'This is an apartheid regime' Does Trump have real leverage over Netanyahu? A handful of electronic components, a few wires and a basic programmable circuit board are enough to bring a group of curious children around a table in central Gaza to learn about robotics. Students at The Next Gen Team: Rising Robotics Engineer Project in Deir el-Balah learn how to integrate sensors into alarm systems, study robotics, understand how motors and drive units work, as well as get firsthand experience with programming. The robotics and early warning systems they are working on could make way for bigger developments such as early detection of gas leaks and fires or help rescue workers access hard-to-reach areas, such as victims trapped under rubble. Today's lesson will also teach students the sciences of the future and help them keep pace with technological advancements happening outside Gaza's borders. Before the war, students had access to schools, universities, incubators and training programmes to pursue an interest in technology.

Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports context-aware optimization, outperforming conventional strategies in differentiating retinal pigment epithelial cells. A web interface enables real-time human-AI collaboration, while a modular backend allows scalable integration with laboratory hardware. These results highlight the feasibility of generalizable, AI-driven laboratory automation and the transformative role of language-based reasoning in biological research.

Over the past year, rural farmers in Malawi have been seeking advice about their crops and animals from a generative AI chatbot. These farmers ask questions in Chichewa, their native tongue, and the app, Ulangizi, responds in kind, using conversational language based on information taken from the government's agricultural manual. "In the past we could wait for days for agriculture extension workers to come and address whatever problems we had on our farms," Maron Galeta, a Malawian farmer, told Bloomberg. "Just a touch of a button we have all the information we need." The nonprofit behind the app, Opportunity International, hopes to bring similar AI-based solutions to other impoverished communities.

In this paper, we aim to generate text classification data given arbitrary class definitions (i.e., user instruction), so one can train a small text classifier without any human annotation or raw corpus. Compared with pioneer attempts, our proposed Incubator is the first framework that can handle complicated and even mutually dependent classes (e.g., "TED Talk given by Educator" and "Other"). Specifically, Incubator is an LLM firstly tuned on the instruction-to-data mappings that we obtained from classification datasets and descriptions on HuggingFace together with in-context augmentation by GPT-4. We then refine Incubator by learning on the cluster centers of semantic textual embeddings to emphasize the uniformity and semantic diversity in generations. We compare Incubator on various classification tasks with strong baselines such as direct LLM-based inference and training data generation by prompt engineering. Experiments show Incubator is able to (1) perform well on traditional benchmarks, (2) take label dependency and user preference into consideration, and (3) enable logical text mining by incubating multiple classifiers.

Google's Area 120 division has been severely affected by the layoffs happening across Alphabet, according to Bloomberg and TechCrunch, which said the unit now has fewer than 100 employees after the most recent round of cuts. Area 120 is known as Google's in-house incubator, which works on experimental apps and products. Those include GameSnacks, an HTML5-based platform that enables users to load and play games quickly even on poor connections and basic smartphones. Sundar Pichai established the division in 2016 to "provide a purpose-built home for bottom-up innovation at Google." The division's website reads: "Area 120 teams work on new products, experiences, and services every day."

Temperature and humidity are two of the rudimentary factors that must be controlled during egg incubation. Improper temperature and humidity levels during the incubation period often result in unwanted conditions. This paper proposes the design of an efficient Mamdani fuzzy interference system instead of the widely used Takagi-Sugeno system in this field for controlling the temperature and humidity levels of an egg incubator. Though the optimum incubation temperature and humidity levels used here are that of chicken egg, the proposed methodology is applicable to other avian species as well. Theinput functions have been used here as per estimated values forsafe hatching using Mamdani whereas defuzzification method, COA, has been applied for output. From the model output,a stabilized heat from temperature level and fan speed to control the humidity level of an egg incubator can be obtained. This maximizes the hatching rate of healthy chicks under any conditions in the field.

So you have a new cutting-edge AI product/service idea. Unfortunately, this is easier said than done! How can a project cover the numerous expenses of AI product development, and remain financially stable? This is exactly where seed funding comes in. What is seed funding?... Seed funding comes before the product/service is ready for commercialization.

In early spring 2020, Smithfield, Tyson, and other industrial food suppliers warned that upwards of millions of pounds of meat could disappear from the U.S. supply chain as a result of the coronavirus. Although it now appears these fears were overblown or possibly a ploy to bolster exports (excepting pork products like pepperoni), tens of thousands of slaughterhouse workers around the world have tested positive for COVID-19, and more than 90 of them have died from the virus. As the health crisis stretches on, the threat to meatpacking, meat processing, and distribution center employees has researchers hunting for a new production model. Even with physical distancing protocols and personal protective equipment like face shields and masks, plant closures are looming -- and the idea of automation is rapidly gaining ground. The U.S. meatpacking industry employed nearly 600,000 workers -- a large portion of whom are immigrants -- at wages averaging $15.92 an hour in 2019.

Apache Hop, a metadata-driven data orchestration tool used to design and build pipelines, today emerged from incubator status and was named a Top-Level Project at the Apache Software Foundation, clearing the way for more intensive production use. Apache Hop, which stands for Hop Orchestration Platform, is a Java-based product designed to help data professionals manage a variety of data and metadata orchestration and integration needs. The software sports a visual design environment that allows users to create ETL pipelines, as well as an execution engine that can run by itself or embedded into Spark, Flink, Google Dataflow, or on AWS EMR via Apache Beam. "Hop is entirely metadata driven," it states on the Apache Hop website. "Every object type in Hop describes how data is read, manipulated or written, or how workflows and pipelines need to be orchestrated. Metadata is what drives Hop internally as well. Hop uses a kernel architecture with a robust engine. Plugins add functionality to the engine through their own metadata."

It's not often that a twitching, snowman-shaped blob of 3D human tissue makes someone's day. But when Dr. Sergiu Pasca at Stanford University witnessed the tiny movement, he knew his lab had achieved something special. You see, the blob was evolved from three lab-grown chunks of human tissue: a mini-brain, mini-spinal cord, and mini-muscle. Each individual component, churned to eerie humanoid perfection inside bubbling incubators, is already a work of scientific genius. But Pasca took the extra step, marinating the three components together inside a soup of nutrients.