Decoding strategies for generative large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Guided by specific hyperparameters, these strategies aim to transform the raw probability distributions produced by language models into coherent, fluent text. In this study, we undertake a large-scale empirical assessment of a range of decoding methods, open-source LLMs, textual domains, and evaluation protocols to determine how hyperparameter choices shape the outputs. Our experiments include both factual (e.g., news) and creative (e.g., fiction) domains, and incorporate a broad suite of automatic evaluation metrics alongside human judgments. Through extensive sensitivity analyses, we distill practical recommendations for selecting and tuning hyperparameters, noting that optimal configurations vary across models and tasks. By synthesizing these insights, this study provides actionable guidance for refining decoding strategies, enabling researchers and practitioners to achieve higher-quality, more reliable, and context-appropriate text generation outcomes.

In its 4 year existence the Israeli start-up Taranis has seen huge growth. Taranis started as a tool to provide farmers with the information to detect and prevent crop disease, weeds and insect damage based on weather forecasts gathered from aerial surveillance. The technology was then further developed by adding visual layers from satellites, planes and drones and leveraged with AI capabilities. Taranis also created a one-of-a-kind, patented hardware that can capture accurate images at a high resolution from a plane flying at 160 km/h, such as a specific insect on a leaf from 200 feet above ground. Taranis CEO Ofir Schlam says the future of the precision farming industry is looking bright, with thousands of start-ups emerging within the last 10 years. According to him, smart farming is projected to create a massive impact on the agricultural economy in the near future and will be dependent on precision technologies, such as the adoption of automated practices and indoor urbanised farming.

People without any knowledge can use artificial intelligence (AI) to produce more profitable cucumbers than the most experienced growers. This was shown in an experiment that Wageningen University & Research (WUR) in the Netherlands conducted last year in a greenhouse complex at their experimental farm in Bleiswijk. WUR researcher Isabella Righini cited this research at the Global Future Farming Summit, which was held in Wageningen recently. Experts there focused on precision agriculture and the robotisation of food production. Righini: "Producing food requires a lot from the grower. He must deal with the supply of raw materials, such as minerals for his crops. He must take care of the crops. And he has to market his crops. That is a complex operation. In the Autonomous Greenhouse project we are testing to what extent artificial intelligence can help growers grow crops."

Valley Insights is the result of the partnership between Valley Irrigation and Prospera, which was announced earlier this year. According to Valley Irrigation, the Valley Insights limited release "has demonstrated successful results this growing season, turning data into useful, actionable information for growers in Washington and Nebraska. The service has exceeded expectations by already reaching its targeted goal of one million acres by 2020." We are gaining traction with the initial launch of Valley Insights. The partnership between Valley and Prospera has exceeded expectations by already reaching its targeted goal of one million acres by 2020.

A level crossing is an important but often dangerous component of rail infrastructure. To rectify the situation, Slovakian IT company GrowthPro has developed Erriate, an AI-based solution that is able to prevent the accidents at level crossings. A lot of rail infrastructure managers spend huge amounts to make level crossings safer for both car and train drivers, pedestrians and train passengers. Usually, they install barriers, signalling equipment or even video cameras. The digital solutions could be a good supplement to these moves and improve safety at the level crossings.