encoded as integers or floating point values for use as input to a machine learning algorithm, called feature extraction (or vectorization). The scikit-learn library offers easy-to-use tools to perform both tokenization and feature extraction of your text data. In this tutorial, you will discover exactly how you can prepare your text data for predictive modeling in Python with scikit-learn.

In this course, you will learn the foundations of deep learning. When you finish this class, you will: – – This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. This is the first course of the Deep Learning Specialization. To help make deep learning even more accessible to engineers and data scientists at large, Google has launched a free Deep Learning Course. This short, intensive course provides you with all the basic tools and vocabulary to get started with deep learning, and walks you through how to use it to address some of the most common machine learning problems.

Apple has released the first public test version of an 11.1 update to its iOS software amid growing complaints from users over the software. It comes just two weeks after the software was released - and a week after Apple was forced to issue an'emergency' 11.0.1 update to deal with major issues. However, it appears many users are still suffering problems ranging from app crashes to reboots, causing Apple to issue a'beta' update of an 11.1 version of the controversial software. The latest update comes just two weeks after the software was released - and a week after Apple was forced to issue an'emergency' 11.0.1 update to deal with major issues. Beta testers who have signed up for will receive the iOS 11.1 beta update over-the-air after installing the proper certificate on an iOS device.

This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.