Artificial Intelligence: Is it the 4th industrial revolution?
It will transform all of our jobs and lives over the next 10 years. However, it is not a new concept! AI’s roots are in the ‘expert systems’ of the ‘70s and ‘80s, computers that were programmed with a human’s ‘expert’ knowledge in order to allow decision-making based on the available facts.
What’s different today, and is enabling this revolution, is the evolution of machine learning systems. No longer are machines just capturing ‘explicit’ knowledge (where a human can explain a series of fairly logical steps). They are now developing a ‘tacit’ knowledge - the intuitive, know-how embedded in the human mind. The kind of knowledge that’s hard to describe, let alone transfer.
This machine learning is already all around us, unlocking our phones with a glance or a touch, suggesting music we like to listen to, and teaching cars to drive themselves. Underpinning all this, however, is the explosion of data that’s happening at the same time.
Data is growing faster than ever before
There will be 50 billion smart connected devices in the world, all developed to collect, analyse and share data. This data is vital to AI. Machine learning models need data… Just as we humans ‘learn’ our tacit knowledge through our experiences, by attempting a task again and again to gradually improve, ML models need to be ‘trained’.
Of course, some data may be ‘difficult’ – it might be unstructured, it may need refinement, it could be in disparate locations and from different sources. So, the next step is to fuse together this data in order to allow analytics tools to find better insight.
The next step in the journey is identifying and understanding the patterns and trends in our data with smart analytics techniques.
Only once these steps of the journey have been completed can we truly progress to AI and machine learning, to gain further insight into the past and future performance of our organisations, and to help us solve business problems more efficiently.