This month Stanford launched a 100-year study of AI (AI100) with a report: Artificial Intelligence and Life in 2030. The 16 member study panel issuing the report sees increasingly useful applications of AI, with potentially profound positive impacts on our society and economy over the next decade. The study identifies eight domains where AI is already having or is projected to have the greatest impact: transportation, healthcare, education, low-resource communities, public safety and security, employment and workplace, home/service robots and entertainment. Check out this Pearson video (and our review of their report): Over the next few months, we'll be exploring developments in these eight categories and the implications for employment and education. This series, #AskAboutAI, will encourage parents, teachers, mentors and advisors to engage young people in a dialog about the emerging automation economy and the ethical and economic implications of artificial intelligence (AI).

Please can you give an overview of the current drug discovery paradigm? In what ways do you think it needs to be leaner? With the current drug discovery paradigm, it takes up to 15 years to translate an idea, such as hypothesizing a certain protein is important in a disease and testing this with targeting the protein with a drug, all the way through to proof of concept. The drug has to be filed with the regulatory authorities, having done all the safety and efficacy testing. Estimates vary, but it's currently reckoned to cost over 1 billion dollars per drug.

I recently read Preparing for the Future of Artificial Intelligence, a paper published by the White House's Executive Office of the President, National Science and Technology Council, Committee on Technology (or EOPNSTCCT for short!). One of the important distinctions it highlights is the difference between Narrow, and General AI. Narrow AI (sometimes referred to as Weak AI) underpins fairly specific services like recommendations on Amazon, ad targeting, and even Facebook's news feed. Watson is unique in that it applies both machine learning, and expert systems, but it's still essentially an advanced'question answering' machine. General AI (also called Artificial General Intelligence, AGI) refers to a notional future AI system that exhibits intelligent behaviours across a complete range of human cognitive tasks.

Erica, the new chatbot from Bank of America, uses advanced AI to deliver personalised, actionable insight that goes beyond users' specific requests. By delivering added value, Erica will help Bank of America to engage more deeply with its customers. Over the last couple of years, banking-related chatbots have moved from the realm of experimental applications into mainstream use. However, often these chatbots are rather unsophisticated and can only deal with simple tasks such as balance checks and transferring funds between accounts. Where requests are more complex, chatbots typically resort to referencing answers from the FAQ sections of banks' websites.

Will the technology be coming for your job next? The question of whether marketing is more science or art has never seemed more relevant now that highly sophisticated cognitive learning technology is able to assume many of the tasks involved in marketing -- in some cases, even doing them better than a human could. But visions of a completely automated campaign may be premature, according to executives from IBM and other companies at the forefront of AI who weighed in on the technology's impact during a panel discussion at ad:tech New York last week. In good news for creative directors, the experts said cognitive technology has the ability to free up marketers to spend more time tackling bigger picture responsibilities, such as finding the inspiration for the right voice and vision to make an emotional connection with consumers. By laying the groundwork for significantly more sophisticated one-to-one marketing, AI could even create a need to beef up analytics, content and other areas for businesses that are able to gain a competitive edge through customer-centric marketing.

Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use. We are first going to look at the different variants of gradient descent.

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During the last fifteen years, a strange parallel economy has covertly developed to the point where it envelops almost all internet users, including you. No money changes hands in this immense network, but it produces enormous transactional benefits nonetheless. In this economy, you labor daily as a trainer, teaching software robots how to perform tasks. In return, the bots then take over much of those tasks for you. You trade your daily labor in exchange for the value produced by the work of your powerful and ubiquitous robot apprentices.

Machine learning software, once thought of as highly technical and complicated, is now widely available. Machine learning, which allows computers to learn from experience without being specifically programmed, heightens an organization's ability to build data-backed applications to enhance the customer experience and make it fully personalized from the first interaction. Well-known e-commerce sites have been using machine learning to advertise and recommend products and services unique to customer profiles, based on buying behavior and browser history. Think about the last time you searched for a product online. Shortly afterward, did you receive advertisements similar to the products you searched for?

What reactions did people have to the movie trailer for Morgan (which was created entirely -- and for the first time -- by an AI bot, and a pretty famous one at that)? Which is a fair reaction. Computers can now write, read, learn and speak. And for some, this is pretty scary -- people are terrified that bots will snatch their jobs and eventually take over the world and render humans useless (films like I, Robot haven't helped this). Many people naturally hold irrational fears.