Introduction
In the previous article, we studied
Tensorflow, its functions, and its python implementations. In this article, we will be studying Artificial Intelligence and more popularly knows as AI. One thing that I believe is that if we are able to correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.
Note: Remember an AI can think or do what a human can do. We are trying to build an AI which has its own brain, but that is 50- 100 years down the line as of the current scenario. So to become an AI or ML expert or practitioner you should primarily have the capability to correlate each and everything with yourself. Once you are able to do this, half of the work is done.
What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
Applications of Artificial Intelligence
1. Gaming
2. Natural Language Processing
3. Expert Systems
4. Vision System
5. Speech Recognition
6. Handwriting Recognition
7. Intelligent Robots
Features of Artificial Intelligence
1) Reduction in Human Error.
2) Takes risks instead of Humans
AI Robots can be used in situations where human intervention can be hazardous.
3) Available 24×7
4) Helping in Repetitive Jobs
5) Digital Assistance
6) Faster Decisions:
7) Daily Applications
8) New Inventions:
Shortcomings of Artificial Intelligence
1) High Costs of Creation
2) Making Humans Lazy
3) Unemployment
4) No Emotions
5) Lacking Out of Box Thinking
What is the difference between Artificial Intelligence and Machine Learning?
| ARTIFICIAL INTELLIGENCE |
MACHINE LEARNING |
| AI stands for Artificial intelligence, where intelligence is defined as the acquisition of knowledge intelligence is defined as an ability to acquire and apply knowledge. |
ML stands for Machine Learning which is defined as the acquisition of knowledge or skill |
| The aim is to increase the chance of success and not accuracy. |
The aim is to increase accuracy, but it does not care about the success |
| It works like a computer program that does smart work |
It is a simple concept machine that takes data and learns from data. |
| The goal is to simulate natural intelligence to solve a complex problem |
The goal is to learn from data on certain tasks to maximize the performance of the machine on this task. |
| AI is decision-making. |
ML allows the system to learn new things from data. |
| It leads to developing a system to mimic humans to respond to behave in circumstances. |
It involves creating self-learning algorithms. |
| AI will go for finding the optimal solution. |
ML will go for the only solution for whether it is optimal or not. |
| AI leads to intelligence or wisdom. |
ML leads to knowledge. |
Basic Terms
Following are some terms that are a pre-requisite for us to understand Artificial Intelligence
1. Knowledge
Theoretical Definition of Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning. Knowledge can refer to a theoretical or practical understanding of a subject. Following are the types of knowledge:
1. Declarative Knowledge:
- Declarative knowledge is to know about something.
- It includes concepts, facts, and objects.
- It is also called descriptive knowledge and expressed in declarative sentences.
- It is simpler than procedural language.
2. Procedural Knowledge
- It is also known as imperative knowledge.
- Procedural knowledge is a type of knowledge that is responsible for knowing how to do something.
- It can be directly applied to any task.
- It includes rules, strategies, procedures, agendas, etc.
- Procedural knowledge depends on the task on which it can be applied.
3. Meta-knowledge:
- Knowledge about the other types of knowledge is called Meta-knowledge.
4. Heuristic knowledge:
- Heuristic knowledge is representing the knowledge of some experts in a field or subject.
- Heuristic knowledge is rules of thumb based on previous experiences, awareness of approaches, and which are good to work but not guaranteed.
5. Structural knowledge:
- Structural knowledge is the basic knowledge of problem-solving.
- It describes relationships between various concepts such as kind of, part of, and grouping of something.
- It describes the relationship that exists between concepts or objects.
2. Intelligence
Theoretical Definition of Intelligence is the ability to learn or understand or to deal with new or trying situations and the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (such as tests). We classify intelligence into the following classes:
1. Naturalist Intelligence
Naturalist intelligence designates the human ability to discriminate among living things (plants, animals) as well as sensitivity to other features of the natural world (clouds, rock configurations)
Musical intelligence is the capacity to discern pitch, rhythm, timbre, and tone. This intelligence enables us to recognize, create, reproduce, and reflect on music, as demonstrated by composers, conductors, musicians, vocalists, and sensitive listeners. Interestingly, there is often an affective connection between music and emotions; and mathematical and musical intelligence may share common thinking processes.
3. Logical-Mathematical Intelligence
Logical-mathematical intelligence is the ability to calculate, quantify, consider propositions and hypotheses, and carry out complete mathematical operations. It enables us to perceive relationships and connections and to use abstract, symbolic thought; sequential reasoning skills; and inductive and deductive thinking patterns.
4. Existential Intelligence
Sensitivity and capacity to tackle deep questions about human existence, such as the meaning of life, why we die, and how did we get here.
5. Interpersonal Intelligence
Interpersonal intelligence is the ability to understand and interact effectively with others. It involves effective verbal and nonverbal communication, the ability to note distinctions among others, sensitivity to the moods and temperaments of others, and the ability to entertain multiple perspectives.
6. Bodily-Kinesthetic Intelligence
Bodily-kinesthetic intelligence is the capacity to manipulate objects and use a variety of physical skills. This intelligence also involves a sense of timing and the perfection of skills through the mind-body union. Athletes, dancers, surgeons, and craftspeople exhibit well-developed bodily-kinesthetic intelligence.
7. Linguistic Intelligence
Linguistic intelligence is the ability to think in words and to use language to express and appreciate complex meanings. Linguistic intelligence allows us to understand the order and meaning of words and to apply meta-linguistic skills to reflect on our use of language.
8. Intra-personal Intelligence
Intra-personal intelligence is the capacity to understand oneself and one’s thoughts and feelings, and to use such knowledge in planning and directing one’s life. Intra-personal intelligence involves not only an appreciation of the self but also of the human condition. It is evident in psychologists, spiritual leaders, and philosophers.
Spatial intelligence is the ability to think in three dimensions. Core capacities include mental imagery, spatial reasoning, image manipulation, graphic and artistic skills, and an active imagination. Sailors, pilots, sculptors, painters, and architects all exhibit spatial intelligence.
3. Understanding
Theoretical Definition of Understanding is Understanding is a psychological process related to an abstract or physical object, such as a person, situation, or message whereby one is able to think about it and use concepts to deal adequately with that object. Understanding is a relation between the knower and an object of understanding. Types of Understanding:
1. Somatic
from birth till about age 2. The main goal is the mastery of mimetic (copying) activities. The main characteristics involve mastery of physical activities and a non-verbal appreciation of the world.
from about ages 3-7. The main goal is the mastery of oral language. The main characteristics involve binary opposites in thinking, metaphors, and stereotypes, including socialization into the culture’s myths and taboos, and gaining a shared sense of right and wrong.
from about ages 8-14. The main goal is the mastery of literacy. The main characteristics involve the acquisition of conventional skills involving getting along, writing and literacy, and gaining an appreciation for finer gradations in perception and thinking (not just the binary opposites of Mythic understanding). There is also a concern with the limits and extremes of human potential.
4. Philosophical
from about ages 15-20. The main goal is the mastery of theoretic abstractions. The main characteristics involve a concern with the theories of the world and one’s position in the world, including its theories. All the facts that the individual had been accruing through Romantic understanding now become sorted and organized into various preferred theories. One develops an ability to both support a theory with the addition of relevant facts as well as to ignore or dismiss facts that may appear inconsistent with that preferred theory.
from about age 21+. The main goal is the mastery of refined reflexiveness. The main characteristics involve skepticism about the various theories (typical of Romantic understanding), or skepticism about the features and interpretation of facts or stories about the human potential (characteristic of Romantic understanding), and so on. Such skepticism can range in how extreme it becomes (from scathing caustic satire on one extreme, to gently skeptical questioning and kind or even silly humor on the other).
4. Intellectual Skills
The theoretical definition is the ability to distinguish, combine, classify, analyze, and quantify objects, events, and symbols; they are divided into discriminations, concrete concepts, defined concepts, rule using, and higher-order rule. List of intellectual skills are
1. Knowledge and Understanding
2. Critical Thinking
3. Bias
4. Problem Solving
5. Creative Skills
5. Practical Skills
The theoretical definition is the skills performed by hand (as in tying a knot) or with human intervention using equipment, tools, or technology requiring guidance, force, or movement (as in utero blood transfusion). Practical skills primarily require physical dexterity, although an understanding of principles, processes, and sequences is also essential, especially for more complex practical skills. List of Practical Skills are:
1. Teamwork
Children learn to be able to work in teams. Even if disagreements occur, hands-on projects teach children to work towards a common goal, while also learning self-control, empathy, time management, and negotiation.
2. Problem-solving
Students discover answers from their own questions and research theories. From this, students learn to think critically, observe and analyze situations in order to form creative solutions based on problems.
3. Communication skills
Communication skills are crucial to any role. Through problem-solving, students learn to effectively communicate, both in writing and orally. Children will learn to say what they mean and explain ideas.
4. Failure is okay
Failure is an important part of finding solutions. Children learn that there often is no single “right” answer in engineering projects. This type of learning removes the stigma that stems from failure and encourages it as a positive way to learn.
5. Leadership skills
During teamwork, leadership skills arise through actions, especially through project management. Common to any job, the “unofficial” project manager will help to establish roles, responsibilities, set priorities, and influence opinions to make decisions.
6. Transferable Skills
Transferable skills are skills and abilities that are relevant and helpful across different areas of life: socially, professionally, and at school. They are 'portable skills. List of Transferable Skills are
1. Team Work
2. Leadership
3. Personal Motivation, Organisation, and Time Management
4. Listening
5. Written Communication
6. Verbal Communication
7. Research and Analytical Skills
8. Numeracy Skills
9. Personal Development
10. Information Technology
7. Sensor
A Sensor is a device that detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors.
8. Actuators
Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc.
9. Effectors
Effectors are devices that affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screens.
Application of above Terms in Artificial Intelligence
1. Knowledge and understanding
You should have a knowledge and understanding of the basic concepts of Artificial Intelligence including Search, Game Playing, KBS (including Uncertainty), Planning, and Machine Learning.
2. Intellectual skills
You should be able to use this knowledge and understanding of appropriate principles and guidelines to synthesize solutions to tasks in AI and to critically evaluate alternatives.
3. Practical skills
You should be able to use a well-known declarative language (Prolog) and to construct simple AI systems.
4. Transferable Skills
You should be able to solve problems and evaluate outcomes and alternatives
History of Artificial Intelligence
Now we would be studying the life of AI, starting with the birth and ending at the current scenario. We will see various phasis of Artificial Intelligence life. As many of you may know about the SDLC (Software Development Life Cycle) or HDLC (Hardware Development Life Cycle). Both are designed as if one thing is created it will get obsolete. In the same way, we are seeing that with time AI models that were once considered to be useful and now getting "obsolete", i.e. now they have just studied as a part of educational curriculum and are not used in industries or I would say not used in production.
Maturation of Artificial Intelligence (1943-1952)
- Year 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons.
- Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning.
- Year 1950: Alan Turing was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test.
The birth of Artificial Intelligence (1952-1956)
- Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program" Which was named "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems.
- Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field.
At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time.
The golden years-Early enthusiasm (1956-1974)
- Year 1966: The researchers emphasized developing algorithms that can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named ELIZA.
- Year 1972: The first intelligent humanoid robot was built in Japan which was named WABOT-1.
The first AI winter (1974-1980)
- The duration between the years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientists dealt with a severe shortage of funding from the government for AI researches.
- During AI winters, an interest in publicity on artificial intelligence was decreased.
A boom of AI (1980-1987)
- Year 1980: After AI's winter duration, AI came back with an "Expert System". Expert systems were programmed that emulate the decision-making ability of a human expert.
- In Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University.
The second AI winter (1987-1993)
- The duration between the years 1987 to 1993 was the second AI Winter duration.
- Again Investors and the government stopped funding for AI research due to high cost but not efficient results. The expert system such as XCON was very cost-effective.
The emergence of intelligent agents (1993-2011)
- Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion.
- Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.
- Year 2006: AI came into the Business world until the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.
Deep learning, big data, and artificial general intelligence (2011-present)
- Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.
- Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction.
- Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test."
- Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well.
- Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointments on call, and the lady on the other side didn't notice that she was talking with the machine.
What are the Types of Artificial Intelligence?
There are various scenario or guidelines on which we can classify AI into different classes:
I. Type 1
The following classification is based on the process type.
a. Symbolic AI
It is concerned with describing and manipulating our knowledge of the world as explicit symbols, where these symbols have clear relationships to entities in the real world. More of a black box testing kind of a thing
b. Sub-symbolic AI (e.g. neural-nets)
It is more concerned with obtaining the correct response to an input stimulus without ‘looking inside the box’ to see if parts of the mechanism can be associated with discrete real-world objects. More of white-box testing of a thing
II. Type 2
This types of classification are based upon the capabilities of an AI
a. Weak or Narrow AI
Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence. Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits. Apple Siri is a good example of Narrow AI, but it operates with a limited pre-defined range of functions. IBM's Watson supercomputer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language processing. Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce sites, self-driving cars, speech recognition, and image recognition.
b. General AI
General AI is a type of intelligence that could perform any intellectual task with efficiency like a human. The idea behind the general AI to make such a system that could be smarter and think like a human on its own. Currently, there is no such system exists which could come under general AI and can perform any task as perfect as a human.
The worldwide researchers are now focused on developing machines with General AI. As systems with general AI are still under research, and it will take lots of effort and time to develop such systems.
c. Super AI
Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than a human with cognitive properties. It is an outcome of general AI. Some key characteristics of strong AI include capability include the ability to think, reason, solve the puzzle, make judgments, plan, learn, and communicate on its own. Super AI is still a hypothetical concept of Artificial Intelligence. The development of such systems in real is still a world-changing task.
III. Type 3
This type of classification is based on functionalities.
a. Reactive Machine
Purely reactive machines are the most basic types of Artificial Intelligence. Such AI systems do not store memories or past experiences for future actions. These machines only focus on current scenarios and react to them as per possible best action. IBM's Deep Blue system is an example of reactive machines. Google's AlphaGo is also an example of reactive machines.
b. Limited Memory
Limited memory machines can store past experiences or some data for a short period of time. These machines can use stored data for a limited time period only. Self-driving cars are one of the best examples of Limited Memory systems. These cars can store the recent speed of nearby cars, the distance of other cars, speed limits, and other information to navigate the road.
c. Theory of Mind AI
Theory of Mind AI should understand human emotions, people, beliefs, and be able to interact socially like humans. This type of AI machines are still not developed, but researchers are making lots of efforts and improvement for developing such AI machines.
d. Self- Awareness AI
Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent and will have their own consciousness, sentiments, and self-awareness. These machines will be smarter than the human mind. Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
AI Agents
An agent can be anything that perceives the environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting. An agent can be:
1. Human-Agent
A human agent has eyes, ears, and other organs which work for sensors, and hands, legs, vocal tract work for actuators.
2. Robotic Agent
A robotic agent can have cameras, an infrared range finder, NLP for sensors, and various motors for actuators.
3. Software Agent
A Software agent can have keystrokes, file contents as sensory input and act on those inputs and display output on the screen.
In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity that acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent). Guidelines for an agent to be intelligent are:
- Rule 1: An AI agent must have the ability to perceive the environment.
- Rule 2: The observation must be used to make decisions.
- Rule 3: Decision should result in an action.
- Rule 4: The action taken by an AI agent must be a rational action.
Agents can be grouped into five classes based on their degree of perceived intelligence and capability. All these agents can improve their performance and generate better action over time.
These are given below:
1. Simple Reflex Agent
Simple reflex agents are the simplest agents. These agents take decisions on the basis of the current percepts and ignore the rest of the percept history. These agents only succeed in the fully observable environment. The Simple reflex agent does not consider any part of percepts history during their decision and action process. The Simple reflex agent works on the Condition-action rule, which means it maps the current state to action. Such as a Room Cleaner agent, it works only if there is dirt in the room.
2. Model-based reflex agent
The Model-based agent can work in a partially observable environment, and track the situation.
A model-based agent has two important factors:
1. Model
It is knowledge about "how things happen in the world," so it is called a Model-based agent.
2. Internal State
It is a representation of the current state-based on percept history.
These agents have the model, "which is knowledge of the world" and based on the model they perform actions.
3. Goal-based agents
The knowledge of the current state environment is not always sufficient to decide for an agent what to do. The agent needs to know its goal which describes desirable situations. Goal-based agents expand the capabilities of the model-based agent by having the "goal" information. They choose action so that they can achieve the goal. These agents may have to consider a long sequence of possible actions before deciding whether the goal is achieved or not. Such considerations of different scenarios are called searching and planning, which makes an agent proactive.
4. Utility-based agent
These agents are similar to the goal-based agent but provide an extra component of utility measurement which makes them different by providing a measure of success at a given state. Utility-based agents act based not only on goals but also on the best way to achieve the goal. The Utility-based agent is useful when there are multiple possible alternatives, and an agent has to choose in order to perform the best action. The utility function maps each state to a real number to check how efficiently each action achieves the goals.
5. Learning agent
A learning agent in AI is the type of agent that can learn from its past experiences, or it has learning capabilities. It starts to act with basic knowledge and then able to act and adapt automatically through learning.
A learning agent has mainly four conceptual components, which are:
1. Learning element
It is responsible for making improvements by learning from the environment
2. Critic
The learning element takes feedback from critics which describes how well the agent is doing with respect to a fixed performance standard.
3. Performance element
It is responsible for selecting external action
4. Problem Generator
This component is responsible for suggesting actions that will lead to new and informative experiences.
Hence, learning agents are able to learn, analyze performance, and look for new ways to improve performance.
Areas of AI
The above figure shows the relationship between various fields of Artificial Intelligence.
Total Turing Test
Elan Turing an English computer scientist, cryptanalyst, mathematician, and theoretical biologist, proposed this test, the test was designed keeping into the fact that the machine can respond either "yes" or "no", hence it has a lot of critics and a lot of alternatives. In addition, a program such as ELIZA could pass the Turing Test by manipulating symbols it does not understand fully. To name a few
1. The Marcus Test
In which a program that can ‘watch’ a television show is tested by being asked meaningful questions about the show's content.
2. The Lovelace Test 2.0
Which is a test made to detect AI by examining its ability to create art.
3. Winograd Schema Challenge
Which is a test that asks multiple-choice questions in a specific format.
According to me, the test is complete in itself but lacks a proper implementation algorithm.
Explanation:
1. Think like Humans
In this, if a system is able to think like a human, given a problem or circumstance, it is known to think like humans
2. Act like Humans
In this, if a system is able to act in the same way or the same as a human may act, given a problem or circumstance, it is known to act like humans.
3. Think Rationally
In this, if a system takes a decision based on a proven or logic-based algorithm, it is known to think rationally.
4. Act Rationally
In this, if a system is able to act based on a prooven or logic-based algorithm, it is known to act rationally.
Conclusion
In this article, we studied artificial intelligence, applications of AI, features of AI, shortcomings of AI, basics terms of AI, History of AI, types of AI, AI Agents, Areas of AI, Total Turing Test. Hope you were able to understand each and everything. For any doubts, please comment on your query.
In the next article, we will learn about Machine Learning.
Congratulations!!! you have climbed your next step in becoming a successful ML Engineer.