Source: Artificial intelligence vs machine learning, deep learning | Sunday Mail (Business)
A FUNDAMENTAL debate in the Fourth Industrial Revolution (4IR), as artificial intelligence (AI) becomes more popular is: Are robots taking over the world, and what will happen next?
Doomsayers would predict that a technological Armageddon is looming, but wait a minute, let me attempt to make a bare-bones approach to this.
Technology is becoming more embedded in our daily lives by the minute, enabling us to talk directly to devices like Alexa or Siri.
We must keep pace with tech terms that have become part of our daily vocabulary.
Artificial intelligence, machine learning (ML) and deep learning (DL) have become buzzwords in the tech world. Although these terms are often used interchangeably, they are quite different.
Let us dig deeper into these terms to clear the mist.
Artificial intelligence is the broader umbrella term for machine learning and deep learning. Machine learning is a subset of AI. And deep learning is a subset of machine learning.
AI is a technique that allows machines to act like humans by replicating their behaviour and nature.
When we were kids, we would put our hands on a hot surface and learn what hot and heat meant.
Although this may sound cruel, that is how we became conscious of hot elements. Similarly, this is what ML enables machines to do. ML is about training machines to detect patterns, learn and adapt based on outcomes and results. The principal aim of ML is to allow the systems to learn by themselves through experience without any human intervention or assistance.
ML algorithms are used in various applications — including speech recognition, image recognition and recommendation systems.
Speech recognition systems use ML to transcribe speech and identify the words spoken. These systems are used in virtual assistants like Siri and Alexa, as well as in call centres and other applications.
DL is a sub-part of the broader family of ML, which makes use of neural networks (like the neurons working in our brain) to mimic human brain-like behaviour.
DL algorithms focus on the information processing mechanism to possibly identify the patterns, just like our human brain does and classifies the information accordingly.
DL works on larger sets of data when compared to ML, and the prediction mechanism is self-administered, through machines. DL algorithms are used in image and video recognition systems to classify and analyse visual data. These systems are used in self-driving cars, security systems and medical imaging.
Further, DL algorithms are used in generative models to create new content based on existing data. These systems are used in image and video generation, text generation and other applications.
Are AI, ML and DL related?
Yes, they are cousins, but there are differences, just like our cousins and us. They are related because AI is the “grandfather” or the umbrella concept under which ML falls.
DL is a subset of ML.
For instance, if self-driving cars are synonymous with AI, the algorithms are the ML modules, and the neural networks that transmit information and data are part of the DL functionalities. AI can help streamline areas of your business and improve your customer experience, and it has many applications that are changing the world.
While creating an AI system that is generally as intelligent as humans remains a pipeline dream, ML already allows the computer to outperform us in computations, pattern recognition and anomaly detection.
As I conclude, this article has clarified that AI is a bigger picture, and ML and DL are subparts.
Have a blessed week, and continue to learn deeply.
John Tseriwa is a tech entrepreneur and a digital transformation advocate focusing on delivering business solutions powered by 4IR technologies. He can be contacted at [email protected] or +263773289802.