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The global artificial intelligence (AI) market is one of the fastest-growing entities of its type in the world, worth approximately $87.04 billion in 2021 despite its relatively nascent nature.
Machine learning is a key driver of this, with this industry niche and sub-sector having the highest concentration of AI start-ups in the whole of the UK.
But what do we mean by machine learning and how does this work? We’ll explore further in the article below!
What is Machine Learning?
Machine learning is a subset of AI, and one that’s focused on building systems that can garner insights from historic data and identify patterns that would otherwise be invisible to the naked eye.
These functions contribute to a highly intuitive technology, and one that can underpin logical and effective decision with little or no need for human interaction.
In this respect, it’s a contemporary and highly effective data analysis method for the digital age, and one that automates the construction of capacious analytical models that can include a much broader range of information sources than traditional alternatives.
The term ‘machine learning’ comes from the technology’s innate ability to adapt to input data and continuously improve the efficacy of outputs, which is central to the appeal of AI as a whole.
How Does it Work?
In terms of the functionality of machine learning, there are four key steps to consider. These include:
- #1. Select and Prepare a Training Data Set: This should be representative of the input data and will help to tune the precise parameters of the model in question.
- #2. Choose an Algorithm and Apply this to the Data: You’ll then need to select a viable algorithm to suit various criteria, including the depth of data in the training set, the issue that the model is aiming to solve and whether the use case is the prediction of a value or not.
- #3. Train the Algorithm to Build the Model: The next step is to train the algorithm, with this process enabling you to finely tune the relevant model variables and deliver more accurate predictions and results.
- #4. Deploy and Enhance the Model: Remember, machine learning drives a continual process, with the last step requiring you to feed new and refined data sets into the model over time. This will improve efficiency and the accuracy of your outcomes, which is crucial to the ongoing success of any machine learning model.
Are There Ethical Issues Associated with Machine Learning?
While machine learning is a high-growth and innovative technology that will benefits brands and customers alike, it also has the potential to introduce new privacy risks and ethical issues.
This is borne out by the numbers, with one study carried out by KPMG revealing that 29% of 1000 respondents thought that the way in which their employers collated personal information was sometimes unethical.
A further 33% stated that consumers should be concerned about how their company uses personal data, highlighting significant concerns about new business models and their impact on customer privacy.
This risk is particularly pronounced for businesses that embrace machine learning and automation, so adopters will have to put measures and safeguards in place to protect their customer’s data.
However, this shouldn’t be enough to undermine the appeal or potential benefits of machine learning, especially in a digital age where the collation and use of data is inevitable.