Nathan Baranowski |
For more info : www . ojosolutions . co . uk |
From traffic lights , and Netflix , to policing and advertising , Big Data is being created at a staggering rate . Unlike the days of old , however , it ’ s actually being used in a remarkably effective manner . Each of these examples uses Big Data as a key resource , but with Machine Learning as a tool to boost its function .
Traffic lights are using information to optimise traffic flows … Netflix , to personalise recommended shows to watch . The police use of facial recognition has been widely documented , along with the marketing data that goes into effective advertising practices .
The well-known figure of 2.5 quintillion bytes ( That ’ s 1,000,000,000,000,000,000 bytes ) of data being created every day has finally found a real-life use-case . That ’ s the equivalent of 931,322,574 high res movies being stored every day .
Now consider that this figure first emerged in 2013 , and since then has been increasing exponentially . We are now seeing estimates that the world will create 163 Zettabytes of data in 2025 . That ’ s the equivalent 409 billion high res movies every day .
Big Data is , in essence , an astronomically large data set . It ’ s defined as a high-volume , low-density phenomenon , with data often of questionable value . Oracle notes that “ This can be data of unknown value , such as
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Twitter data feeds , clickstreams on a webpage or a mobile app , or sensor-enabled equipment . For some organisations , this might be tens of terabytes of data .”
Big data is often characterised by the 3Vs – the extreme volume , the wide variety of data types , and the velocity at which data must be processed . Big Data is truly here to stay , but it ’ s far beyond the power of most solutions to actually do anything with it .
Enter : Machine Learning . Machine learning is described as “ a method of data analysis that automates analytical model building ”. In short , a branch of artificial intelligence which allows businesses to derive real meaning from Big Data . For scale , picture the famous ‘ needle in a haystack ’ analogy … then have that haystack significantly grow to the size of the UK or the US perhaps . Now we have big data .
The simple truth is that , often , all this takes place in the background . Most people don ’ t realise we ’ re actually using big data and machine learning solutions on a day to day basis .
This is where Microsoft comes in with its Cognitive Services . The tech giant , aiming to increase the number of intelligent applications in the world , has realised its own set of Machine Learning algorithms for the translation
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of Big Data . The goal of these cognitive services , essentially , is to provide developers with the right tools to work AI solutions into their own applications – making AI more accessible .
Currently these services are segmented into five categories :
• Vision - analyse images and videos for content and other useful information .
• Speech - tools to improve speech recognition and identify the speaker .
• Language - understanding sentences and intent rather than just words .
• Knowledge - tracks down research from scientific journals for you .
• Search - applies machine learning to web searches .
These services have provided businesses the world over with the means of taking AI and integrating it within their existing offerings . From Coca-Cola using AI for customer engagement , to Uber using the software to verify that drivers are indeed who they say they are , there are a myriad of uses .
For the brewing aficionados out there , these services have
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also been used to automate Great Lakes Brewing with the Microsoft Bot Framework . It ’ s a ready-made infrastructure supported by Azure , which means brewers can spend more time brewing , and less time on tasks that can be completed by a machine .
In short , Machine Learning allows businesses to transform Big Data into outcome-based solutions . Data and information that was previously ignored can now be used to drive real change and efficiency .
Traditionally , there has been fear around AI ‘ taking peoples jobs ’. Just as we saw during the last industrial revolution , the introduction of ‘ innovative ’ new tools did not reduce the total number of roles available – instead , it supercharged them . While low-skilled roles will likely be replaced , they will be replaced with tasks only a human can do .
What this should mean is fewer boring jobs , and more which require creativity , humanto-human interaction , and a flair for innovation . In the case with the brewery firm in the US , it ’ s resulted in workers being able to spend more time perfecting their next batch , and less time worrying about machine maintenance .
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