WHAT IS ARTIFICIAL INTELLIGENCE?

Artificial intelligence (AI) is the emulation of human intellect in devices that have been designed to behave and think like humans. The phrase may also be used to refer to any computer that demonstrates characteristics of the human intellect, such learning and problem-solving.

HISTORY OF ARTIFICIAL INTELLIGENCE AND ROBOTICS

THE HISTORY OF ARTIFICIAL INTELLIGENCE IS LONG AND ROBUST, GOING BACK TO THE 1940S.

A Brief History of Artificial Intelligence

Intelligent robots and artificial beings first appeared in the ancient Greek myths of Antiquity. Aristotle’s development of syllogism and its use of deductive reasoning was a key moment in mankind’s quest to understand its own intelligence. While the roots are long and deep, the history of artificial intelligence as we think of it today spans less than a century. The following is a quick look at some of the most important events in AI.

1940s

(1943) Warren McCullough and Walter Pitts publish “A Logical Calculus of Ideas Immanent in Nervous Activity.” The paper proposed the first mathematical model for building a neural network.

(1949) In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.

1950s

(1950) Alan Turing publishes “Computing Machinery and Intelligence, proposing what is now known as the Turing Test, a method for determining if a machine is intelligent.

(1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.

(1950) Claude Shannon publishes the paper “Programming a Computer for Playing Chess.”

(1950) Isaac Asimov publishes the “Three Laws of Robotics.” 

(1952) Arthur Samuel develops a self-learning program to play checkers.

(1954) The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English.

(1956) The phrase artificial intelligence is coined at the “Dartmouth Summer Research Project on Artificial Intelligence.” Led by John McCarthy, the conference, which defined the scope and goals of AI, is widely considered to be the birth of artificial intelligence as we know it today.

(1956) Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program.

(1958) John McCarthy develops the AI programming language Lisp and publishes the paper “Programs with Common Sense.” The paper proposed the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans do. 

(1959) Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving.

(1959) Herbert Gelernter develops the Geometry Theorem Prover program.

(1959) Arthur Samuel coins the term machine learning while at IBM.

(1959) John McCarthy and Marvin Minsky founded the MIT Artificial Intelligence Project.

 

1960s

(1963) John McCarthy starts the AI Lab at Stanford.

(1966) The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects.

(1969) The first successful expert systems are developed in DENDRAL, a XX program, and MYCIN, designed to diagnose blood infections, are created at Stanford.

1970s

(1972) The logic programming language PROLOG is created.

(1973) The “Lighthill Report,” detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for artificial intelligence projects.

(1974-1980) Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s “Lighthill Report,” artificial intelligence funding dries up and research stalls. This period is known as the “First AI Winter.”

1980s

(1980) Digital Equipment Corporation’s develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first “AI Winter.”

(1982) Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.

(1983) In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and artificial intelligence.

(1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.

(1987-1993) As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.

1990s

(1991) U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.

(1992) Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.

(1993) DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations.

(1997) IBM’s Deep Blue beats world chess champion Gary Kasparov

2000s

(2005) STANLEY, a self-driving car, wins the DARPA Grand Challenge.

(2005) The U.S. military begins investing in autonomous robots like Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”

(2008) Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.

2010 – 2014

(2011) IBM’s Watson trounces the competition on Jeopardy!

(2011) Apple releases Siri, an AI-powered virtual assistant through its iOS operating system.

(2012) Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in the breakthrough era for neural networks and deep learning funding.

(2014) Google makes the first self-driving car to pass a state driving test.

(2014) Amazon’s Alexa, a virtual home is released

2015 – 2021

(2016) Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.

(2016) The first “robot citizen”, a humanoid robot named Sophia, is created by Hanson Robotics and is capable of facial recognition, verbal communication and facial expression.

(2018) Google releases natural language processing engine BERT, reducing barriers in translation and understanding by machine learning applications.

(2018) Waymo launches its Waymo One service, allowing users throughout the Phoenix metropolitan area to request a pick-up from one of the company’s self-driving vehicles.

(2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.

Introduction to Artificial Intelligence

The short answer to What is Artificial Intelligence is that it depends on who you ask.

A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.

If you ask about artificial intelligence to an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So, to summarize, Artificial Intelligence meaning is:

Artificial Intelligence Definition

  • An intelligent entity created by humans.
  • Capable of performing tasks intelligently without being explicitly instructed.
  • Capable of thinking and acting rationally and humanely.

 

How do we measure if Artificial Intelligence is acting like a human?

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

  • Turing Test
  • The Cognitive Modelling Approach
  • The Law of Thought Approach
  • The Rational Agent Approach

 

How Artificial Intelligence (AI) Works?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational prowess to surpass what we are capable of.

To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and understand how those domains could be applied into the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.

 

  1. Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
  2. Deep Learning: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
  3. Neural Networks: Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
  4. Natural Language Processing: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
  5. Computer Vision: Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
  6. Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output.
  7. Robotics: Robotics is the study of a reprogrammable, multifunctional manipulator designed to move material, parts, tools or specialized devices through variable programmed motions for the performance of a variety of tasks: Robot Institute of America, 1979.

What are the Types of Artificial Intelligence?

Not all types of AI all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and that’s how they vary. AI can be classified based on Type 1 and Type 2 (Based on functionalities). Here’s a brief introduction the first type.

Types of Artificial Intelligence

  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI)

 

Strong and Weak Artificial Intelligence

Extensive research in Artificial Intelligence also divides it into two more categories, namely Strong Artificial Intelligence and Weak Artificial Intelligence. The terms were coined by John Searle in order to differentiate the performance levels in different kinds of AI machines. Here are some of the core differences between them.

Weak AI Strong AI
It is a narrow application with a limited scope. It is a wider application with a vaster scope.
This application is good at specific tasks. This application has an incredible human-level intelligence.
It uses supervised and unsupervised learning to process data. It uses clustering and association to process data.
Example: Siri, Alexa, Google search, Image recognition software and other personal assistants Self-driving cars, IBM’s Watson

 

Example: Advanced Robotics

 

What is the Purpose of Artificial Intelligence?

The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.

Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.

That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidized by corporations and government agencies before it becomes accessible to everyday laymen. To learn more about the purpose of artificial intelligence and where it is used, you can take up an AI course and understand the artificial intelligence course details and upskill today.

 

Where is Artificial Intelligence (AI) Used?

AI is used in different domains to give insights into user behavior and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organizations to make an end user’s life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:

  • Searching within data, and optimizing the search to give the most relevant results
  • Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters
  • Pattern-detection to identify significant patterns in large data set for unique insights
  • Applied probabilistic models for predicting future outcomes

Advantages and Disadvantages of Artificial Intelligence?

There’s no doubt in the fact that technology has made our life better. From music recommendations, map directions, mobile banking to fraud prevention, AI and other technologies have taken over. There’s a fine line between advancement and destruction. There are always two sides to a coin, and that is the case with AI as well. Let us take a look at some advantages of Artificial Intelligence.

Prerequisites for Artificial Intelligence?

  • As a beginner, here are some of the basic prerequisites that will help get started with the subject.
  • A strong hold on Mathematics – namely Calculus, Statistics and probability.
  • A good amount of experience in artificial intelligence programming languages.
  • A strong hold in understanding and writing algorithms.
  • A strong background in data analytics skills.
  • A good amount of knowledge in discrete mathematics.
  • The will to learn machine learning languages.

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