This posting is inspired from our recent meal time conversations, why one of my Fellow THD has now had a difficult time reading. She said she used to read a lot when she was young, today she barely read. I think the reasons why is this happening to her is explained in the following post (From My Face Book Page).
Reading seems simple, but a new meta-analysis by neuroscientists at the Max Planck Institute reveals just how complex the brain’s activity is during reading. By analyzing data from 163 brain imaging studies with over 3,000 readers, the researchers mapped how the brain dynamically processes every stage of reading, from letters to full texts showing that different brain areas are involved depending on the reading task. They found that all reading activity mainly occurs in the left hemisphere of the brain but changes based on what’s being read. Reading single letters activates a small visual area, while reading words engages larger networks including frontal and parietal regions. Sentence reading recruits areas linked to understanding syntax and meaning, and reading longer texts calls on working memory centers. This means the brain reconfigures itself depending on how complex the reading is, rather than just scaling up the same processes.
The study also showed clear differences between reading aloud and silently. Reading aloud activates auditory and speech motor areas, while silent reading relies more on executive networks that help form internal speech and suppress actual vocalization, showing silent reading is an active mental effort.
Comparing real words to nonsense pseudowords revealed distinct brain pathways: real words engage memory and meaning areas, while pseudowords activate regions for sounding out unfamiliar terms. This supports a key theory that the brain uses two routes for reading depending on familiarity.
Interestingly, the cerebellum-traditionally linked to movement—was also active during reading, especially the right side across all tasks and the left side during silent reading and word recognition, suggesting a bigger role in language than previously thought.
The researchers caution that common reading tasks used in studies, like deciding if a word is real or not, don’t fully capture natural reading’s brain activity. Understanding these differences could improve how we study reading difficulties like dyslexia.
Brazil just taught a robot how to harvest crops using smell
In a remote field outside São Paulo, a robotic breakthrough is quietly transforming how humans detect ripe fruit not with cameras or weight sensors, but through synthetic olfaction. Scientists at the Federal University of São Carlos have designed the world’s first autonomous robot capable of identifying fruit ripeness by smell alone.
The robot’s nose is built with arrays of bioengineered smell receptors, mimicking the olfactory proteins found in animals. These are connected to a neural network trained to decode the subtle chemical signatures of ripeness in bananas, mangoes, and passionfruit. When it detects the correct blend of esters and alcohols, it makes its move.
Unlike vision-based bots that fail in fog, dust, or shade, this machine works in rain, night, or heat, tracking fruit aroma even through leaves. It not only harvests with gentle precision, but can also detect rotting or disease-prone produce before visual symptoms appear saving both time and waste.
This breakthrough could revolutionize farming in tropical regions, where ripening happens fast and inconsistently. For small farmers with labor shortages, smell-based robotics may finally offer a low-cost, high-accuracy alternative to human pickers.
The team is now adapting it to coffee, cocoa, and even wine grapes where smell determines millions in value.
Lastly, Do You Know How AI Learns?
How AI learns- From the Washington Post
Return to menuArtificial intelligence software is nothing without data.
The tools develop intelligence through machine learning, a process that allows computers to “learn” on their own, without requiring a programmer to tell them each step. Feed a computer massive amounts of data, and it eventually can recognize patterns and predict outcomes.
Key to this process are neural networks, mathematical systems that act like a computerized brain, helping the technology find connections in data. They’re modeled after the human brain, with layers of artificial “neurons” that communicate information to one another. Even experts don’t necessarily understand all the intricacies of how neural networks work.
Large language models, or LLMs, are a type of neural network that learns to write and converse with users; they back all of the chatbots that have swooped onto the scene in recent months. They learn to “speak” by hoovering up massive amounts of text, often websites scraped from the internet, and finding statistical relationships between words. When these systems pattern-match, it can lead to feats of creativity: A chatbot can create song lyrics closely matching Jay-Z’s style because it’s absorbed the patterns of his entire discography. But LLMs don’t have awareness of the meanings behind words.
Parameters, which are numerical points across a large language model’s training data, dictate how proficient it is at its tasks, such as predicting the next word in a sentence.
In the future, some researchers say, the technology will approachartificial general intelligence, or AGI, a point at which it matches or exceeds the intelligence of humans. The idea is core to the mission of some artificial intelligence labs, like OpenAI, which lists achieving AGI as its goal in its founding documents. Other experts contest that AI is anywhere close to achieving that kind of sophistication, with some critics contending that it’s a marketing term.
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