Artificial Intelligence is a fascinating study. If you are a beginner or simply curious about Artificial Intelligence, this article covers the basics for you...
Today I'm gone a show you the real concept of Artificial Intelligence. As per I think You have heard about the term Artificial Intelligence but didn't clear the concept behind it..so this article covers the basics behind it ...
The concept of what defines AI has changed over time, but at the core, there has always been the idea of building machines which are capable of thinking like humans. According to John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in a 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.
Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc.
Fig: Artificial Intelligence is Picking Up the Era by Native AI Smartphone Penetration Rate
“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.
Let's see, 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 to new situations.
Types of Intelligence & The examples of it:
- Linguistic intelligence - Narrators, Orators
- Musical intelligence - Musicians, Singers
- Logical-mathematical intelligence - Mathematicians, Scientists
- Spatial intelligence - Map readers, Astronauts
- Bodily-Kinesthetic intelligence - Players, Dancers
- Interpersonal intelligence - Interviewers
Difference between Human and Machine Intelligence:
- Humans perceive by patterns whereas the machines perceive by a set of rules and data.
- Humans store and recall information by patterns, machines do it by searching algorithms. For example, the number 40404040 is easy to remember the store, and recall as its pattern is simple.
- Humans can figure out the complete object even if some part of it is missing or distorted; whereas the machines cannot do it correctly.
Real Life Applications of AI Research Areas:
- Expert Systems:
- Natural Language Processing:
- Neural Networks:
- Robotics:
Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc.
- Fuzzy Logic Systems:
Fig: Artificial Intelligence is Picking Up the Era by Native AI Smartphone Penetration Rate
What is Exactly AI Technique?
In the real world, knowledge has some unwelcomed properties −
- Its volume is huge, next to unimaginable.
- It is not well-organized or well-formatted.
- It keeps changing constantly.
A Technique is a manner to organize and use the knowledge efficiently in such a way that −
- It should be perceivable by the people who provide it.
- It should be easily modifiable to correct errors.
- 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.
As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for tasks like understanding language.
Others were inspired by the importance of learning to human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data.
The field hit milestone after milestone, as computers mastered more
tasks that could previously be done only by people.
Deep learning, the rocket fuel of the current AI boom, is a revival of one of the oldest ideas in AI. The technique involves passing data through webs of math loosely inspired by how brain cells work, known as artificial neural networks. As a network processes training data,
connections between the parts of the network adjust, building up an ability to interpret future data.
(The above Video will explain you the overall concept of Artificial Intelligence)
Applications of AI:
AI has been dominant in various fields such as −
- Gaming − AI plays a crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where the machine can think of a large number of possible positions based on heuristic knowledge.
- Natural Language Processing − It is possible to interact with the computer that understands the natural language spoken by humans.
- 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.
- Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example,
- A spying airplane 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.
- 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.
- 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 them into editable text.
- 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.
How Agricultural Sector Uses Artificial Intelligence?
The above image coverup the Robotics and AI how it actually used by some of the famous industries in the Agricultural Sector
The above image coverup the Robotics and AI how it actually used by some of the famous industries in the Agricultural Sector
- Analyzing Satellite Images
- In-Field Monitoring
- Agricultural Robots
- Predictive Analytics
- Soil Health
Importance Of AI:
Nowadays, AI is most important in our day to day life usually we didn't aspects it or take it much important But in this case it's now used in every sector. The following Image will cover up the point of How important is it that AI be used to help solve the following societal issues? However, ok I'll give you the list of the exact ratio which is used to solve by their problems:
Seven Spectrums of Outcomes for AI:
- Perception
- Notification
- Suggestion
- Automation
- Prediction
- Prevention
- Situational Awareness
The Future of Artificial Intelligence :
Even if progress on making artificial intelligence smarter stops
tomorrow, don’t expect to stop hearing about how it’s changing the
world.
Big tech companies such as Google, Microsoft, and Amazon have amassed
strong rosters of AI talent and impressive arrays of computers to
bolster their core businesses of targeting ads or anticipating your next purchase.
The understandable concern has led to the foundation last year, by a number
of tech giants including Google, IBM, Microsoft, Facebook, and Amazon, of
the Partnership in AI.
This group will research and advocate for ethical implementations of
AI, and to set guidelines for future research and deployment of robots
and AI.
They’ve also begun trying to make money by inviting others to run AI
projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware,
growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.