There is no surprise that artificial intelligence is taking the world by storm. The technology is increasingly being used across diverse business functions and revolutionizing all aspects of life and work. AI enables computers to learn from a voluminous amount of data to perform menial and complex tasks. Its applications have been of great value for both an organization or an individual, assisting them in doing their work with ease and getting things done on time. As AI is different from rule-based automation solutions and uses machine learning and NLP, this tech is expected to be as important for humans as electricity and the internet.

In the United States, more than half of all households are expected to have a digital assistant or smart speaker like Google Home or Amazon Echo by 2022, and many people already today use these devices for shopping. In the Nordic region, however, relatively few consumers have purchased or plan to purchase an AI-based digital assistant. Those Nordic residents who do have one primarily use assistants to play music, do research and manage to-do lists. Yet when it comes to online shopping, the purchasing journey is to a high degree driven by convenience. In the next few years, AI solutions that save customers time and energy are expected to become increasingly common.

Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-source framework to empower researchers and engineers to quickly build and evaluate Reinforcement Learning agents for recommendations in marketplaces. MARS-Gym addresses the whole development pipeline: data processing, model design and optimization, and multi-sided evaluation. We also provide the implementation of a diverse set of baseline agents, with a metrics-driven analysis of them in the Trivago marketplace dataset, to illustrate how to conduct a holistic assessment using the available metrics of recommendation, off-policy estimation, and fairness. With MARS-Gym, we expect to bridge the gap between academic research and production systems, as well as to facilitate the design of new algorithms and applications.

Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices. Yet despite considerable research, except for very special cases, the number of samples required to efficiently recover the underlying matrices has not been known. We determine the onset of learning in several common latent-variable settings. For all of them, we show that learning $k\times k$, rank-$r$, matrices to normalized $L_{1}$ distance $\epsilon$ requires $\Omega(\frac{kr}{\epsilon^2})$ samples, and propose an algorithm that uses ${\cal O}(\frac{kr}{\epsilon^2}\log^2\frac r\epsilon)$ samples, a number linear in the high dimension, and nearly linear in the, typically low, rank. The algorithm improves on existing spectral techniques and runs in polynomial time. The proofs establish new results on the rapid convergence of the spectral distance between the model and observation matrices, and may be of independent interest.

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in these carousels is a challenging task, as items are numerous and as users have different preferences. In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app. Along with this paper, we publicly release industrial data from our experiments, as well as an open-source environment to simulate comparable carousel personalization learning problems.

A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains. To progress research in this direction, we introduce DialoGLUE (Dialogue Language Understanding Evaluation), a public benchmark consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks, designed to encourage dialogue research in representation-based transfer, domain adaptation, and sample-efficient task learning. We release several strong baseline models, demonstrating performance improvements over a vanilla BERT architecture and state-of-the-art results on 5 out of 7 tasks, by pre-training on a large open-domain dialogue corpus and task-adaptive self-supervised training. Through the DialoGLUE benchmark, the baseline methods, and our evaluation scripts, we hope to facilitate progress towards the goal of developing more general task-oriented dialogue models.

Machine learning, for marketing, refers to a process of configuring marketing programs in ways that help to analyze customer data and generate intelligent marketing decisions. Machine learning should not be confused with marketing automation systems since this is different from rules-based automation processes that are based on the specific programming and definite instructions from marketers and other users. Big brands such as Netflix, Google, and Amazon have already adopted machine learning tools to analyze customers' behavior to deliver them better-personalized information, content, and solutions via their email marketing campaigns. What's even better is that the costs of machine learning systems have significantly reduced of late that these tools are now easier to afford to adopt even in small and medium businesses. The efficacy of machine learning and hyper-personalization systems in revenue generation has led these tools to rise to popularity that businesses of different sizes and industries are resorting to these technologies.

How does Amazon help Alexa understand what people mean and not just what they say? And, we couldn't be talking about Alexa, smart home tech, and AI at a better time. During this week's Amazon Devices event, the company made a host of smart home announcements, including a new batch of Echo smart speakers, which will include Amazon's new custom AZ1 Neural Edge processor. In August this year, I had a chance to speak with Evan Welbourne, senior manager of applied science for Alexa Smart Home at Amazon, about everything from how the company is using AI and ML to improve Alexa's understanding of what people say, Amazon's approach to data privacy, the unique ways people are interacting with Alexa around COVID-19, and where he sees the future of voice and smart tech going in the future. The following is an transcript of our conversation edited for readability. Bill Detwiler: So before we talk about maybe IoT, we talk about Alexa, and kind of what's happening with the COVID pandemic, as people are working more from home, and as they may have questions that they're asking about Alexa, about the pandemic, let's talk about kind of just your role there at Amazon, and what you're doing with Alexa, especially with AI and ML. So I lead machine learning for Alexa Smart Home. And what that sort of means generally is that we try to find ways to use machine learning to make Smart Home more useful and easier to use for everybody that uses smart home. It's always a challenge because we've got the early adopters who are tech savvy, they've been using smart home for years, and that's kind of one customer segment. But we've also got the people who are brand new to smart home these days, people who have no background in smart home, they're just unboxing their first light, they may not be that tech savvy.

Suppose you're trying to engage in a conversation with a founder or CEO, you'll probably hear them speaking about artificial intelligence (AI) and machine learning (ML). And they'll probably tell you how these innovative technologies can transform their business. Machine learning (ML) has real-life applications, so typically that we often tend to overlook it! From switching on the phone by facial recognition to more complicated recommender algorithms that influence your decision to watch or shop next, machine learning is making quite a noise for now. ML is described as making machines learn to imitate human actions through complex coding started in Python, R, C, C#, Java, etc.

The LG Stylo 6 phone comes with a 6.8 inch screen that's bigger than the iPhone 11 Pro Max, has a stylus and a hefty 64 GB of internal storage – on par with the iPhone and Galaxy. What's also different is the retail price: $299 vs. the other phones, which both start in the $1,000 range? The phone could appeal to those looking for basic calls, texts and email reading, but when it comes to moving files, watching video and the like, you'll probably want to shop elsewhere. The Stylo 6 has picked up good reviews for most of its features except for one very important one – sluggish performance. That's the tradeoff you're going to have to make.