Almost every week brings a whole new LLM application, each with its own specific output speed, cost, and quality needs. Additionally, the models that offer the best performance for the job need to be made apparent. Because of this, there are a lot of manual signups, model tests, custom benchmarks, etc. The problem is difficult… →

Google AI Researchers introduced Human I/O to address the issue of situationally induced impairments and disabilities (SIIDs). SIIDs are temporary challenges that hinder our ability to interact with technology due to environmental factors such as noise, lighting, and social norms. These impairments can significantly affect our ability to use our hands, vision, hearing, or speech… →

Recent advancements in ML are revolutionizing how we evaluate treatments by predicting the causal impact of treatments on patient outcomes, known as causal ML. This approach leverages data from randomized controlled trials (RCTs) and real-world data sources like clinical registries and electronic health records to estimate the effects of treatments. A major advantage of causal… →

Generative vision-language models (VLMs) have revolutionized radiology by automating the interpretation of medical images and generating detailed reports. These advancements hold promise for reducing radiologists’ workloads and enhancing diagnostic accuracy. However, VLMs are prone to generating hallucinated content—nonsensical or incorrect text—which can lead to clinical errors and increased workloads for healthcare professionals. The core issue… →

Computer vision focuses on enabling devices to interpret & understand visual information from the world. This involves various tasks such as image recognition, object detection, and visual search, where the goal is to develop models that can process and analyze visual data effectively. These models are trained on large datasets, often containing noisy labels and… →

A major challenge in diffusion models, especially those used for image generation, is the occurrence of hallucinations. These are instances where the models produce samples entirely outside the support of the training data, leading to unrealistic and non-representative artifacts. This issue is critical because diffusion models are widely employed in tasks such as video generation,… →