Siri, Cortana, Alexa, Watson, Bixby, Viv, M, Google Assistant and the list goes on and on……. Along with all the above personal assistants in the real world, one of the most famous personal assistants of 21st century is the one and only'Jarvis' from the movie'Iron man' & 'Avengers', big ticket projects from the Marvel Cinematic Universe. The sheer concept of a personal assistant facilitated by technology is groundbreaking and similar is the stature of breakthrough technology which has been leveraged to accomplish it, which goes by the name of, Cognitive Computing. Cognitive Computing was used for the very first time by IBM. It developed Watson, a unique response-capable computing system, which was built to compete against humans on the popular game show called, Jeopardy.

Voice assistants have been successfully adopted for simple, routine tasks, such as asking for the weather or setting an alarm. However, as people get more familiar with voice assistants, they may increase their expectations for more complex tasks, such as exploratory search-- e.g., "What should I do when I visit Paris with kids? Oh, and ideally not too expensive." Compared to simple search tasks such as "How tall is the Eiffel Tower?", which can be answered with a single-shot answer, the response to exploratory search is more nuanced, especially through voice-based assistants. In this paper, we outline four challenges in designing voice assistants that can better support exploratory search: addressing situationally induced impairments; working with mixed-modal interactions; designing for diverse populations; and meeting users' expectations and gaining their trust. Addressing these challenges is important for developing more "intelligent" voice-based personal assistants.

Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users' data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this paper, we propose a novel Privacy preserving POI Recommendation (PriRec) framework. First, to protect data privacy, users' private data (features and actions) are kept on their own side, e.g., Cellphone or Pad. Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices. Those public data include: (1) static data only related to the status of POI, such as POI categories, and (2) dynamic data depend on user-POI actions such as visited counts. The dynamic data could be sensitive, and we develop local differential privacy techniques to release such data to public with privacy guarantees. Second, PriRec follows the representations of Factorization Machine (FM) that consists of linear model and the feature interaction model. To protect the model privacy, the linear models are saved on users' side, and we propose a secure decentralized gradient descent protocol for users to learn it collaboratively. The feature interaction model is kept by the recommender since there is no privacy risk, and we adopt secure aggregation strategy in federated learning paradigm to learn it. To this end, PriRec keeps users' private raw data and models in users' own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.

Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this paper, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have rich resources of user social information. It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems. It is anticipated to combine the social information with the user-item ratings to improve the overall recommendation performance. Most existing recommendation models are built based on the assumptions that the social information are available. However, different platforms are usually reluctant to (or cannot) share their data due to certain concerns. In this paper, we first propose a SEcure SOcial RECommendation (SeSoRec) framework which can (1) collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and (2) securely keep the raw data of both platforms. We then propose a Secret Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and prove its correctness and security theoretically. By applying minibatch gradient descent, SeSoRec has linear time complexities in terms of both computation and communication. The comprehensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed SeSoRec and SSMM.

"Hey Google, read this page." That's a new command for the Google Assistant that will see the robot reading web pages aloud. Use cases: catching on news that doesn't have a podcast component while driving, having pages translated to you in other languages (say if you're traveling) or just general help for people who are vision-impaired. Fine print: The feature is only available, starting today, on Google's Android smartphone platform. Whenever a web article is displayed on your browser in your Android phone, you can say, "Hey Google, read it" or "Hey Google, read this page" it will immediately read aloud the content of the web page," says Yossi Matias, a Google vice-president. "And to help people follow along at a convenient pace without having to touch the screen, your browser will highlight the words being read out and auto-scroll the page.

Deciding who picks up the bill on a first date may be a cinch for some lucky users of the dating app, Hinge. According to the company, it will award up to 2,500 of its users a $100 Visa gift card that can be used to pay for a date in a bid to help them'unplug' from their phones and meet someone in-person. To activate the promotion, users must pause their profiles from 4 pm on Friday March 6th until Saturday March 7th, during which time they're expected to go on a date. After the two parties go on their date, they're then asked to go into the Hinge app and confirm that they actually attended the meetup up by clicking on their date's profile and selecting'met'. The daters are then asked to say whether they would go on another date with each other and are then are allowed to file for the Visa card on unplugwithhinge.com.

Tinder has posted a warning for its users telling them that coronavirus safety is'more important' than dating and urging them to wash their hands frequently. The dating app also encourages its singletons to carry hand sanitiser, avoid touching their face and'maintain social distance' when out in public. The warning says: 'Tinder is a great place to meet new people. While we want you to continue to have fun, protecting yourself from the coronavirus is more important'. It appears as a pop up while users are flipping between potential matches to warn of the dangers of the potentially deadly virus now called COVID-19. The pop-up warning also includes a link to the latest advice and information from the World Health Organisation (WHO) website.

The result: eBay's copywriters are now able to devote more time to more creative work, such as crafting prose, according to Molly Prosser, associate creative director at eBay and a big believer in AI writing tools. "All the time I've spent lingering over the length of a subject line, wondering'Is this word more engaging, or is this word more engaging', or'How do I convey urgency without seeming too cliché'," says Prosser. "The hours I've spent editing and having team members pour over these things -- it's just meaningless work when we have a piece of AI that can do that for us. The new capability enables Naylor to offer a personalized newsletter for each association member it services -- based on his or her interests and reading habits. The AI tool incorporates personalized content that Naylor generates in-house, as well as content it finds on the Web –including job ads that match the specific job skills of the reader. Essentially, AI enables newsletters to become ever-more personalized over time by monitoring how each reader interacts with his or her personalized newsletter, and making adjustments accordingly, says Amith Nagaranjan, executive chairman, Rasa.io.

Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These so-called problems are present in all fields - the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence (AI) is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.