Do we really know the potential and the pitfalls of artificial intelligence? Maybe not, say the experts and innovators at the TED conference in Vancouver. Jane Wakefield hears from the creator of the infamous "fake Obama" videos, Dr Supasorn Suwajanakorn. And AI expert and pioneer Max Tegmark of MIT explains why we can't make any assumptions about the future, and must decide now how to navigate the problems of AI such as whether to ban autonomous weapons. Plus we hear from Pierre Barreau about his AI music, and why computers sometimes need reminding that human musicians need to take a breath.

There is a set of techniques covering all aspects of machine learning (the statistical engine behind data science) that does not use any mathematics or statistical theory beyond high school level. So when you hear that some serious mathematical knowledge is required to become a data scientist, this should be taken with a grain of salt. Because of this, you need to really be math savvy to get a "standard" job, so sticking to standard math-heavy training and standard tools work for people interested in becoming a data scientist. To make things more complicated, most of the courses advertised as "math-free" or "learn data science in three days" are selling you snake oil (it won't help you get a job, and many times the training material is laughable.) You can learn data science very quickly, even on your own if you are a self-learner with a strong background working with data and programming (maybe you have a physics background) but that is another story.

Learn to use Python Artificial Intelligence for data science. Do you want to learn how to use Artificial Intelligence (AI) for automation? You will learn how to code in Python, calculate linear regression with TensorFlow, analyze credit card fraud and make a stock market prediction app. AI is code that mimics certain tasks. You can use AI to predict trends like the stock market.

In a first for machine-learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene. The breakthrough could have significant implications for our understanding of bacterial biochemistry and for the development of new medications. One thrust of modern pharmacology is focused on alleviating ailments by developing drugs that target specific proteins that reside in the membranes of our bodies' cells. These proteins, known as integral membrane proteins (IMP), act as receptors or "gates" that allow materials into and out of cells. Examples of IMPs are G-protein-coupled receptors, which relay information to a cell about its environment, and ion channels, which control the interior environment of a cell by acting as gatekeepers that selectively allow ions to pass in and out of the cell. IMPs are the targets of nearly 50 percent of all drugs on the market.

Artificial Intelligence (AI) is transforming many human activities ranging from daily chores to highly sophisticated tasks. But unlike many other industries, the higher education sector has yet to be really influenced by AI. Uber has disrupted the taxi sector, Airbnb has disrupted the hotel industry and Amazon disrupted first the bookselling sector, then the whole retail industry. It is only a matter of time then until the higher education sector undergoes a significant transformation. Within a few short years, universities may well have changed beyond all recognition.

Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, very few prior studies have explored this problem from a deep learning perspective. In this paper, we recast the student performance prediction problem as a sequential event prediction problem and propose a new deep learning based algorithm, termed GritNet, which builds upon the bidirectional long short term memory (BLSTM). Our results, from real Udacity students' graduation predictions, show that the GritNet not only consistently outperforms the standard logistic-regression based method, but that improvements are substantially pronounced in the first few weeks when accurate predictions are most challenging.

We consider the online path learning problem in a graph with non-additive gains/losses. Various settings of full information, semi-bandit, and full bandit are explored. We give an efficient implementation of EXP3 algorithm for the full bandit setting with any (non-additive) gain. Then, focusing on the large family of non-additive count-based gains, we construct an intermediate graph which has equivalent gains that are additive. By operating on this intermediate graph, we are able to use algorithms like Component Hedge and ComBand for the first time for non-additive gains. Finally, we apply our methods to the important application of ensemble structured prediction. Keywords: online learning, experts, non-additive losses or gains, structured prediction, bandit.

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http://github.com/locuslab/TCN .