Deep Science AI: Introspective & Disaster-Chasing Systems

The Rapid Pace of Machine Learning Research

The sheer volume of research papers published today makes it impossible for any single person to stay current with all developments. This is particularly true within the rapidly evolving field of machine learning, which now impacts nearly every sector and generates a constant stream of new publications. This article highlights some of the most significant recent discoveries and papers, focusing on artificial intelligence and related areas, and explains their potential impact.

Self-Aware AI and Autonomous Vehicles

Developing an AI capable of recognizing and learning from its own errors is a crucial step towards more reliable autonomous systems. Researchers at the Technical University of Munich are working on a system for self-driving cars that focuses on “introspective failure prediction.”

The proposed system analyzes past instances where human intervention was required, allowing the car to identify its own limitations. For example, when encountering heavy traffic, the vehicle’s AI can quickly determine, by comparing the current situation to previous experiences, whether it can safely proceed or if disengagement is necessary.

This rapid assessment – potentially saving six or seven seconds – can be critical for a safe handover of control.

Autonomous Interaction for Military Applications

The ability for robots and autonomous vehicles to operate independently, without constant communication with a central command, is vital, especially in scenarios demanding swift and decisive action. The Army Research Lab is investigating methods for enabling autonomous interaction between ground and air vehicles.

One approach involves a ground vehicle equipped with a large QR code landing pad. A drone can autonomously locate and land on this pad without requiring coordination, permission, or precise GPS signals. Future iterations could utilize shape recognition to identify suitable landing zones, relying on logical deduction to confirm the vehicle’s identity.

AI-Powered Cell Tracking in Medical Imaging

Artificial intelligence is increasingly being applied to automate tedious tasks in medical research. Tracking the movement of individual cells in microscopy images is one such example. While not an insurmountable challenge for humans, it is a time-consuming process that researchers would prefer to avoid.

Software developed by researchers at Nagoya City University in Japan automates this process using image analysis and advanced object tracking capabilities. This allows for the automatic tracking of cell movements across multiple depths of a petri dish.

Early Detection of Skin Cancer

Similar to cell tracking, identifying potentially dangerous skin pigmentation is a task well-suited for machine learning. MIT researchers have created a model that can classify skin pigmentation, highlighting the riskiest areas for further examination.

Trained on over 20,000 images from 133 patients, the model demonstrated a high degree of accuracy in a lab setting, correctly identifying 90% of “suspicious pigmented lesions” detected by experts. While challenges remain in capturing images of hard-to-reach areas, such as the back, this technology holds promise for early cancer detection.

Unconventional Data Sources: Cows and Hot Pixels

Researchers are exploring diverse data sources for machine learning applications. In one unusual case, researchers manually highlighted 31,000 cows in satellite imagery to track their movements, demonstrating the potential for AI-powered animal tracking.

Samsung is also tackling a different image-related problem: identifying “hot pixels” in camera sensors. These bright pixels can degrade image quality, and Samsung’s model aims to eliminate them during the image processing pipeline. While the model achieves high accuracy, reducing false positives remains a challenge.

Predicting Debris Flows with Machine Learning

Early warning systems are crucial for mitigating the risks associated with natural disasters. Researchers at ETH Zurich have developed a machine learning model to predict debris flows – a particularly dangerous type of mudslide.

By analyzing seismic data, the model can distinguish between ordinary seismic activity and the characteristic vibrations of a debris flow, providing over 20 minutes of additional warning time. The team is now investigating whether this model can be adapted for use in other high-risk areas.

Social Media Analysis for Disaster Response

Following a disaster, communication networks are often overwhelmed, and social media becomes a primary source of information. However, sifting through the flood of messages to identify critical information can be challenging.

Researchers at Virginia Tech are developing a machine learning model to quickly assess social media content during a disaster, prioritizing messages related to people and places in need of assistance. Simultaneously, Tohoku University is exploring the use of news footage to assess building damage, with potential applications for analyzing images from platforms like Instagram and TikTok.