When it comes to technology and artificial intelligence, there is much hype as well as uncertainty. AI is an extremely broad topic, and there are many different competing definitions of what it means. However, the one thing that all AI has in common is that it’s about making machines follow rules and learn from their experiences. This can take a variety of forms, but the general principle stays the same: machines follow rules based on their experience.
What is Artificial Intelligence?
The definition of AI has been changing since the term was coined in 1956. A computer scientist named John McCarthy came up with the term artificial intelligence to describe “the science and engineering of making intelligent machines.” The definition has evolved over the years since then, with experts taking different approaches to define the concept. There are two main approaches: functional and philosophical.
Pros of Artificial Intelligence
AI can automate tedious and repetitive tasks
– AI systems can undertake tedious tasks that humans would rather not do, allowing employees more time to focus on higher-value activities. Examples of this type of AI include computer vision systems that can be trained to identify defects in products or chatbots that help customers with basic enquiries.
AI can help to scale businesses by increasing efficiency –
AI systems are often able to work around the clock without getting tired. This means AI systems can help businesses scale without hiring more employees. This is especially relevant in industries like healthcare where demand for services is high.
AI can boost sales by increasing customer satisfaction
– AI systems can be programmed to give customers exactly what they want. This means customers are more likely to be satisfied and come back again. AI can also be used to monitor customer behaviour patterns and provide insights that can improve future service provision.
Cons of Artificial Intelligence
AI systems don’t always work as expected
– Every technology has glitches, but with AI this is often a result of the system not following the rules that were programmed into it. In order for AI to generate results, it needs to be trained on a consistent basis to follow the rules that were set out for it. If these rules are not followed consistently, the AI will not generate the expected results.
AI systems can be difficult to change
– If you need to make a change to an AI system that has been trained, you will likely need to re-train it from scratch. This means that mistakes made during the training process are difficult to fix. For example, if your computer vision system is trained to detect defective products but ends up misclassifying a certain type of product as defective if it can’t fix the mistake.
AI can be used to reinforce biases in society
– Although the technology behind AI has been improving, there are still societal issues that occur because of it. For example, algorithms can be programmed to respond to certain scenarios in a certain way. If these scenarios and ways of responding are based on flawed rules, they can reinforce biases in society.
Key takeaway
Artificial intelligence is the science and engineering of making intelligent machines. The definition of AI has evolved over the years since it was first coined in 1956. It can be broken down into three core tasks: automated decision-making, automation, and pattern recognition. AI can help to scale businesses, improve efficiency, boost sales, and automate tedious tasks. However, AI systems don’t always work as expected, can be difficult to change, and can be used to reinforce biases in society.
