Google has successfully applied deep learning artificial intelligence algorithms to the diagnosis of breast cancer. In a study carried out by researchers taking part in Google's Brain Residency Program – a 12-month educational course in machine and deep learning – an algorithm was trained to detect breast cancer tumours in a dataset of digitised pathology slides provided by Dutch medical institute the Radboud University Medical Center. After'training' the algorithm, researchers were able to achieve a 92% sensitivity in picking out tumour cells from the slides – significantly higher than the 73% achieved by trained pathologists with no time constraint. In addition, the team recreated the accuracy in different datasets taken from other hospitals and scanning machinery. The team did report an average of eight false positive per slide compared to none from trained pathologists.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. Here's how AI and its derivatives are gradually finding their way into the classroom, and beyond.

In the course of our work with Intel's data science and artificial intelligence initiatives, we often encounter people who are excited about the potential of artificial intelligence, and eager to learn more about the things Intel is doing to drive the industry forward. In many cases, these people have read about Intel's focus on AI, and now they are asking how they can get more involved in this forward-looking field. They often ask how they can advance their data science careers in the direction of AI. In Part 1 of this blog series, we talked about steps organizations can take to cultivate in-house expertise in advanced analytics and data science. In this second part of the post, we will take things down to a more personal level, and talk about steps individuals can take to chart a future that involves creating AI solutions.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. We're inviting 250 to exhibit at TNW Conference and pitch on stage!

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. TNW Conference won best European Event 2016 for our festival vibe.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

Featured will be 24-hour content from our annual CX conference, SCORE, as well as a handful of live webcasts--serving CX professionals responsible for driving superior customer experiences with innovative, yet proven, strategies. Learn how to differentiate your brand with a fast, simple purchasing process saving your customers time and effort. You'll transform your buying experience to create loyal, raving fans, while empowering your reps to spend more time selling. Reach people on the phone, via live chat, email, through social media, and even in person. Use visitor tracking and email analytics to know what your customers are seeing.