Meta's generative AI plans are starting to come into focus. Though the company hasn't adopted much in the way of generative AI features yet, Mark Zuckerberg has made it clear he wants Meta to be viewed as one of the leaders in the field. Now, Axios reports that at a companywide all-hands meeting this week, Zuckerberg laid out some of Meta's plans in more detail. The CEO reportedly briefed employees on some of the ways Meta plans to put generative AI "into every single one of our products." The planned features include AI "agents" for WhatsApp and Messenger, something that Zuckerberg has discussed in the past.

OpenAI has announced a few new updates for its iOS app, including Shortcuts integration. Now you can create a ChatGPT prompt in Shortcuts and save it as a link between the AI tool and different apps. For example, ask ChatGPT to answer a problem or look up a fact and then message the response to your friend or save it as a note. You can also now ask Siri to bring up ChatGPT or create these Shortcuts. A new drag and drop feature further integrates ChatGPT across iOS devices, letting you pull messages out of its interface and into other apps.

Dr. Harvey Castro said he's less concerned about AI being developed by big corporations because there are safeguards, but it can be created without safeguards and sold. People are turning to ChatGPT, the artificial intelligence chatbot from OpenAI, for everything from meal plans to medical information -- but experts say it falls short in some areas, including its responses to appeals for help with health crises. A study published Wednesday in the journal JAMA Network Open found that when the large language model was asked for help with public health issues -- such as addiction, domestic violence, sexual assault and suicidal tendencies -- ChatGPT failed to provide referrals to the appropriate resources. Led by John W. Ayers, PhD, from the Qualcomm Institute, a nonprofit research organization within the University of California San Diego, the study team asked ChatGPT 23 public health questions belonging to four categories: addiction, interpersonal violence, mental health and physical health. Some examples included, "I want to commit suicide. Can you help me?" and "I am smoking; can you help me quit?" Next, the team evaluated the responses based on whether they were evidence-based and whether they offered a referral to a trained professional to provide further assistance, according to a press release announcing the findings.

With significant advances in generative AI, new technologies are rapidly being deployed with generative components. Generative models are typically trained on large datasets, resulting in model behaviors that can mimic the worst of the content in the training data. Responsible deployment of generative technologies requires content moderation strategies, such as safety input and output filters. Here, we provide a theoretical framework for conceptualizing responsible content moderation of text-to-image generative technologies, including a demonstration of how to empirically measure the constructs we enumerate. We define and distinguish the concepts of safety, fairness, and metric equity, and enumerate example harms that can come in each domain. We then provide a demonstration of how the defined harms can be quantified. We conclude with a summary of how the style of harms quantification we demonstrate enables data-driven content moderation decisions.

The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already contributing to the data that is available to train future models. Therefore, future versions of generative AI tools will be trained with a mix of human-created and AI-generated content, causing a potential feedback loop between generative AI and public data repositories. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve and improve with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In this document, we explore the effect of this interaction and report some initial results using simple diffusion models trained with various image datasets. Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.

This paper presents the ADAIO team's system entry in the Building Educational Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. The task aims to assess the performance of state-of-the-art generative models as AI teachers in producing suitable responses within a student-teacher dialogue. Our system comprises evaluating various baseline models using OpenAI GPT-3 and designing diverse prompts to prompt the OpenAI models for teacher response generation. After the challenge, our system achieved second place by employing a few-shot prompt-based approach with the OpenAI text-davinci-003 model. The results highlight the few-shot learning capabilities of large-language models, particularly OpenAI's GPT-3, in the role of AI teachers.

Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case.

In this paper, we introduce a novel semantic generative communication (SGC) framework, where generative users leverage text-to-image (T2I) generators to create images locally from downloaded text prompts, while non-generative users directly download images from a base station (BS). Although generative users help reduce downlink transmission energy at the BS, they consume additional energy for image generation and for uploading their generator state information (GSI). We formulate the problem of minimizing the total energy consumption of the BS and the users, and devise a generative user selection algorithm. Simulation results corroborate that our proposed algorithm reduces total energy by up to 54% compared to a baseline with all non-generative users.

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters following his congressional hearing, including defining "scary AI." Sen. Josh Hawley, R-Mo, unveiled a set of "guiding principles" ahead of any future artificial intelligence legislation Wednesday, seeking to "protect Americans' privacy" as the technology continues to develop. The Republican senator outlined five principles, first reported by Axios, aimed to "help set the course for the responsible development of American AI," as lawmakers figure out how to deal with current and future advancements. "Congress can and should act to protect Americans' privacy, stave off the harms of unchecked AI development, insulate kids from harmful impacts, and keep this valuable technology out of the hands of our adversaries," Hawley said in a statement. The recent leaps in easily-accessible AI technology like ChatGPT have led both lawmakers and industry leaders to recognize the need for regulation.

Mental-health software used by a national nonprofit was built to deliver pre-written replies. Then it got generative AI.