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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