GT1 ARTIFIN

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Intelligent systems

Intelligent systems

can be defined as technologically advanced machines that perceive and respond to the world around them.

Thinking

is the activity of using your brain to consider a problem or to create an idea.

Intelligence

the ability to learn and understand, to solve problems and to make decisions

John McCarthy, Father of Artificial Intelligence (A.I)

Who said "The science and engineering of making intelligent machines, especially intelligent computer programs".

Artificial Intelligence (AI)

is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent.

• General AI (cognitive modeling, philosophical foundations)

• Expert systems and applications

• Automated programming

• Deduction and theorem proving

• Formalism and methods for knowledge representation

• Machine learning

• Understanding and processing of natural and artificial languages

• Problem solving, control methods, and state space search

• Robotics

• Computer vision, pattern recognition, and scene analysis

• Distributed artificial intelligence

BRANCHES OF ARTIFICIAL INTELLIGENCE

intelligere

Latin of Intelligence meaning to understand, comprehend

Cognitive science

is an interdisciplinary study of the mind

• Linguistic intelligence

• Musical intelligence

• Logical-mathematical intelligence

• Spatial intelligence

• Bodily-Kinesthetic intelligence

• Intra-personal intelligence

• Interpersonal intelligence

TYPES OF INTELLIGENCE

Reasoning

It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction.

Inductive Reasoning

It conducts specific observations to makes broad general statements.

Deductive Reasoning

It starts with a general statement and examines the possibilities to reach a specific, logical conclusion.

Learning

It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. _______ enhances the awareness of the subjects of the study.

Auditory Learning

It is learning by listening and hearing. For example, students listening to recorded audio lectures.

Episodic Learning

To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly.

Motor Learning

It is learning by precise movement of muscles. For example, picking objects, Writing, etc.

Observational Learning

To learn by watching and imitating others. For example, child tries to learn by mimicking her parent.

Perceptual Learning

It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations.

Relational Learning

It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding 'little less' salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt.

Spatial Learning

It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road.

Stimulus-Response Learning

It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell.

Problem Solving

It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles.

Perception

It is the process of acquiring, interpreting, selecting, and organizing sensory information.

Linguistic Intelligence

It is one's ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.

• 1950 TURING TEST

• 1955 A.I. BORN

• 1961 UNIMATE

• 1966 ELIZA

• 1966 SHAKEY

• 1997 DEEP BLUE

• 1998 KISMET

• 1999 AIBO

• 2002 ROOMBA

• 2011 SIRI

• 2011 WATSON

• 2014 EUGENE

• 2014 ALEXA

• 2016 TAY

• 2017 ALPHAGO

THE AI TIMELINE

Mark 1 Perceptron (1957): Frank Rosenblatt created the first computer program for this, called the Mark 1 Perceptron.

SAINT or Symbolic Automatic INTegrator (1961): This program, created by MIT researcher James Slagle, helped to solve freshman calculus problems.

ANALOGY (1963): This program was the creation of MIT professor Thomas Evans. The application demonstrated that a computer could solve analogy problems of an IQ test.

STUDENT (1964): Under the supervision of Minsky at MIT, Daniel Bobrow created this AI application for his PhD thesis. The system used Natural Language Processing (NLP) to solve algebra problems for high school students.

ELIZA (1966): MIT professor Joseph Weizenbaum, designed this program, which instantly became a big hit. It even got buzz in the mainstream press. It was named after Eliza (based on George Bernard Shaw's play Pygmalion) and served as a psychoanalyst.

Computer Vision (1966): In a legendary story, MIT's Marvin Minsky said to a student, Gerald Jay Sussman, to spend the summer linking a camera to a computer and getting the computer to describe what it saw.

Mac Hack (1968): MIT professor Richard D. Greenblatt created this program that played chess. It

Golden Age of AI: Programs

Strong AI

Weak AI

Types of AI according to John Searle

Strong AI

This is when a machine truly understands what is happening.

Weak AI

With this, a machine is pattern matching and usually focused on narrow tasks. Examples of this include Apple's Siri and Amazon's Alexa.

Reactive Machines Limited Memory Theory of Mind Self-Awareness

The four classification types of AI promoted by Arend Hintze

Reactive Machines

The machines you see beating humans at chess or playing on game shows are examples of __________. A ______________ has no memory or experience upon which to base a decision. Instead, it relies on pure computational power and smart algorithms to recreate every decision every time. This is an example of a weak AI used for a specific purpose.

Limited Memory

A self-driving car or autonomous robot can't afford the time to make every decision from scratch. These machines rely on a small amount of memory to provide experiential knowledge of various situations. When the machine sees the same situation, it can rely on experience to reduce reaction time and to provide more resources for making new decisions that haven't yet been made. This is an example of the current level of strong AI.

Theory of Mind

A machine that can assess both its required goals and the potential goals of other entities in the same environment has a kind of understanding that is feasible to some extent today, but not in any commercial form. However, for self-driving cars to become truly autonomous, this level of AI must be fully developed. A self-driving car would not only need to know that it must go from one point to another, but also intuit the potentially conflicting goals of drivers around it and react accordingly

Self-Awareness

This is the sort of AI that you see in movies. However, it requires technologies that aren't even remotely possible now because such a machine would have a sense of both self and consciousness. In addition, instead of merely intuiting the goals of others based on environment and other entity reactions, this type of machine would be able to infer the intent of others based on experiential knowledge.

• Megabyte 1,000 kilobytes

• Gigabyte 1,000 megabytes

• Terabyte 1,000 gigabytes

• Petabyte 1,000 terabytes

• Exabyte 1,000 petabytes

• Zettabyte 1,000 exabytes

• Yottabytes 1,000 zettabytes

Types of data levels

Structured Unstructured Semi - Structured

Types of Data

Structured Data

which is usually stored in a relational database or spreadsheet. a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs.

Unstructured

is information that has no predefined formatting.

Semi - Structured

The data that is a hybrid of structured and unstructured sources. The information has some internal tags that help with categorization.

BIG DATA

is a collection of data that is huge in volume, yet growing exponentially with time. It is so large and complex that none of traditional data management tools can store it or process it efficiently. ______ is also a data but with huge size.

Volume Variety Velocity Veracity Variability Value Visualization

Characteristics of Big Data

Volume

This is the scale of the data, which is often unstructured.

Variety

This describes the diversity of the data, say a combination of structured, semistructured, and unstructured data (explained above).

Velocity

This shows the speed at which data is being created.

Veracity

This is about data that is deemed accurate.

Value

This shows the usefulness of the data.

Variability

This means that data will usually change over time.

Visualization

This is using visuals—like graphs—to better understand the data.

TURING TEST

In 1950, Alan Turing introduced a test to check whether a machine can think like a human or not, this test is known as the Turing Test. In this test, Turing proposed that the computer can be said to be an intelligent if it can mimic human response under specific conditions.

ELIZA Parry Eugene Goostman

Chatbots to attempt the Turing test:

Chinese Room Argument

In the year 1980, John Searle presented "Chinese Room" thought experiment, in his paper "Mind, Brains, and Program," which was against the validity of Turing's Test. According to his argument, "Programming a computer may make it to understand a language, but it will not produce a real understanding of language or consciousness in a computer."

• Automation-spurred job loss

• Privacy violations

• 'Deepfakes'

• Algorithmic bias caused by bad data

• Socioeconomic inequality

• Weapons automatization

RISKS AND ETHICAL ISSUES OF ARTIFICIAL INTELLIGENCE