Artificial Intelligence and Machine Learning are terms that are currently filling the air in the tech world and across a range of industries, as plans for massive disruption begin to take shape.
Both technologies are set to be central to the future, particularly in critical fields such as cybersecurity, and the financial services. Technology is developing at a gallop, and an ever widening skills gap is causing concern, driving the need for technology to fill in for the lack of human skills.
However, it is common that the two terms are haphazardly thrown together under the banner of automation, or simply used together without distinction. Because of this, CBR is setting out to find out the main differences, what they are best applied to, and who is standing out in each of the popular spaces.
AI can be described as machines that operate with intelligence; devices that can to some extent think for themselves and experience the surrounding world. Many exciting developments towards this are have and are being made, but it could be argued that this level has not truly yet been reached.
However, many advances are included under the banner of AI, for example machines have become able to understand human speech, autonomous cars becoming ever more advanced, and platforms are taking on human champions, with one recently dethroning the world champion of the game Go.
The world is also familiar with talking to and interacting with the likes of Alexa, Siri and Cortana, all the while ‘AI’ personalities are becoming more common in corporate applications too. For example, Infor recently debuted Coleman, an AI assistant underpinning the organisation’s cloud based platform for the enterprise.
It could be also be argued that AI is an ever changing term, as a phenomena called the AI effect notes that when machine is made capable of doing something new, it is no longer considered intelligence. This move the goal posts to some extent, as the abilities of machines edge ever closer to our own.
Machine Learning is essentially a feature of artificial intelligence, albeit a crucial one. Machie Learning i an area of computer science in which machines are able to learn from experience without needing to be patched or implemented with new information. For an example of AI to truly be worth its name, it needs the capability to learn of its own accord, representing human volition.
Another decisive piece of information in our endeavour to separate the two is the fact that Arthur Samuel, an American AI pioneer, actually came up with the term Machine Learning, to some extent solving the chicken or the egg question. Samuel came up with the term in 1959, at which point it related to simple pattern recognition.
While AI prompts science fiction imaginings, Machine Learning is more of a component, an essential internal tool. This concept could be further ratified by the fact that it is somewhat symbiotic with computational statistics, which is connected with making predictions and mathematical optimisation.
It also has a place in data analytics, as machine learning can be used in the creation of complex algorithms for commercial use. Machine Learning has already been applied in various financial services positions; unsurprisingly it is replacing jobs in database management, for example.
Who is leading the way?
Machine learning has stood out and been leveraged for some time, a period spanning more than ten years for many organisations. As AI has progressed and become more of a reality, machine learning has been brought under the overarching term.
Leading the way in artificial intelligence are the likes of Microsoft, DeepMind, Facebook, IBM, Salesforce, Apple and Amazon. Facebook for example has a designated team conducting research into the field, recently hitting the headlines when during testing two AIs had to be shut down after developing and using their own, more efficient language.
This example highlights how far humanity has brought the technology, with today’s AI capable of learning at a rate even unexpected by the scientists monitoring them. Salesforce is a frontrunner in using AI to build customer experiences, with Einstein capable of learning from vast quantities of data.
In summary, machine learning continues to be an essential tool that emerged in the late 1950s. In recent years, AI has become so sophisticated that machine learning has been absorbed within it. Machine learning continues to be a valuable tool on its own, but it is also an integral component within artificial intelligence.
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