Computer vision, a field focusing on enabling devices to interpret and understand visual information from the world, faces a significant challenge: aligning vision models with human aesthetic preferences. Even modern vision models trained on large datasets sometimes fail to produce visually appealing results that align with user expectations for aesthetics, style, and cultural context. In…
The field of radiology has seen a transformative impact with the advent of generative vision-language models (VLMs), automating medical image interpretation and report generation. This innovative tech has shown potential in reducing radiologists’ workload and improving diagnostic accuracy. However, a challenge to this technology is its propensity to produce hallucinated content — text that is…
Machine learning (ML) is transforming the healthcare industry by enhancing the evaluation of treatments through the prediction of treatment impacts on patient outcomes. This methodology, known as causal ML, uses data from various sources including randomized controlled trials, clinical registries, and electronic health records to measure treatment effects. By providing personalized outcome predictions under different…
Language model applications (LLMs) are released frequently, each promising its own specific speed, cost, and quality advantages. But selecting the best model can be challenging due to the constant influx of new options, manual signups and custom benchmarks, and for some, the output quality and speed are less than satisfactory. In response to these constraints,…
