Machine learning is generally used to refer to the technology and computation that allows systems to be able to identify patterns and enhance outputs. 

These patterns help the system to ‘learn’ and then make decisions and present solutions based on this data.

This is an ongoing process which draws on complex algorithms to enable the computer system to become more and more sophisticated and to continually improve through the experience of data collection and decision-making.

Software developers can create machine learning by building programs to enable computers to analyse vast amounts of data to solve complex problems. This process is used to create and inform Artificial Intelligence (AI).

Machine learning is typically used to enable AI to make predictions and to automate tasks, reduce errors, and provide very specific information based on ‘learned’ behavioural data and models. For example, Amazon uses this approach to ‘recommend’ items to purchase.

There are other ways to create AI using tools such as ‘deep learning’, ‘neural networks’, ‘computer vision’ and ‘natural language processing’, which all work to inform AI.

AI is essentially a broad term used to refer to machines that are able to mimic human cognitive functioning and intelligence - and so essentially machine learning is a subset of AI.

AI, however, is not always dependent on machine learning. For example, a chatbot is able to respond with pre-defined answers, which are based on inputted keywords and data.  

The field of machine learning is ever-evolving. Programmers continually seek ways to test the limits of the outputs generated to continually improve the perception and actions of computers and as technology advances, so do the applications made possible.

At BentoBot, we utilise machine learning within our learning technology platform to bolster the user's experience and the effectiveness of the training. With machine learning we can  provides a far more immersive platform, boosting engagement and enabling a more targeted learning and training programme. 

For example, BentoBot asks the learner about the learning content, times the response and records accuracy, helping to pinpoint learning individual knowledge gaps. Utilising sophisticated algorithms, learning material is then presented to the user based on this data, focussing on areas that the learner knows less well and resulting in a far more efficient and effective training experience. 

For more information, and to learn how BentoBot can advance your training and learning programmes, contact hello@bentobot.com