Visions of bright future

"One way race logic strives to save energy is by addressing the shortest-path problem. In one scenario, cars set off in multiple directions trying to find the fastest route. When the first car arrives, all the other cars stop, saving energy.Credit: B. Hayes and J. Wang/NIST" (Nist.gov/Brain-Inspired Computing Can Help Us Create Faster, More Energy-Efficient Devices — If We Win the Race) When we select a route for data, we always select the shortest possible route. But what if there are two routes whose length is the same? That causes a situation in which the data packages that travel through those routes can reach the same point. At the same time. The situation is similar with two cars that reach at the same moment the same parking lot. That thing causes conflict in the system.  If those cars are lorries, that must only deliver material to the house the system can tell that another driver can go to take some coffee. And the other car can deliver its cargo. In computer systems

The brain-inspired computer can revolutionize the spontaneously learning AI. The idea of brain-inspired computers is that those systems act like human brains. In human brains, neurons can connect and disconnect their connections. Every neuron involves a small data structure. When neurons make connections they connect those data structures.  They form virtual neurons that can connect those data structures in billions of ways. Every data structure involves some skill. And why neurons remove the connections. That makes it easier to control that connection structure or neural network. The brain is the morphing neural network. That morphs its structures all the time.  Above: Von Neumann architecture is used in binary computers. The brain-inspired structure can involve even billions of networked Von Neumann architecture structures. (Wikipedia, Von Neumann architecture)  The biggest difference between brain-inspired morphing neural networks and traditional systems is this: in traditional syst

  "Scientists created a minimally invasive brain–machine interface using functional ultrasound (fUS) to accurately map brain activity related to movement planning with a resolution of 100 micrometers. Credit: Caltech" (ScitechDaily, Reading Minds With Ultrasound: Caltech’s New Brain–Machine Interface) "The latest advancements in Brain-Machine Interfaces feature functional ultrasound (fUS), a non-invasive technique for reading brain activity. This innovation has shown promising results in controlling devices with minimal delay and without the need for frequent recalibration. Credit: SciTechDaily.com (ScitechDaily, Mind Control Breakthrough: Caltech’s Pioneering Ultrasound Brain–Machine Interface) The Caltech researchers worked with the brain-machine interface (BMI) that uses ultrasound for reading the mind. And the new systems can make it possible to turn thoughts into text.  Researchers at Caltech created a new AI-powered Brain Machine Interface (BMI) that can transform

 Harnessing the RNAi is the tool for next-generation genetic experiments.  "Alnylam Pharmaceuticals is translating the promise of RNA interference (RNAi) research into a new class of powerful, gene-based therapies. In this rendering, the green strand is the targeted mRNA, and the white object is the RNA-induced silencing complex (RISC) that can prevent the expression of the target mRNA’s proteins. The orange strand is RNAi. Credit: Courtesy of Alnylam Pharmaceuticals" (ScitechDaily, Harnessing RNAi: Alnylam’s Path From Lab Discovery to Life-Changing Treatments) Harnessing RNA interference (RNAi) is the tool that makes next-generation, life-changing treatments possible. The other names for RNAi are co-suppression and post-transcriptional gene silencing (PTGS). And  quelling . That  thing  makes it possible to turn genome silence like a programmer sometimes "kills code" in computer coding. RNAi is one of the most effective tools for genetic engineering.  The idea of t

"Six layers of excitatory neurons color-coded by depth. Credit: Google Research and Lichtman Lab" (SciteechDaily, Harvard and Google Neuroscience Breakthrough: Intricately Detailed 1,400 Terabyte 3D Brain Map) Harvard and Google created the first comprehensive model of human brains. The new computer model consists of 1400 terabytes of data. That thing would be the model. That consists comprehensive dataset about axons and their connections. And that model is the path to the new models or the human brain's digital twins.  The digital twin of human brains can mean the AI-based digital model. That consists of data about the blood vessels and neural connections. However, the more advanced models can simulate electric and chemical interactions in the human brain.  This project was impossible without AI. That can collect the dataset for that model. The human brain is one of the most complicated structures and interactions between neurotransmitters, axons, and the electrochemica

In game theory, all organisms want to maximize their benefit. Game theory can used as a tool that makes programming learning machines easier than it has been. The idea is that the AI will get points for every correct answer. And when the AI gives the wrong answer, the system gets zero points. That thing allows the simple model, that is easy to program for computers.  When the AI plays chess against people, it can have a system where each chess button has points. That allows the AI to model the game. The soldier can be one or two points. And the queen can be worth 1000 points. When the number of those chess buttons decreases, the system can get new numbers for those buttons. The winner of the game is, who gets more points, and loses fewer buttons. The lost button can decrease the points as the value of those lost buttons is.  Image: Quanta magazine In classic machine learning models the computer selects the answers. That brings benefit to it for storing in its memory. But the problem ha

"Researchers are investigating bacteriophages, particularly “jumbo” phages with large genomes, as potential tools to combat antibiotic-resistant bacteria. These phages might be engineered to deliver antibiotics directly to infections, offering a new strategy in the fight against deadly pathogens." (ScitechDaily, When Giants Fight Microscopic Wars: Jumbo Viruses Tackle Superbugs) Jumboviruses can act as nanomachines that carry medicals in the right cells. In that case, the researchers can replace giant viruses or jumbo viruses DNA using the long-chain proteins. The system can pump those proteins into the targeted cells.  The AI is an ultimate tool to control complicated molecule production. The next-generation AI-powered environments are tools that can make breakthroughs in medical work. The long-chain molecules are themselves good tools for medicines. The protein can fill targeted cells. And that makes new types of medicals possible. The only problem is this: how do make sure