Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. To advance the state of understanding and enable machine learning system development and optimization for the commerce domain, we aim to define an industry-relevant recommendation benchmark for the MLPerf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets. We then summarize the discussions and advice from the MLPerf Recommendation Advisory Board.

This work aims to explore the quality of cold-start recommendations derived from collective matrix factorization models [11] in collaborative filtering with explicit-feedback data in the form of ratings. Recommender systems based on collaborative filtering are typically constructed solely based on data about useritem interactions [6], such as movies rated by different users, which result in domain-independent and easily-implementable models, but have the disadvantage of only being able to make recommendations about users and items for which there is interactions data available (known as warm-start recommendations in the literature). In many settings however, there is oftentimes additional side information available about users and/or items, which is not used in the most common models such as low-rank matrix factorization [6] or kNN-based formulas [10], but which can be used both to improve recommendation models that take interactions data, and to make recommendations in the absence of interactions data (so-called cold-start recommendations). This work focuses on the second case: studying recommendations from matrix factorization models that are based on attributes data without interactions data.

In today's world that we live in, it seems as if every industry is using artificial intelligence in one way or another and raving about its benefits. Artificial intelligence has made it possible for machines to receive information, process it using the record of past patterns in their database, and perform tasks that could previously only be performed by humans. From automated systems to self-driving cars and smart applications, there are many examples of artificial intelligence that we come across every day. However, the concept still appears to be unclear to most people. The common man does not know what AI is, how it used in different industries, and the fantastic benefits it has to offer.

When Kursat Ceylan, who is blind, was trying to find his way to a hotel, he used an app on his phone for directions, but also had to hold his cane and pull his luggage. He ended up walking into a pole, cutting his forehead. This inspired him to develop, along with a partner, Wewalk - a cane equipped with artificial intelligence (AI), that detects objects above chest level and pairs with apps including Google Maps and Amazon's Alexa, so the user can ask questions. Jean Marc Feghali, who helped to develop the product, also has an eye condition. In his case his vision is severely impaired when the light is not good. While the smart cane itself only integrates with basic AI functions right now, the aim is for Wewalk, to use information gathered from the gyroscope, accelerometer and compass installed inside the cane.

Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android? Thanks to books and movies, each generation has formed its own fantasy of a world ruled (or at least served) by robots. We've been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner. But if the age of AI is here, why don't our lives look more like the Jetson's?

Conversational AI is a huge technology advancement – as momentous as the unveiling of the Internet in 1983 or when Steve Jobs launched the iPhone in January 2007. But within the last year or two, Conversational AI has evolved into a cornerstone of innovation. Gone are the days of single-use chatbots that execute pre-scripted, single-path programs or recite your service manual to customers. With Conversational AI, we are talking about complex, Machine Learning (ML)-powered, intelligent Digital Assistants that can drive unmatched customer and employee interactions based on the current context, past history, even predicting the flow of conversations and delivering next best actions – based on naturally expressive voice or text. Where the engagement with a digital assistant is intelligent enough that you think of it as a "cobot" – a co-pilot in your journey as a customer, employee, vendor or partner.

Technology is a tool that helps us live out our God-given callings. This is one of the most important things for us to learn as we engage the topic of technology and artificial intelligence. Because we often see the tremendous power that technology has over our lives, we are tempted to treat technology as more than a tool, as something with a value similar to our own if it is powerful enough or does enough work on its own. Technology will be misused and abused by broken people just like you and me. Nowhere in Scripture is a tool or a technology condemned for being evil.

Hinge is going to give people in the US a $100 Visa online gift card to go on date, a decision that'll presumably encourage them to schedule plans while also helping the company market itself and grow. The dating app previously partnered with bars to give its daters discounts, but this is the first time the company has given people what essentially amounts to cash. The company has $25,000, or enough money for 250 daters, set aside for the promotion. To qualify, Hinge users have to pause their accounts from 4PM ET on Friday, March 6th until 4PM ET on Saturday, March 7th. After reactivating, people have to click on their date's profile and select that they "met," which tells the app that they met in-person and prompts it to ask whether this is the type of person this dater would want to see again.

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).

This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.