Is Machine Intelligence the new AI?
It is an exciting time to be working in the technology landscape. Today we are witnessing technologies that were fodder for engaging science fiction movies become now our reality. We have seen how technologies such as Artificial Intelligence (AI) has moved beyond the realm of fiction and become a mainstream technology, one that promises to deliver unprecedented value to organizations irrespective of the industry.
It hardly then comes as a surprise that this market is set to continue experiencing massive growth, with revenues increasing from around $10 Billion in 2018 to an expected $126 Billion by 2025.
Machine Learning (ML), a subset of AI that gives machines the ‘intelligence’ they need, is also a market that is growing fast. The global machine learning market was valued at $1.58 Billion in 2017 and is expected to reach $20.83 Billion in 2024, growing at a CAGR of 44.06% between 2017 and 2024.
While we thought that the rise of the machines had reached its crescendo, we are bowled over, yet again, with a new kid on the block – Machine Intelligence.
A primer to Machine Intelligence?
Artificial Intelligence, Machine Learning, and Machine Intelligence are not interchangeable terms. While they are related to one another, they are, at the same time, distinct from each other. There are nuanced differences that separate these technologies to understand which combination is best for a specific undertaking.
AI, for example, is a set of algorithms, processes, and methodologies to enable computer systems to perform tasks that usually require human intelligence. Machine Learning focuses on developing algorithms to enable self-learning in machines and help them continuously learn, improve, and adapt from the data without needing explicit programming. Machine Learning, owing to its capabilities, is a crucial part of the AI ecosystem.
AI and Machine Learning look for trends and patterns that swim in vast seas of data and leverage those findings to draw conclusions. Machine Learning algorithms then use the data to develop new programs taking these conclusions into account and make the technology more accurate and efficient in problem-solving.
Machine Intelligence takes this concept a step ahead and is created when machines begin to get programmed with some aspects of human intelligence. This includes learning, prioritization, and problem-solving. Machine Intelligence exists at the intersection of AI and ML and is essentially advanced computing that allows a machine to intelligently interact with its existing environment.
Machine Intelligence – what does it involve?
The thought of Machine Intelligence might lead to some loud gasps of horror. After all, does this mean that the march of the machines is on its way? Is fiction becoming a reality? Or is it just a natural progression of already-intelligent technologies?
Machine Intelligence leverages both AI and Machine Learning to learn to act proactively. The aim of Machine Intelligence is to create an artificial intelligence system that can efficiently solve problems while accounting for context and be capable of thinking critically and perform tasks that need comprehension and judgment.
Machine Intelligence involves the use of deductive logic – the machine can understand when it has made a mistake, watch out for similar data that could lead to a similar mistake and then, avoid doing so. To gain these capabilities, Machine Intelligence has to employ a suite of Machine Learning methods and a host of automation techniques. Using this, Machine Intelligence can smartly prioritize and sequentially deploy these methods and techniques in the right order, with the right timing, to achieve specific business goals.
The impact of Machine Intelligence
Given the data explosion and increased computing prowess, industries are becoming increasingly data-driven with a heavy reliance on computers, automated systems, and machines. With Machine Intelligence, organizations will gain the capability to further improve their efficiencies by leveraging data for more predictive actions – be it for data classification, optimizing and further automating processes, trend and pattern detection, contextual assistance, etc.
With enough data and higher computational power, it is time for machine learning algorithms to reach higher performance levels and assist in enabling amplified enterprise intelligence.
Would it then be wrong to say that Machine Intelligence is the natural and higher evolution of Machine Learning topped up with prioritization goals…almost like a stepping-stone to the path of true AI?
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