It is 2020 and it's all about technology these days. You knowingly or unknowingly use machine learning in your day-to-day life. Let me give you some examples, Google spam filter, Netflix recommendation system, Facebook face recognition, weather forecast, and much more. All of these are Machine Learning. It has become one of the hottest industries and has become like a trend for beginners.

Valentine's Day is just around the corner -- and if you want to maintain the interest of a woman on Tinder, a funny chat-up line is the way to go, scientists have revealed. US researchers tested out various online chat-up lines on 237 young, heterosexual adults -- finding that humour was a better opening gambit than compliments. In fact, they found that men who used funny introductions were seen as more attractive to women, who rated them as more intelligent, kind and trustworthy. Even if some of the lines were a little cheesy, they found that women still responded to them better than bland, unimaginative greetings like'Hi, how are you?' Men, in contrast, were found to overwhelmingly base their evaluations of prospective dates on how attractive they found the woman's profile. The team noted that, thanks to the COVID-19 pandemic closing bars and clubs around the world, singles have surged to apps like Tinder for their dating needs.

Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. In this work, we explore the feasibility of automatically detecting the level of trust that a user has on a virtual assistant (VA) based on their speech. We developed a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a VA. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the VA's skills, they are first informed that the VA was previously rated by other users as being either good or bad; subsequently, the VA answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different trust conditions. Using this protocol, we collected a speech corpus in Argentine Spanish. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present results of a perceptual study of the degree of trust performed by expert listeners. Finally, we found that the subject's speech can be used to detect which type of VA they were using, which could be considered a proxy for the user's trust toward the VA's abilities, with an accuracy up to 76%, compared to a random baseline of 50%.

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified "Hallmarks" of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.

Amazon announced that their voice assistant Alexa can now sign business agreements with health providers under the Health Insurance Portability and Accountability Act, or HIPAA. Third-party health developers can meet the rules that govern how sensitive health information is shared, and major health providers and companies have launched a number of voice programs to help users manage chronic conditions. If you have an Amazon Echo at home, you need to dive into the privacy settings. There are a few important things to lockdown. Don't forget to turn off voice purchasing if you never use that feature, or at least set up a PIN.

After years of being forced to use Apple services on the iPhone by default, those restrictions are finally easing up a bit. As noticed by MacRumors earlier today, the iOS 14.5 beta appears to let you set third-party music services as default with Siri. This means you can ask Siri to play a particular song or album and it'll go straight to Spotify or YouTube Music. Currently, Siri only searches and plays things from whatever music you have in the Apple Music app, be it your own collection of songs or the Apple Music subscription catalog. It sounds like after iOS 14.5 is installed, Siri will ask you what music service you want to use when you ask it to play a song.

If you're looking for an affordable color smart bulb that works with both Amazon Alexa and Google Assistant, the Sylvania A19 Smart Full Color bulb is a fine candidate, although you can't buy just one. This standard A19 bulb connects directly to Wi-Fi networks, meaning there's no need for a bridge or smart-home hub. Sylvania's mobile app offers a variety of grouping options, lighting scenes, and powerful automation tools. That said, we still prefer the more polished apps that come bundled with our favorite A19 smart bulbs from LIFX and Philips Hue. The Sylvania bulb also lacks the HomeKit support that LIFX and Hue bulbs deliver, but that will matter only to Apple users who use that ecosystem.

Clustering is a fundamental unsupervised learning task used for various applications, such as anomaly detection (Leung and Leckie, 2005), recommender systems (Shepitsen et al., 2008) and cancer diagnosis (Zheng et al., 2014). In recent years, research on sequential clustering has been actively studied, motivated by applications in which data arrives sequentially, such as online recommender systems (Nasraoui et al., 2007) and online community detection (Aggarwal, 2003). In this work, we study k-median clustering in the sequential no-substitution setting, a term first introduced in Hess and Sabato (2020). In this setting, a stream of data points is sequentially observed, and some of these points are selected by the algorithm as cluster centers. However, a point can be selected as a center only immediately after it is observed, before observing the next point. In addition, a selected center cannot be substituted later. This setting is motivated by applications in which center selection is mapped to a real-world irreversible action, such as providing users with promotional gifts or recruiting participants to a clinical trial. The goal in the no-substitution k-median setting is to obtain a near-optimal k-median risk value, while selecting a number of centers that is as close as possible to k.

The development of Artificial Intelligence technology was thought to be rapid, of course. But in the last few years, there has been an exponential increase in the number of platforms, applications, and tools based on machine learning and artificial intelligence technologies. Scientists and developers continue to design and develop intelligent machines that can mimic reasoning, develop and learn knowledge, and attempt to mimic how humans think. Of course, it is really difficult to follow technologies that are developing so fast. For this reason, in order to keep up with these technological developments, we tried to gather Top 10 Artificial Intelligence Technology trends for you.

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance.