We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visualization questions to compare inference accuracies between the agen-tic GPT -5 and multimodal GPT -4V, for difficult image instances, where GPT -4V failed to produce correct answers. Our results show that model architecture dominates the inference accuracy: GPT - 5 largely improved accuracy, while prompt variants yielded only small effects. Pre-registration of this work is available here; the Google Drive materials are here. Benchmarking visual literacy, i.e., "the ability and skill to read and interpret visually represented data and to extract information from data visualizations" [1] shapes progress in measuring AI's ability in handling visualization images. Often, the same tasks as designed to assess visual literacy questions traditionally performed by human observers are now being assigned to algorithms. Following this trend, our goal in this paper is to quantify the new GPT -5's ability to read charts. Specifically, we used questions where GPT -4V failed and other LLMs achieved only low accuracy, as reported in V erma et al.'s CHART -6 benchmark [2].

The number of collisions between aircraft and birds in the airspace has been increasing at an alarming rate over the past decade due to increasing bird population, air traffic and usage of quieter aircraft. Bird strikes with aircraft are anticipated to increase dramatically when emerging Advanced Air Mobility aircraft start operating in the low altitude airspace where probability of bird strikes is the highest. Not only do such bird strikes can result in human and bird fatalities, but they also cost the aviation industry millions of dollars in damages to aircraft annually. To better understand the causes and effects of bird strikes, research to date has mainly focused on analyzing factors which increase the probability of bird strikes, identifying high risk birds in different locations, predicting the future number of bird strike incidents, and estimating cost of bird strike damages. However, research on bird movement prediction for use in flight planning algorithms to minimize the probability of bird strikes is very limited. To address this gap in research, we implement four different types of Long Short-Term Memory (LSTM) models to predict bird movement latitudes and longitudes. A publicly available data set on the movement of pigeons is utilized to train the models and evaluate their performances. Using the bird flight track predictions, aircraft departures from Cleveland Hopkins airport are simulated to be delayed by varying amounts to avoid potential bird strikes with aircraft during takeoff. Results demonstrate that the LSTM models can predict bird movement with high accuracy, achieving a Mean Absolute Error of less than 100 meters, outperforming linear and nonlinear regression models. Our findings indicate that incorporating bird movement prediction into flight planning can be highly beneficial.

If the denizens of the tech world are grumpy about spending the energy reserves they built up over the holidays to survive CES, they should try working in the auto industry. For the folks creating and covering the future of cars, the Vegas show is merely the nose-bloodying jab that sets up the jaw-clacking undercut: the North American International Auto Show in Detroit. Both shows, however, were unusually quiet this year, at least compared to the overhyped displays of the recent past. Because while major changes are on the horizon, other problems are already within firing range. The auto industry is hunkering down amid predictions of slowing sales: GM and Ford are reworking their lineups to suit American tastes.

Most airports are working to keep drones away, but a new AI-powered breed could help to improve safety by steering birds clear of airspace. While rare, birds can pose a serious problem to aircraft – from broken windshields to engine failures. That's not to say bird strikes themselves are uncommon. Between 1990-2015, there were 160,894 bird strikes on US aircraft. Each year, bird strikes cost US airlines an estimated $1.2 billion.

Police are investigating a reported mid-air collision between a drone and a British Airways jet from Geneva that was approaching London's Heathrow Airport. BA said the Airbus A320 was not damaged when the object hit the nose of the plane, which landed safely with no injuries reported to anyone on board. Since April last year there have been 25 near misses between aircraft and drones, figures from the UK Airprox Board suggest. A dozen of these were denoted "Class A" which indicates there was a serious risk of collision. The Heathrow incident comes only weeks after the British Airline Pilots Association called for rules governing the use of drones to be enforced more strictly.