Artificial Intelligence - III

Artificial Intelligence - III

            So we are continued with third post on Artificial Intelligence. In this article we are learn about the actual work of AI. We can introducing brief explanation of Intelligence System. Also learn about AI Types and their Information.

What is Intelligence?

The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations.

What is Intelligence Composed of?

The intelligence is intangible. It is composed of:

1. Reasoning

2. Learning

3. Problem Solving

4. Perception

5. Linguistic Intelligence

What are Agent and Environment?

An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.

1. A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.

2. A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.

3. A software agent has encoded bit strings as its programs and actions.

What are Expert Systems?

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Characteristics of Expert Systems

1. High performance

2. Understandable

3. Reliable

4. Highly responsive

Types of AI 

In the AI there is some types are included there main types are Strong AI and Weak AI. There is also other types we are introduce one by one. 

            Strong AI

           The work aimed at genuinely simulating human reasoning tends to be called strong AI in that any result can be used to not only build systems that think but also explain how humans

Think as well. Genuine models of strong AI or systems that are actual simulations of human cognition have yet to be built.

Weak AI

          The work in the second school of thought, aimed at just getting systems to work, is usually called weak AI in that while we might be able to build systems that can behave like humans, the results tell us nothing about how humans think. One of the prime examples of this was IBM’s Deep Blue, a system that was a master chess player but certainly did not play in the same way that humans do and told us very little about cognition in general.

TYPE I AI: REACTIVE MACHINES

            Some other types are described in below. We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities. This type of intelligence involves the computer

TYPE II AI: LIMITED MEMORY

            This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this.

TYPE III AI: THEORY OF MIND

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

TYPE IV AI: SELF-AWARENESS

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligences. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

This are all about the AI types and there brief explanation. This information can take much time to read it but it’s very interesting because it can provide some important knowledge about Artificial Intelligent. I hope it will help you to get ideas on AI.

Keep in touch to next article on series of AI. Thank you.