Under NVIDIA founder Jensen Huang's iconic leather jacket is one of the tech industry's sharpest CEOs -- a man who can not only talk eloquently about GPU architecture, machine learning and the limits of Moore's Law, but do so for hours without a strict script. It's an impressive feat, but if you're not well versed in the technology of server GPUs, his talks can be a little hard to digest. That's why we cut Huang's two hour GTC keynote into an easily digestible clipshow. NVIDIA's GPU Technology Conference may be mostly aimed at developers and businesses, but the company still had a few exciting things to show off, including Project Holodeck (no, not that project holodeck), NVIDIA's take on high-end virtual reality teleconferencing. Still, most of the presentation was focused on how the company's latest supercomputer GPU facilitates deep learning artificial intelligence -- the kind of algorithmic wizardry that lets computers teach themselves how to identify and touch up photos, play golf and autonomously drive cars. You get the bulk of them in just under 13 minutes by watching the embedded video above.

Ikea recently launched a survey to gauge how consumers feel about artificial intelligence, and what they are looking for in AI and virtual assistant capabilities, a move which may be a precursor to the retailer launching its own such solution. The survey, called "Do You Speak Human?," was created by Ikea's Space10 innovation and design lab and asks questions such as whether consumers want your AI to be human-like, if it should be male, female or gender-neutral, and even if it should be religious, among other questions. An Ikea official stressed that the retailer remains at an information-gathering stage, New Atlas reports. The company recognizes that AI presents a "tremendous opportunity," but for now it is simply curious how people feel about AI. Consumers can see how their answers stack up to others immediately after taking the survey and have the option to submit an e-mail address to be kept in the loop as the survey progresses.

A new working paper finds that the arrival of one new industrial robot in a local labor market coincides with an employment drop of 5.6 workers. These papers have not been peer-reviewed, but are circulated by their authors for comment and discussion. With the NBER's blessing, Making Sen$e is pleased to feature these summaries regularly on our page. The following summary was written by the NBER and doesn't necessarily reflect the views of Making Sen$e. With America's workers already squeezed by forces ranging from international competition to offshoring to new information technologies, concern is growing about the impact of robots on jobs and wages.

As artificial intelligence becomes more human, to co-exist, does human intelligence need to become more artificial? We've spent a lot of time philosophizing about where Artificial Intelligence is going to take us, how far we are to achieving general AI and the implications it will have on humanity – all not without the sky net scenarios! Hype aside, there are companies out there who are focusing on how we can use artificially intelligent applications to improve the human experience, sustain life on our planet and significantly boost the economy. This pioneering technology could well see the next world-changing scientific discovery hailing from Silicon Valley, especially considering the significant increase in investment over the past few years. According to the Alzheimer's Association, there are more than 5 million Americans living with Alzheimer's today, with a predicted 16 million by 2050, and a further 850,000 people with dementia in the UK.

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerges from physics and geometry and these links have been extensively studied by a series of authors through the last thirty years. However, there is currently a gap between the intuitions and knowledge of users of the methodology and our deep understanding of these theoretical foundations. The aim of this review is to provide a comprehensive introduction to the geometric tools used in Hamiltonian Monte Carlo at a level accessible to statisticians, machine learners and other users of the methodology with only a basic understanding of Monte Carlo methods. This will be complemented with some discussion of the most recent advances in the field which we believe will become increasingly relevant to applied scientists.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well. The primary motivation and driving force for these areas of study, and for developing these techniques further, is that the solutions required to solve certain problems are incredibly complicated, not well understood, nor easy to determine manually.

Our fourth installment of Research for Practice covers two of the hottest topics in computer science research and practice: cryptocurrencies and deep learning. First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open access Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. This is a topic with a long history in the academic literature that has recently come to prominence with the rise of Bitcoin, blockchains, and similar implementations of advanced, decentralized protocols. These developments--and colorful exploits such as the DAO vulnerability in June 2016--have captured the public imagination and the eye of the popular press. In the meantime, academics have been busy, delivering new results in maintaining anonymity, ensuring usability, detecting errors, and reasoning about decentralized markets, all through the lens of these modern cryptocurrency systems.

Google's offering voice assistants to your next DIY computing project, we review the new BlackBerry phone (yes, it is 2017), and test-ride an electric dirt bike. Raspberry Pi has teamed up with Google to bring voice integration to the Pi with a clever combination of hardware and software. Packed with the same tech that powers Google Home, the companies have released a kit that transforms a regular Raspberry Pi 3 into your very own virtual assistant. The collaboration marks the first time that Google has produced something for hobbyists. The initiative is called "Artificial Intelligence Yourself" (AIY), and Google's project director said that he wants to create more hobbyist uses for Google software.

The Drum has launched a survey of its readers to explore how brands are investing in artificial intelligence (AI). The research survey, in partnership with Sysomos, will explore how AI is set to make a huge impact in the marketing industry and will form the basis of The Drum Market Insight Report - Artificial Intelligence Edition. Brand investment in AI is starting to pick up. Forrester has predicted there will be a 300% increase in investment in AI in 2017. According to a study by researchers at Oxford University and Deloitte, the likelihood of a robot taking a marketer's job is 33%.