“Big Data”, “Machine Learning” and the intriguing “Deep Learning” are some of the biggest buzzwords surrounding Artifical Intelligence today.
Often, we pick up a general feel for what a term means from the context we hear it used in. But when it comes to considering the relevant issues in-depth, we can struggle to get a foothold. If we want to fully engage with developments in tech, it can be handy to have a working knowledge of what these things mean.
So, just what is Deep Learning and how does it relate to Artificial Intelligence?
Deep Learning is a subset of Machine Learning, which itself sits under the umbrella of Artificial Intelligence. AI encompasses all functions that would usually require human brains to be achieved successfully. Machine Learning goes one step further, describing machines that can develop their skills without the need for any human intervention. These machines learn purely through their own experiences.
A further subset is Deep Learning, which refers to artificial neural networks that have been inspired by the human brain. These networks learn from processing massive sets of data. What they learn from this data they use to solve complex problems. It is their capacity for layered or ‘deep’ thinking that allows them to tackle these issues. They are even able to accomplish this with messy, disorganised data.
But why now?
We generate an increasingly massive amount of data on a daily basis, and we have the strongest computing power that has ever been available. We are also currently in an era where AI is being used more than ever before, with SMEs able to afford and utilise service bots and chatbots. The possibilities seem truly endless.
Whilst the full potential of Deep Learning is yet to be discovered, it is evident that it will provide a wealth of benefits to a number of different industries. Some current areas of expertise include the detection of fraud and money laundering – it will be interesting to see the next major area where this algorithm will offer its expertise.