The hive rulers produce a pheromone that helps keep workers loyal. What happens when it's gone? Breakthroughs, discoveries, and DIY tips sent every weekday. A once-powerful ruler is sick. The virus threatens the entire kingdom.

Tiny but mighty, honeybees play a crucial role in our ecosystems, pollinating various plants and crops. They also support the economy. These small producers contribute billions of dollars to Canada's agriculture industry, making Canada a major honey producer. However, in the winter of 2024, Canada's honey industry faced a severe collapse. Canada lost more than one-third of its beehives, primarily due to the widespread infestation of Varroa mites.

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers; (ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data.

The beekeeping sector has undergone considerable production variations over the past years due to adverse weather conditions, occurring more frequently as climate change progresses. These phenomena can be high-impact and cause the environment to be unfavorable to the bees' activity. We disentangle the honey production drivers with tree-based methods and predict honey production variations for hives in Italy, one of the largest honey producers in Europe. The database covers hundreds of beehive data from 2019-2022 gathered with advanced precision beekeeping techniques. We train and interpret the machine learning models making them prescriptive other than just predictive. Superior predictive performances of tree-based methods compared to standard linear techniques allow for better protection of bees' activity and assess potential losses for beekeepers for risk management.

Since I was a child in Brazil, I've been fascinated by bees, and I still dream, like a sort of modern Sherlock Holmes, of becoming a beekeeper when I retire. My retirement dream is to be a small-time beekeeper with a selected number of colonies of bees, rearing quality queens that can survive in our harsh environment while introducing the traits I would like to see in them on a pretty small scale. Over the last few years, I've been in contact with some passionate beekeepers, some that want to do it for a hobby, and some that want to do it for the experience for a long time; I've been exploring many hypotheses of applying technology, in particular, AI to enhance and protect beekeeping, helping them to survive our tough metropolitan environments. Fortunately, there has been a growing awareness about our pollinators and the risk they face from the pressures of human activity. In response, many researchers, farmers, and citizens are coming together to help protect these essential insects and their habitats.

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis re-veals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.

Buckfast honey bees fly near a beehive in Illinois, U.S. Photographer: Daniel Acker/Bloomberg Beewise, a agtech startup, has created the first fully autonomous beehive called Beehome that comes complete with a beekeeping robot that acts as both medic and guardian to complement the natural intelligence of bees. Beehome utilizes artificial intelligence, (AI) machine learning and precision robotics to rescue and protect the hives bees. The modular commercial AI-powered robotic apiary also has 24/7 monitoring and smart technology that increases pollination capacity and honey production. Saar Safra, CEO of Beewise says that Beehome is poised to protect the global food supply chain, stem the impacts of climate change and increase sustainability. "If the bees are protected, entire ecosystems are too."

To be perfectly clear: Insects aren't evil. They don't have morals or ethical guidelines. They certainly cannot commit murder. That said, there's a reason why the Asian giant hornet was nicknamed the "murder hornet" in the North American press and not, say, "gentle sweetie bee." These apex predators look like they flew in from the Carboniferous era.

Bega Cheese has launched a network of smart beehives that can automatically detect parasites in a bid to safeguard Australian honey production. The Purple Hive Project uses 3D printed components to house 360 degree cameras that feed into an artificial intelligence algorithm that is capable of identifying healthy bees from those carrying the deadly varroa destructor mite. The solar-powered devices immediately send an alert to beekeepers to quarantine the affected hive to contain the spread of the mite, which has devastated bee colonies on every other continent. An initiative from Bega Cheese's B Honey brand, the devices can be fixed to existing beehives for round the clock monitoring at high-risk entry points to Australia, saving beekeepers from having to perform manual inspections. Unchecked infestations of varroa mites can cripple and even kill off entire hives within three to fours years, industry group BeeAware said.

When honeybees are ready to establish a new colony, they initiate a coordinated procedure called swarming. For beekeepers, swarming provides an opportunity to capture the departing bees and establish a new hive. To forecast a swarm, beekeepers regularly inspect their hives for the presence of larger honeycomb cells that host developing future queens. But those regular inspections are laborious. Now Martin Bencsik of Nottingham Trent University in the UK and his colleagues are automating the process by using machine learning.