Intelligence creating intelligence a brief story AI
In this article, we are going to get to know the roots of this fast-growing field.
We have answered seemingly impossible questions about things that are light years away from our big blue planet, yet we haven’t solved a few fundamental questions!
- How does intelligence actually work?
- How can we artificially replicate human intelligence?
Controversy is generated from its definition to our own understanding of it, which differs from person-to-person. With that, what is intelligence?
From Intelligence: Knowns and Unknowns (1995), a report published by the Board of Scientific Affairs of the American Psychological Association:
“Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person’s intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of “intelligence” are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions.”
According to Wikipedia, intelligence is most often studied in humans but has also been observed in both non-human animals and in plants. Intelligence in machines is called artificial intelligence(AI), which is commonly implemented in computer systems using programs and, sometimes, appropriate hardware.
Now there is a lot of misconceptions and controversy when it comes to AI too. I’m making this article as a means to clear some if not all those misconceptions for you.
What is AI?
AI is an area of computer science that aims in creating machines that demonstrate human-like intelligence and/or superior to the one displayed by humans.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
- Knowledge
- Reasoning
- Problem-solving
- Perception
- Learning
- Planning
- Ability to manipulate and move objects
AI sometimes called machine intelligence is a term that was coined by American computer scientist and cognitive scientist John McCarthy — this man was very influential in the early development of AI.
How it all began
The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”. The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.
The field of AI research was born at a workshop at Dartmouth College in 1956. Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954) (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.
In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.
Moore’s law is the observation that the number of transistors in a densely integrated circuit doubles about every two years.
This advancement is important since its tightly correlated with processing speed and the price of electronic devices(i.e. GPU cards, smartphones and etc).
In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world №1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.
AI subfields
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others.
We should not mistake today’s AI with General Inteligence mostly know as AGI(Artificial General Intelligence). Where AGI is said to be the intelligence of a machine that could successfully perform any intellectual task that a human being can. We are not there yet, today’s AI is known as Narrow AI, meaning it can only handle on a particular task but fails to generalise across a variaty of different tasks, but note that we are moving towards AGI.
This is where things get really complicated for many people to understand, AI is a vast field within this field there exist many subfields that spread across many fields, I will talk about a couple of them, which I believe are very important.
- Machine Learning
- Deep Learning
Machine learning(ML)
The image says everything but let me clear it for you — machine learning are algorithms that learn the mapping between x and y, where x can be the size and number rooms of a house and y can be the price.
Just fifty years ago, machine learning was still the stuff of science fiction. Today it’s an integral part of our lives, helping us do everything from finding photos to driving cars. We’ve come very far, very fast, thanks to countless philosophers, filmmakers, mathematicians, and computer scientists who fueled the dream of learning machines.
Types ML algorithms:
I’m only going to talk about two of them because it is out of the scope of this article, if you want me to make a dedicated article, please comment down bellow.
- Supervised learning
- Unsupervised Learning and etc.
Supervised learning
In supervised learning, we are given a data set and already know what our correct output should look like(also called labelled data), having the idea that there is a relationship between the input and the output. The algorithm is then trained with this data where there are examples of what we would like to see as an output and then it's feed new data so it can predict the output.
Unsupervised learning
Unsupervised learning occurs when an algorithm learns from plain examples without any associated response, leaving to the algorithm to determine the data patterns on its own. This type of algorithm tends to restructure the data into something else, such as new features that may represent a class or a new series of uncorrelated values. They are quite useful in providing humans with insights into the meaning of data and new useful inputs to supervised machine learning algorithms.
As a kind of learning, it resembles the methods humans use to figure out that certain objects or events are from the same class, such as by observing the degree of similarity between objects. Some recommendation systems that you find on the web in the form of marketing automation are based on this type of learning.
The marketing automation algorithm derives its suggestions from what you’ve bought in the past. The recommendations are based on an estimation of what group of customers you resemble the most and then inferring your likely preferences based on that group.
Deep Learning
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
This Subfield of ML has been responsible for more than 70% real-world application of AI systems, from crop-disease detection to autonomous cars and trucks, deep learning changed our lives even the way we take pictures, you don’t believe? If you take a portrait picture taken from your mobile with bokeh mode guess what? It is Deep Learning powered.
This and so many other achievements are all from a fairly YOUNG and not fully DEVELOPED field that hasn’t reached half of its potential but is already being projected that it will take millions of jobs and disrupt many industries. In contrast to that, it is creating many more jobs than it’s taking but that means that many will have to pivot.
What do you think, is it just hype or it has everything to make a significant change the world we know today?
Thank you for reading. If you have any thoughts, comments or critics please comment down below.
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