The black holes and antimatter can help to solve mysteries of the universe.

"The Fireball collaboration at CERN has generated a powerful electron-positron plasma beam to study black hole jets, significantly advancing our understanding of these cosmic phenomena and supporting simulations with experimental data. Credit: SciTechDaily.com" (ScitchDaily, Matter/Antimatter Black Hole Jets Recreated in CERN’s Laboratory)

Schwinger effect or wave-particle duality is the thing that proved that material or particles are one energy form. And energy and material are the same thing. The wave-particle duality must be possible between gravity waves and dark energy. That means dark energy and gravity waves can also create material or particles, that are unknown to us. 

For the first time, CERN created the laboratory version of the black hole's antimatter and material jets. And that thing opens interesting visions to dark energy and wormhole research. 

One reason why black hole relativistic jets are so powerful is that they form antimatter. The antimatter forms when material. That comes from the black hole's axle impacts energy from the material disk around the black hole. That high-energy reaction turns the spin of some particles to the opposite. And that forms a high-energy interaction in the black hole's relativistic jet. This thing makes this relativistic jet more high energy than it should be. 

The relativistic jet is an electromagnetic wormhole itself. But could that thing be a so-called real or gravitational wormhole? So, can the electromagnetic tunnel turn so tight that it will not let radiation travel through its shell? Maybe antimatter-material annihilation can make the Einstein-Rose bridges possible in the real world. The idea is that there is a spiral-shaped structure around the hollow center. 

And material-antimatter annihilation happens in that spiral. The extremely high energy reaction can deny that the outcoming energy or quantum field cannot travel through that channel. And the high-energy reaction pumps energy to objects that travel in that wormhole. That thing denies energy travel out from the objects. 

So, in that model. Annihilation makes that structure as tight as gravity wormholes. The high-energy annihilation reaction at the electromagnetic tunnel's shell can turn electromagnetic wormholes similar to gravity wormholes that can transport energy and particles through the universe. The thing in wormholes is that they don't let energy travel out from particles. 

The energy level in particles stays stable, and energy flows out from them is aging. Denying energy flow out from particles denies their aging because energy cannot flow out from them. 

In the time-arrow model when particles get older they deliver energy to particles and other objects around them. That thing means that if some particle can send a pulse. With a high enough energy level that thing pushes those other particles back in time. 

When the big bang happened. we can think of that event as a time arrow. When that energy or radiation was released it turned time travel opposite around it. In some models, the Big Bang happened because time moved opposite. So in the thing or point before the Big Bang time could travel opposite direction than in our universe. 

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We can explain why we have no antimatter in the universe like this: Schwinger effect formed particle-antiparticle pairs. Just after the Big Bang, and then those particle-antiparticle pairs annihilated.  

Again, the Schwinger effect formed new particles. That annihilation happened in a powerful energy flow that turned those particles spin in the same direction, forcing the material into the form we know it. 

So, as I  wrote before. 

Schwinger effect or wave-particle duality is the thing that proved that material or particles are one energy form. And energy and material are the same thing. The wave-particle duality must be possible between gravity waves and dark energy. That means dark energy and gravity waves can also create material or particles, that are unknown to us. 

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That causes an interesting question: Does time travel backward somewhere in the universe? In black holes time travels backward, because their escaping velocity is higher than the speed of light.  It's possible. That time travels backward in wormholes if there is a structure that pumps energy into particles. And if particles cannot send that energy away, it turns younger. 

The great annihilation just after the Big Bang formed material in the form as we know it. That annihilation created a situation where the impacting wave movement formed particle-antiparticle pairs. But those particle-antiparticle pairs formed in an extremely powerful energy flow. That energy flow turned all particle's spin in the same direction. That energy flow forced all known particles to turn into material. 

In the Big Bang, formed the same way matter and antimatter particles. Annihilation between particles and antiparticles just after that event formed the material in the form as we know it. The Big Bang might have happened just before the particle-antiparticle-pairs formed. And those particle-antiparticle pairs could be something that we never imagined. That particle-antiparticle annihilation formed the chaotic condition where impacting wave movement formed the particles. 

The reason for the lack of antimatter might be this. When those first elementary particles formed, they formed in an energy flow that traveled in one direction. That direction made those particles spin in the same direction. That energy flow made homogenously spinning particles that form material. The thing is that the different or opposite spins make some particles antiparticles and some particles material. 

When we think about the Big Bang, which released time and material into space, we must realize that dark matter and dark energy are mysteries. It's possible. That dark energy and dark matter can exist before the Big Bang. 

Maybe the first Big Bang that formed particle-antiparticle pairs formed dark energy and dark matter. The second Big Bang was annihilation that formed material as we know it. In that process, the first entropy formed the Big Bang. 

Then, the second entropy formed particle-antiparticle pairs. And the thing that formed material was the great annihilation that maximized entropy. Entropy or disorder in a system makes wave movement impact forming elementary particles.  The thing is that we are in energy that formed in the Big Bang.

https://bigthink.com/starts-with-a-bang/all-nothing-expanding-universe/

https://scitechdaily.com/matter-antimatter-black-hole-jets-recreated-in-cerns-laboratory/

https://en.wikipedia.org/wiki/Arrow_of_time

https://en.wikipedia.org/wiki/Schwinger_effect

https://en.wikipedia.org/wiki/Wave%E2%80%93particle_duality

The new AI leaps from language into logic.

"Natural language embedded programs (NLEPs) have been introduced to enhance the functionality of large language models. By generating Python code to address queries, NLEPs increase accuracy, efficiency, and transparency. This approach allows models to handle diverse tasks more effectively and could also benefit data privacy and smaller models. Credit: SciTechDaily.com" (ScitechDaily, Programmatic Breakthrough: AI’s Leap From Language to Logic To Solve Complex Problems)

How to make sure that the data that the system searches and uses is right? The system must use two sources. The problem with AI is how it finds the data from homepages. Well-sorted data that has a determinator before it is easy to handle. So if somebody asks the date of birth of some U.S. president, the AI must only search "date of birth". 

And then output the data after that word. The system might involve the package that involves the alternative for "date of birth", that is "birthdate". That kind of structure in databases makes large language models (LLM) very large. There are many ways to say one thing. And those alternatives must be determined for the AI, or it doesn't understand them. But when the AI must find data from the text, it has a problem. 

Because there are more alternatives in databases. That makes new LLMs more complex than previously. The new AIs are more effective and flexible than existing AIs. And they make fewer errors than the existing LLMs. The new powerful language model uses modular methods to solve problems. In that system, the programmers think. The system collects the answers from modules. Each module is like a package that involves some information. 

So when the user asks for the names of the U.S. presidents and years in office, the language model collects the information using modules U.S. presidents. And then it can search when they were in office. The LLM requires data from trusted sources. And one of the most difficult things in LLM is how it should select trusted sources. Then another thing that the LLM must do is to pick the necessary information from the questions. 

Of course, users can ask questions like: what are the names of the Donald Duck nephews? That kind of information might be interesting. But it doesn't bring a lot of new things for business or programming. The problem with existing LLMs is that they are not interactive. When those LLMs get a mission, they will just go to search for information. 

That thing makes the AI an interesting tool, but the interactive AI could be more accurate. Interactive AI discusses with its user. It will ask questions like where the computer program will go if some user wants to use it for the programming. The more information the system receives, the more accurately it can operate. 

When LLM makes things, like computer programs, it faces the same problems as humans. If the knowledge of the customer's system is incomplete, the AI makes incomplete code the same way as humans. Incomplete data about the requirements means that the product is incomplete. 

The limits of the LLM can seen in the question, which of the U.S. presidents elected after 1950 was born on a Wednesday? 

"For example, a large language model might be able to memorize and recite a list of recent U.S. presidents and their birthdays, but that same model could fail if asked the question: “Which U.S. presidents elected after 1950 were born on a Wednesday?” (The answer is Jimmy Carter.)" (ScitechDaily, Programmatic Breakthrough: AI’s Leap From Language to Logic To Solve Complex Problems) 

The thing, that makes the name of the weekday, October 1, 1924, so difficult for the AI is that the AI must use the two-stage model to make the answer. First, it must find Jimmy Carter's birthday. Then it must know where it should search for the name of the weekday. We know that the source is a calendar, but the AI might not know that thing. The AI must search the net to find data, where it can find names of the weekdays. And where it can connect those names with certain dates. 

In interactive AI models the AI could ask, where that data usually is? And then the user can say that the answer is in the calendar. We all know that that date is easy to find on the calendar. But that is not so natural thing for the AI. If programmers don't determine that thing for the system, the AI must try to find answers from the internet. 

And there the AI faces one of the most interesting things. The birthdates of the U.S. presidents are well documented and sorted. There is the word "date of birth" and then the AI can search for the next words and numbers. The AI must only recognize the months, weekdays, and numbers for making the solution, that it introduces for the user. Weekday's names are not often mentioned in data sources like web-based dictionary books. 

That requires that the user knows where that answer is. And in an interactive model, the AI can learn things. Most of the queries that the AI gets can be, that the people don't have time or they just don't want to search lots of homepages. 

The birth dates of the U.S. presidents are easy to find. But the names of those days are not listed. So we might know that Jimmy Carter was born on October 1, 1924. But for searching the name of that day, the system must go to search that data from the calendar. In the calendar, data is sorted differently. But the main problem is that the AI must know to find the name of that weekday from the calendar. 

https://scitechdaily.com/programmatic-breakthrough-ais-leap-from-language-to-logic-to-solve-complex-problems/