“AI teaches itself.” “AI, Machine Learning and Deep learning are all the same.” “I don’t need AI.”
These are some of the common myths about Artificial intelligence (AI). With a topic like AI, it is important to understand the truth and to separate it from all of the myths and misconceptions.
Myth #1: AI learns on its own
It is a common myth that intelligent machines learn from themselves. However, AI does not work like magic. Instead, it is trained on historical data and uses its knowledge to make judgements. After checking the judgements made by AI against reality, engineers measure the accuracy of a model and then the model can be re-trained. This can be done by reframing the problem, removing potential bias in the training data and integrating new data into the next learning cycle. AI cannot do this on its own – for now.
Myth #2: AI is the same as Machine Learning and Deep Learning
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are often used interchangeably. However, it is important to understand that the three terms are related but not the same. For example, when a machine solves problems based on a set of rules (algorithms), such intelligent behaviour is called Artificial Intelligence or AI.
ML, on the other hand, enables machines to learn from data and make accurate predictions. In other words, ML is a technique for realizing AI and training algorithms to learn. ML is a subset of AI.
Then there is DL, which represents the next evolution of ML and another subset of AI. DL uses neural networks (NNs) to accomplish tasks, inspired by information processing patterns found in the human brain. However, when compared to ML, DL needs considerably greater amounts of training data to deliver accurate results.
Myth #3: My organisation does not need AI
Every organisation should at least investigate how AI could transform their business. It helps organisations in delivering business goals more efficiently, reducing operational costs and improve customer experience. The exploitation of AI leads to the next phase of automation, which will ultimately place organisations at a competitive advantage.
For example, the AI-enabled Sirius Insight platform provides many operators in the marine environment with invaluable insights that enhance their decision-making. Sirius Insight does this by analysing data sourced from satellite, air and terrestrial-based sensors and digital sources, identifying unusual behaviour and acting upon this.