Google Assistant is honoring prominent women in March with the help of its latest feature launched for Women's History Month. Anyone with Google's smart speaker can wish it a "Happy International Women's Day" and count on their smart device to read out loud information related to a trailblazer. On devices with screens, such as a smartphone or Google Home smart display, you can also see an image of the woman with a written summary. International Women's Day is celebrated on Sunday, March 8. Google has chosen to highlight 12 women from diverse nationalities and disciplines, including labor rights activist Dolores Huerta, architect Zaha Hadidand environmental scientist Rachel Carson, among others. "Our goal is to showcase some examples of the far-ranging impact women have had on all aspects of culture, and inspire women and girls to be their own trailblazers," said Google Assistant's senior director of product management, Lilian Rincon.

To many, Machine Learning may be a new word, but it was first coined by Arthur Samuel in 1952, and since then, the constant evolution of Machine Learning has made it the go-to technology for many sectors. Right from robotic process automation to technical expertise, Machine Learning technology is extensively used to make predictions and get valuable insight into business operations. It's considered as the subset of Artificial Intelligence (intelligence demonstrated by machines). If we go by the books, Machine Learning can be defined as a scientific study of statistical models and complex algorithms that primarily rely on patterns and inference. The technology works independently of any explicit instruction, and that's its strength.

For doctors that are exhausted from long hours typing up patient medical records, it could be game-changing, says Punit Soni, founder and CEO of Suki AI, a virtual assistant app for clinicians. The startup just raised a $20 million Series B round from Flare Capital Partners, First Round Capital, and Venrock, doubling its total funding to $40 million since its 2017 launch. The premise of Suki AI is simple: It's Alexa for doctors. Similar to how people can order Amazon's voice-enabled digital assistant to set a reminder or tell them their schedule, doctors can use Suki to take notes during patient appointments and those notes will automatically fill out electronic health records (EHRs). That's increasingly important as doctors spend more time logging data and less face time with patients.

"Alexa, turn on the 30C." (Photo: Eufy) Purchases you make through our links may earn us a commission. Back in the day, robot vacuums were the kind of investment that might have struck some people as a little excessive. But nowadays with smart tech making everything we hate about daily life--like cooking dinner and finishing chores--so much easier, it makes sense that robot vacuums would become more ubiquitous, too. A smart robot vacuum like the Eufy Robovac 30C can make vacuuming suck a lot less, and right now you can get this top-rated device for its second-lowest price of all-time on Amazon, at just $190. At full price, the 30C is advertised as being $299.99,

Coronavirus concerns have now spread to dating apps. Tinder is urging its users to stay safe amid the COVID-19 outbreak with pop-up alerts. The notification randomly appears when swiping for matches with the headline, "your wellbeing is our #1 priority." The popular dating app also features a series of safety tips within the alert before linking users back to the World Health Organization (WHO) to learn more. The safety tips, aligned with WHO's recommendations, include avoiding touching your face and carrying hand sanitizer.

How do we know the profile of each of our products? A product's profile should represent the key underlying characteristics of the product, especially the ones that users care about. A very simple product profile would just be the category. For example for books, the profiles would be history, fantasy, science fiction, nonfiction, biography, romance, humor, etc. Quantitatively, books would just be represented something like this: And we would try to find out what categories (genres) our customers liked to read. Let's say we go with approach 2 -- Jeremy gives us a list of 7 fantasy books and 3 romance books.

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.