An increasing number of companies are adopting the use of artificial intelligence and smart technology. The terms are becoming more frequent in public conversation and while the growing interest is certainly positive, we’ve found that the terms are sometimes used as though they are interchangeable. In this article we will clarify what is meant by ‘artificial intelligence’ (AI) and ‘machine learning’, highlighting the key differences between them.
What is Artificial Intelligence?
AI can be broadly defined as the technology which enables computers to simulate human behaviour using rules and logic. Artificial intelligence has been around for decades now and has been used in a wide range of scenarios – it’s what powers Siri and allows Tesla to make self-driving cars. Traditionally, an artificially intelligent system would have to be programmed with a range of logic and ‘if-then’ rules. ‘If-then’ rules are rules whereby if certain conditions are met, then an action will be performed. A system uses these rules to analyse data and react accordingly to a given input. For example, if a person asked an AI voice assistant ‘what is the weather today?’, the system would be triggered to search for the weather and speak the answer. The limits of traditional AI are that when the system encounters an unfamiliar scenario, its initial programming will prove inadequate. For instance, consider an AI voice assistant that has been programmed to answer questions about the weather. If the question was phrased in a way that wasn’t detailed in its programming, it would be unable to perform its task.
What is Machine Learning?
Machine learning is a subset of AI rather than a distinct field. The term describes a process through which a computer system automatically improves based on the data it’s fed. In other words, machine learning mimics a human’s ability to find patterns and learn from experience. One of the key differences between machine learning and AI is how the two approach data input and analysis. Rather than programming a system with rules in order to sort data, machine learning works by feeding the system large amounts of data and having the system form rules based on the data. This allows the computer system to be more accurate, to better account for exceptions to the rules, and be applicable in a wider range of situations. In this sense, the similarities with the human brain become more apparent. We learn from experience, and our brains find connections and patterns to help us make sense of the world. For example, we’re exposed to lots of spoken sounds as a child, and as the amount of “data” we receive increases, our brains form neural connections and our knowledge of language increases. It’s how we become able to recognise, understand, and speak a complex language – even with different accents and dialects. This is essentially how machine learning works. Machine learning systems use neural networks, similar to the human brain. The more data the network is exposed to the stronger the connections are, meaning the system can better recognise inputs going forward, classify them based on their properties, and even make inductive predictions for the future. These predictions become increasingly accurate and refined as the system learns when it’s right and when it’s wrong. For instance, AI chatbots become more effective at solving complex queries as they learn more about customers’ unique needs. We encounter many examples of machine learning in everyday life. Take navigation services for example; when calculating your journey time, the technology incorporates a number of factors such as time of day, traffic, engineering works, and more. It analyses data from millions of users who have made similar journeys, helping it recommend the fastest or simplest route for your particular circumstances. This process of constant learning and improvement results in increasingly accurate predictions of journey times and potential setbacks. The system will also keep track of common routes you take, such as your commute to work, and preemptively provide updates on congestion or transport delays. It’s an excellent example of how machine learning can be of great service to us. Just imagine the possibilities on a larger scale.
What is the difference?
Looking for the difference between AI and machine learning is a slightly misleading approach, as this assumes they’re completely distinct. It’s like asking the difference between mathematics and algebra – algebra is one of many disciplines of mathematics. All algebra falls under mathematics, but not all mathematics is algebra. All machine learning can be classified as AI, but not all AI is machine learning. With basic, rule-based AI, many of today’s innovative technologies wouldn’t be possible. Voice assistants and translation apps for instance use machine learning to constantly improve. This capacity for constant and autonomous improvements is machine learning’s greatest strength as a form of technology. As cliché as it sounds, the possibilities are endless. In the digital age, data is in abundance and processing power continues to skyrocket, meaning machines can learn at increasingly rapid speeds. Machine learning is becoming more and more prevalent in our lives, and AI chatbots are a prime example. Chatbots that incorporate machine learning improve each time they’re used, providing long-term value for businesses and customers alike. Intelligent chatbots streamline the time-consuming aspects of customer service, so managers can focus on growing their business and delivering on their audience’s expectations. Harness the power of AI to take your business to the next level. Provide 24-hour support, quickly resolve queries, and improve your customers’ experiences with a level of efficiency you’ve never seen before. Get in touch for more information.