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Machine learning is increasing in terms of significance and reach. As its influence continues to grow, the software raises questions regarding its reliability, cost-effectiveness and potential. Our 15th Wirelive asks the question, ‘What is Machine Learning?’ During the livestream, Wirehive’s Principal Cloud Solution Architect, Andy Readman, sought to explore this question with Adi Polak, a Senior Software Engineer and a Developer Advocate at Microsoft. 

What's So Exciting About Machine Learning?

Machine learning and AI are already impacting societies across the world in remarkable ways. Microsoft’s Premonition is an example of how AI is working to protect communities against pathogen-caused disease. As it stands, Premonition is operating on a small scale, but its potential to positively impact the lives of countless individuals is remarkable.  

Premonition operates by sampling the blood consumed by between 10,000 – 15,000 mosquitoes a night (all without harming the insect). By comparing the DNA contained within the sample against a database of over 3 trillion genomic sequences, scientists can ascertain which diseases are circulating within which species, and within which regions. Consequently, diseases that are spread through pathogens will be easier to monitor and control. 

What is the Significance of Machine Learning?

The technology behind artificial intelligence has developed exponentially in recent years. Most people will be interacting with the software daily without even realising. Yet, the way in which we use machine learning across industries has a long way to go until it’s fully optimised and operating at its full potential.

Adi notes that historically, businesses have employed the best data scientists that money can buy and been disappointed when the insights into their data weren’t as detailed as initially expected. Simply employing a data scientist isn’t enough. Unless that data scientist is provided with the necessary tools and infrastructure, they’re fundamentally unable to analyse mass quantities of data at the depth expected and required by the employer.

Looking to the future, Adi predicts that machine learning will become an even greater presence in day-to-day business operations. The expected growth of machine learning will inevitably entail more thorough regulation.

Andy adds that currently, there’s a 5-10 year gap between the software’s development and the ability of general businesses to put the technology into action. Whilst technological progression is an undeniable positive, unless the advanced software is democratised and accessible to businesses and users across the globe the benefits are limited.

How Does Wirehive Work With Data?

Wirehive strongly believes that the technology operating behind a business needs to reflect the people, processes, culture and core strategy of that business. Technology is a core part to any business- it's equally as important as the four components noted above.

Andy reflects that Wirehive adheres to two mottos when it comes to artificial intelligence: ‘be more jellyfish’ and ‘be more dolphin.’ In other words, technology should enable the team to perform their day-to-day jobs without having to excessively consider how particular processes and procedures will be impacted by the technology behind the business. Machine learning should enable the employee to work in accordance with their instinct rather than just logic.

‘Be more dolphin’ alternatively relates to the way in which machine learning algorithms can enable employees to work more effectively as a team. Just as a pod of dolphins communicate and coordinate via echolocation, AI can coordinate the ‘movements’ of a business, streamlining the way in which the team operates.

What Can Go Wrong with Machine Learning?

According to Adi, the biggest risk that artificial intelligence poses to a modern business is the fact that unless it’s properly implemented and supported with adequate tools and infrastructure, it’s likely to be a waste of money. There’s no denying that machine learning is an investment; the tools, infrastructure and data scientists to put them to use are costly. Yet, the rewards to be reaped by the business are significant. It’s important to understand that it's important to be fully committed to the implementation of machine learning prior to taking the plunge.

Andy recognises that there can be a preconception regarding AI and machine learning that it can be detrimental to businesses- this is not the case. Machine learning poses as much of a threat to a business as a scientific calculator.

Has the Development of Machine Learning Plateaued?

According to Adi, the development of machine learning has not plateaued- quite the opposite. We’ll inevitably see more supporting tools, Adi posits. People are beginning to understand that we need to support the technology, as well as vice versa. We need to ensure that the AI software has everything it needs to provide the accurate and in-depth insights that its capable of providing. Under these conditions, the software will continue to improve and its ability to analyse big data and identify bigger and more significant patterns will emerge.

To Conclude 

Artificial Intelligence and machine learning are already shaping our lives to a remarkable degree. As the technology continues to improve, we can expect improved IT support and a greater uptake of those opting to use the software. Business’ understanding of their data will become more nuanced and acquired insight into the data will make for greater business opportunities and successes.

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