AI Takes Root in Farming

Farmers are adopting artificial intelligence more readily than many other emerging technologies. Agriculture is particularly suited to AI because farms generate large amounts of data from machinery, sensors, satellites, weather services and field observations. AI can turn this information into practical recommendations, helping farmers predict yields, identify pests and diseases, optimise irrigation and fertiliser use, and make better operational decisions. Generative AI is also emerging as a kind of virtual agronomist, allowing farmers to obtain advice through natural-language queries and potentially making sophisticated agricultural knowledge easier to access.

Targeted applications of AI could generate around $100 billion in additional on-farm value globally. The appeal is largely driven by measurable benefits, higher productivity, reduced input and labour costs, improved resource efficiency and greater resilience to increasingly unpredictable environmental conditions. However, adoption is not uniform. Connectivity, data quality, affordability and concerns over control of farm data remain significant obstacles, particularly for smaller farms and farmers in regions with limited digital infrastructure. The broader message is that AI is gaining traction in agriculture because it can address concrete, everyday farming problems.

More information:

https://finance.yahoo.com/technology/ai/articles/farmers-embracing-ai-more-other-110000541.html

Brain Implant Enables Speech and Gestures Through an Avatar

Researchers at the University of California developed a BCI that allows people with severe paralysis to communicate using both speech and upper-body gestures through a digital avatar. The system uses one implant containing 253 electrodes positioned on the surface of the sensorimotor cortex to capture neural activity associated with attempted speech and movements. Machine-learning algorithms decode these signals in real time, enabling an animated avatar to speak while simultaneously performing gestures such as waving or nodding. The study involved two participants with severe paralysis and demonstrated that neural signals for speech and movement can be extracted from the same cortical region and translated into coordinated communication.

The research represents a step toward BCIs that reproduce the richness of natural human communication rather than simply generating text, speech, or cursor movements. Because conversation relies heavily on non-verbal cues, combining vocal expression with gestures could make interactions more intuitive and socially expressive for people affected by conditions such as ALS or brainstem stroke. Importantly, the findings suggest that a single neural interface can support multiple functions simultaneously, potentially reducing the need for separate implants dedicated to different tasks. The researchers ultimately envision multifunctional systems capable of restoring a broader range of communication and interaction abilities for people with severe paralysis.

More information:

https://www.science.org/content/article/single-brain-implant-lets-paralyzed-people-both-talk-and-gesture-avatar

Beijing’s Robot Kindergarten

A new robot kindergarten has opened in Beijing’s Shijingshan district to explore a different approach to training robots: allowing them to learn continuously through physical interaction, touch, trial and error, rather than relying primarily on human demonstrations. The facility was jointly established by tactile-sensing company Tashan Technology and a team led by Turing Award winner and reinforcement-learning pioneer Richard Sutton. Robots repeatedly attempt movements, receive feedback from their environment and modify their behaviour based on the results, for example, learning to avoid an obstacle after colliding with it. Sutton described the facility as a controlled environment where robots can learn about their own bodies and the physical world, making mistakes an essential source of training data.

The initiative reflects a broader effort to develop robots capable of continuous, autonomous learning after deployment, rather than having their behaviour largely fixed by pre-training, simulation or teleoperation. Researchers argue that robots could eventually generate their own experience-based datasets and potentially share what individual machines learn across common models, helping them adapt to situations not covered during initial training. Significant challenges remain, particularly hardware durability and the complexity of learning sophisticated humanoid behaviours. For now, autonomous experiential learning is expected to complement rather than replace imitation learning and simulation, while potentially becoming increasingly important as robots enter factories, services and homes.

More information:

https://www.globaltimes.cn/page/202609/1369570.shtml

Brain Implant Controlled Across Continents

Researchers from KAIST and Yonsei University have demonstrated a wireless brain implant that can be controlled across continents. Their system, called RAPIDO, received commands sent from Chicago to a laboratory in Daejeon, South Korea with an average response time of about 109 milliseconds. Designed for experiments with freely moving animals, the implant combines two capabilities: precisely delivering drugs into specific brain regions and providing targeted light stimulation for optogenetic experiments. Its refillable cartridge and internet-based control could enable longer-term studies while reducing the need for researchers to physically approach or repeatedly handle experimental animals.

In experiments with rats, RAPIDO successfully delivered controlled doses of cocaine into the nucleus accumbens over several weeks, producing measurable dose-dependent behavioral effects. In a separate experiment, researchers used the implant's light stimulation to activate the RhoA signaling pathway, preventing rats from developing the same learned preference for a cocaine-associated environment. The researchers stress that this does not constitute a treatment for addiction; rather, the work demonstrates a platform for remotely manipulating specific neural processes and studying their effects on behavior. Significant challenges must be addressed before such technology could be considered for human clinical applications.

More information:

https://www.sciencealert.com/a-brain-implant-in-korea-was-just-controlled-from-the-us

LLM-Personalised Autonomous Driving

Researchers at Delft University of Technology have developed an approach that allows passengers to personalise how an autonomous vehicle drives using natural-language commands. Instead of allowing a large language model (LLM) to control the vehicle directly, the system uses the LLM to interpret requests or indications that the passenger feels dizzy. These preferences are translated into adjustments to parameters used by a conventional, safety-aware motion planner, affecting characteristics such as speed, acceleration, steering smoothness and collision avoidance. The system also keeps the passenger in the loop by explaining the proposed changes in everyday language and asking for confirmation before applying them. Tests using the nuPlan autonomous-driving simulator showed that the system could successfully adapt driving behaviour to eight different passenger prompts, producing smoother driving when comfort was prioritised and higher speeds when urgency was expressed.

The approach is significant because it combines the flexibility and natural-language understanding of LLMs with the predictability of traditional autonomous-driving controllers. Rather than relying on an LLM for real-time driving decisions—which can suffer from latency, hallucinations and a lack of performance guarantees—the researchers use GPT-4o-mini only to translate subjective passenger preferences into changes within predefined safe limits. This separation means the underlying motion planner remains responsible for vehicle control, reducing the potential consequences of incorrect LLM outputs. However, researchers note that maintaining safe operating constraints still requires substantial engineering, and the Delft approach does not yet provide mathematically provable safety. Other work is therefore exploring additional verification layers that mathematically check AI-generated driving decisions against traffic rules and predicted road-user behaviour before they are executed.

More information:

https://spectrum.ieee.org/autonomous-vehicles-motion-planner-llm

Flexible Brain Circuits Enable Cognitive Switching

MIT neuroscientists discovered that the brain could reuse the same neural circuits for different cognitive tasks, helping explain how a finite number of neurons supports such a wide range of behaviors. In experiments with mice, researchers examined activity in the prefrontal and parietal cortices while the animals compared auditory tones and remembered information needed to decide. They found that while neurons in the parietal cortex mainly retained sensory information, a group of neurons in the prefrontal cortex could flexibly switch roles, first maintain a sensory memory of a tone and later store the action the animal planned to perform.

The findings support the concept of modular or compositional cognition, in which neural circuits function like reusable building blocks rather than dedicated systems for individual tasks. By applying the same computational mechanisms to different kinds of information (such as sensory inputs or motor plans) the brain can efficiently combine and repurpose neural resources to produce flexible behavior and learn new tasks without constructing entirely new circuits. The researchers plan to test this mechanism further by selectively inhibiting these modules during different stages of tasks to determine their causal role in cognitive flexibility.

More information:

https://news.mit.edu/2026/flexible-brain-circuits-can-switch-between-different-tasks-0817

AI-Designed Viruses Raise Safety Concerns

Scientists at Stanford University have created what are described as the first functional viruses whose genomes were designed by artificial intelligence. Researchers used genome language models called Evo1 and Evo2, trained on genetic information from about 2 million bacteriophages, viruses that infect bacteria, to generate thousands of candidate viral genomes. Nearly 300 were synthesised in the laboratory, producing 16 viable bacteriophages. A mixture of these AI-designed viruses successfully attacked strains of E. coli that had developed resistance to naturally occurring phages, demonstrating potential for developing more adaptable phage therapies against antibiotic-resistant bacterial infections.

The breakthrough also raises significant biosafety and biosecurity concerns because it demonstrates that generative AI can design complete, biologically functional viral genomes. The researchers deliberately excluded genetic data from viruses that infect humans, animals and plants, but experts warn that similar approaches applied to pathogenic organisms could create novel biological threats. Biosecurity specialists therefore argue that governance needs to develop alongside the technology, combining controls on AI-model development and access with responsible research oversight, DNA-synthesis screening, laboratory biosafety and restrictions on potentially dangerous genome production.

More information:

https://www.theguardian.com/science/2026/aug/06/safety-fears-as-scientists-make-first-viruses-designed-by-ai

Teaching Drones to Play Tag with AI

Researchers at Sandia National Laboratories are using reinforcement learning (RL) to teach autonomous drones how to cooperate and make rapid decisions in a strategic version of tag. In the experiment, two evader drones attempt to reach a protected base while two pursuer drones try to intercept them. The evaders learn through simulated rewards and penalties to coordinate their movements, including strategies that can cause the pursuers to interfere or collide with one another. Unlike the RL-trained evaders, the pursuers use a conventional proportional-navigation algorithm. The research demonstrates how multi-agent reinforcement learning can produce flexible cooperative behaviours without engineers having to explicitly program every possible manoeuvre.

The project is overcoming the simulation-to-reality gap: strategies that perform well in simulation can fail on physical drones because of factors such as battery depletion, aerodynamic interactions, communication delays and other unmodelled dynamics. Sandia therefore tests the algorithms on small, inexpensive quadrotors in its CAMINO facility, using high-precision motion capture to evaluate and refine their behaviour before moving toward more costly systems. Beyond the game itself, the work could contribute to autonomous systems capable of pursuit, evasion and coordinated swarm behaviour in rapidly changing environments, including potential applications for protecting critical infrastructure against hostile drones and other national-security scenarios.

More information:

https://www.sandia.gov/labnews/2026/08/13/teaching-drones-to-play-tag/