Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or exclusively, while attempting to maximize specific outcomes. How should we design such systems to serve users more fairly? This paper explores this question in the case where a group of users works toward a shared goal in a social exergame called Step Heroes. We identify adverse outcomes in traditional multi-armed bandits (MABs) and formalize the Greedy Bandit Problem. We then propose a solution based on a new type of fairness-aware multi-armed bandit, Shapley Bandits. It uses the Shapley Value for increasing overall player participation and intervention adherence rather than the maximization of total group output, which is traditionally achieved by favoring only high-performing participants. We evaluate our approach via a user study (n=46). Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.

Artificial intelligence (AI) is rapidly becoming a more prominent component of several global industries, including education. But in some industries, it has reached a point where workers are now concerned about whether or not their jobs are safe. When it comes to EdTech what makes a user interface engaging for a student? I've personally seen students open up an EdTech product, including Google Classroom, and immediately groan out loud. Lloyd Alexander once said, "We learn more by looking for the answer to a question and not finding it than we do from the answer itself." I love this quote because I've witnessed the truth of it firsthand in the classroom.

The graph represents a network of 1.023 Twitter users whose recent tweets contained "#ChatGPT #Education lang:en", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 2.000 tweets. The network was obtained from Twitter on Saturday, 11 February 2023 at 16:52 UTC. The tweets in the network were tweeted over the 9-day, 6-hour, 56-minute period from Thursday, 02 February 2023 at 08:19 UTC to Saturday, 11 February 2023 at 15:16 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

China has developed a new drone that go function in air and water. ChatGPT was launched back in November 2022 by OpenAI and has been a big hit thus far – but not always for the right reasons. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER Students have begun taking advantage of the AI model as well by using it to help them cheat on their homework. The AI-bot, as it is called, essentially does everything a student is supposed to do while developing critical thinking in a learning environment. Now, educators and experts behind anti-cheating software are doing their best to stop this from happening.

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' Generative artificial intelligence like ChatGPT is susceptible to several forms of bias and could cause harm if not properly trained, according to artificial intelligence experts. "They absolutely do have bias," expert Flavio Villanustre told Fox News Digital. "Unfortunately, it is very hard to deal with this from a coding standpoint. It is very hard to prevent bias from happening."

Rep. Bill Huizenga, R-Mich., joined'Fox & Friends First' to discuss the latest details surrounding the fatal shooting at Michigan State University and an upcoming briefing on the flying objects in U.S. airspace. Members of the Vanderbilt staff apologized on Friday for using ChatGPT, an artificial intelligence (AI) generator, to send an email to students calling for the community to come together following the shooting at Michigan State University. The email was sent on Thursday by the Peabody Office of Equity, Diversity, and Inclusion (EDI) at the university's Peabody College and included a note at the bottom that indicated the email had been written using ChatGPT, Vanderbilt's official student newspaper, The Vanderbilt Hustler, first reported on Friday. Associate Dean Nicole Joseph sent another email on Friday and said using ChatGPT to write the email was "poor judgment," according to the Hustler. OpenAI ChatGPT seen on mobile with AI Brain seen on screen.

Before viewing the Assistant Professor at University of North Carolina at Charlotte job, please verify that you are a human and not a robot overlord.

We show hardness of improperly learning halfspaces in the agnostic model, both in the distribution-independent as well as the distribution-specific setting, based on the assumption that worst-case lattice problems, such as GapSVP or SIVP, are hard. In particular, we show that under this assumption there is no efficient algorithm that outputs any binary hypothesis, not necessarily a halfspace, achieving misclassfication error better than $\frac 1 2 - \gamma$ even if the optimal misclassification error is as small is as small as $\delta$. Here, $\gamma$ can be smaller than the inverse of any polynomial in the dimension and $\delta$ as small as $exp(-\Omega(\log^{1-c}(d)))$, where $0 < c < 1$ is an arbitrary constant and $d$ is the dimension. For the distribution-specific setting, we show that if the marginal distribution is standard Gaussian, for any $\beta > 0$ learning halfspaces up to error $OPT_{LTF} + \epsilon$ takes time at least $d^{\tilde{\Omega}(1/\epsilon^{2-\beta})}$ under the same hardness assumptions. Similarly, we show that learning degree-$\ell$ polynomial threshold functions up to error $OPT_{{PTF}_\ell} + \epsilon$ takes time at least $d^{\tilde{\Omega}(\ell^{2-\beta}/\epsilon^{2-\beta})}$. $OPT_{LTF}$ and $OPT_{{PTF}_\ell}$ denote the best error achievable by any halfspace or polynomial threshold function, respectively. Our lower bounds qualitively match algorithmic guarantees and (nearly) recover known lower bounds based on non-worst-case assumptions. Previously, such hardness results [Daniely16, DKPZ21] were based on average-case complexity assumptions or restricted to the statistical query model. Our work gives the first hardness results basing these fundamental learning problems on worst-case complexity assumptions. It is inspired by a sequence of recent works showing hardness of learning well-separated Gaussian mixtures based on worst-case lattice problems.

Multimodal summarization (MS) aims to generate a summary from multimodal input. Previous works mainly focus on textual semantic coverage metrics such as ROUGE, which considers the visual content as supplemental data. Therefore, the summary is ineffective to cover the semantics of different modalities. This paper proposes a multi-task cross-modality learning framework (CISum) to improve multimodal semantic coverage by learning the cross-modality interaction in the multimodal article. To obtain the visual semantics, we translate images into visual descriptions based on the correlation with text content. Then, the visual description and text content are fused to generate the textual summary to capture the semantics of the multimodal content, and the most relevant image is selected as the visual summary. Furthermore, we design an automatic multimodal semantics coverage metric to evaluate the performance. Experimental results show that CISum outperforms baselines in multimodal semantics coverage metrics while maintaining the excellent performance of ROUGE and BLEU.

Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of financial crises reveals significant weaknesses and vulnerabilities in the foundations of modern economies. Another significant issue currently is people quitting their jobs in large numbers. Moreover, many organizations have a diverse workforce comprising multiple generations posing new challenges. Transformative approaches in economics and labour markets are needed to protect our societies, economies, and planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorised them into 5 macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues, and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualisation methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of AI-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.