Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. "Citizen Kane" has lost its edge. The 1941 movie directed by Orson Welles is known as one of the best films in history, and until very recently, held a 100% Fresh score on popular movie review aggregator site Rotten Tomatoes. However, the website recently added an 80-year-old review of the movie to its already-compiled collection that dropped the score to 99%.

If you follow Apple, you're aware its latest update delivers significant privacy changes. But what else can you do in iOS 14.5? On Monday, Apple launched the latest version of the operating software that powers iPhones and iPads. The biggest change requires apps to ask your permission to track your online activity. For example, if you open up an app like Facebook, you'll see a prompt seeking permission from you to track your activity across other apps and websites.

Nowadays, almost everybody is aware of the effect Artificial Intelligence (AI) has on our every day lives. AI is already a part of many people's lives and maybe already a part of your life too -- whether you realize it or not. Alexa), Google Home, and Apple's HomePod (with Siri) are perhaps the three most popular products in the thriving field of AI assistants. It's estimated that Amazon has sold about 25 million Echo devices up to now, and they expect that number to go double or more by 2020. These AI assistants products understand spoken commands and speak in humanlike voices using natural language.

Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remains to be investigated. In this paper, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability: the ability to detect "actual" performance differences as opposed to those observed by chance; (2) fidelity: the ability to agree with ultimate user preference; and (3) intuitiveness: the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics varies significantly across different scenarios whereas consistent with prior studies, existing metrics only achieve a weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.

Dating apps use AI algorithms to help match singles, and a new study finds the systems may be influencing users to swipe right on certain potential mates. Scientists in Spain wanted to find out what influences users, so they presented a group of test subjects with a series of fictitious suitors. Some of them were overtly promoted as highly compatible while other were favored more subtly--their photos just appeared more often. The researchers found participants were more likely to choose profiles that appeared frequently than those explicitly labeled as'an ideal partner.' This suggests people accept'scientific' advice for more intellectual subjects like politics, the researchers say, but prefer to go on intuition when it comes to romance.

We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives. These systems normally rely on machine learning models evolving over time to provide quality user experience. However, the development and improvement of the models are challenging because they need to support both high (head) and low (tail) usage scenarios, requiring fine-grained modeling strategies for specific data subsets or slices. In this paper, we explore the recent concept of slice-based learning (SBL) (Chen et al., 2019) to improve our baseline conversational skill routing system on the tail yet critical query traffic. We first define a set of labeling functions to generate weak supervision data for the tail intents. We then extend the baseline model towards a slice-aware architecture, which monitors and improves the model performance on the selected tail intents. Applied to de-identified live traffic from a commercial conversational AI system, our experiments show that the slice-aware model is beneficial in improving model performance for the tail intents while maintaining the overall performance.

Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users' preference on target items by the items they have interacted with. Recent models use methods such as attention mechanism and deep neural network to learn the user representation and scoring function more accurately. However, despite their effectiveness, such models still overlook a problem that performance of ICF methods heavily depends on the quality of item representation especially the target item representation. In fact, due to the long-tail distribution in the recommendation, most item embeddings can not represent the semantics of items accurately and thus degrade the performance of current ICF methods. In this paper, we propose an enhanced representation of the target item which distills relevant information from the co-occurrence items. We design sampling strategies to sample fix number of co-occurrence items for the sake of noise reduction and computational cost. Considering the different importance of sampled items to the target item, we apply attention mechanism to selectively adopt the semantic information of the sampled items. Our proposed Co-occurrence based Enhanced Representation model (CER) learns the scoring function by a deep neural network with the attentive user representation and fusion of raw representation and enhanced representation of target item as input. With the enhanced representation, CER has stronger representation power for the tail items compared to the state-of-the-art ICF methods. Extensive experiments on two public benchmarks demonstrate the effectiveness of CER.

It's a good time to buy a no-frills smart speaker. Amazon is running a sale that drops the price of the fourth-generation Echo Dot smart speaker to $30 (down from $50) for the basic model and $40 (down from $60) for its clock-equipped upgrade. This is very nearly a record low price for both, and makes them easy picks if you're looking for an entry-level device. Be sure to act quickly if you're interested, though -- this is a limited-time deal. The Echo Dot doesn't do a lot, but it does its job well.

It's a well-known fact that the science of speech recognition has made some fantastic progress since IBM introduced its first speech recognition machine in 1962. As the innovation has advanced, speech recognition has gotten progressively embedded in our everyday lives with voice-driven applications like Apple's Siri, Amazon's Alexa, Microsoft's Cortana, or the many voice-responsive highlights of Google. From our phones, PCs, watches, and refrigerators, each new voice-interactive gadget that we bring into our lives develops our reliance on AI and ML. Speech recognition in AI is the cycle that empowers a PC to perceive and react to verbally expressed words and afterwards changing over them in a format that the machine gets it. The machine may then change over it into another type of information relying upon the ultimate aim.

I greatly enjoyed reading and reviewing this delightful new book, Machine Learning for Kids: A Project-based Introduction to Artificial Intelligence, by Dale Lane, which was developed to introduce machine learning technology to children. It is well-written and includes everything needed to jump-start a kid's life in data science. The book is just the thing to motivate a young person to extend their innate curiosity to data centric experimentation. Included are the solutions to many contemporary problems where the reader turns machine learning models into computer games and apps such as a Rock, Paper Scissors game that recognizes hand shapes, an interactive virtual assistant like Siri and Alexa, a movie recommendation app, and an AI motivated Pac Man app. Kids learn about the building blocks of programming for machine learning using the Scratch-based companion website, implementing an extension to Scratch that allows kids to train their own ML models and use them from their Scratch programs.