Whenever we hear the word ‘Artificial Intelligence’, what comes to our mind at first? Google Voice Search? Siri? Cortana? (Or nothing?) But a complete notion of artificial intelligence is way beyond these automated voice assistants introduced by Google, Apple or Microsoft. Trust me, if you carry on reading this article, you’ll get really intrigued about this Artificial Intelligence and you’ll probably thank me later!
Artificial Intelligence is expected to carry us into a dream future, to say the least. The huge prospect of Artificial Intelligence cannot be put into words this easily. Nor can the total concept of Artificial Intelligence be expressed clearly to everyone. It won’t be even a little bit exaggerated if I say artificial intelligence is the future of mankind.
What is Artificial Intelligence:
Well, what is AI (Yeah, it’s much shorter)??? AI is a field of computer science which discusses about the machines working as human minds. AI is a system that perceives information from vicinity as input and takes convenient actions that maximize the chance of success for a definite goal. Here we’ll take a wee look at some of the major aspects of AI.
The study of logical reasoning and deduction began with philosophers and mathematicians in antiquity. Artificial Intelligence may have started its way when humans first made machines with a view to calculating numbers. In the 19th century, George Boole refined those ideas into logical theories, which eventually paved the way for Boolean Algebra. George Boole, who did some really intensive and amazing works on logical reasoning, is one of the pioneers in this field.
During the 1940s, Alan Turing’s theory of computation deduced that a machine, using symbols as simple as “0” and “1”(Binary), 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. Alan Turing is the greatest forerunner in the field of logical reasoning. He also became a World War 2 hero, when his team successfully managed to break the German enigma code during the war. He is widely regarded as the father of ‘Computer Sceince’.’The Imitation Game’, a 2014 movie, was basically his biography for which Benedict Cumberbatch received an Oscar nomination.
Charles Babbage, the inventor of modern calculator, (And he smoothed the way towards the first computer) was also a major scientist to work for the development of AI.
The field of AI research was founded at a conference at Dartmouth College in 1956. The attendees, including John McCarthy,Marvin Minsky, Allen Newell, Arthur Samuel and Herbert Simon, became the leaders of AI research. They and their students wrote programs that were, to most people, simply astonishing. This was the first time when computers were winning at checkers, solving word problems in algebra, proving logical theorems and speaking English and various other languages.
After this, AI went through many ups and downs for the next few decades. Recently, it’s become one of the most promising field of Computer Science and is expected to have a far-reaching effect on human civilization. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997. This was a huge milestone for the advancement of AI.
Since then, many researchers are working relentlessly with a view to achieving more milestones on the way to bolster our current AI technology.
Humans use step-by-step reasoning when they have to solve critical problems. They make logical reasoning and deduce the most likely outcome. Machines have also started to solve problem by means of logical reasoning and deductions. AI research has developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
Knowledge representation and knowledge engineering are central to AI research. Some of the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains.
Machine learning is a process that even the most primitive animal used on a daily basis. That is, to learn from experience. By processing a huge amount of information and analyzing the pattern among them, computers can work somewhat close to human minds, which is being called the neural network. Actually it analyzes very large and complicated data, find the mutual patterns among them, and then it tries to learn how a human mind works. Then it tries to make a perfect deduction to solve the given problem.
After many false dawns, AI has made extraordinary progress in the past few years, thanks to this versatile technique named ‘Machine learning’.’Deep learning’ is a subfield of this machine learning, and it’s the main pillar behind the ode of glory now we’re hearing from AI. Given enough data, large (or ‘deep’)neural networks, modelled on the brain’s architecture, can be trained to do all kinds of things. They power Google’s search engine, Facebook’s automatic photo tagging, Apple’s voice assistant, Amazon’s shopping recommendations and Tesla’s self-driving cars. Actually, machine learning has been central to AI research since its inception.
Robotics and Natural Language Processing :
Natural language processing provides the machine with the ability to read and understand the languages that we humans speak. A powerful natural language processing system would acquire knowledge directly from human-written sources, such as news or texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation.
The very promising field of Robotics is entirely dependent on AI. Data analysis, reasoning, deduction, machine learning, intellect, skills, motion, manipulation, navigation and what not – everything a robot needs is basically pure AI.
Researchers have set some primary goals to attain. Artificial intelligence will reach a new milestone when these goals are reached. Some of them are giving the machines the ability to –
- Behave skillfully in social endeavor
- Be creative
- Show signs of general intelligence
- Get as close as possible to Neural network (i.e. human mind)
Researchers have set some levels to evaluate the progress of the AI of a definite machine. One classification is somewhat like this:
- Optimal: it is not possible to perform better.
- Strong super-human: performs better than all humans.
- Super-human: performs better than most humans.
- Sub-human: performs worse than most humans.
For example, performance at checkers is optimal, performance at chess is super-human and nearing strong super-human and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.
No matter what we say, getting a complete notion of human brains is impossible. What we know about the capabilities of human brains is next to nothing. Human brain is so complicated that experts say the way it functions can never be utterly unraveled. The brain is made up of approximately 100 billion neurons. And there are approximately 1 quadrillion synapses(Connection between Neurons) in the human brain. That’s 1,000,000,000,000,000 synapses! And guess what? Albert Einstein, the greatest scientist the mankind has ever seen, used only 10% of his total brain capacity! A normal human usually applies only 5-6% of his total brain capacity. Apart from this, human beings often act on hunches and intuitions instead of logical reasoning. They often take decisions out of sentiments or feelings. To put these traits in a machine is impossible till now.
So imitating human mind is no piece of pie.There is no established unifying paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neural science? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems? Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require ‘sub-symbolic’ processing? Answers to these questions tend to vary a lot when asked to different researchers.
Some of the approaches made include brain simulation, cognitive simulation, statistical approach, knowledge-based approach, neurological approach, psychological approach, probabilistic methods for uncertain reasoning, statistical learning methods etc. Researchers are passing their days trying to figure out the proper way to ensure the march of the machines.
‘The Perceptron‘, widely known as an ’embryo’ of a machine, was a small machine invented by Frank Rosenblatt. It had one layer of ‘Deep learning’ (Nowadays we have eight) and was expected to grow into a machine which can make logical reasoning, take proper decisions and act like a normal human being after further research. It was the first neural network to be recorded.
Deep learning in artificial neural networks with many layers has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others. A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
Reference in Literature and Movies:
The implications of artificial intelligence have been a persistent theme in science fiction. Early stories typically revolved around intelligent robots. The word ‘Robot’ itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for “Rossum’s Universal Robots”. Mary Shelley’s ‘Frankenstein’, a best-seller which is probably known to every bookworm of the world, was the first instance of an artificially created being that could think and act like humans. It also described how it could be a potential threat to mankind. Arthur C. Clarke also used an AI named HAL in his famous ‘2001: A Space Odyssey’. Isaac Asimov also wrote about in numerous science fictions.
AI has since become firmly rooted in popular culture. Movies like ‘The Terminator Franchise'(A blockbuster) and ‘A.I. Artificial Intelligence’(2001). The latter is a great instance of how AI robots works and also a classical masterpiece which everyone will surely appreciate.
Pros and Cons:
Talking about the pros of AI is like trying to count the stars in the night sky. It never seems to cease. If we can extract our abilities into machines, they will be able to work much more efficiently than us. We can set computers in almost every sector which requires labor now. Machines can simulate significantly more information than we can in a definite time. So they can work a million times quicker than us, and also, they won’t make any mistakes or won’t get tired of working. We’ll get rid of all the hard labor we have to do now, even from our daily chores. If we can develop AI technology, we may have domestic robots to do our chores while we spend our times in sheer recreation. The pros of AI is never ending, so I won’t go further than this.
Now let’s analyze the cons, shall we? Experts warn hat ‘The substitution of machinery for human labor’ may render a huge population redundant. Machines containing AI will take over all the jobs that need hard labor, or routined calculation, checking etc.Such fears are expressed today by those who worry that advances in AI could destroy millions of jobs.
Another major con of AI is the prospective ‘Existential threat’ it poses on mankind. Yeah, a ‘Terminator’-like threat on mankind. Rogue AI turning evil is the plot of countless Sci-fi films. Prominent physicist (The best of this age) Stephen Hawking, Microsoft founder Bill Gates and the CEO of SpaceX ( A rocket company) and Tesla Motors, the famous business tycoon Elon Musk, have all expressed their concern about robots taking control of human civilization in distant future. But although AI systems are impressive, until now they can perform only very specific tasks. So a general AI capable of outwitting its human creators remains a distant and uncertain prospect. Andrew Ng, an AI researcher said,
Worrying about it is like worrying about overpopulation on Mars before colonists even set foot there.
Artificial Intelligence A.K.A. AI is suck a vast topic that to discuss it in such a short span of time is practically impossible. In the next article, we’ll try to put emphasis on the pros and cons of AI in details. We’ll also discuss how the AI machines will possibly influence our world.
Here I’ve pointed out a few elementary concepts of AI. If you’re interested, just surf on Internet. You’ll find numerous amazing articles on AI and will have a clear idea how it works. Here are some links.( I also took some help from these articles, they’re really cool!)
Good luck exploring Artificial Intelligence. Hoping that AI will take us to a future we never even thought of before! Adieu!!!