AI, Machine Learning and Deep Learning, what’s the difference?

AI, Machine Learning and Deep Learning, what’s the difference?

A quick guide on the definitions, differences and application of Ai, machine learning and deep learning

Artificial Intelligence

Put in simple terms, artificial intelligence is creating machines can think like we do. This has been a concept or more so a dream since the 1950s when mainstream computing was emerging. We’ve seen plenty of examples in science fiction of what the world hopes for AI to become, think a friendly assistant like C3PO but in reality we are still a while off this point. 

What we can achieve with AI is described as ‘Narrow AI’ and describes specific task carried out by machines with a degree of cognition which sets this practice apart from ordinary computing. Examples of this are the AI systems currently writing simple news articles (not this one promise..) and facial recognition software used by companies like Facebook and uber. 

These tasks are achieved by methods such as machine learning and deep learning which exist in the ‘circle’ of artificial intelligence. 

Machine Learning

Using a selection of algorithms to filter through large data sets which various methods such as inductive logic programming. clustering and reinforcement learning this allows conclusions and predictions to be drawn from the data. This is what sets a machine learning system apart from say a machine system, by the real world conclusions and consideration of the data that the computer can provide. 

Although it does require a lot of code input to create these systems it is considered a very basic model for artificial intelligence. It may be easier than you think to create a machine learning system for yourself given the amount of free algorithm libraries available right now. 

In a chemistry perspective it can provide capabilities such as parameter optimisation and predictions of stabilities and reaction pathways. Some of these we have discussed in previous articles. 

Deep Learning

Deep learning takes a bit more time to get your head around, it is a subset of machine learning that adds complexity and sophistication to it’s methods. Sometimes described as deep structured learning, it is inspired by information and processing nodes in living systems (e.g. our brains) and recreates the multi-layered system that operates. By creating artificial neural networks (ANNs). Take image processing for example, deep learning systems can extract parts of the image in different layers to piece together and represent and interpret the image by carrying out multi-level analysis of the image. So it works efficiently by splitting the data and running simultaneous analysis to extract a higher level of perception. 

There’s no easy way of effectively defining this model due to it’s complex nature. But this is representative of the complexity of intelligence as we know it and the task of recreating this artificially.  

This domain has gained much popularity recently and has already produced results that surpass human capabilities. For example in drug discovery there is many examples of the sheer power of implementing deep learning within modern day technologies.  

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