Maths and AI — The ultimate power-couple

I can count on one finger the number of times I've had to calculate the length of the hypotenuse of a triangle — in life, or in business.

Admittedly, this may go some way to explaining my track record of DIY failures and hesitance with woodworking. That being said, I do recognise why there's a recent drive from Government to improve the use and knowledge of Mathematics.

Mathematics is a crucial component of Data Science, which involves the use of data to gain insights and knowledge. From simple statistical analysis to complex Machine Learning (ML) algorithms, maths helps us understand and interpret data in a structured and objective way.

Employees and managers often find it challenging to understand statistical concepts and the potential risks associated with the use of Machine Learning (ML) and Artificial Intelligence (AI), partly because Statistics and Data Science can be complex and technical.

In the UK, only 1.8% of students chose to study Maths or Statistics at degree level in 2021/22. However, it's vital that workers and managers understand these concepts so they can make informed decisions about how to use ML & AI in their businesses.

To address this, they can take steps to learn more about Statistics and Data Science. This could involve taking a training course or enrolling onto a Data Apprenticeship or Skills Bootcamp to improve their working knowledge on the subject.

They can also collaborate with Data Technicians, Analysts, Scientists, and other experts to help ensure that the data used to train predictive models is diverse, accurate, ethical, and unbiased.

Recently, there's been a surge of interest in developing Large Language Models (LLMs) that can generate natural language text with high accuracy.

GPT-3 is one of the most advanced and largest LLMs that's been created so far and uses statistical and probabilistic methods extensively to understand and generate natural language.

These models are built using massive amounts of data and use statistical methods to identify patterns and relationships in the data, enabling them to produce text that sounds like it was written by a human.

However, there are some risks involved in using LLMs. Because they're trained on vast datasets of text, there's a danger that they may learn biases or inaccurate information. This could lead to problems if LLMs are used to more frequently to generate text for a broad range of applications.

For example, if an LLM is trained on a biased dataset, it may generate text that continues to perpetuate those biases. This could be a serious issue in areas such as news reporting, where biased or inaccurate information can have severe consequences.

The future use of LLM’s and AI is uncertain. Many in the tech sector have called for a pause on development of AI; however, others believe that AI will be revolutionary and change the way that many roles are performed in business.

Undoubtedly, there will be interesting challenges around the ways organisations monetise data used to train LLMs as they start to grow and earn revenue. This may lead to organisations requiring their own internal LLM models built on public and their own private data to support workers and managers in day-to-day activities.

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