Artificial Intelligence has disrupted the tech industry. From generating images to getting a fabulous caption for your Instagram-worthy pictures, AI has been successfully imitating human efforts. In the field of computer science, AI mainly focuses on designing systems that are capable of executing tasks normally carried out by humans. These tasks involve the ability to focus on thinking, solving problems, understanding natural language, learning from experience, and acquiring awareness of the environment.
The main objective of artificial intelligence is to come up with systems that either act on their own completely or function with little human intervention. AI as a term stands apart, but the meaning may be related to several technologies included within it, such as machine learning, natural language processing, computer vision, and robotics, among others. Curious to learn more? Let us understand what artificial intelligence at its core is and how it came into existence.
AI and its Essence
Essentially, artificial intelligence is the replication of human intelligence in machines that are programmed to deliberate, reason, and learn like humans. Unlike traditional computer programs, which are designed for specific tasks, AI systems are built on algorithms and rugged volumes of data that allow them to learn patterns, make decisions and progressively enhance their performance.
These include technologies such as machine learning, natural language processing, computer vision and robotics. These technologies empower AI systems to carry out sophisticated tasks, like speech recognition and face detection, with astounding accuracy. Artificial intelligence consists of several building blocks or core components that distinguish it from any other technology. They are:
Machine Learning
Machine learning is one of the core components of artificial intelligence. It facilitates machines to learn and enhance from data. Algorithms are used to recognize patterns, analyse data and make decisions. The more data they’re exposed to, the more they learn and improve their performance.
Neural Networks
A neural network is a model that is used to replicate a human brain in machines. It consists of nodes known as artificial neurons that perform a function similar to that of neurons in the brain. They help computers to figure out patterns and solve problems in the various areas of artificial intelligence.
Deep Learning
Deep learning is a subset of machine learning that uses complex neural networks with many layers, which is why it is called ‘deep’ and is used to analyse various data factors. It plays a key role in applications such as image and speech recognition.
Natural Language Processing (NLP)
NLP deals with how people use human language to communicate with computers, enabling machines to understand, interpret, and generate human language at usable levels. Because people produce text data from social media sites to research papers every single second of the day, automatic extraction of needed information and automation of many processes have made NLP a very important tool in the 21st century.
Robotics
Robotics refers to the building of machines that are programmed to perform various tasks, such as working in an assembly line or conducting complex procedures. It is often associated with AI because it involves both mechanical and cognitive dynamics.
Cognitive Computing
This is an AI method that solves complex problems and issues through the process of recognizing patterns and natural languages, as well as data mining.
Expert Systems
These are systems of artificial intelligence that are capable of replicating human skills, such as making decisions by drawing on knowledge and reason to come to a conclusion.
Types of AI
AI Systems are broadly broken down into two categories. They are:
Narrow AI
The term “Narrow AI” stands for models trained to carry out ordinary tasks perfectly. Narrow AI is quite limited to the tasks it is programmed to perform. It is unable to think beyond its initial programming or to generalize broadly. For instance, narrow AI includes virtual assistants like Apple Siri and Amazon Alexa, and recommendation engines like those on streaming platforms such as Spotify and Netflix.
General AI
General AI is a form of AI that does not exist for now and is more commonly referred to as Artificial General Intelligence (AGI). If it is ever designed, AGI would be an entity that can perform complex tasks just like a human being. To achieve this, an AGI would need the capacity to deploy reasoning in different areas of knowledge to comprehend complicated problems that are not particularly meant to be addressed. To make this possible, AI would have to use a method called fuzzy logic: a technique which, when implemented, allows for the existence of grey areas and gradations of uncertainty rather than bringing out only binary, black-and-white solutions.
The Advent of Artificial Intelligence
Artificial Intelligence is all around us, whether it acts as our virtual assistant or simplifies tasks that would take hours for us to complete manually. But is AI a recent development, or has it been around for longer than we’ve realized?
Believe it or not, the earliest record of artificial intelligence goes back to ancient Greek mythology. It all began with an artificial being known as Talos, who was created to guard the majestic island of Crete. Talos, unlike humans, was not made of flesh and bones but of bronze by the Greek god of fire and blacksmithing, Hephaestus.
However, modern AI began taking shape between the 1940s and 1950s. This started with the proposal of an idea. Alan Turing, a mathematician and computer scientist, first mooted the concept of the Turing Machine, a general-purpose machine to carry out any form of computation; this idea eventually gave rise to AI. The bold experiment involved what is now called the Turing Test to check if human-like intelligence can be replicated in machines. The Turing Test is an inquiry method to test the intelligence of a computer. A machine can pass the test if a human being is unable to distinguish the replies from the machine and the replies from another human to questions put forward to both. In 1955, the Dartmouth Conference presented the first event by John McCarthy in launching AI as a research field. This was important in laying the bedrock in the academic arena.
The period between the 1950s and 1970s was a game-changer for AI. Symbolic AI was generated by systems, basically using pre-defined rules for arriving at logical decisions, such as expert systems. Among the successful chatbots that came through during this era were ELIZA, one of the first chatbots, and SHRDLU, one of the early natural language processing programs. However, these systems were not totally unsupervised, and once given more challenging or ambiguous tasks, they frustrated the researchers.
Despite a lot of effort put in, most of the problems remained in the AI research area, including computational complexity, high-level expectations, and unrealistic results. During this period, funding and interest in the subject had dropped owing to the AI Winter. The problems that came with AI were much more intricate than considered before, and early efforts did not lead to successful results in real-world applications.
The inflection points in the 2000s was where big data met high-performance computing hardware, such as Graphic Processing Units. Along with this turn, symbolic AI disappeared, along with other systems that explicitly represent meaning through symbols and then provide meaning by means of production rules. Coupled with big data and the power of machine learning and deep learning, AI began to execute tasks such as speech recognition and image recognition at unimaginable speeds. Large neural networks were used to attain this.
The deep learning model developed by Geoffrey Hinton in 2012 was declared the winner in an image classification competition; this marked a renewed interest in AI research. Algorithm development, availability of data, including machine learning, and increased computational speed have combined to make AI super acceptable and practical in real life. The usage of AI has become very prominent in day-to-day popular technologies, such as voice assistants, Siri, and Alexa, recommendation systems such as Netflix and YouTube, and self-driving cars.
AI has been able to write articles, answer questions, and generally generate human-like text. Reinforcement learning- meaning training through trial and error- has also been used in AI in gaming, robotics, and other more complex simulations.
AI, as we’ve delved into, is an intricate field that will continue to shape our current and future state of being. AI has come a long way, from its early theoretical foundations to the innovative technologies we see today, and is now impacting industries, transforming how we work, communicate, and solve complex problems. But this is just the beginning.
In the weeks and months ahead, we are going to explore the many aspects of AI in more depth with a series of articles dedicated to helping you understand the intricacies, applications and future impact of this technology. Whether you’ve just found AI or want to do a deep dive into even more, there’s plenty still to come — so stay tuned for more takes and explorations into this amazing field!