A new program trained on iPhone photos could curb food waste. Avocados have a carbon footprint that is three times higher than bananas. Breakthroughs, discoveries, and DIY tips sent every weekday. The days of buying a rock-tough avocado in the hopes of avoiding mushy food waste may soon be over. Machine learning researchers at Oregon State University (OSU) recently designed an artificial intelligence program that visually assesses avocado quality and ripeness .

With the growing deployment of Vision-Language Models (VLMs) in real-world applications, previously overlooked safety risks are becoming increasingly evident. In particular, seemingly innocuous multimodal inputs can combine to reveal harmful intent, leading to unsafe model outputs. While multimodal safety has received increasing attention, existing approaches often fail to address such latent risks, especially when harmfulness arises only from the interaction between modalities. We propose SIA (Safety via Intent Awareness), a training-free, intent-aware safety framework that proactively detects harmful intent in multimodal inputs and uses it to guide the generation of safe responses. SIA follows a three-stage process: (1) visual abstraction via captioning; (2) intent inference through few-shot chain-of-thought (CoT) prompting; and (3) intent-conditioned response generation. By dynamically adapting to the implicit intent inferred from an image-text pair, SIA mitigates harmful outputs without extensive retraining. Extensive experiments on safety benchmarks, including SIUO, MM-SafetyBench, and HoliSafe, show that SIA consistently improves safety and outperforms prior training-free methods.

This paper proposes a novel method for analyzing food retail processes with a focus on reducing food waste. The approach integrates object-centric process mining (OCPM) with stochastic process discovery and analysis. First, a stochastic process in the form of a continuous-time Markov chain is discovered from grocery store sales data. This model is then extended with supply activities. Finally, a what-if analysis is conducted to evaluate how the quantity of products in the store evolves over time. This enables the identification of an optimal balance between customer purchasing behavior and supply strategies, helping to prevent both food waste due to oversupply and product shortages.

The field of Artificial Intelligence (AI) is constantly evolving and growing more sophisticated. This presents both challenges and opportunities for the Manufacturing Industry. As, AI technology advances, it is becoming more and more capable of Automating tasks that have traditionally been done by Human workers. This can result in increased efficiency and productivity for manufacturers, but it can also lead to job loss as machines replace human workers. AI is also being used to create more personalised and customised products.

Even in inflationary times, commissaries, supermarkets and other places where food is made or sold are increasingly turning to Artificial Intelligence and Machine Learning technologies to help them streamline operations, improve the customer experience, reduce waste and give their bottom lines a boost. San Ramon, Calif.-based AI and ML specialist Impulse Logic delivers advanced predictive analytics to create the optimal product flow through a retailer's store to optimize labor availability, ensure product availability, reduce waste, and increase profits, said Matt Frost, the company's CEO. "By optimizing the journey from warehouse to shop floor, stores can improve the way they manage their inventory to drive sales," Frost said. "Our innovative AI and ML-based solution does this by reading in-store data every two seconds." That ensures that store associates can make decisions based on accurate insights and ultimately deliver outstanding customer service and satisfaction.

AI in food production with predictive analytics will monetize the food industry in novel ways Like many other industries, artificial intelligence (AI) is having a major impact on the food and beverage industry. Companies in the industry are becoming more aware of how AI can increase efficiency and profits, reduce waste, and protect against supply chain disruptions. These are all part of what is known as Industry 4.0. With AI, businesses can traditionally predict what their customers want through research and adapting to new trend. By mining vast amounts of data about sales patterns and taste preferences by demographic, manufacturers can now predict future trends and design new products that capitalize on them more quickly.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Researchers in Texas have created an enzyme variant that can break down plastics that would typically take hundreds of years to dissolve in a matter of hours or days. The creation by officials at The University of Texas at Austin could solve the problem of how to rid the world of billions of tons of plastic piling up in landfills and polluting natural lands and water. "The possibilities are endless across industries to leverage this leading-edge recycling process," Hal Alper, professor in the McKetta Department of Chemical Engineering at UT Austin said in a statement.

Artificial intelligence (AI) technology has quickly become an invaluable tool in heavy industry, and startups are constantly developing new AI tools to help businesses overcome existing challenges. Facility waste management, for example, is increasingly important as manufacturers look to boost efficiency, improve productivity and make their manufacturing processes more sustainable. Identifying sources of waste and potential solutions can be difficult, however. New AI and machine learning (ML) solutions could help manufacturers optimize their facility waste management. For every facility that uses raw materials or components to create a product, waste will be a serious issue.

Fukuoka – The city of Fukuoka, jointly with the Japan Weather Association, is conducting an experiment to reduce food waste using artificial intelligence. In the experiment, AI is used to predict sales of products in line with weather conditions, allowing stores to adjust their order and production volumes. Participating stores were able to reduce waste and boost sales in the fiscal year that ended in March. The experiment uses the JWA's weather-based demand prediction service, which analyzes mainly weather conditions, temperatures, social media posts and past retail sales data to predict demand for more than 660 products, including fresh food and prepared food, in seven stages. In the experiment last fiscal year, six of the eight participating companies in the city saw their food waste decline, while seven logged increased sales.

Fero Labs, the only Explainable Machine Learning software solely dedicated to the industrial sector, today announced the closing of a $9 million Series A round led by Innovation Endeavors, with participation from Deutsche Invest VC. This funding will support Fero Labs in expanding its product offerings to new sectors and ultimately push the industrial manufacturing community forward. The industrial sector has just begun to implement technologies into its processes to reduce waste and increase efficiency and profits. More than half of leaders in the manufacturing and utilities sector expect artificial intelligence to control high-value assets such as industrial plants, equipment, machines and its processes in the next five years, according to Next-Gen Industrial AI - and due to the pandemic, the industry observed a steady increase in artificial intelligence and machine learning adoption across industries including energy, manufacturing, heavy industry, infrastructure, and transportation sectors. "At Fero Labs, we develop our technology around the needs of customers, delivering the best of machine learning, AI technologies and scalable automated infrastructure," said Berk Birand, CEO of Fero Labs.