Efficient Brain - Viral DNA - Myopia Epidemic - Humanoid Robots

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May 29, 2024, 7:09:31 AMMay 29

https://nautil.us/why-is-the-human-brain-so-efficient-237042/

Why Is the Human Brain So Efficient?

By Liqun Luo

The brain is complex; in humans it consists of about 100 billion neurons, making on the order of 100 trillion connections. It is often compared with another complex system that has enormous problem-solving power: the digital computer. Both the brain and the computer contain a large number of elementary units—neurons and transistors, respectively—that are wired into complex circuits to process information conveyed by electrical signals. At a global level, the architectures of the brain and the computer resemble each other, consisting of largely separate circuits for input, output, central processing, and memory.1

Which has more problem-solving power—the brain or the computer? Given the rapid advances in computer technology in the past decades, you might think that the computer has the edge. Indeed, computers have been built and programmed to defeat human masters in complex games, such as chess in the 1990s and recently Go, as well as encyclopedic knowledge contests, such as the TV show Jeopardy! As of this writing, however, humans triumph over computers in numerous real-world tasks—ranging from identifying a bicycle or a particular pedestrian on a crowded city street to reaching for a cup of tea and moving it smoothly to one’s lips—let alone conceptualization and creativity.

So why is the computer good at certain tasks whereas the brain is better at others? Comparing the computer and the brain has been instructive to both computer engineers and neuroscientists. This comparison started at the dawn of the modern computer era, in a small but profound book entitled The Computer and the Brain, by John von Neumann, a polymath who in the 1940s pioneered the design of a computer architecture that is still the basis of most modern computers today.2 Let’s look at some of these comparisons in numbers (Table 1).

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https://theconversation.com/depression-schizophrenia-and-bipolar-disorder-linked-with-ancient-viral-dna-in-our-genome-new-research-230490

Depression, schizophrenia and bipolar disorder linked with ancient viral DNA in our genome

Rodrigo Duarte

Around 8% of human DNA is made up of genetic sequences acquired from ancient viruses. These sequences, known as human endogenous retroviruses (or Hervs), date back hundreds of thousands to millions of years – with some even predating the emergence of Homo sapiens.

Our latest research suggests that some ancient viral DNA sequences in the human genome play a role in susceptibility to psychiatric disorders such as schizophrenia, bipolar disorder and major depressive disorder.

Hervs represent the remnants of these infections with ancient retroviruses. Retroviruses are viruses that insert a copy of their genetic material into the DNA of the cells they infect. Retroviruses probably infected us on multiple occasions during our evolutionary past. When these infections occurred in sperm or egg cells that generated offspring, the genetic material from these retroviruses was passed on to subsequent generations, becoming a permanent part of our lineage.

Initially, scientists considered Hervs to be “junk DNA” – parts of our genome with no discernible function. But as our understanding of the human genome has advanced, it’s become evident that this so-called junk DNA is responsible for more functions than originally hypothesised.

First, researchers found that Hervs can regulate the expression of other human genes. A genetic feature is said to be “expressed” if its DNA segment is used to produce RNA (ribonucleic acid) molecules. These RNA molecules can then serve as intermediaries leading to the production of specific proteins, or help to regulate other parts of the genome.

Initial research suggested that Hervs regulate the expression of neighbouring genes with important biological functions. One example of this is a Herv that regulates the expression of a gene involved in modifying connections between brain cells.

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https://www.nature.com/articles/d41586-024-01518-2

A myopia epidemic is sweeping the globe. Here’s how to stop it

By Elie Dolgin

The COVID-19 pandemic didn’t just reshape how children learn and see the world. It transformed the shape of their eyeballs.

As real-life classrooms and playgrounds gave way to virtual meetings and digital devices, the time that children spent focusing on screens and other nearby objects surged — and the time they spent outdoors dropped precipitously. This shift led to a notable change in children’s anatomy: their eyeballs lengthened to better accommodate short-vision tasks.

Study after study, in regions ranging from Europe to Asia, documented this change. One analysis from Hong Kong even reported a near doubling in the incidence of pathologically stretched eyeballs among six-year-olds compared with pre-pandemic levels1.

This elongation improves the clarity of close-up images on the retina, the light-sensitive layer at the back of the eye. But it also makes far-away objects appear blurry, leading to a condition known as myopia, or short-sightedness. And although corrective eyewear can usually address the issue — allowing children to, for example, see a blackboard or read from a distance — severe myopia can lead to more-serious complications, such as retinal detachment, macular degeneration, glaucoma and even permanent blindness.

Rates of myopia were booming well before the COVID-19 pandemic. Widely cited projections in the mid-2010s suggested that myopia would affect half of the world’s population by mid-century (see ‘Rising prevalence’), which would effectively double the incidence rate in less than four decades2 (see ‘Affecting every age’). Now, those alarming predictions seem much too modest, says Neelam Pawar, a paediatric ophthalmologist at the Aravind Eye Hospital in Tirunelveli, India. “I don’t think it will double,” she says. “It will triple.”

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https://www.sciencenews.org/article/reinforcement-learn-ai-humanoid-robots

Reinforcement learning AI might bring humanoid robots to the real world

By Matthew Hutson

ChatGPT and other AI tools are upending our digital lives, but our AI interactions are about to get physical. Humanoid robots trained with a particular type of AI to sense and react to their world could lend a hand in factories, space stations, nursing homes and beyond. Two recent papers in Science Robotics highlight how that type of AI — called reinforcement learning — could make such robots a reality.

“We’ve seen really wonderful progress in AI in the digital world with tools like GPT,” says Ilija Radosavovic, a computer scientist at the University of California, Berkeley. “But I think that AI in the physical world has the potential to be even more transformational.”

The state-of-the-art software that controls the movements of bipedal bots often uses what’s called model-based predictive control. It’s led to very sophisticated systems, such as the parkour-performing Atlas robot from Boston Dynamics. But these robot brains require a fair amount of human expertise to program, and they don’t adapt well to unfamiliar situations. Reinforcement learning, or RL, in which AI learns through trial and error to perform sequences of actions, may prove a better approach.

“We wanted to see how far we can push reinforcement learning in real robots,” says Tuomas Haarnoja, a computer scientist at Google DeepMind and coauthor of one of the Science Robotics papers. Haarnoja and colleagues chose to develop software for a 20-inch-tall toy robot called OP3, made by the company Robotis. The team not only wanted to teach OP3 to walk but also to play one-on-one soccer.

“Soccer is a nice environment to study general reinforcement learning,” says Guy Lever of Google DeepMind, a coauthor of the paper. It requires planning, agility, exploration, cooperation and competition.