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.
