Various companies across almost every industry have been applying AI to solve complex problems and create more personalised experiences.
John McCarthy coined the term artificial intelligence back in 1956. However, it wasn’t until the ImageNet dataset collection that the field made a tremendous leap.
ImageNet originally had an academic objective to collect and map out a dataset of objects. It rapidly evolved into an annual challenge where teams would compete using algorithms to accurately recognise images.
In 2012, there was a defining moment – a team of researchers from the University of Toronto participated in the competition and submitted Alexnet – a deep convolutional neural network architecture that significantly outperformed the competition in identifying images.
The competition is seen as a primary catalyst in the field of AI. Since then, the application of AI has expanded to areas such as computer vision, natural language processing, voice recognition, among others.
In our daily routines, it is pretty easy to spot where artificial intelligence is at work. For example, Facebook and its image-tagging feature, recognising particular people, or Netflix’s highly predictive algorithm used to provide the film recommendations.
To gain more insights and understanding into artificial intelligence, we must first define what AI is. Afterward, we can distinguish it from other related terms such as machine learning (ML), neural networks (NN) and deep learning (DL).
Defining artificial intelligence
With it’s growing popularity, AI has faced some challenges due to a lack of clarity in terminology and in public misconception. Some widely spread mistakes are that AI and ML are interchangeable terms or that artificial intelligence cannot be applied to creative fields.
So, what exactly is AI?
Artificial intelligence, otherwise known as machine intelligence, is a type of intelligence created by the machines to resemble the organic intelligence shown by humans. To achieve this goal, AI acts as an enabler to computer systems so that they can perform tasks such as visual perception or speech recognition.
Why is AI important?
Artificial intelligence plays a significant role in making discoveries through data and iterative learning as it automatically improves its capability to withdraw findings.
However, don’t confuse AI with robotic and hardware-driven automation. Instead of automating manual tasks, it can continuously process high volume, frequent and computerised tasks at a higher accuracy rate than a human being ever could.
How does it work?
To mimic human intelligence, AI employs machine learning so that computers can learn how to respond to specific actions. Artificial intelligence adjusts via progressive learning algorithms and enables data to utilize experience to continuously update the programming.
Simply put, finding regularities and structure within data allows AI to acquire a new skill. This way, it can teach itself to recommend you a product based on your past purchases, meaning AI can either predict or classify.
Narrow vs General AI
Broadly speaking, there are two categories of AI—general and narrow.
- General AI (AGI) would have all of the characteristics of human intelligence and would successfully exhibit it. However, experts are yet to reach a consensus about whether something like AGI is possible to fully implement in the real world.
- Narrow AI (ANI) displays one or more aspects of human intelligence and can perform a particular facet exceptionally well. However, it will often underperform in other areas. For example, a machine can be great at interpreting your eyesight, but it wouldn’t be capable of anything else.
What roles do machine learning, neural networks and deep learning play in AI?
Machine learning, neural networks, deep learning and artificial intelligence are all related, but all of them have significant differences that set them apart. Imagine them as one being a branch of the other—like ML is a subset of AI technique that uses statistical methods.
Artificial intelligence → Machine learning → Neural networks → Deep learning
ML is one of the means of achieving AI. In other words, it’s a form and application of artificial intelligence that is automating and learning patterns with data.
The underlying premise here is that every lesson learned is memorised, so the machine gets smarter with every task performed. This way, the algorithm is trained on how to perform tasks without being provided with a set of instructions every single time.
When new data is being fed to the software, this method of data analysis enables artificial intelligence systems to learn how to make decisions, predictions and self-improve.
As the name suggests, neural networks are designed as a replica of neurons in the brain. They are a set of algorithms and components of larger machine-learning applications required for reinforcement-based learning.
With the use of artificial intelligence, neural networks are capable of resolving extremely convoluted relationships. These networks are designed to recognise patterns, so they can decipher various sensory data, which is processed by machines to classify or cluster the received raw input.
A specific function of machine learning called deep learning is used for describing more complex networks with several layers. The layers are inspired by the structure and function of the brain.
In this case, artificial neural networks are algorithms replicating how the brain operates in terms of neurons and their connections. Each layer selects a specific nuance to learn, for example, identifying contours in photos.
In essence, DL is an autonomous and self-teaching system that enables a computer to develop a set of rules and solve its problems, instead of people teaching machines to do so. The ability to train itself, at the core, is based on neural networks.
The benefit of the added layers is that these networks are capable of developing higher levels of abstraction, which is needed for some complex tasks such as automatic translation and image recognition.
Emerging technologies, together with machine learning, deep learning and neural networks, have provided great accuracy and stable ground for the rapid development of AI.
It’s important to note that AI is not a product or service in its own right, but more a way to make improvements for existing products or services. This serves both businesses and users, as companies make more precise predictions and provide superior solutions, customers are being delivered a more desired experience based on their preferences.