From buzzword to boardroom – what’s next for machine learning?
- by 7wData
Businesses are moving past the tired buzz of machine learning and realising that the technology has fresh applications when combined with big data
Artificial intelligence is considered to be the next quantum leap. More UK businesses than ever before are making machine learning a top priority as it promises to fundamentally revolutionise multiple industries . Yet the concept of machine learning isn’t, in principle, anything new. So why the hype now?
What has changed today is the technical framework that makes implementing artificial intelligence possible in practice. This includes greater computing power, as well as affordable storage, powerful in-memory databases such as SAP HANA, highly developed algorithms but above all, big data – a corollary of Digital Transformation and the basis of machine learning.
At the same time, the pressure on companies is increasing: today, they are dependent on the automation of business processes to withstand the increasing pressure to be competitive and innovative, and to compensate for the shortage of IT specialists in the UK.
A match made in heaven
The huge data treasures that result from the execution of digital processes are buried in the systems of most companies and many organisations are trying to generate added value from them.
This is a task however, which until now required too many resources. Machine learning opens up entirely new dimensions here; as well as humans, intelligent algorithms are now also analysing the data – so quickly, comprehensively and intelligently that they can identify any interconnections even within the largest data volumes.
As demonstrated in previous experiments , no human brain is able to process as much data at comparable speed and accuracy as machine learning systems can and as a result, deliver a sound, data-based result within nanoseconds.
To date, pattern recognition is the most frequently used variant of machine learning: to make connections between large volumes of data in the process is only a sub-field. It is more important that the algorithm learns how a task can be accomplished.
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