With a focus in computer vision and machine learning, Wang has been interested in artificial intelligence ever since his time as an undergraduate.

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Adobe Photoshop and Premiere Elements 2020 are now available, and both have some new AI-enabled features. The simplified versions of the company's flagship creative applications help amateurs edit high-quality photos and videos, and with the new Sensei-powered tasks, they're easier to use. The full-featured versions of Photoshop and Premiere can be overwhelming, and the methods to reach a desired outcome are rarely obvious. In other words, you really have to know what you're doing. The Elements versions of the software offer straightforward workflows that avoid obscure menus and hotkeys, as well as a one-time purchase rather than Adobe Creative Cloud's monthly subscription fee.

Ask anyone who knows a bit about technology, and you'll more than likely hear them say a few things about machine learning and/or deep learning. These two concepts are catapulting artificial intelligence into strata never seen before, and sometimes it seems as though they are the same thing – but look closely and it becomes clear that they are two different beasts, and both must be tamed before artificial intelligence achieves its next breakthrough. At its simplest, it is possible to describe machine learning as a process by which machines use algorithms to take in data, learn from that data, and use what they've learned to make more nuanced, informed decisions. This involves a great deal of data and usually brings machine and social networking into the picture. For example, music streaming services are capable of looking at what you listen to, finding other users who listen to the same music, and suggesting more music for you based upon the preferences of users similar to yourself.

Spotify is doing everything it can to get you to listen to more music. The company has created algorithms to govern everything from your personal best home screen to curated playlists like Discover Weekly, and continues to experiment with new ways to understand music, and why people listen to one song or genre over another. While competitors like Apple Music, Amazon Prime Music, and Google Music rely on a mix of paid humans and community-created playlists, Spotify's main differentiating factor is the level of customization and expansion of music knowledge offered to customers. Spotify needs to continue building out these algorithms because it's the only way to create custom listening experiences for each of its over 200 million users. As Spotify struggles to grow its business, that differentiating factor needs to be a compelling reason to subscribe to the service.

Artificial intelligence (AI) is transforming the way to work, live, and do business. From smart homes to robots, companies are depending on AI to expand their business. It allows them to invent new things to improve the lives of their customers. In this process, the government is playing its role by offering SRED financing. The Canada SR&ED (scientific research and experimental development) provides support for partnerships, individuals, and corporations who are involved in empirical development and scientific research in Canada.

Ford's transition from automaker to mobility company took another step forward in a small office space in downtown Ann Arbor this week.

In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named \emph{fact-checkers}, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30\% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.

Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoder-decoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5\% absolute f-score in low-resource cases, validating the usefulness of our approach.