Artificial Intelligent-IV

Artificial Intelligent-IV

Hello ,

             So  we have go forward to learn new about Artificial Intelligent Series. In this post I have introduce you with Machine Learning, Artificial Intelligent And  Deep Learning Topics. First I have introduce about these three topic and then the difference of that.
                

                                          
Artificial intelligence(AI)

            AI is the most comprehensive way to think about the best of advanced computer intelligence. At the Dartmouth Artificial Intelligence Conference held in 1956, AI was suitably described as: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” It can range from a computer program playing chess or a voice-recognition system like Alexa by Amazon that is capable of speech response and interpretation.

Machine Learning (ML)

            ML is a subfield of AI. The core principle of ML is that machines learn by themselves by taking data from various sources. Presently, it is considered to be one of the most promising tools of the AI kit that is suitable for businesses.

            ML systems are capable of fast application of knowledge and getting trained from large sets of data to be able to do tasks like speech recognition, facial recognition, translation, and object recognition with ease and efficiency. ML allows machines to make predictions based on the recognition of complex data sets and patterns. This is what makes ML different from hand-coding a software program that requires specific instructions for task completion.

Deep Learning

            Deep learning is one of the subsets of machine learning. Whenever the term deep learning is used,  it is generally referred to the deep artificial neural networks, and at times of deep reinforcement learning. Deep artificial neural networks are algorithm sets are extremely accurate especially for problems like sound recognition, image recognition, recommender systems, etc. A few examples would be – deep learning is a part of DeepMind’s popular AlphaGo algorithm, which had beaten former world champion Lee Sedol at Go in 2016, and the current world champion Ke Jie in 2017.

            Deep is a technical term referring to the layers of the neural network. A superficial network has a single hidden layer, and a network that is deep has multiple layers. These hidden layers are the layers that allow deep neural networks to acquire data features from a feature hierarchy. This is due to the fact that simple features recombine from the existing layers to form complex features. Intensive computations form the basis of deep learning, and this is why GPUs are in great demand- to provide training in the deep-learning models.

Difference

              After the above information you have getting enough idea About AI,ML and Deep Learning. So the is some difference on these three fields can described below.

  

            While discussing about Artificial intelligence vs Machine learning vs Deep learning, one needs to understand that data lies at the heart of everything. Whether it is an algorithm that is being used or machine learning or artificial intelligence, one aspect is certain: if flawed data is being used, the extracted information and insights would most definitely be flawed. Algorithms can be flawed quite like the humans that they are replacing but more the usage of data more is the scope for flaws.

             Therefore data cleansing becomes critical. Data cleansing is defined as the process of correcting and detecting inaccurate or corrupt records from a table, record set, or database and deleting/ modifying irrelevant/incorrect data. According to the Crowd Flower Data Science report, data scientists need to cleanse data though it is not something that they enjoy doing. A major part of their time is spent in data cleansing since the output needs to be trustworthy and this can only happen when data is cleansed.

            The concept of Artificial intelligence is broader than that of machine learning, the latter uses computers to imitate the cognitive human functions. Artificial intelligence, therefore, can be defined as machines carrying out various tasks based on algorithms in a perfectly intelligent way. Machine learning is a subset of AI and its’ focus lies on the capability of machines to not only receive data sets but also learn and relearn for themselves, change the algorithms according to the information that they are processing.

            Deep learning networks need to visualize large quantities of items to be trained. Instead of being programmed with the edges that define items, the systems learn from exposure to millions of data points. An early example would be of Google Brain learning to recognize cats after being shown over ten million images. Deep learning networks do not need to be programmed with the criteria that define items; they are able to identify edges through being exposed to large amounts of data.

            So we have understanding the concept of Artificial intelligence, Machine learning, Deep learning. And there difference also. This information can take some time to read,but it’s very interesting because it can provide some important knowledge about Artificial Intelligent. I hope it will help you to grow your knowledge on AI.

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

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.

Artificial intelligence

Hello Friends,
            Today I have Introduce My new series On Artificial intelligence. I Hope you like it and get new knowledge from my blog. Also I will come with new series on Blockchain, Bitcoin, Deep web and much more fresh topic.
             So please keep In touch with my new series and my blog and Learn Something New..!!! And also share this with your friends and follow my blog.
     So Let’s, Start...This is First post on Artificial intelligence series. 

Introduction

             Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn.
It is also a field of study which tries to make computers "smart". According to the father of Artificial Intelligence John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.
           Artificial Intelligence (AI) is the key technology in many of today's novel applications, ranging from banking systems that detect attempted credit card fraud, to telephone systems that understand speech, to software systems that notice when you're having problems and offer appropriate advice. These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades.
           Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

           AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?”

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.
         

           Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.
           This is the introduction part of Artificial intelligence.

History

            Objects that look and act like humans exist in every major civilization. The first appearance of artificial intelligence is in Greek myths, like Talos of Crete or the bronze robot of Hephaestus. Humanoid robots were built by Yan Shi, Hero of Alexandria, and Al-Jazari. Sentient machines became popular in fiction during the 19th and 20th centuries with the stories of Frankenstein and Rossum's Universal Robots.
             Formal logic was developed by ancient Greek philosophers and mathematicians. This study of logic produced the idea of a computer in the 19th and 20th century. Mathematician Alan Turing's theory of computation said that any mathematical problem could be solved by processing 1's and 0's. Advances in neurology, information theory, and cybernetics convinced a small group of researchers that an electronic brain was possible.
              AI research really started with a conference at Dartmouth College in 1956. It was a month long brainstorming session attended by many people with interests in AI. At the conference they wrote programs that were amazing at the time, beating people at checkers or solving word problems. The Department of Defense started giving a lot of money to AI research and labs were created all over the world.

          AI revived again in the 90s and early 2000s with its use in data mining and medical diagnosis. This was possible because of faster computers and focusing on solving more specific problems. In 1997, Deep Blue became the first computer program to beat chess world champion Garry Kasparov. Faster computers, advances in deep learning, and access to more data have made AI popular throughout the world. In 2011 IBM Watson beat the top two Jeopardy! Players Brad Rutter and Ken Jennings, and in 2016 Google's Alpha Go beat top Go player Lee Sedol 4 out of 5 times.
          

            I know this is boring reading for you that’s why I will make series on it. So you can read it carefully and get complete Knowledge on Artificial intelligence. 

           So keep in touch with the series. And most important thing is Share my blog with your friends and suggest me your Ideas in comment, so I will try to give brief information on your ideas. And please give me your feedback on each post on that series.

            Thank You… Keep Reading Next Post to know more on Artificial intelligence.
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Artificial Intelligent-II

Artificial Intelligent-II

                  So how was the first Artificial Intelligent post? I hope it will informative to all my readers, Friends. Also you can get basic knowledge of Artificial Intelligent.
Now we are continue with second post on Artificial Intelligent. In this post we are cover the same topic related with Artificial Intelligent. In that we can get knowledge on Artificial Intelligent Applications, there Goals, and there Issues.

                     Artificial Intelligent is the best option to handle some areas like Gaming, Natural Language Processing, Speech Recognition, and Intelligent Robots etc. It’s very helpful to fast growth of world because it can do the hard job in easy way and also fastly. So it’s very time consuming than other human working process.
Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.
                     Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.
Out of the following areas, one or multiple areas can contribute to build an intelligent system.

Goals of AI
1. To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
2. To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.

What is AI Technique?
In the real world, the knowledge has some unwelcomed properties:
1. Its volume is huge, next to unimaginable.
2. It is not well-organized or well-formatted.
3. It keeps changing constantly.

AI Technique is a manner to organize and use the knowledge efficiently in such a way that:
1. It should be perceivable by the people who provide it.
2. It should be easily modifiable to correct errors.
3. It should be useful in many situations though it is incomplete or inaccurate.
AI techniques elevate the speed of execution of the complex program it is equipped with.

Applications of AI
AI has been dominant in various fields such as:

1. Gaming
AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

2. Natural Language Processing
It is possible to interact with the computer that understands natural language spoken by humans.

3. Expert Systems
There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

4. Vision Systems
These systems understand, interpret, and comprehend visual input on the computer. For example,

• A spying aero plane takes photographs which are used to figure out spatial information or map of the areas.

• Doctors use clinical expert system to diagnose the patient.

• Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

5. Speech Recognition
Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

6. Handwriting Recognition
The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

7 Intelligent Robots
Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

AI ISSUES
                   AI is developing with such an incredible speed, sometimes it seems magical. There is an opinion among researchers and developers that AI could grow so immensely strong that it would be difficult for humans to control.
Humans developed AI systems by introducing into them every possible intelligence they could, for which the humans themselves now seem threatened.
Threat to Privacy an AI program that recognizes speech and understands natural language is theoretically capable of understanding each conversation on e-mails and telephones.

                Threat to Human Dignity AI systems have already started replacing the human beings in few industries. It should not replace people in the sectors where they are holding dignified positions which are pertaining to ethics such as nursing, surgeon, judge, police officer, etc.
Threat to Safety the self-improving AI systems can become so mighty than humans that could be very difficult to stop from achieving their goals, which may lead to unintended consequences.
So this are the some Artificial Intelligent Applications, Goals, and Issues. These topic are small information on AI because our AI field is changing constantly day by day so this information also updating .I try to provide latest updated information on Artificial Intelligent.so keep in touch with that series get complete information on AI. And your eyes on next post I will post as soon.

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