Digital marketing firm Voro is using ChatGPT, the popular new artificial intelligence program, to "supercharge" content creation for clients. Before ChatGPT, content had been "incredibly expensive" to create -- especially individualized content necessary for search engine visibility, said Chris Gauron, partner and CEO at the Minneapolis firm. Voro has created an artificial intelligence-assisted, but human-edited, process that increases speed at the same time it lowers cost. ChatGPT's power and potential have fueled explosive growth, reaching 100 million users in just two months. Reports that it passed exams in four University of Minnesota law courses, at the Wharton School of Business and the exam to become a licensed physician have only heightened interest globally.

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.

Platforms are increasingly adopting Artificial Intelligence (henceforth, AI) algorithms (for example, the ChatGPT (Ouyang, Wu, Jiang, Almeida, Wainwright, Mishkin, Zhang, Agarwal, Slama, Ray, et al. (2022)) and Eloundou, Manning, Mishkin, and Rock (2023)) to intelligentize services and price their products, and this tendency is likely to be extended to other business areas, particularly contract design. In this paper, we ask whether contracting algorithms may "autonomously" learn to be incentive compatible, especially for the contracting problem with multiple sides, which we also refer to as the multi-sided contracting problem. We emphasize that AI algorithms can be used to automatically optimize the terms of a contract by taking into account the preferences of both sides and the legal and economic environment in which the agreement must be implemented. Note that this contract negotiation process is automatic, requiring very little external guidance. In light of these developments, concerns have been voiced by scholars and organizations alike, that AI algorithms may create an AI alignment problem due to differences between the specified reward function and what relevant humans (the designer, the user, others affected by the agent's behavior) actually value (see Hadfield-Menell and Hadfield (2019) and Gabriel (2020)). We highlight that this AI alignment problem has a clear analogy to the principal-agent problem (see Hadfield-Menell and Hadfield (2019)), and the analysis of incentive compatibility for the incomplete contracting via AI algorithms can provide an insightful framework for understanding the alignment among algorithms. But how real is the risk of misalignment among AI algorithms? That is a difficult question to answer, both empirically and theoretically. On the empirical side, alignment is notoriously hard to detect from market outcomes, and firms typically do not disclose details of the financial or employment contracts they have.

The potential of large language models (LLMs) to reason like humans has been a highly contested topic in Machine Learning communities. However, the reasoning abilities of humans are multifaceted and can be seen in various forms, including analogical, spatial and moral reasoning, among others. This fact raises the question whether LLMs can perform equally well across all these different domains. This research work aims to investigate the performance of LLMs on different reasoning tasks by conducting experiments that directly use or draw inspirations from existing datasets on analogical and spatial reasoning. Additionally, to evaluate the ability of LLMs to reason like human, their performance is evaluted on more open-ended, natural language questions. My findings indicate that LLMs excel at analogical and moral reasoning, yet struggle to perform as proficiently on spatial reasoning tasks. I believe these experiments are crucial for informing the future development of LLMs, particularly in contexts that require diverse reasoning proficiencies. By shedding light on the reasoning abilities of LLMs, this study aims to push forward our understanding of how they can better emulate the cognitive abilities of humans.

Fox News contributor Jonathan Turley reacts to a dean at Stanford Law School joining students in heckling a conservative judge on'America Reports.' EXCLUSIVE – A curriculum developed under Yale Medical School is using emotional persuasion tactics to trigger children attending thousands of public schools to become angry about social justice causes and aid them in developing an "intersectional identity," parents worry. Fox News Digital reviewed the tightly guarded curriculum, created by the Center for Emotional Intelligence at the medical school's Child Study Center. Yale's clients are forbidden from sharing its contents with anyone who is not employed at the district, according to the contract it has signed with partners. The lessons probed deeply and, oftentimes intrusively, into the student's emotions, personal relationships, traumas, beliefs and triggers. "Conversations around triggers and Meta-Moments are an excellent way to discuss power and privilege in who, in our society, is required to regulate more strictly in public spaces. Consider examining stereotypes in the context of emotional regulation as they relate to race, gender, sexuality, religion, and other forms of difference," the curriculum said.

At Adobe Summit on March 21, 2023, Adobe announced and delivered its generative AI offering known as Adobe Firefly. Adobe Firefly (In beta), is a collection of generative AI models built for creative applications. The offering joins the platform's AI services. Adobe plans to integrate Firefly into their Creative Cloud, Document Cloud, and Adobe Express product lines. Future models will target additional use cases and content types, potentially leveraging other technology and training data.

If you want to explore this area further, here's everything you need to know about what data and AI can mean for your future energy costs and how these trends will impact your utility bill over time! The average American home uses more than one trillion kilowatt-hours of electricity annually, or enough to power 3 million homes. Nearly half of that is used by appliances. The good news is that there are several ways to save on energy costs without compromising comfort or convenience. For example, LED bulbs can last up to 50 times longer than traditional incandescent bulbs and use 75 percent less electricity; smart thermostats can automatically adjust the temperature settings in your home based on where people are located, saving you an average of 10% on heating and cooling costs. Data and artificial intelligence (AI) fundamentally change how we consume, manage, produce and distribute energy.

As part of the 37th AAAI Conference on Artificial Intelligence (AAAI2023), 32 different workshops were held, covering a wide range of different AI topics. We continue our round-up of these workshops with summaries from the organisers of two of the workshops, who tell us their key takeaways from their respective events. This workshop included contributions spanning theory, methods and systems, for application to web-based healthcare, with a focus on applications in population and personalized health. This workshop focussed on both the theoretical and practical challenges related to the design of privacy-preserving AI systems, including multidisciplinary components, such as policy, legal issues, and societal impact of privacy in AI. You can read the first round up in our series of AAAI workshop summaries here.

Different from conventional image matting, which either requires user-defined scribbles/trimap to extract a specific foreground object or directly extracts all the foreground objects in the image indiscriminately, we introduce a new task named Referring Image Matting (RIM) in this paper, which aims to extract the meticulous alpha matte of the specific object that best matches the given natural language description, thus enabling a more natural and simpler instruction for image matting. First, we establish a large-scale challenging dataset RefMatte by designing a comprehensive image composition and expression generation engine to automatically produce high-quality images along with diverse text attributes based on public datasets. RefMatte consists of 230 object categories, 47,500 images, 118,749 expression-region entities, and 474,996 expressions. Additionally, we construct a real-world test set with 100 high-resolution natural images and manually annotate complex phrases to evaluate the out-of-domain generalization abilities of RIM methods. Furthermore, we present a novel baseline method CLIPMat for RIM, including a context-embedded prompt, a text-driven semantic pop-up, and a multi-level details extractor. Extensive experiments on RefMatte in both keyword and expression settings validate the superiority of CLIPMat over representative methods. We hope this work could provide novel insights into image matting and encourage more follow-up studies. The dataset, code and models are available at https://github.com/JizhiziLi/RIM.

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the industrial plant's production. We exhibit several trade-offs between reducing environmental impact and maintaining production activities. The predictive component of our framework employs various machine learning models, such as gradient-boosted tree-based models and ensemble methods, for time series forecasting. The prescriptive component utilizes interpretable optimal policy trees to propose multiple trade-offs, such as reducing dangerous emissions by 33-47% and unnecessary costs by 40-63%. Our deployed models significantly reduced forecasting errors, with a range of 38-52% for less than 12-hour lead time and 14-46% for 12 to 48-hour lead time compared to official weather forecasts. We have successfully implemented the predictive component at the OCP Safi site, which is Morocco's largest chemical industrial plant, and are currently in the process of deploying the prescriptive component. Our framework enables sustainable industrial development by eliminating the pollution-industrial activity trade-off through data-driven weather-based operational decisions, significantly enhancing factory optimization and sustainability. This modernizes factory planning and resource allocation while maintaining environmental compliance. The predictive component has boosted production efficiency, leading to cost savings and reduced environmental impact by minimizing air pollution.