Have you ever considered how the Google search engine can so accurately predict what you are looking for, even after typing just 2 solitary letters? It all comes down to a concept called Machine Learning, or the process of computer systems undertaking explicit tasks without ever being specifically programmed to do so. As such, Artificial Intelligence (AI) systems are often criticized for an array of reasons, not least for having the inability to feel emotions like their human counterparts. However, in the ongoing debate regarding AI’s place in society, there remains a fact that must be accepted at a holistic level: Computers are capable of teaching themselves.
Records are Tumbling
The implications of such potent technological capacities have not been lost on many of the world´s largest companies, with 2017 seeing an unprecedented level of AI and Machine Learning firms being acquired to the aggregate sum of USD$17 Billion; a figure that exceeds all recorded years of AI-related acquisitions combined (451 Research’s M&A KnowledgeBase). However, in considering that 2017’s list of acquirers included the likes of Apple, Facebook, Google, Microsoft and Cisco, the figure comes as little surprise. In saying this, the marquee purchase of the year went to IT-giant, Intel, who acquired Israeli tech firm, MobileEye, for a lazy USD$15.3bn; all for the sake of adding an autonomous transportation company to its already extensive portfolio.
As is largely indicated by the previous list of major acquirers from 2017, it can be firmly understood that the majority of AI take-overs are stemming from the US. With the nation acquiring and investing in 4 to 5 times more Machine Learning based entities than that of Europe and Asia respectively, the USA certainly remains the land of opportunity for AI firms. As such, the 1st quarter of 2018 saw no less than 116 deals take place, seeing USD$1.9 Billion being directly invested into US-based AI companies: This constitutes a 29% quarterly increase from the closing of 2017.
What the Fuss of Machine Learning is All About
One of the fundamental reasons why companies are so driven to invest in Machine Learning systems are due to their unique capacity to independently analyse enormous amounts of information into succinct, easy-to-comprehend reports. This all derives from the “Big Data” phenomena, in which businesses across all sectors are being exposed to a never-before seen level of facts, figures and statistics; which of course need to be accurately evaluated in turn. With AI systems becoming increasingly capable of not just analysing these massive data-sets, but also making inferences, extrapolations and predictions from them, the concept of Machine Learning is in practice, just another step in the evolution of modern-day technology.
In what are extremely encouraging signs for companies specialising in Machine Learning, nearly USD$2 Billion worth of early-stage venture capital investments were made in AI companies across the highly productive 1st quarter of 2018. Unsurprisingly, the vast majority of these investments have been directed towards Machine Learning related businesses that are vying in what are becoming extremely lucrative markets, such as Autonomous Transportation, Predictive Analytics and Robotic Automation (Global Banking & Finance Review). These figures are in itself enough to suggest that the AI sector could be in for another record-breaking year.
Context in the Chaos
During this period of transition towards a more AI-oriented work environment, one of the genuine challenges for businesses will be finding the right balance between embracing the hype and maintaining stability in their modus operandi.’ Currently, the world is experiencing a moment whereby people and companies alike have a certain perception of the world, in which it is sensed that the ´age’ of the ‘smart machine’ is upon us. In turn, this perception is leading to a spike in innovation with respect to AI, and how it is integrated into the world’s working environment.
During this time, expectations regarding the capacity of Machine Learning systems are increasing, which might actually result in a sudden fall in modernization as a direct result of pessimism or disillusionment towards the rise of AI. It is during and following this point in the adoption cycle where it will be up to the true AI pioneers to take the initiative, gain the first movers advantage, and lead the world into an environment that can sustainably maintain a balance between human and technological synergy. If successful, it is at this point where the modern and mainstream workforce will truly embrace the capacities of AI and Machine Learning.
The Next Step
One could argue that the next frontier for AI is that of the manufacturing sector, in which Machine Learning systems could help to improve the efficiency of supply-chains, internal production systems, and overall product quality. As such, the predictive nature of a Machine Learning system could help to address specific flaws and problems within a company’s daily operations, which would otherwise take months to fix or even identify. Beyond this, an advanced algorithm could even go as far as being able to forecast an overall system failure, by way of identifying and isolating a malfunctioning ‘part’ of a company’s process. For example, Australian-based start-up, VROC Artificial Intelligence (www.vroc.ai), is one of countless companies from around the world that are implementing interpretative systems to improve business output. With their “preventative maintenance solution,” VROC uses advanced “predictive analysis and machine learning to identify unscheduled breakdowns before they occur.”
Undisputedly, Machine Learning is truly on the precipice of redefining the way in which companies across all industries go about their daily business. With computers becoming capable of teaching themselves processes that are otherwise understood to be lengthy and complex, it invariably allows human capital to be redistributed to new roles that will allow the leveraging of the respective comparative advantages of man and technology.