Bye bye algorithms.

We are waiting for the new step in the AI development and research process. Many big technology bosses say that this is the end of algorithms. And the next step is self-learning AI. That system can communicate with robots and all other systems. The self-learning system can learn in two ways. It can create new models that it uses in certain situations. Or it can connect a new module into itself. The reason why advances in AI go to self-learning systems is simple. 

New algorithms are very complicated. And their training requires so much time that the self-learning models are better. The thing that makes this kind of thing very complicated is that the new AI must operate in larger areas. They must control things like street-operating robots. So they need a more effective way to learn things. The street-operating robots can use platforms that look like computer games to learn how to cross the roads, and where those robots find things like apples if they go to shop for their owner. But then those robots must face unexpected things. 

Robots can share their mission records with the entire system. And that helps to develop methods on how to operate in natural situations. Basically, the difference between a learning system and a normal system is that the learning system can create new models and then compare the original model with that new model. There are parameters that determine which way to act is better. And if the new model is better, it replaces the old one. This means that the fixed model turns into a flexible model. That model lives with its environment. 

That thing is the AGI or artificial general intelligence. That kind of AI is everywhere, and it can connect multiple different systems that seem different under one dome. The biggest difference between AI and modern algorithms is that the system can bring new data from sensors to data flow that travels in the system. The AGI is a system that might be "god-like" but if that system cannot create genetic codes like manufacture the DNA it might have no ability to control living organisms. However, the system has many ways to manipulate evolution. 

The AGI can make couples that have certain skills. The fact is that dating applications are effective for dating. And it's possible that the AGI can also make it possible to select perfect spouses. So people who are not "perfect" leave without a couple. And that means only people who are suitable, or similar can make descendants. This causes segregation and loss of diversity. 

And that is a sad thing for humans. Self-learning AI is a tool that can learn from its mistakes. It learns what to do, and what it must not do. The thing is that the self-learning AI is the new common tool that can make almost everything. The system learns like humans. And that makes it the so-called AGI. One tool fits all. The system can control things like robots.

Robots can collect data for that system. The AGI works like this. One robot sits on a chair and then the teacher teaches things for, and through that thing. Then that robot shares new things across the entire AI and network. For training that kind of system, a lot of information. And companies like Meta have that data. And AI makes it possible to create things like AI agents that sneak and observe what happens in the network. Robots can learn from other robots. When one robot makes a mistake, it scales over the network. Other robots must know that they don’t make the same mistake again. 

Chat GPT outperforms university students as text producers.

Chat GPT outperforms university students as text producers. 

The Chat GPT is a very good tool. It outperforms many things and the next-generation coders and other workers might only advise to Chat GPT or some of its successors and the AI makes the calculations and codes for the person who uses it. So maybe Chat GPT is the end of the era of coders or analysts. 

When we think about students, they have to learn to work with the newest possible tools. The business and military environments require flexibility and effective tools. The purpose of studies is to give a person the capacity to operate in real-life business and technical environments. Nobody sits in school forever. 

We know that some students are cheating. But those students cheat anyway. That means the Chat GPT is a system that requires an honest attitude. Students are cheated throughout history. The most common way to cheat in studies is to hire some other person to do their work. And that means the AI just gives that possibility to people who have no money and relationships that make essays for those students. 

The Chat GPT and other similar algorithms are a challenge. But people must just respond to that challenge. They must learn to use those new programs because they are necessary tools. The Chat GPT and other similar applications can already used for making military systems. And Ukrainian army uses those applications for the R&D of their equipment. 

Who work with mechanical areas in computers and especially programming technology. When we are talking about the end of coders we are talking about the end of the era of low-paid one-time-used workers. The Chat GPT and its "family algorithms" like Bing are tools that are making prima-code very fast. And that's why they are the ultimate tools for programming. 

When we think about AI and its abilities we must remember that only imagination is limited. The fact is that AI is the tool that will turn everything around. It's a game changer in business, engineering, and war. The AI-based metaverse applications have many possibilities that even their developers cannot predict. Maybe in the future, we will live in a metaverse that interconnects multiple physical and virtual systems into one entirety. 

The 3D printer systems that can connect with AI can make everything. There is a possibility that the metaverse is a matrix where people can use virtual characters or they can use physical robots as external bodies. Those external robot bodies can turn all vehicles and aircraft into robot systems. 

The BCI systems can make it possible for people can see things like animals see them. The BCI can transmit things that things like birds are seeing. And that thing can turn every single animal into a biosensor or biorobot that can be used in many missions in scientific, law enforcement, and military areas. The thing is that the metaverse along with BCI means that people can live in the virtual world. Architects can see what their house looks like in real places. Aircraft and car designers can give instructions for AI. And that thing makes models by following those instructions. 

The AI-based design tool makes it possible for people can make customized cars and other vehicles by using simple design tools. If we interconnect that design tool with a 3D printer system that follows orders and makes it possible to create customized vehicles and other merchandise for customers. 

The metaverse allows customers to see the product in the natural environment. The vacuum technology where that printer operates allows them to create high-class, high-temperature products. Also, new carbon fiber- chromium-steel composites are making the new hardness to those materials that the high-temperature 3D systems can use. 

And of course, people say that AI and combat robots are not humanitary weapons. The fact is anyway, the purpose of the military is to be effective. Without killer drones, Ukraine would fall. The thing is that those killer robots are effective tools for non-humane purposes. And war is a non-human action. But if the nation wants to defend itself it must have effective military forces. 

https://scitechdaily.com/new-study-chatgpt-outperforms-university-students-in-writing/

Large neural networks have a larger number of abilities.

Above this text is the model of the neural network system. Every point in that diagram is the ability or skill that the network has. And every line in that image is the connection between those skills. So every point in that image is the database. And the line is the database connection. Every single database can have a limited number of connections. Everything that the robot must do is stored in databases. And there is a series of actions that are connected to a certain table. 

The neural network can be physical. It can be the network of physical systems like computers and surveillance cameras. 

Or it can be virtual. The neural network can be the network of skills. And whenever the system learns a new skill it expands. The network of skills means that every single action requires sub-actions. The sub-actions like turning the steering wheel can use in many places. Same way robots can turn cars, tractors, and forklifts. So that means the robot can use the same skill to turn every vehicle that has steering wheels. The virtual system means a large number of networked databases. 

The things like red traffic lights can act as triggers that launch a certain reaction. So when a robot sees red traffic light. That thing launches certain action. A great number of databases means that AI can search solutions more effectively. If the system already knows that the screwdriver is the tool it can search that thing faster. In that kind of operation, the robot goes to the toolbox. If there is no screwdriver it might ask alternative places. So the operator tells that maybe that tool is on the table. So the robot searches that place. If that screwdriver is there, the robot would involve the table to list places where the screwdriver could be. 

Of course, the robot can search automatically screwdriver from the floor. The AI uses image recognition for separating the objects. If there is no screwdriver the AI can search the image of that tool from its memory and ask is this screwdriver? The human operator can check that there is the right image in the memory of the AI. If the image is wrong the operator can change it to right. The robot is not necessarily physical. It can be the algorithm that collects data from the Internet. 

The learning system means the number of databases increases. And the number of connections between them also increases. That means that if there is a lot of databases at the beginning of the independent learning that system can use more connections at the beginning of operations. And that makes the system can use databases versatile if there is a large number of data for use at the beginning of the self-learning. 

Self-learning or autonomous learning process means that the system can increase the number of databases and database connections without human assistance. There is a theory that all databases on the internet can interconnect to one large database entirety. And that thing makes it possible to create the ultimate artificial intelligence that can interconnect all computers and other systems to one entirety.  

The self-learning process can turn more effective. If the databases are pre-sorted by using certain parameters. In that model the database groups are sorted under topics like "visiting shop", cleaning the house" etc. Those databases involve what kind of things the AI must use for completing the mission successfully. 

So when an AI-controlled robot is taking the order to go shopping it can interconnect the databases that are involving things how the robot should walk to the shop. Where are traffic lights, how must react to traffic lights etc.? The thing is that robots can use the same databases for multiple uses. The knowledge of how to react when traffic lights are red. Can use in all missions like walking, driving cars, and other things. 

Image)https://www.quantamagazine.org/computer-scientists-prove-why-bigger-neural-networks-do-better-20220210/

https://thoughtsaboutsuperpositions.blogspot.com/

Do you trust AI?

   

At the beginning of this text, I must say that AI is a computer program. Computer programs are like machines. They cannot handle every problem on Earth. They meant to use for some certain purpose. And if we want to use some AI algorithm outside its operational sector, that thing causes catastrophe. The world is full of algorithms. 

Some algorithms are meant to use for things like collecting marketing information from limited systems. The other AI systems are meant to control physical robots. So if we want to use AI for something. We must make sure that the program is meant for that purpose. 

We must realize that if we want to use the marketing analysis programs for controlling robots that thing would cause disaster. If we want to improve the skills of AI. That requires more complicated code than the AI that has only one skill. Every single skill that the AI has must be programmed to that thing. Machine learning is making independently learning machines possible. 

There are three types of learning machines. 

1) Semi-automatic learning systems. 

Whenever the system faces a new problem it calls the operator.  The operator makes the solution and stores that thing in the memory of computers. 

X) Independently learning machines. 

Those machines can create the databases automatically. And then those systems can automatically connect the database to a certain action series. 

2) Hybride systems

Those systems can make the solution or connections between databases automatically. But if that system cannot find the database that fits the problem it can ask for assistance from the operators. That kind of system can respond to multiple problems. 

Hybrid systems are close to the human way to learn things. If the system would not find a match for the case. It would not know how to respond to the case that it faces. In that case. The system will ask for help in solving the problem from the human operators. 

Whenever the system gets the new answer for problems. That thing increases the data mass that the system can create more connections. And it turns more independent. 

When the system is creating the solution. Or the controller solves a problem that solution stored in the memory of the artificial intelligence. for similar cases. That thing increases the number of skills of the AI. 

There are two types of AI

1) Passive AI. 

That system just collects data and analyzes it. 

2) Active AI.

That system interacts with the real world. The system collects data from the sensors. Then it analyzes that data. And then it sends signals to the communication tool. That tool might be the traffic lights if the AI controls traffic. 

The thing is that AI is not a stand-alone operating tool. The system requires tools like an internet connection or a physical robot for making things. If the AI is interacting with the real world. 

It requires sensors and is connected to the sensors that it can get the data mass that it processes. But the AI needs the tool how to interact with the real world. If it controls things like traffic it needs a connection to the traffic lights. Without that connection, AI does not affect the real world. 

All artificial intelligence programs or algorithms are meant to operate in certain sectors. 

The thing in artificial intelligence is that it doesn't make mistakes. If we are saying that the AI makes mistakes. We can same way say that some regular programs like text-handling tools make mistakes. Every mistake that the AI makes is encoded in its code. 

Another way that causes mistakes for the AI is that the data that the AI handles is somehow disturbed. If the sensor that sends data to AI is corrupted accidentally or in purpose. That means the data flow to the system is not relevant. 

The corruption of the sensor means as an example,  that the camera might be dirty.  So that means the system would not get real information. When we are thinking about the trust of AI, we must realize that we must check every single part of the system. The code itself must be completed and tested. But cable connections and the function of the sensors are the same way the important things. 

https://scitechdaily.com/measuring-trust-in-artificial-intelligence-ai/

Image: https://scitechdaily.com/measuring-trust-in-artificial-intelligence-ai/

https://thoughtsaboutsuperpositions.blogspot.com/

Artificial intelligence is the very good thing when the massive quantity of the data must be processed.

http://crisisofdemocracticstates.blogspot.fi/p/artificial-intelligence-is-very-good.html

http://crisisofdemocracticstates.blogspot.fi/p/artificial-intelligence-is-very-good.html