The company behind Oreo cookies has, by its own admission, been quietly creating new flavors using machine learning.

As the Wall Street Journal reports, Mondelez — the processed food behemoth that manufactures Oreos, Chips Ahoy, Clif Bars, and other popular snacks — has developed a new AI tool to dream up new flavors for its brands.

Used in more than 70 of the company’s products, the company says the machine learning tool is different from generative AI tools like ChatGPT and more akin to the drug discovery algorithms used by pharmaceutical companies to find and test new medications rapidly. Thus far the tool, created with the help of the software consultant Fourkind, has created products like the “Gluten Free Golden Oreo” and updated Chips Ahoy’s classic recipe, per the WSJ.

The fascinating stories and secrets behind hit Japanese products, plus parts and machines that boast the top share of niche markets. In the first half: the story behind canned bread developed by a Japanese bakery in 1996 which doesn’t go stale easily and has a long shelf-life. In the second half: a machine that makes dorayaki, a Japanese sweet with red bean paste sandwiched by pancakes. We introduce this unique machine that’s also being used to make other sandwich pancakes around the world.

Editor’s note: This story has been updated to clarify the type of trees affected by Phanerochaete velutina.

A species of wood-eating fungus didn’t need a brain to pass a cognitive test with flying colors, and researchers say this first-of-its-kind discovery could have broader implications for understanding consciousness and intelligence in a variety of life forms.

A team of researchers at Japan’s Tohoku University, led by Yu Fukasawa, associate professor in the Graduate School of Agricultural Science, set out to determine whether fungi could recognize shapes. Their study, published in the journal Fungal Ecology in October, found evidence that these bottom feeders possess memory and decision-making abilities despite not having a central nervous system.

Basically chat gpt, gemini, and apple intelligence all can be a great teaching tool that can teach oneself nearly anything. Essentially college even can be quickly solved with AI like chat gpt 4 because it can do more advanced thinking processing than even humans can in any subject. The way to think of this is that chat gpt 4 is like having a neuralink without even needing a physical device inside the brain. Essentially AI can augmented us to become god like just by being able to farm out computer AI instead needing to use our brains for hard mental labor.

I created a prompt chain that enables you to learn any complex concept from ChatGPT.

To harness the power of the sun and make sugars for energy storage, plants use photosynthesis. But some plants are more efficient at it than others. For the first time, researchers have identified a key step in the transformation between old-fashioned C3 photosynthesis and new and improved C4 photosynthesis — which could lead to the development of more efficient, more resilient “super crops,” SciTechDaily reports.

Scientists at the Salk Institute in San Diego, California, collaborated with researchers at the University of Cambridge to make the breakthrough, charting the evolution of plants over millions of years.

While 95% of plants use C3 photosynthesis, SciTechDaily explained, a new group of plants evolved to use C4 photosynthesis around 30 million years ago.

I believe that vertical farming will be able to meet the demand of 9.7 billion people by 2050 or even be able to feed eventually the entire globe or even space stations. The leading vertical farming company I like is aero farms:3.

By 2050, we’ll need to produce 70% more food to feed over 9 billion mouths. Luckily, a wide range of vertical farming companies are developing innovative solutions to redefine production, expand urban agriculture and transform consumers into green-fingered growers.

PlantRNA-FM, an AI model trained on RNA data from over 1,100 plants, decodes genetic patterns to advance plant science, improve crops, and tackle global agricultural challenges.

A groundbreaking Artificial Intelligence (AI) model designed to decode the sequences and structural patterns that form the genetic “language” of plants has been launched by a research collaboration.

Named Plant RNA-FM, this innovative model is the first of its kind and was developed by a partnership between plant researchers at the John Innes Centre and computer scientists at the University of Exeter.

Precision agriculture leverages cutting-edge machine learning algorithms to transform farming, boosting productivity and sustainability. From Random Forest for crop classification to CNNs for high-resolution imagery analysis, these tools optimize resources, detect diseases early, and improve yield prediction. Discover the top algorithms shaping modern agriculture and how they empower smarter, data-driven decisions.

A study published in the journal Optica demonstrates live plant imaging of several representative plant samples, including the biofuel crop sorghum. By employing a novel detector, researchers obtained clear images of living sorghum plants with a light far dimmer than starlight. This advance enables imaging of delicate, light-sensitive samples, such as biofuel crops, without disturbing or damaging the plants.

A method called quantum ghost imaging (QGI) allows scientists to capture images at extremely low light levels. QGI also enables the use of one low intensity color, best matched to the sample and a different color at higher intensity, sufficient to form the image of the sample. This approach improves imaging in regions of light where traditional cameras struggle.

By using label-free infrared imaging, researchers can gather critical information about important plant processes, such as water content and photosynthesis, even in low-light conditions. This is particularly beneficial for studying biofuel crops, where researchers want to optimize plant growth and health to maximize yield and sustainability.

Inside every jar of honey lies a taste of the local environment. Its sticky-sweet flavor is shaped by the flowers that nearby bees choose to sample. However, a new study from Tulane University has revealed that honey can also provide insights into local pollution.

The study, published in Environmental Pollution, analyzed 260 honey samples from 48 states for traces of six toxic metals: arsenic, lead, cadmium, nickel, chromium, and cobalt. None of the samples contained unsafe levels of these metals based on a typical serving size of one tablespoon per day, and the concentrations in the United States were generally lower than global averages. Still, researchers identified regional variations in toxic metal distribution: the highest arsenic levels were detected in honey from a cluster of Pacific Northwest states (Oregon, Idaho, Washington, and Nevada); the Southeast, including Louisiana and Mississippi, showed the highest cobalt levels; and two of the three highest lead levels were found in samples from the Carolinas.

Overall, the study highlights a potential dual role for honey as both a food source and a tool for monitoring environmental pollution.