There will be no fourth season for Netflix's dark comedy series Santa Clarita Diet as the streaming giant has opted to cancel the series starring Drew Barrymore and Timothy Olyphant. The news comes almost a month after the March 29 release of the show's third season. Santa Clarita Diet is the latest Netflix series canceled after three seasons. Following Deadline's story examining the trend, fans of the show started a #SaveSantaClaritaDiet Twitter campaign, rallying for a Season 4 renewal. Like with the recently axed Netflix comedy series One Day At a Time, also after three seasons, the cancellation for Santa Clarita Diet comes after a very strong season, which scored 100% on Rotten Tomatoes.

Garage door openers are certainly convenient, but compared to the connected devices around our homes, they're nor very smart. But it doesn't have to be that way. Today, you can choose from two smart garage door remotes at all-time low Amazon prices to beef up your existing system: the Chamberlain MyQ for $50Remove non-product link, down from a list price of $80, or the Nexx Garage for $70Remove non-product link, down from a list price of $100. The MyQ smart garage door hub connects to any compatible garage door opener and Wi-Fi to bring smarts to your existing garage setup. Once connected, you'll be able to use the app to open and close your door, get alerts when the door opens, closes, or is left open too long, and schedule automatic close times.

Some weeks, there are so many tall-skinny cylinders sitting around my apartment you might think I spend my time as a wizard in The Cones of Dunshire. The job of a WIRED Gear reviewer does not include making civilizations to collect cones. It does involve testing a ton of can-shaped portable speakers. The JBL Link 20 is one my favorites, and it's half off through April 27. Usually speakers come in, and I test them, and then they leave my cluttered life.

Forty-one percent of voice assistant users are concerned about trust, privacy and passive listening, according to a new report from Microsoft focused on consumer adoption of voice and digital assistants. And perhaps people should be concerned -- all the major voice assistants, including those from Google, Amazon, Apple and Samsung, as well as Microsoft, employ humans who review the voice data collected from end users. But people didn't seem to know that was the case. So when Bloomberg recently reported on the global team at Amazon that reviews audio clips from commands spoken to Alexa, some backlash occurred. In addition to the discovery that our AI helpers also have a human connection, there were concerns over the type of data the Amazon employees and contractors were hearing -- criminal activity and even assaults in a few cases, as well as the otherwise odd, funny or embarrassing things the smart speakers picked up.

Most modern successful recommender systems are based on matrix factorization techniques, i.e., learning a latent embedding for each user and each item from the given rating matrix and use the embeddings to complete the matrix. However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen users/items or new tasks. In this paper, we aim to learn an inductive model for recommender systems based on the local graph patterns around user-item pairs. The inductive model can generalize to unseen nodes/items, and potentially also transfer to other tasks. To learn such a model, we extract a local enclosing subgraph for each training (user, item) pair, and feed the subgraphs to a graph neural network (GNN) to train a rating prediction model. We show that our model achieves highly competitive performance with state-of-the-art transductive methods, and is more stable when the rating matrix is sparse. Furthermore, our transfer learning experiment validates that the learned model is transferrable to new tasks.

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long-lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent-assisted health coaching system to support health intervention delivery to individuals and groups. This research provides three main contributions to the preventive healthcare & healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain-independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one-month validation study on physical activity, healthy diet and stress management. Introduction Adhering to a healthy lifestyle is among the most contributor to health promotion and disease prevention [27,28]. A varied diet and regular physical activity have significant benefits for individuals' overall health [27,28,6]. Similarly, mental wellness is associated with social competence and coping skills that lead to positive outcomes in adulthood and later stages of individuals life [29,9]. Although the benefit of pursuing a healthy lifestyle, several barriers exist in the process of health promotion. For instance, individuals' motivation to change, their demographics and preparedness are all factors that contribute to their intention to follow a healthy lifestyle. Several studies tackled the issue of poor lifestyle through mobile technologies. Approaches [31,32] developed mobile applications to mitigate the risk of poor diet, sedentary lifestyle and anxiety. That said, the learning curve associated with mobile apps is still an issue, especially for individuals with low digital literacy.

It emerged earlier this month that thousands of Amazon employees are reviewing some Alexa recordings (which are captured after you've said the wake word). The auditors transcribe, annotate and analyze a selection of commands to help improve Alexa. But it seems these workers could view users' personal information too, according to Bloomberg. At least some employees are said to have had access to location data, addresses and phone numbers. There's no indication any workers have tried to look up a customer's home (say, on Google Street View) using the data from these tools.

With increasing physicians' workload and patients' needs for care, there is a need for technology that facilitates physicians work and performs continues follow-up with patients. Existing approaches focus merely on improving patient's condition, and none have considered managing physician's workload. This paper presents an initial evaluation of a conversational agent assisted coaching platform intended to manage physicians' fatigue and provide continuous follow-up to patients. We highlight the approach adapted to build the chatbot dialogue and the coaching platform. We will particularly discuss the activity recommender algorithms used to suggest insights about patients' condition and activities based on previously collected data. The paper makes three contributions: (1) present the conversational agent as an assistive virtual coach, (2) decrease physicians workload and continuous follow up with patients, all by handling some repetitive physician tasks and performing initial follow up with the patient, (3) present the activity recommender that tracks previous activities and patient information and provides useful insights about possible activity and patient match to the coach. Future work focuses on integrating the recommender model with the CoachAI platform and test the prototype with patient's in collaboration with an ambulatory clinic.

How to Do It is Slate's sex advice column. Send your questions for Stoya and Rich to howtodoit@slate.com. I have been sexually active since I was 17. I am now 29 years old. A majority of the sex I had between 17 and 21 was only when I was drunk, so I don't remember most of it, but I know I didn't climax.

There are three quite distinct ways to train a machine learning model on recommender system logs. The first method is to model the reward prediction for each possible recommendation to the user, at the scoring time the best recommendation is found by computing an argmax over the personalized recommendations. This method obeys principles such as the conditionality principle and the likelihood principle. A second method is useful when the model does not fit reality and underfits. In this case, we can use the fact that we know the distribution of historical recommendations (concentrated on previously identified good actions with some exploration) to adjust the errors in the fit to be evenly distributed over all actions. Finally, the inverse propensity score can be used to produce an estimate of the decision rules expected performance. The latter two methods violate the conditionality and likelihood principle but are shown to have good performance in certain settings. In this paper we review the literature around this fundamental, yet often overlooked choice and do some experiments using the RecoGym simulation environment.