It’s been a holy term of science fiction for more than sixty years – “artificial intelligence” (AI); machine software that can mimic human intelligence at a capable level for targeted, trained workload efficiency. We’ve all heard of it, whether it’s from Hollywood, or echoing in your living room from a Google Home device. Nevertheless, AI’s exotic family of Machine Learning (ML) and Deep Learning (DL) have avoided being the scrutinised poster-child for depictions of calculating killers and murderous machines.

We’ve all heard of AI but what about Machine Learning and Deep Learning? Today, ML and DL are making vast changes across our technological landscape, changing the very fabric of how we work. Yet, for many, what differentiates them begin to blur.

Just what is the difference between artificial intelligences’ Machine Learning and Deep Learning? They’re making more strides in our lives today than you’d ever believe.


Artificial intelligence was birthed in 1956 with the creation of AI research at Dartmoth university, describing machines that are programmed to execute tasks only capable by those with human intelligence. This is a severely simplified definition with extremely complex outputs that range in abilities from language translation to facial recognition.

However, human intelligence can be hard to define – what precisely construes our own abilities and intellect? If artificial intelligence mimics are own, will it only be fully complete once it can reflect emotional responses? Acknowledge specific events? Modern AI lacks in true intelligence, more the programmed reflection of such. What we know of AI now is defined into two terms, narrow and general. General AI is truly aware of its surroundings and even has characteristics of human intelligence. Narrow AI lacks in actual thinking, instead designated to certain areas, be it searching through images for certain objects or listening to audio for various aspects that can be useful to businesses and corporations.

This is where machine learning enters. It is a step towards creating narrow artificial intelligence.


Machine Learning is a system that lives within the definitions of artificial intelligence, being defined as a machine being able to learn without having any detailed programming. Without machine learning, artificial intelligence would require millions of lines of code to achieve fully learning.

In principal, machines are given data that they use to teach themselves for assignment to a specific designated role. Using massive data sets, machine learning systems quickly become “experts” at their tasks by recognising patterns within data and using this to make prediction. This has proven to be vastly more efficient than hard coding a software program for these roles.

Machine learning is one subfield of AI. The core principle here is that machines take data and "learn" for themselves. It's currently the most promising tool in the AI kit for businesses. ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.


Deep Learning was influenced by the very structure and functions of our own brains neurons. Creating Artificial Neural Network algorithms that copied the biology of our brain, these complex networks tapped into the same logistics of how we perceive decision-making.

These artificial neurons have layers which entangle certain features to learn, such as understanding the technical specifics of an image to recognise. This has led to the term ‘deep learning’, the depth that is created with layer upon layer of connections to other neurons, in oppose to machine learnings singular learning layer.

As a subset of machine learning, Deep learning adapts the techniques granted to machine learning in order to tackle issues that may require decision-making and considerate analysis which may not have room for human-error.

Deep learning is far more intensive and vast than machine learning, with its layers demanding massive datasets in order to train itself. The algorithm needs sections of information to decipher and understand so that it can understand aspects of, say, an image of a living room, to differentiate it from a thousand other forms of rooms.

Its deep learnings complexity and need for even the most minor of details that makes it an effective tool for businesses and organisations. With applications useful for trawling through huge amounts of data – millions of images, for example, its algorithms are trained to be able to recognise even the most specific of characteristics.


With its intensive methods, deep learning has applications that could change the very way that you work. As technology expands and evolves, technologies are shrinking as they become more intelligent. From artificial intelligences inception in 1956, machine learning and deep learning have paved the way for its future. As they too continue to grow, the next step in advanced machine intelligence will be a powerful tool to utilise.

Contact Novatech for more help and information on how Deep Learning could take your industry to the next level. 

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