Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748 samples for evaluating the co-temporal comprehension and reasoning abilities of LLMs. Our extensive experiments reveal a significant gap between the performance of current LLMs and human-level reasoning on CoTempQA tasks. Even when enhanced with Chain of Thought (CoT) methodologies, models consistently struggle with our task. In our preliminary exploration, we discovered that mathematical reasoning plays a significant role in handling co-temporal events and proposed a strategy to boost LLMs' co-temporal reasoning from a mathematical perspective. We hope that our CoTempQA datasets will encourage further advancements in improving the co-temporal reasoning capabilities of LLMs. Our code is available at https://github.com/zhaochen0110/Cotempqa.

On June 6, 2015, the U.S. Department of Justice brought the first-ever online market-place prosecution against a price-fixing cartel. One of the special features of the case was that prices were set by algorithms. Topkins and his competitors designed and shared dynamic pricing algorithms that were programmed to act in conformity with their agreement to set coordinated prices for posters sold online. They were found to engage in an illegal cartel. Following the case, the Assistant Attorney General stated that "[w]e will not tolerate anticompetitive conduct, [even if] it occurs...over the Internet using complex pricing algorithms."

Artificial Intelligence is already changing society. Algorithms and machine learning are trading millions of euros in financial markets; they are predicting what people want to search for online and what shows they might like to watch on Netflix; AI is already helping police identify criminals using facial recognition (albeit with mixed results), and sifting through climate change data. Soon, AI could be driving our cars and trains (even our ships and planes). How will these new technologies transform our workplaces, our homes, our cities, and our lives? Inevitably, there will be disruption.

The European Union is looking to connected vehicles and autonomous driving to reduce traffic fatalities, after a disappointing year for road safety. Last year, 26,000 died on European roads, up 1 percent on the previous year. "The latest figures are disappointing. For the second year in a row, we have not managed to reduce the number of victims on our roads," said European Commissioner for Transport Violeta Bulc, presenting the EU's latest study of traffic accident statistics in Brussels on Thursday. Disappointing though the rise is, EU roads are still among the safest in the world, with traffic fatalities down 17 percent since 2010, after a reduction of 43 percent in the previous decade.