Miscommunication and communication challenges between instructors and students represents one of the primary barriers to post-secondary learning. Students often avoid or miss opportunities to ask questions during office hours due to insecurities or scheduling conflicts. Moreover, students need to work at their own pace to have the freedom and time for the self-contemplation needed to build conceptual understanding and develop creative thinking skills. To eliminate barriers to student engagement, academic institutions need to redefine their fundamental approach to education by proposing flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a power language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system will serve as a voice-enabled helper capable of answering course-specific questions concerning curriculum, logistics and course policies. It is envisioned to improve access to course-related information for the students and reduce logistical workload for the instructors and TAs. Its GPT-3-based knowledge discovery component as well as the generalized system architecture is presented accompanied by a methodical evaluation of the system accuracy and performance.

The dating app Tinder changed the way we date by mainstreaming the swipe: Swipe left to reject, swipe right to see if there's a spark. Now, in the age of pernicious romance scams, Tinder is going hard on another feature: The block. Tinder, which is owned by Match Group, said recently it was introducing a feature that lets users block someone's profile as soon as it's suggested by the app. Previously Tinder members could only block someone after there was a match and one party subsequently filed a report. Now, blocking can happen right away.

You can help prevent others from falling victim to the same romance scam and remember if something seems too good to be true. Online dating is the most common way for singles to get together these days. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER If you've heard of "The Tinder Swindler" on Netflix, you may already know this, yet schemes are getting extreme when it comes to online dating. Romance scams are skyrocketing, Americans were scammed out of over $500 million last year because of them. So how can you make sure you don't have a crazy person come after you when you're swiping left and right?

We're at the height of a global technological revolution, and yet this is the modern state of dating: You swipe left, swipe left again, and again, and again--in fact, you mind-numbingly swipe left so many times that when the app finally lands on a person you deem worthy of swiping right, you accidentally swipe left on them, too. My thumbs are bloody with disappointment that dating apps, once the face of innovation, have become relics of the status quo. But I've seen the light on the horizon in the form of generative AI programs like ChatGPT. These have now been crowned the virtual assistant of the future--so why shouldn't we use it for dating, which most people already describe as a second job? AI can put the fun back into dating--but we can't expect the end result to change if we don't let go of our preconceived notions of what meeting and talking to partners should look like. I was inspired by a viral TikTok of a self-described "Tinder veteran" who used ChatGPT to write pickup lines for his matches.

It only took three days of swiping before he popped up. I froze, thumb hovering over the X. I scrolled through his photos and prompts, looking at what he had changed since I'd seen it the first time. The first photo was the same: him holding a climbing rope somewhere remote, curly hair bursting from underneath a baseball cap. His simple pleasures were still "mountain roads, forests and alarm free mornings."

Velocity has integrated this latest advancement in artificial intelligence with its existing analytics tool – Velocity Insights. Velocity Insights is India's largest eCommerce analytics platform that is trusted by 3000 Indian eCommerce brands to make informed business decisions. Brands that utilize Insights receive a daily business report on Whatsapp. Further, these brands can also access benchmarks to evaluate their performance relative to other brands in the industry. Velocity Insights currently provides advanced analytics that contains information about an eCommerce business's sales and marketing performance.

Recommendation systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications, which recently present two emerging trends: (i) Collaboration: single-sided model trained on-cloud (separate learning) to the device-cloud collaborative recommendation (collaborative learning). (ii) Real-time Dynamic: the network parameters are the same across all the instances (static model) to adaptive network parameters generation conditioned on the real-time instances (dynamic model). The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication. Despite promising, we argue that most of the communications are unnecessary to request the new parameters of the recommendation system on the cloud since the on-device data distribution are not always changing. To alleviate this issue, we designed a Intelligent DEvice-Cloud PArameter Request ModeL (IDEAL) that can be deployed on the device to calculate the request revenue with low resource consumption, so as to ensure the adaptive device-cloud communication with high revenue. We envision a new device intelligence learning task to implement IDEAL by detecting the data out-of-domain. Moreover, we map the user's real-time behavior to a normal distribution, the uncertainty is calculated by the multi-sampling outputs to measure the generalization ability of the device model to the current user behavior. Our experimental study demonstrates IDEAL's effectiveness and generalizability on four public benchmarks, which yield a higher efficient device-cloud collaborative and dynamic recommendation paradigm.

They are man's best friend – but dogs could also be your best wingman, too. That's according to a new study that found showcasing a pooch in your photos could help boost your chances of finding love. When it comes to curating a dating profile, singletons can spend hours deciding on the best photos to try and attract a potential partner. But those who have a dog in their pictures could be more likely to get a match, according to a study. A poll of 1,000 British adults revealed two-thirds of people said they're more likely to match with someone whose dating profile includes a picture of a dog.

Now they're at the center of a landmark legal case that ultimately has the power to completely change how we live online. On February 21, the Supreme Court will hear arguments in Gonzalez v. Google, which deals with allegations that Google violated the Anti-Terrorism Act when YouTube's recommendations promoted ISIS content. It's the first time the court will consider a legal provision called Section 230. Section 230 is the legal foundation that, for decades, all the big internet companies with any user generated stuff--Google, Facebook, Wikimedia, AOL, even Craigslist--built their policies and often businesses upon. As I wrote last week, it has "long protected social platforms from lawsuits over harmful user-generated content while giving them leeway to remove posts at their discretion."

The $K$-armed dueling bandits problem, where the feedback is in the form of noisy pairwise preferences, has been widely studied due its applications in information retrieval, recommendation systems, etc. Motivated by concerns that user preferences/tastes can evolve over time, we consider the problem of dueling bandits with distribution shifts. Specifically, we study the recent notion of significant shifts (Suk and Kpotufe, 2022), and ask whether one can design an adaptive algorithm for the dueling problem with $O(\sqrt{K\tilde{L}T})$ dynamic regret, where $\tilde{L}$ is the (unknown) number of significant shifts in preferences. We show that the answer to this question depends on the properties of underlying preference distributions. Firstly, we give an impossibility result that rules out any algorithm with $O(\sqrt{K\tilde{L}T})$ dynamic regret under the well-studied Condorcet and SST classes of preference distributions. Secondly, we show that $\text{SST} \cap \text{STI}$ is the largest amongst popular classes of preference distributions where it is possible to design such an algorithm. Overall, our results provides an almost complete resolution of the above question for the hierarchy of distribution classes.