Animal trials helped to restore order when the unspeakable happened. In 1457, a sow and her piglets were put on trial for the murder of a child in the village of Savigny in Burgundy, France. The sow was ultimately found guilty and her piglets were acquitted. Breakthroughs, discoveries, and DIY tips sent six days a week. It's a common scene in many films set in medieval Europe: a wooden cart wheeling its way through a jeering crowd of townsfolk, taking a condemned prisoner to the gallows.

We present the results of cognitive tests on conceptual combinations, performed using specific Large Language Models (LLMs) as test subjects. In the first test, performed with ChatGPT and Gemini, we show that Bell's inequalities are significantly violated, which indicates the presence of 'quantum entanglement' in the tested concepts. In the second test, also performed using ChatGPT and Gemini, we instead identify the presence of 'Bose-Einstein statistics', rather than the intuitively expected 'Maxwell-Boltzmann statistics', in the distribution of the words contained in large-size texts. Interestingly, these findings mirror the results previously obtained in both cognitive tests with human participants and information retrieval tests on large corpora. Taken together, they point to the 'systematic emergence of quantum structures in conceptual-linguistic domains', regardless of whether the cognitive agent is human or artificial. Although LLMs are classified as neural networks for historical reasons, we believe that a more essential form of knowledge organization takes place in the distributive semantic structure of vector spaces built on top of the neural network. It is this meaning-bearing structure that lends itself to a phenomenon of evolutionary convergence between human cognition and language, slowly established through biological evolution, and LLM cognition and language, emerging much more rapidly as a result of self-learning and training. We analyze various aspects and examples that contain evidence supporting the above hypothesis. We also advance a unifying framework that explains the pervasive quantum organization of meaning that we identify.

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.

Chinese farmers recently began testing a new AI system that uses a combination of machine vision, voice recognition, and temperature sensors to keep track of pigs' location, health, and wellbeing. A new artificial intelligence (AI) project from tech conglomerate Alibaba could alleviate some of the myriad problems facing Chinese farmers in the pork industry. China is the world's largest producer and consumer of pork, and keeping track of the nation's estimated 700 million animals is notoriously difficult for farmers. They need to pay careful attention to ensure that piglets aren't crushed to death by their mothers, sows aren't bred past their prime, and sick pigs don't pass their illnesses on to the rest of the population. Currently, farmers track pigs by clipping wireless radio frequency identification (RFID) tags to the animals' ears. These can be expensive, and farmers don't always have time to fit each pig with a tag and scan them.

An artificial intelligence (AI) powered system has been developed that can reportedly clone pigs better than humans can. A new report by the South China Morning Post has revealed that a new AI-powered system created by researchers from the University of Nankai's College of Artificial Intelligence has already created seven piglets via surrogate. The new system created the piglets without any human intervention, and according to the researchers behind the project who spoke to the publication, the system may pave the way forward to commercialized cloning. Notably, most cloning processes consist of somatic cell nuclear transfer, which is a laboratory strategy that involves a scientist manually transferring cells and their nuclei, which can take hours. The manual process also involves a lot of injuries for the cells, which is AI-system dramatically reduces as it can "can calculate the strain within a cell and direct the robot to use minimal force to complete the cloning process," according to Liu Yaowei, who spoke to the publication.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In this Mixed Model example, I will use a somewhat more advanced dataset containing the bodyweight growth of piglets across different levels.

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don't. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language. Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast "what happens next" given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.

An AI has been studying the cookbooks and has taught itself how to make intriguing new pie recipes -- including Scotch egg pies and one with a salad filling. Working with a Sussex-based pie makers, the algorithm has produced thousands of recipes, five of which have been selected for production and will be going on sale. The AI works by looking for patterns in existing recipes and then trying to make its own based on what it learnt. While some of the early recipes it proposed were perhaps less-than-mouth-watering, with a little guidance it soon got the hang of cooking up new pie concepts. The experiment illustrates how artificial intelligence can provide new insights for small businesses and help dream up novel products to take to market.

Most livestock and field crops rely heavily on the weather for their comfort, and providing water and energy. But China's more than 1.3 billion residents, a growing number of whom are becoming mid-income earners, are building up such an appetite that farmers are having to change the way they grow and sell food. In order to transform an ancient business that was largely run using intuition, the modern answer is technology. Artificial intelligence has come to the farmyard, helping to ensure the country's increasing numbers of pigs remain active and crop yields grow ever larger. This is the case for Wang Degen and his company Tequ Group, a major hog farm in Southwest China's Sichuan province.

A new artificial intelligence (AI) venture from tech giant Alibaba could ease a portion of the various issues confronting Chinese farmers in the pork business. China is the world's biggest maker and consumer of pork, and monitoring the country's estimated 700 million creatures is famously troublesome for farmers. They have to give careful consideration to guarantee that piglets aren't squashed to death by their moms, sows aren't reared past their prime, and sick pigs don't pass their diseases on to the remaining populace. Right now, agriculturists track pigs by clipping wireless radio frequency identification (RFID) labels to the creatures' ears. These can be costly, and farmers don't generally have room schedule-wise to fit each pig with a tag and scan them.