The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown that discrimination can occur even when identity terms are not specified explicitly. Studies of bias in LM responses to open-ended prompts (where identity classifications are left unspecified) are lacking and have not yet been grounded in end-consumer harms. Here, we advance studies of generative LM bias by considering a broader set of natural use cases via open-ended prompting. In this "laissez-faire" setting, we find that synthetically generated texts from five of the most pervasive LMs (ChatGPT3.5, ChatGPT4, Claude2.0, Llama2, and PaLM2) perpetuate harms of omission, subordination, and stereotyping for minoritized individuals with intersectional race, gender, and/or sexual orientation identities (AI/AN, Asian, Black, Latine, MENA, NH/PI, Female, Non-binary, Queer). We find widespread evidence of bias to an extent that such individuals are hundreds to thousands of times more likely to encounter LM-generated outputs that portray their identities in a subordinated manner compared to representative or empowering portrayals. We also document a prevalence of stereotypes (e.g. perpetual foreigner) in LM-generated outputs that are known to trigger psychological harms that disproportionately affect minoritized individuals. These include stereotype threat, which leads to impaired cognitive performance and increased negative self-perception. Our findings highlight the urgent need to protect consumers from discriminatory harms caused by language models and invest in critical AI education programs tailored towards empowering diverse consumers.

Neural architecture search (NAS) has brought significant progress in recent image recognition tasks. Most existing NAS methods apply restricted search spaces, which limits the upper-bound performance of searched models. To address this issue, we propose a new search space named MobileNet3-MT. By reducing human-prior knowledge in omni dimensions of networks, MobileNet3-MT accommodates more potential candidates. For searching in this challenging search space, we present an efficient Multi-trial Evolution-based NAS method termed MENAS. Specifically, we accelerate the evolutionary search process by gradually pruning models in the population. Each model is trained with an early stop and replaced by its Lottery Tickets (the explored optimal pruned network).In this way, the full training pipeline of cumbersome networks is prevented and more efficient networks are automatically generated. Extensive experimental results on ImageNet-1K, CIFAR-10, and CIFAR-100 demonstrate that MENAS achieves state-of-the-art performance.

Proudly "voted the best place to work" in 2021-2022, Foodics, one of the most promising SaaS companies in MENA, was founded in 2014 in KSA with headquarters in Riyadh and offices in the United Arab Emirates Jordan, Kuwait, Egypt, Pakistan, and the Netherlands. FOODICS is the leading Restaurant-Tech company in MENA and a pioneer in the regional F&B industry. Foodics is undergoing rapid expansion across MENA, Pakistan, Africa and Asia, servicing over 20,000 brands, and has achieved three rounds of funding, with the latest raising $170 million in the largest SaaS funding round in MENA, boosting its innovation capabilities to better serve business owners. We provide a cloud-based point-of-sale SaaS ecosystem with tools that help F&B, and retail businesses start, track and grow. Our customers use Foodics to accept payments, track inventory, monitor sales, process orders, digitize menus, manage employees, create analytics and smart reports, provide secure cloud storage and enable the integration of third-party apps.

Founders of new era in Artificial Intelligence announced their METAHUMAN platform during GITEX 2022 in Dubai. DRIPS.TV is a Metahuman Artificial Intelligence platform that literally can replace human being at News channels as a beginning, speaking all languages in any shape and look. Matti K. from Finland and Mohammed Ebrahim Al Fardan from the Kingdom of Bahrain, have been working day and night during the pandemic to create the next unicorn. "It is METAHUMAN at last, speaking all languages on earth with almost perfect face and body impressions to deliver any broadcast, the future is now." Said Mohammed Al Fardan, founder and technology expert.

Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than epsilon-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.

We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM) algorithm for mixtures based on entropic optimal transport. sEM differs from the classic EM algorithm in the way responsibilities are computed during the expectation step: rather than assign data points to clusters independently, sEM uses optimal transport to compute responsibilities by incorporating prior information about mixing weights. Like EM, sEM has a natural interpretation as a coordinate ascent procedure, which iteratively constructs and optimizes a lower bound on the log-likelihood. However, we show theoretically and empirically that sEM has better behavior than EM: it possesses better global convergence guarantees and is less prone to getting stuck in bad local optima. We complement these findings with experiments on simulated data as well as in an inference task involving C. elegans neurons and show that sEM learns cell labels significantly better than other approaches.

Students at Lebanese American University (LAU) participate in a hardware design workshop that leverages the tools of the fourth Industrial Revolution. We are in the midst of the greatest industrial revolution in human history. The Fourth Industrial Revolution (4ID) is an economic transformation a thousand-times wider and deeper than anything that has come before it. "The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril," according to Professor Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF). The 4ID is characterized by the confluence of next generation technologies like: quantum computing, artificial intelligence and machine learning, autonomous transportation and robotics, the Internet of Things, additive manufacturing including 3D printing, biotechnology, and more generally; the merging of the digital and physical worlds.