We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.

We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.

Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. The large dataset is labelled with a Cascading Random Forest Model using a na\"ive pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. The pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning; a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility.

DALL-E 2, Stable Diffusion, and others transformed the image generation space. We saw more powerful language models, PaLM, and of course ChatGPT. We saw foundation models being developed for speech, music, proteins, and many other data modalities. And, for the first time, these models are now being widely deployed and utilized by consumers to accomplish a wide breadth of useful tasks. What is clear is that while foundation models have opened up unprecedented new possibilities, they are also still raw, imperfect research artifacts that we do not entirely understand. In 2021, we founded the Center for Research on Foundation Models (CRFM), recognizing the critical role of foundation models. CRFM's mission is to understand and improve foundation models from both a technical and societal perspective.

DALL-E 2, Stable Diffusion, and others transformed the image generation space. We saw more powerful language models, PaLM, and of course ChatGPT. We saw foundation models being developed for speech, music, proteins, and many other data modalities. And, for the first time, these models are now being widely deployed and utilized by consumers to accomplish a wide breadth of useful tasks. What is clear is that while foundation models have opened up unprecedented new possibilities, they are also still raw, imperfect research artifacts that we do not entirely understand. In 2021, we founded the Center for Research on Foundation Models (CRFM), recognizing the critical role of foundation models.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Like Rodin's The Thinker, there was plenty of thinking and pondering about the large language model (LLM) landscape last week. There were Meta's missteps over its Galactica LLM public demo and Stanford CRFM's debut of its HELM benchmark, which followed weeks of tantalizing rumors about the possible release of OpenAI's GPT-4 sometime over the next few months. That's when Meta AI and Papers With Code announced a new open-source LLM called Galactica, that it described in a paper published on Arxiv as "a large language model for science" meant to help scientists with "information overload." The "explosive growth in scientific literature and data," the paper's authors wrote, "has made it ever harder to discover useful insights in a large mass of information."

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. In the world of artificial intelligence (AI) and machine learning (ML), 2022 has arguably been the year of foundation models, or AI models trained on a massive scale. From GPT-3 to DALL-E, from BLOOM to Imagen -- another day, it seems, another large language model (LLM) or text-to-image model. But until now, there have been no AI benchmarks to provide a standardized way to evaluate these models, which have developed at a rapidly-accelerated pace over the past couple of years. Don't miss our new special issue: Zero trust: The new security paradigm.

The pace has only accelerated this year and moved firmly into the mainstream, thanks to the jaw-dropping text-to-image possibilities of DALL-E 2, Google's Imagen and Midjourney, as well as the options for computer vision applications from Microsoft's Florence and the multimodal options from Deep Mind's Gato. That turbocharged speed of development, as well as the ethical concerns around model bias that accompany it, is why one year ago, the Stanford Institute for Human-Centered AI founded the Center for Research on Foundation Models (CRFM) and published "On the Opportunities and Risks of Foundation Models" -- a report that put a name to this powerful transformation. "We coined the term'foundation models' because we felt there needed to be a name to cover the importance of this set of technologies," said Percy Liang, associate professor in computer science at Stanford University and director of the CRFM.