Through the course of this Artificial Intelligence tutorial, we will look at various concepts such as the meaning of artificial intelligence, the levels of AI, why AI is important, it’s various applications, the future of artificial intelligence, and more.
Usually, to work in the field of AI, you need to have a lot of experience. Thus, we will also discuss the various job profiles which are associated with artificial intelligence and will eventually help you to attain relevant experience. You don’t need to be from a specific background before joining the field of AI as it is possible to learn and attain the skills needed. While the terms Data Science, Artificial Intelligence (AI) and Machine learning fall in the same domain and are connected, they have their specific applications and meaning. Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial.
What is Artificial Intelligence?
The answer to this question would depend on who you ask. A layman, with a fleeting understanding of technology, would link it to robots. 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. Both of these answers are right. So to summarize, Artificial Intelligence is:
- An intelligent entity created by humans.
- Capable of performing tasks intelligently without being explicitly instructed.
- Capable of thinking and acting rationally and humanely.
At the core of Artificial Intelligence, it is a branch of computer science that aims to create or replicate human intelligence in machines. But what makes a machine intelligent? Many AI systems are powered with the help of machine learning and deep learning algorithms. AI is constantly evolving, what was considered to be part of AI in the past may now just be looked at as a computer function. For example, a calculator may have been considered to be a part of AI in the past. Now, it is considered to be a simple function. Similarly, there are various levels of AI, let us understand those.
Why is Artificial Intelligence Important?
The goal of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. From a technical standpoint, that is the main goal of AI. When we look at the importance of AI from a more philosophical perspective, we can say that it has the potential to help humans live more meaningful lives that are devoid of hard labour. AI can also 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, Artificial Intelligence is shared by all the different tools and techniques have been invented by us over the last thousand years – to simplify human effort, and to help us make better decisions. Artificial Intelligence is one such creation that will help us in further inventing ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.
History of Artificial Intelligence
The concept of intelligent beings has been around for a long time and have now found its way into many sectors such as AI in education, automotive, banking and finance, AI healthcare etc. The ancient Greeks had myths about robots as the Chinese and Egyptian engineers built automatons. However, the beginnings of modern AI has been traced back to the time where classical philosophers’ attempted to describe human thinking as a symbolic system. Between the 1940s and 50s, a handful of scientists from various fields discussed the possibility of creating an artificial brain. This led to the rise of the field of AI research – which was founded as an academic discipline in 1956 – at a conference at Dartmouth College, in Hanover, New Hampshire. The word was coined by John McCarthy, who is now considered as the father of Artificial Intelligence.
Despite a well-funded global effort over numerous decades, scientists found it extremely difficult to create intelligence in machines. Between the mid-1970s and 1990s, scientists had to deal with an acute shortage of funding for AI research. These years came to be known as the ‘AI Winters’. However, by the late 1990, American corporations once again were interested in AI. Furthermore, the Japanese government too, came up with plans to develop a fifth-generation computer for the advancement of AI. Finally, In 1997, IBM’s Deep Blue defeated the first computer to beat a world chess champion, Garry Kasparov.
As AI and its technology continued to march – largely due to improvements in computer hardware, corporations and governments too began to successfully use its methods in other narrow domains. The last 15 years, Amazon, Google, Baidu, and many others, have managed to leverage AI technology to a huge commercial advantage. AI, today, is embedded in many of the online services we use. As a result, the technology has managed to not only play a role in every sector, but also drive a large part of the stock market too.
Today, Artificial Intelligence is divided into sub-domains namely Artificial General Intelligence, Artificial Narrow Intelligence, and Artificial Super Intelligence which we will discuss in detail in this article. We will also discuss the difference between AI and AGI.
Levels of Artificial Intelligence
Artificial Intelligence can be divided into three main levels:
- Artificial Narrow Intelligence
- Artificial General Intelligence
- Artificial Super-intelligence
Artificial Narrow Intelligence (ANI)
Also known as narrow AI or weak AI, Artificial narrow intelligence is goal-oriented and is designed to perform singular tasks. Although these machines are seen to be intelligent, they function under minimal limitations, and thus, are referred to as weak AI. It does not mimic human intelligence; it stimulates human behaviour based on certain parameters. Narrow AI makes use of NLP or natural language processing to perform tasks. This is evident in technologies such as chatbots and speech recognition systems such as Siri. Making use of deep learning allows you to personalise user experience, such as virtual assistants who store your data to make your future experience better.
Examples of weak or narrow AI:
- Siri, Alexa, Cortana
- IBMs Watson
- Self-driving cars
- Facial recognition softwares
- Email spam filters
- Prediction tools
Artificial General Intelligence (AGI)
Also known as strong AI or deep AI, artificial general intelligence refers to the concept through which machines can mimic human intelligence while showcasing the ability to apply their intelligence to solve problems. Scientists have not been able to achieve this level of intelligence yet. Significant research needs to be done before this level of intelligence can be achieved. Scientists would have to find a way through which machines can become conscious through programming a set of cognitive abilities. A few properties of deep AI are-
- Recognition
- Recall
- Hypothesis testing
- Imagination
- Analogy
- Implication
It is difficult to predict whether strong AI will continue to advance or not in the foreseeable future, but with speech and facial recognition continuously showing advancements, there is a slight possibility that we can expect growth in this level of AI too.
Artificial Super-intelligence (ASI)
Currently, super-intelligence is just a hypothetical concept. People assume that it may be possible to develop such an artificial intelligence in the future, but it doesn’t exist in the current world. Super-intelligence can be known as that level wherein the machine surpasses human capabilities and becomes self-aware. This concept has been the muse to several films, and science fiction novels wherein robots who are capable of developing their feelings and emotions can overrun humanity itself. It would be able to build emotions of its own, and hypothetically, be better than humans at art, sports, math, science, and more. The decision-making ability of a super-intelligence would be greater than that of a human being. The concept of artificial super-intelligence is still unknown to us, its consequences can’t be guessed, and its impact cannot be measured just yet.
Goals of Artificial Intelligence
So far, you’ve seen what AI means, the different levels of AI, and its applications. But what are the goals of AI? What is the result that we aim to achieve through AI? The overall goal would be to allow machines and computers to learn and function intelligently. Some of the other goals of AI are as follows:
1. Problem-solving: Researchers developed algorithms that were able to imitate the step-by-step process that humans use while solving a puzzle. In the late 1980s and 1990s, research had reached a stage wherein methods had been developed to deal with incomplete or uncertain information. But for difficult problems, there is a need for enormous computational resources and memory power. Thus, the search for efficient problem-solving algorithms is one of the goals of artificial intelligence.
2. Knowledge representation: Machines are expected to solve problems that require extensive knowledge. Thus, knowledge representation is central to AI. Artificial intelligence represents objects, properties, events, cause and effect, and much more.
3. Planning: One of the goals of AI should be to set intelligent goals and achieve them. Being able to make predictions about how actions will impact change, and what are the choices available. An AI agent will need to assess its environment and accordingly make predictions. This is why planning is important and can be considered as a goal of AI.
4. Learning: One of the fundamental concepts of AI, machine learning, is the study of computer algorithms that continue to improve over time through experience. There are different types of ML. The commonly known types of are Unsupervised Machine Learning and Supervised Machine Learning. To learn more about these concepts, you can read our blog on what ML means and how it works.
5. Social Intelligence: Affective computing is essentially the study of systems that can interpret, recognize, and process human efforts. It is a confluence of computer science, psychology, and cognitive science. Social intelligence is another goal of AI as it is important to understand these fields before building algorithms.
Thus, the overall goal of AI is to create technologies that can incorporate the above goals and create an intelligent machine that can help us work efficiently, make decisions faster, and improve security.
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