New artificial intelligence learns by using the "cause and effect" methodology.

Image I

The cause and effect methodology means the AI tests simultaneously the models that are stored in its memory. And when some model fits a case that the AI must solve, the AI stores that model to other similar cases. And in that case, the AI finds a suitable solution for things that it must solve. It selects the way to act that is most suitable for it. The most beneficial case means that the system uses minimum force for reaching the goal.

The "cause and effect method" in the case that the AI-controlled robot will open the door might be that the first robot is searching marks about things that help to determine which way the door is opening. Then the robot first just pulls the door and turns the handle. Then the robot tries the same thing but it pushes the door. Then the robot can note that the door is locked and find another way to get in.

But if a robot must get in it might have a circular programming architecture. If the robot cannot open the door by using the methods that are found in the first circle. It will step to the next level and use more force. And then the robot will try to kick the door in or some other way to break it. The idea is that robot always uses minimum force. But the problem is how to determine the case. The robot is allowed to do in cases that it faces the door.

There are cases where the cause and effect methodology is not suitable. If the alone operating robot would be on ice it cannot test the strength of the ice. But if the robot group is operating under the control of the same AI which operates them as an entirety the system might use the cause and effect methodology.

There is the possibility that artificial intelligence is located in the computer center. And it can operate radio-controlled cars by using the remote control. So the moving robots are dummies and work under the control of the central computer. There is the possibility that this kind of robot system is someday sent to another planet.

Image II:

The model of the large robot groups is taken from ants. The ants are moving robots. And anthills are the central computer of the entirety.

The cause and effect methodology would be suitable for the groups of simple robots that are operating under the same AI. Those cheap and simple moving robots are easy to replace if they are damaged. And the AI that operates those sub-robots can be at the computer center and control those robots by using regular data remote-control systems.

The supercomputer that drives AI would be at different capsule or orbiting trajectories. And the simple robot cars are operating on the ground. The system might have two stages. At the first stage. The main computer that orbits the planet will send the instructions to the ground-based computers. Those are in the landing capsules. And then those capsules are controlling the robot cars and quadcopters. Keeping the moving robots as simple as possible. Is making it possible to replace destroyed individuals from the group easily.

The AI sends the robot simultaneously to the route over the icy terrain. And the robot tells all the time its condition. If the ice breaks under it can send the data to its mates about the strength of the ice. Robots are sending information about their location all the time.

The system knows the last position of the robot. And the strength of the ice can measure by using the last images of that robot. The system knows to avoid the place where ice collapses. And the next robot knows to avoid that place. That thing means that the cause and effect methodology is suitable for large groups of robots where individual robots are not very complicated.

Artificial intelligence can operate remote-controlled robots. And that means the robots that are forming the group are simple. They might be more remote-control cars than complicated robots. The central computer that is operating the entirety is intelligent. The reason why those robots have only necessary sensors is that they are easy to replace. And maybe robot factories can make those robots in the operational area.

()https://scitechdaily.com/ai-that-can-learn-cause-and-effect-these-neural-networks-know-what-theyre-doing/

Image I: https://scitechdaily.com/ai-that-can-learn-cause-and-effect-these-neural-networks-know-what-theyre-doing/

Image II: https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/AntsStitchingLeave.jpg/800px-AntsStitchingLeave.jpg

https://visionsofbrightfuture.blogspot.com/

The LK-99 could be a fundamental advance even if it cannot reach superconductivity in 400K.

The next step in superconducting research is that LK-99 was not superconducting at room temperature. Or was it? The thing is that there is needed more research about that material. And even if it couldn't reach superconductivity in 400K that doesn't mean that material is not fundamental. And if LK-99 can maintain its superconductivity in 400K that means a fundamental breakthrough in superconducting technology. The LK-99 can be hype or it can be the real thing. The thing is, anyway, that high-voltage cables and our electric networks are not turning superconducting before next summer. But if we can change the electric network to superconducting by using some reasonable material. That thing can be the next step in the environment. Superconductors decrease the need to produce electricity. But today cooling systems that need lots of energy are the thing that turn superconductors that need low temperatures non-practical for everyday use. When the project begins there is lots of ent

Black holes, the speed of light, and gravitational background are things that are connecting the universe.

Black holes, the speed of light, and gravitational background are things that are connecting the universe. Black holes and gravitational waves: is black hole's singularity at so high energy level that energy travels in one direction in the form of a gravitational wave. We normally say that black holes do not send radiation. And we are wrong. Black holes send gravitational waves. Gravitational waves are wave movement or radiation. And that means the black holes are bright gravitational objects. If we can use water to illustrate the gravitational interaction we can say that gravitational waves push the surface tension out from the gravitational center. Then the other quantum fields push particles or objects into a black hole. The gravitational waves push energy out from the objects. And then the energy or quantum fields behind that object push them into the gravitational center. The elementary particles are quantum fields or whisk-looking structures. If the gravitational wave is

The CEO of Open AI, Sam Altman said that AI development requires a similar organization as IAEA.

We know that there are many risks in AI development. And there must be something that puts people realize that these kinds of things are not jokes. The problem is how to take control of the AI development. If we think about international contracts regarding AI development. We must realize that there is a possibility that the contract that should limit AI development turns into another version of the Nuclear Non-Proliferation Treaty. That treaty didn't ever deny the escalation of nuclear weapons. And there is a big possibility that the AI-limitation contracts follow the route of the Nuclear Non-Proliferation Treaty. The biggest problem with AI development is the new platforms that can run every complicated and effective code. That means the quantum computer-based neural networks can turn themselves more intelligent than humans. The AI has the ultimate ability to learn new things. And if it runs on the quantum-hybrid system that switches its state between binary and quantum states,

Cogito ergo sum. I think; therefore I am (René Descartes, French philosopher)

Search This Blog