Joe Biden has defeated President Donald Trump in the 2020 presidential election and will become the 46th president of the United States. Although Trump has not conceded, Biden has been named the president-elect by multiple outlets, including AP News. The decision to name the 77-year-old the president-elect sent Twitter into a frenzy, which resulted in Biden's supporters using Alexa, Amazon's virtual assistant, to taunt Trump and celebrate the democrat's victory. On Saturday, "Alexa" began trending on Twitter as people began sharing the songs they wanted to play to celebrate the president-elect and say goodbye to Trump. In the song, Meek Mill raps, "See my dreams unfold, nightmares come true It was time to marry the game and I said, 'Yeah, I do' If you want it you gotta see it with a clear-eyed view."

Artificial Intelligence (AI) has been a leading factor in numerous eye-opening marketing experiments in the past few years. Studies show that 80% of B2B marketers believe that AI will revolutionize marketing in the coming years. AI can emulate the human mind's capacity while making more precise business decisions. Using big data and intelligent machine learning, AI can help B2B marketers make more informed decisions and put their money into the right places. Let's look at how AI is changing the marketing world and leading to massive customer engagement and revenue improvements.

Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021. Companies have swiftly started leveraging the potential of AI.

Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021.Companies have swiftly started leveraging the potential of AI. Companies like Amazon, Microsoft, and Google have grown immensely due to the incorporation of AI for forecasting, adapting to changing market conditions and generating profit.

Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.

The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism. We first show in theory that applying supervised learning to detect user preferences may end up with inconsistent results in the absence of exposure information. The counterfactual propensity-weighting approach from causal inference can account for the exposure mechanism; nevertheless, the partial-observation nature of the feedback data can cause identifiability issues. We propose a principled solution by introducing a minimax empirical risk formulation. We show that the relaxation of the dual problem can be converted to an adversarial game between two recommendation models, where the opponent of the candidate model characterizes the underlying exposure mechanism. We provide learning bounds and conduct extensive simulation studies to illustrate and justify the proposed approach over a broad range of recommendation settings, which shed insights on the various benefits of the proposed approach.

Unlike humans, conversational artificial intelligence (AI), most commonly deployed today via chatbots, are "up" 100% of the time. Beyond chatbots, automated voice response systems (as annoying as they may still be) and virtual voice assistants all utilize conversational AI to power human-to-machine dialog. Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).

The holiday shopping season is in full swing -- and yes, we know it's only early November. Retailers like Amazon, Walmart and Best Buy have already kicked off the first rounds of their big sale events, and there will be more to come in the lead-up to Black Friday. This week, we saw AirPods drop to a new all-time low; a great price for Samsung's Galaxy Watch 3; and a bunch of worthwhile deals at Walmart for Instant Pots, robot vacuums and more. Here are the best deals from this week that you can still get today. Now's the time to grab Apple's classic AirPods for someone on your list, or for yourself.

A smart clock is a great addition to a bedroom. Not only does it tell the time and other pertinent information, but it's also smart enough to dim at night and brighten up in the morning. Plus, they usually don't have cameras, making them better than a typical smart display for intimate spaces like your bedside. Lenovo's Smart Clock series features Google Assistant integration, too, so you can easily control your connected devices or get weather and traffic reports by speaking to the device. When the company's basic Smart Clock Essential launched this year, it was pretty affordable at just $50.

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.