Most of the successful commercial applications to date--in areas such as computer vision, speech recognition, machine translation, and natural language processing--have involved supervised learning, taking advantage of labeled datasets. However, most of the world's data is unlabeled. In this book, we will cover the field of unsupervised learning (which is a branch of machine learning used to find hidden patterns) and learn the underlying structure in unlabeled data. According to many industry experts, such as Yann LeCun, the Director of AI Research at Facebook and a professor at NYU, unsupervised learning is the next frontier in AI and may hold the key to AGI. For this and many other reasons, unsupervised learning is one of the trendiest topics in AI today.

Last year, Amazon announced the beta release of Alexa Guard, a new service that lets customers who are leaving the house instruct their Echo devices to listen for glass breaking or smoke and carbon dioxide alarms going off. At this year's International Conference on Acoustics, Speech, and Signal Processing, our team is presenting several papers on sound detection. I wrote about one of them a few weeks ago, a new method for doing machine learning with unbalanced data sets. Today I'll briefly discuss two others, both of which, like the first, describe machine learning systems. One paper addresses the problem of media detection, or recognizing when the speech captured by a digital-assistant device comes from a TV or radio rather than a human speaker.

Revealed before its upcoming software developer conference, the company is now said to be focusing more on providing cloud-based computing and software services to other businesses. One of Microsoft's new offerings is an AI-powered service that helps online retailers recommend products based on shopping history. Scott Guthrie, Microsoft's head of artificial intelligence, spoke about how the new solution can be combined with a retailer's existing recommendation engine: "They're using ours in addition to the existing system they had and seeing some tremendous wins in terms of productivity as part of it. "I don't think it's going to be necessarily always a winner takes all." When it comes to #AI, there's a difference between can and should. When it comes to @SatyaNadella's Vision Keynote at #MSBuild this year, you can and you should tune in. May 6. Get ready ---- pic.twitter.com/L8JutUpJdi In addition to this, Microsoft will launch a cloud-based blockchain service in collaboration with JP Morgan Chase & Co. According to Guthrie, this system has multiple uses, such as documenting transactions and tracking goods as the move along the logistics chain. Guthrie added: "That enables them, from a quality control perspective, to dramatically improve the end-to-end supply chain and deliver a better product.

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This time, artificial intelligence is literally taking jobs. Documents obtained by The Verge show how Amazon used a computer system to automatically track and fire hundreds of fulfillment center employees between for failing to meet productivity quotas -- a grim glimpse of a future in which AI is your boss. While not every decision was made by a computer system, the documents -- including a signed letter by an Amazon attorney describing the system -- reveal how deeply automated the process really is. It's not clear whether Amazon is still using the system. "Amazon's system tracks the rates of each individual associate's productivity," reads the letter as quoted by The Verge, "and automatically generates any warnings or terminations regarding quality or productivity without input from supervisors." After this story ran, Amazon spokesperson Ashley Robinson reached out with a statement that pushed back against The Verge's reporting -- but failed to provide specific examples of inaccuracies.

So you're the CEO of a clothing retailer, a rental car agency, or a payroll processing company, and you hear that artificial intelligence is changing the world. What are you supposed to do? The short answer, says Paul Oyer at Stanford Graduate School of Business, is to start learning fast. "Artificial intelligence will affect every industry, whether it's clothing or shipping," says Oyer, a professor of economics and the codirector of a new multidisciplinary course on AI for senior executives. "We need to find a complementary relationship between those who deal with the technology of AI and the managers who understand what drives their companies. Managers don't need to learn all the technical details, but they do need to understand the implications for their business."

In e-commerce, product content, especially product images have a significant influence on a customer's journey from product discovery to evaluation and finally, purchase decision. Since many e-commerce retailers sell items from other third-party marketplace sellers besides their own, the content published by both internal and external content creators needs to be monitored and enriched, wherever possible. Despite guidelines and warnings, product listings that contain offensive and non-compliant images continue to enter catalogs. Offensive and non-compliant content can include a wide range of objects, logos, and banners conveying violent, sexually explicit, racist, or promotional messages. Such images can severely damage the customer experience, lead to legal issues, and erode the company brand. In this paper, we present a machine learning driven offensive and non-compliant image detection system for extremely large e-commerce catalogs. This system proactively detects and removes such content before they are published to the customer-facing website. This paper delves into the unique challenges of applying machine learning to real-world data from retail domain with hundreds of millions of product images. We demonstrate how we resolve the issue of non-compliant content that appears across tens of thousands of product categories. We also describe how we deal with the sheer variety in which each single non-compliant scenario appears. This paper showcases a number of practical yet unique approaches such as representative training data creation that are critical to solve an extremely rarely occurring problem. In summary, our system combines state-of-the-art image classification and object detection techniques, and fine tunes them with internal data to develop a solution customized for a massive, diverse, and constantly evolving product catalog.

You don't have to look far to see the impact that artificial intelligence is having on the world around us. Across multiple facets of work and play, we're surrounded by smart devices and applications that are strangely prescient at anticipating our wants and needs. But one industry where AI adoption has been surprisingly slow is retail -- particularly around demand forecasting, where AI's potential has scarcely been scratched. A 2018 report from the McKinsey Global Institute concluded that AI has the potential to boost global GDP by 16% by 2030. In dollar terms, that's a gain of $13 trillion, which is a huge number, to be sure.