Introduction to Artificial Intelligence
Introduction
Artificial Intelligence is an attempt to replicate the human brain that perceives, learns and reacts to its surroundings, according to the situation at hand. When we say that a machine is intelligent, we refer to the machine having the capability to understand its self, its needs, believes and desires and also having the ability to empathize with the surrounding entities. We believe that it is capable of expressing with logical reasoning. However, this journey of establishing artificial intelligence has been bumpy, whose final destination is to create a program more intelligent than the humans themselves, seems too far ahead. Surprisingly, the start of this journey, seemed to be an inevitable mishap.
Atificial Intelligence definition
Artificial Intelligence is a process of using Machine Learning, Deep Learning, Natural language Processing, and many other techniques to build artificially intelligent models that can perform high-level computations and solve complex problems.
It describes a machine that is capable of imitating and performing intelligent human behavior. Some of these tasks could include problem-solving and decision-making or specific activities requiring acute perception, recognition, or translation abilities.
The primary goals of AI include deduction and reasoning, knowledge representation, planning, natural language processing (NLP), learning, perception, and the ability to manipulate and move objects. Long-term goals of AI research include achieving Creativity, Social Intelligence, and General (human level) Intelligence.
Types of AI
While there are various forms of AI as it’s a broad concept, we can divide it into the following three categories based on AI’s capabilities:
Weak AI, which is also referred to as Narrow AI, focuses on one task. There is no self-awareness or genuine intelligence in case of a weak AI. iOS Siri is a good example of a weak AI combining several weak AI techniques to function. It can do a lot of things for the user, and you’ll see how “narrow” it exactly is when you try having conversations with the virtual assistant.
Strong AI, which is also referred to as True AI, is a computer that is as smart as the human brain. This sort of AI will be able to perform all tasks that a human could do. There is a lot of research going on in this field, but we still have much to do. You should be imagining Matrix or I, Robot here.
Artificial Superintelligence is going to blow your mind if Strong AI impressed you. Nick Bostrom, leading AI thinker, defines it as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.”
Artificial Superintelligence is the reason why many prominent scientists and technologists, including Stephen Hawking and Elon Musk, have raised concerns about the possibility of human extinction.
Key AI terminologies
1. Machine Learning
First thing to understand about machine learning is that it is an application of AI. It is the process by which we create systems that have ability to learn with experience. For instance, the systems which automatically identify spam emails are trained to do so by exposing them to millions of e-mails which are spammy and non-spammy in nature. With more data, the program is able to understand and learn what makes an email.
Machine learning is different from a fully-AI system in the way that it does not know what it was trained to do. For instance, the above mentioned AI system can identify spams but it will never be able to tell why it is doing that. Or if a new type of email comes in, it will fail to understand it.
2. Cognitive Analytics
As the name suggests, this field of study deals with the ‘thinking’ part of an AI. This system can not only analyse data to create obvious results, but it can train itself to deduce and synthesize new thoughts. For instance, if this system reads a Harry Potter book and is asked who were the two best friends of Harry, it can reply with Hermoine and Ron. To put it more clearly, this system can make sense of unstructured data.
This system is being used a lot in chat bots and customer service platforms. Many recommender systems on the internet are using this type of systems to give custom content recommendation.
3. Robotics
Although a more general term that existed before the onset of the AI wave, robotics is getting a new push with the rise of AI. In the recent years, various companies have launched AI backed robotic systems. For example, MIT’s Cheetah II and IPsoft’s Amelia have taken AI based robotics to another level.
Robotics, academically, is defined as a programmed machine that can perform rule-based tasks. With AI, the ‘rule-based’ part broadens as the robot learn with its experiences.
Techniques Used in AI
Myriads of AI techniques have emerged in the past decade for implementing and building AI systems.
1. Natural Language Processing
In a one-liner, natural language processing is the study of how a computer interacts with a human language. Broadly, in application sense, it refers to speech recognition and speech synthesis in human language.
This field of study is already in application phase and companies are using it in their voice assistants. Apple’s Siri, Google Assistant, Microsoft’s Cortana, and Amazon’s Alexa relies a lot on natural language processing.
Natural language processing further uses different techniques for implementation like parsing techniques, text recognition, and part-of-speech tagging.
2. Neural Networks
Neural networks are available in living beings. Humans and animals uses a complex network of billions of neurons (which makes neural systems) to take decisions in day-to-day life and learn new things to do. Building artificial neural networks is an attempt to create neural networks modelled on our own brains!
These networks can identify patterns in inputs as it processes a lot of data and learn from it. It uses different learning methods: supervised learning, unsupervised learning, and reinforced learning. Neural networks have wide applications in pattern recognition, machine learning, and deep learning.
3. Vector machines
Vector machines are really capable in solving classification problems. For instance, an email system like Gmail for classifying an email as ‘Social’ or ‘Promotion’ or ‘Personal’ in nature and categorizing them in their respective categories.
The fundamental of vector (or sometimes called support vector) machines is to create parameters that draws the line between do distinct objects dividing them into two classes. This technique of AI has wide applications in image recognition, face recognition, and text recognition systems.
4. Heuristics
This is probably the most basic technique in AI. And it totally comes from our understanding of human behaviour in learning processes. We make mistakes and learn. That’s how heuristics work. Heuristics work on the principle of trial-and-error. You keep doing things until you chance upon the answer or the right solution.
Heuristics are good for solving problems where it is hard to come upon a solution definitely. For instance, telling which route is shorter on a map. The best way to solve this problem is by identifying all possible routes and then identifying the shortest one or the one which has least traffic.
Conclusion:
Rise of AI has given way to five new technologies which are bound to change the way we live our lives. They are:
- Cloud computing
- Internet of things
- Big data
- Application programming interfaces (or APIs)
- Open source technologies
If you want to develop your career or create an outstanding product, you can choose any specific field of AI rather than trying to cover them all. You cannot have it all. AI is such a large and new field that it is practically impossible for a single company to cover everything.