Artificial intelligence (AI) is a broad term used to describe computer systems that mimic human intelligence in that they can perform tasks that normally require human intelligence such as visual recognition, making decisions based on information and recognizing human speech. Machine learning is a subset of AI in that it enables the system to learn without human instruction by analyzing large amounts of data and finding correlations in it without being explicitly programmed to do so. When people talk about AI learning, they are usually talking about the machine learning component of an AI system.
The most significant recent advances in AI, in terms of practical applications, depend on machine learning so when people talk about AI today it is almost synonymous with machine learning. These applications include automated chat bots, voice controlled devices in search, natural language translation and image recognition systems (such as those used to identify diseases from x-rays in medicine). These systems learn from large datasets and are then apply the patterns and correlations they have ‘learnt’ to new datasets so that they can quickly identify them or make predictions based on previous data.
Machine learning has become ubiquitous in business with a recent survey finding that 67% of companies are using the technology in some form and 97% are currently using, or intend to use it, in the near future. The technology is used in a wide array of applications from retail to finance and even traditional businesses have found ways to use the technology to spur innovation and boost their efficiency. This means that it is likely to have a significant impact on almost every aspect of the way that business is done and will, therefore, also impact the general public.
This means that people need to understand how this technology works in general terms, without having to know the technical details, so that they are aware of how it functions and the limits of what it can and cannot do. There are significant social, ethical and societal issues associated with machine learning and it is important that people understand how these tools work and how it is best to use them. Although it has many benefits and can do much good, there are cases in which machine learning has reflected biases that are built-in, as it were, by their creators – not intentionally but through the use of datasets that unintentionally contain prejudiced and biased information. It is important to be aware of these downsides as AI learning becomes more and more prevalent in people’s lives.
