The Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game.

  The Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game. 

The Chat GPT,  Bing, and many other AI-based chatbot versions are massive systems that should fit every situation. The problem with common AI is that these kinds of systems require lots of capacity, and there are lots of sources that those systems must use. 

This thing means that the trustworthiness of sources is problematic. The reason for this thing is that the AI doesn't think. It collects data by following certain parameters. That thing makes those systems vulnerable in cases where they should search for information that is not very common. 

The smaller-size specific AI-based systems that can use the same engines with Chat GPT and Bing are more suitable for things like scientific writing. The AI is an ultimate tool if it has a pre-programmed list of trusted and estimated sources. If a writer wants information about some very uncommon things like quantum mechanics. The AI can use sources. That passed scientific estimation. The results are best in business.

The limited AIs can act as independently operating modules in the networked AI-based systems. In that case, those limited AIs act as event handlers for the common AIs. 

Maybe we think that those small AIs operate independently. But the fact is that those independently operating smaller AIs can used as event handlers. That system is connected with bigger AI:s. In that model, those limited AIs can form independently operating module networks, which makes the common AIs more powerful, and accurate than ever before. 

Those independently operating limited AIs can network below the Chat GPT style AIs. The idea is that the limited AIs can form the entirety under the common AI control. This means that the limited AI:s can form a network. That the bigger AIs can be used as event handlers. 

Next comes two examples of limited and powerful AIs. Those things can turn game changers. 

The Finnish AI predicted very accurately where the wildfire started. 

One of the examples of specific highly accurate AI is the AI that predicts wildfire. The researchers from Finnish Aalto-University have created an AI that can predict wildfires. And that AI has shown its success. In this case, the AI uses parameters like humidity in the air, air temperature, and wind speed. The system also can use statistics about the conditions and places where wildlife is starting. 

Also, things like the frequency of lightning and things that are lightning common along with rain or in dry weather are things, that help to predict wildlife. The system also can follow volcanic activity and how often and in what kinds of conditions people are making fire. If there are no spark arresters in chimneys that increases the risk of wildfire.  In that case, the specific AI follows only a limited number of variables. And that thing makes it very accurate. 

The military AI can predict where the enemy attack comes from. 

The military AI can use variables like how hard the ground is, is there some muddy river bottoms, and other kinds of things to predict the place where the enemy might want to attack. There are also many other variables like enemy vehicles and weapons that can affect to that place.

But if the enemy uses tanks the ground's hardness is extremely important. Another thing that the system must know is how steep the riverbed is. That is important information for tanks. In the cases that they cannot use bridges. 

There are, of course, many other variables that the system must have. But those two things are examples of small, and specific AIs. Those RISCs- AI:s are not as flexible as Chat GPT and Bing, but they are highly accurate. And the limited operational areas make variable handling easier than in some common AIs.  

https://www.dezeen.com/2023/08/24/ai-wildfire-model-firecnn-aalto-university-aitopia/

https://www.aalto.fi/en/news/new-ai-system-predicts-how-to-prevent-wildfires

ChatGPT passes the U.S. medical licensing examination.

 

The biggest difference between machine learning and human is that human uses fuzzy logic. The machine uses precise logic for its actions. When a machine operates, it uses precisely described action chains. The problem is that no pre-programmed action chain has a match for a real-life situation. 

On the streets are some surprises. Like suddenly coming cars are everyday situation. Nobody can predict all possible situations that robots can face in real life. And that makes the system hard to program. 

Precise logic means that programmers must describe every single thing that requires notice or reactions to the machine. The machine cannot operate. If something is missing from its code. 

When the learning system will face that kind of situation it says "unknown situation". And then ask for orders from the human operators. 

The reason. Why ChatGPT passed the U.S. medical examination, but the AI cannot recognize U.S. marines that closed the base because the type of information that AI must handle in the examination is limited. 

In the examination, the AI uses a precise and limited type of information. The AI must just handle letters with precise ASCII values. AI is best in that kinds of missions. 

And when marines closed the target. The AI must handle uncertain types of information. The difference between human and machine learning is that the coders must precisely describe the missions to the AI. 

Sometimes that the person takes a box or some unexpected item like red paper. And cover the head by using that item is enough to cheat the AI. The reason for that is simple. Nobody ever described a person whose head is covered with paper to AI. And that means the AI goes into a trap. 

In the test, marines used trees and boxes. And the AI didn't recognize them. Nobody even describes the case. The two men walk to the target area and keep the box over their heads. Nobody describes. The situation for the AI is that the box with four feet are closing it. 

The AI is the support system. Not the main operator. But the systems advancing. And that kind of test is needed for the R&D work of that system. The same thing that made it easy to cheat the AI is making it hard to make robots that can go shopping for people. 

There are so many variables that making perfect descriptions for every possible situation is hard. Programmers are hard to describe all possible situations, and blocks to the computer. 

But why the AI that mission is to launch an alarm if some unauthorized person is in house works? Sometimes regular alarm systems make alarms. When the burglary alarm launches that system sends information to the guard, who checks the situation from the screen. 

The fact is that the AI uses precise logic. If the alarm system is on, the AI simply knows that nobody should move in some area. And then if something moves in some room, it sends information to the human operators. The AI can detect cases like possible fights in public transportation. And then make an alarm to human operators who make decisions on how to act in that case.

https://www.unilad.com/news/us-marines-defeat-pentagon-ai-robot-cardboard-boxes-175338-20230121

https://scitechdaily.com/the-rise-of-artificial-intelligence-chatgpts-stunning-results-on-the-us-medical-licensing-exam/

https://shorttextsofoldscholars.blogspot.com/