The following is an excerpt from Max Tegmark's new book Life 3.0: Being Human in the Age of Artificial Intelligence. What does it mean to be human in the present day and age? For example, what is it that we really value about ourselves, that makes us different from other life forms and machines? What do other people value about us that makes some of them willing to offer us jobs? Whatever our answers are to these questions at any one time, it's clear that the rise of technology must gradually change them.

In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

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.

Using the human brain as a model, machine learning teaches AI computers how to learn new things, recognize patterns and make decisions. We learn by doing, Aristotle once said. Today, experiential learning doesn't apply just to humans -- machines are increasingly able to sense, reason, act and adapt based on learned experience. It's unlikely the ancient Greek philosopher ever dreamed artificially intelligent machines would also learn by doing -- to improve precision medicine and self-driving cars, and even analyze data using processes similar to his own logic systems. It's taken more than 60 years for computer scientists to figure out how to make machines smarter, and this work still continues today.

XGBoost is a library that is designed for boosted (tree) algorithms. It has become a popular machine learning framework among data science practitioners, especially on Kaggle, which is a platform for data prediction competitions where researchers post their data and statisticians and data miners compete to produce the best models. For structured learning problems on Kaggle, it can be difficult to get into the top 10 without including XGBoost. Typically, data scientists use multi-thread single machines to train XGBoost models. Very few people have deployed XGBoost on a distributed environment and achieved good performance.

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.

Preparing for the unknown is not as hard as it may seem, though it implies fundamental shifts in our policies on education, employment and social insurance. Were we to plan for specific changes, we would start revamping curriculums to include skills we thought would be rewarded in the future. For example, computer programming might become even more of a staple in high schools than it already is. Maybe that will prove to be wise and we will have a more productive work force. But perhaps technology evolves quickly enough that in a few decades we talk to, rather than program, computers.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Dr. Kidd is focused on innovating within the rapidly changing healthcare technology market. He is the founder and CEO of Catalia Health, a company that delivers patient engagement across a variety of chronic conditions. You can find all the interviews here. We'll be posting them regularly on Robohub.

As of last month Alphabet Inc.'s AI division, Google DeepMind, has open-sourced their new machine learning platform DeepMind Lab. Artificial Intelligence is the technology of the moment, constantly debated and attracting massive attention from investors. Despite warnings from influential figures including Professor Stephen Hawking, Google's decision to open up their software to other developers is part of a mass movement to advance the capabilities of AI. Facebook open sourced its own deep learning software last year, and Elon Musk's non-profit organisation OpenAI recently released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI and others made these platforms public, and how will this affect the adoption of Artificial Intelligence and machine learning as a whole?

In this article, we look at two machine learning (ML) techniques, Naive Bayes classifier and neural networks, and demystify how they work. With all the hype surrounding self-driving cars and video-game-playing AI robots, it's worth taking a step back and reminding ourselves how machine learning programs actually "learn". In this article, we look at two machine learning (ML) techniques–spam filters and neural networks–and demystify how they work. And if you're not sure what machine learning even is, read about the difference between artificial intelligence, machine learning, and deep learning. One common machine learning algorithm is the Naive Bayes classifier, which is used for filtering spam emails.