Lifeboat Foundation

AI is becoming an increasingly powerful technology that can benefit humanity and sustainable development, but empowering local AI ecosystems across all countries needs to be prioritized to build the tools and coalitions necessary to ensure robust safety measures. Here, we make a case for reimagining trust and safety, a critical building block for closing the AI equity gap.

“Trust and safety” is not a term that developing countries are always familiar with, yet people are often impacted by it in their everyday interactions. Traditionally, trust and safety refers to the rules and policies that private sector companies put in place to manage and mitigate risks that affect the use of their digital products and services, as well as the operational enforcement systems that determine reactive or proactive restrictions. The decisions that inform these trust and safety practices carry implications for what users can access online, as well as what they can say or do.

Advances in cognitive analytics are set to usher in a new age of AI that will see systems work autonomously to complete assigned tasks.

Generative models, artificial neural networks that can generate images or texts, have become increasingly advanced in recent years. These models can also be advantageous for creating annotated images to train algorithms for computer vision, which are designed to classify images or objects contained within them.

While many generative models, particularly generative adversarial networks (GANs), can produce synthetic images that resemble those captured by cameras, reliably controlling the content of the images they produce has proved challenging. In many cases, the images generated by GANs do not meet the exact requirements of users, which limits their use for various applications.

Researchers at Seoul National University of Science and Technology recently introduced a new image generation framework designed to incorporate the content users would like generated images to contain. This framework, introduced in a paper published on the arXiv preprint server, allows users to exert greater control over the image generation process, producing images that are more aligned with the ones they were envisioning.

Learn how AlphaProteo is transforming healthcare, drug development, and environmental sustainability with AI-designed custom proteins.

AWS’ megacluster of chips for AI startup Anthropic will be among the world’s largest, it said, and its new giant server will lower the cost of AI as it seeks to build an alternative to Nvidia.

In a recent study published in the journal Cell Reports, researchers used the machine learning (ML)-based Variational Animal Motion Embedding (VAME) segmentation platform to analyze behavior in Alzheimer’s disease (AD) mouse models and tested the effect of blocking fibrinogen-microglia interactions. They found that AD models showed age-dependent behavioral disruptions, including increased randomness and disrupted habituation, largely prevented by reducing neuroinflammation, with VAME outperforming traditional methods in sensitivity and specificity.

Background

Behavioral alterations, central to neurological disorders, are complex and challenging to measure accurately. Traditional task-based tests provide limited insight into disease-induced changes. However, advances in computer vision and ML tools, such as DeepLabCut, SLEAP, and VAME, now enable the segmentation of spontaneous mouse behavior into postural units (motifs) to uncover sequence and hierarchical structure, offering scalable, unbiased measures of brain dysfunction.

Nvidia CEO Jensen Huang has a plan to bring AI infrastructure to countries around the world, and he’s pitching it in person.

Large language models such as OpenAI’s o1 have electrified the debate over achieving artificial general intelligence, or AGI. But they are unlikely to reach this milestone on their own.

I had wondered if AI could just learn and advance from it s users.

During your first driving class, the instructor probably sat next to you, offering immediate advice on every turn, stop and minor adjustment. If it was a parent, they might have even grabbed the wheel a few times and shouted “Brake!” Over time, those corrections and insights developed experience and intuition, turning you into an independent, capable driver.

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