Karthik Ramakrishnan

Amazon AGI, Aaron Langford, Aayush Shah et al. · amazon-science

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text. Amazon Nova Micro is a text-only model that delivers our lowest-latency responses at very low cost. Amazon Nova Canvas is an image generation model that creates professional grade images with rich customization controls. Amazon Nova Reel is a video generation model offering high-quality outputs, customization, and motion control. Our models were built responsibly and with a commitment to customer trust, security, and reliability. We report benchmarking results for core capabilities, agentic performance, long context, functional adaptation, runtime performance, and human evaluation.

Sam Chauhan, Estelle Duguet, Karthik Ramakrishnan et al.

Post hoc explanation methods, such as LIME and SHAP, provide interpretable insights into black-box classifiers and are increasingly used to assess model biases and generalizability. However, these methods are vulnerable to adversarial manipulation, potentially concealing harmful biases. Building on the work of Slack et al. (2020), we investigate the susceptibility of LIME and SHAP to biased models and evaluate strategies for improving robustness. We first replicate the original COMPAS experiment to validate prior findings and establish a baseline. We then introduce a modular testing framework enabling systematic evaluation of augmented and ensemble explanation approaches across classifiers of varying performance. Using this framework, we assess multiple LIME/SHAP ensemble configurations on out-of-distribution models, comparing their resistance to bias concealment against the original methods. Our results identify configurations that substantially improve bias detection, highlighting their potential for enhancing transparency in the deployment of high-stakes machine learning systems.

Karthik Ramakrishnan, Gokul P, Preet Batavia et al.

Climate change, which is now considered one of the biggest threats to humanity, is also the reason behind various other environmental concerns. Continued negligence might lead us to an irreparably damaged environment. After the partial failure of the Paris Agreement, it is quite evident that we as individuals need to come together to bring about a change on a large scale to have a significant impact. This paper discusses our approach towards obtaining a realistic measure of the carbon footprint index being consumed by a user through day-to-day activities performed via a smart phone app and offering incentives in weekly and monthly leader board rankings along with a reward system. The app helps ease out decision makings on tasks like travel, shopping, electricity consumption, and gain a different and rather numerical perspective over the daily choices.