Target Audience? For those of you out there with little to no previous knowledge or experience of Machine Learning (ML), but looking for a non-technical explanation of what exactly it is.
The Goal? Provide you with, in layman’s terms, a basic understanding of Machine Learning (ML).
Prior knowledge? NONE! This blog is intended for all readers, regardless of background or technical expertise….
NOPE – let’s bury this myth from the get-go…PHEW!! 😊
While many of your favourite films over the years such as Terminator and The Blade Runner have depicted robots developing emotions and self-awareness beyond the control of humans, don’t expect anything similar to happen in reality. While the thoughts of self-controlling robots shooting laser beams from their eyes sounds exciting/terrifying, machine learning instead focuses on:
1. Detection (interpreting the present)
2. Prediction (learning from the present and predicting future outcomes)
Well, no not really. Before we tell you why it’s not as boring as you might think (albeit with no real-life Terminator), let’s examine what ML concept actually is first.
ML has advanced hugely over the last 10 years, with algorithms now achieving human like performance or better on a range of tasks including facial and object recognition. The algorithms used depend upon the type of learning required, with there been three main types of learning:
Organisations nowadays are creating and collecting more data than ever. To gain a better understanding of what ML can achieve, let’s examine how some organisations are using ML to create more innovate solutions using data:
Getvisibility provides a product that utilises the latest technology in ML for data classification to give companies visibility, control and a strong understanding of their data as it is been created.
As the previous examples show, most industries working with large amounts of data have recognised the value of ML technology, and appreciate the competitive advantage it can create for them. We now see many organisations scrambling to integrate machine learning into their functionality and offerings, and with that, the demand for data scientists and ML experts has grown exponentially. Well – trained machines now possess the capabilities to do high-frequency repetitive tasks with high accuracy without getting bored. In fact, we experience some facet of ML each and every day, even though we may not even realise it:
The ML process can be explained quite simply:
Despite all the buzz and hype that ML and AI has created already, it is clear that the field is at a relatively infant stage when you consider and appreciate the potential of the technology and how it can drastically disrupt the world, we live in. The technology will continue to develop and grow with better training and research into its capabilities and potential functionalities. Although we have been given various examples of how the technology can redefine various industries, it is evident that the full capabilities of the technology will not be felt for many years yet as experts and data scientists attempt to grasp a better understanding of the AI field.
There is no doubt that the future will be extremely interesting and exciting for those working in the AI field and for organisations who attempt to and successfully integrate the technology into their offerings. The various branches of AI will continue to grow, and new branches will emerge as times passes. Many organisations may feel they need to adopt AI and ML in order to remain competitive, and whilst this may be true in the long term, AI is better suited to solving complex issues using large buckets of data which obviously may not be feasible for some companies.
The future of AI and ML is unpredictable and dynamic, and who knows what direction the likes of Elon Musk might decide to take it. What is clear is that the algorithms that support for example, natural language processing (NLP) and speech recognition will only improve and as a result will become more integrated into our daily lives as technology users.
We hope this blog has provided you with a better understanding of what ML and AI is, whilst removing as much of the technical jargon as possible. As you can see, this blog only covers the tip of the ice berg, but the hope is that after reading this blog, your understanding of the topic and desire to further improve your knowledge of all things AI and ML has increased!!!
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