Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data

The integration of artificial intelligence into everyday devices, such as watches, phones, and home assistants, is becoming increasingly prevalent, prompting concerns about overconsumption driven by these technologies. This trend highlights how AI is reshaping consumer behavior and expectations in daily life.

A new automated machine learning framework has been developed for high-throughput imaging and profiling of the morphology of 316L powder particles, crucial for Selective Laser Melting (SLM) in additive manufacturing. This method addresses the limitations of traditional powder characterization techniques, which are often low-throughput and qualitative, by effectively capturing the heterogeneity of industrial-scale batches.

A new machine learning-driven system has been developed to estimate black carbon (BC) concentrations from urban traffic, addressing the lack of local data on BC emissions that disproportionately affect marginalized communities. The model utilizes visual information from traffic videos combined with weather data, achieving a notable R-squared value of 0.72 and RMSE of 129.42 ng/m3.

Tyche introduces a novel approach to medical image segmentation by utilizing stochastic in-context learning, allowing for predictions on new tasks without retraining. This model addresses the limitations of existing methods that require extensive resources and expertise for each new segmentation task, and it acknowledges the inherent uncertainty in segmentation outcomes by generating multiple predictions.

A recent study has shown that simple AI agents can outperform human experts in optimizing biomedical imaging workflows. This research highlights the challenges faced by scientists in adapting complex computer vision tools to specific datasets, often requiring extensive manual coding and large annotated datasets that are not always available. The introduction of a systematic evaluation framework for agentic code optimization has proven beneficial in this context.

Recent research has focused on the scalability of GPU-accelerated multi-hypothesis tracking, particularly through the Generalized Labeled Multi-Bernoulli (GLMB) filter, which allows for multiple detections per object. This method addresses the computational challenges associated with maintaining multiple hypotheses in multi-target tracking systems, especially in distributed networks of machine learning-based virtual sensors.

Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, are significantly enhancing big data analytics and management. This development focuses on large language models (LLMs) like ChatGPT, Claude, and Gemini, which are transforming industries through improved natural language processing and autonomous decision-making capabilities.

The rise of Multimodal Large Language Models (MLLMs) marks a significant advancement in artificial intelligence, enabling machines to process and generate content across various modalities, including text, images, audio, and video. This meta-review surveys current benchmarks and evaluation methods for MLLMs, addressing foundational concepts, applications, and ethical concerns.