Demystifying AI and ML: What They Are and How They Work

TLDR: AI and machine learning are rapidly transforming various industries, from healthcare to finance to entertainment. By processing and analyzing vast amounts of data, these technologies can help improve efficiency, accuracy, and decision-making. However, their implementation also raises ethical, social, and economic questions that need to be addressed.

Artificial Intelligence (AI) and Machine Learning (ML) are two of the most talked-about technologies today. They promise to revolutionize the way we live and work by automating tasks, analyzing data, and making predictions. But what exactly are AI and ML, and how do they work? In this article, we'll demystify these buzzwords and explain what they really mean.

"Artificial intelligence, deep learning, and other machine learning techniques are making computers more intelligent, but more importantly, they are helping people become more productive, creative, and effective problem-solvers." - Andrew Ng, AI pioneer and founder of deeplearning.ai

AI is the ability of machines to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. ML is a subset of AI that involves the use of algorithms and statistical models to enable machines to learn from data, without being explicitly programmed. In other words, ML allows machines to automatically improve their performance on a specific task by learning from examples, rather than by following a pre-defined set of rules.

So how does ML work? At the heart of ML is the concept of a model, which is a mathematical representation of a system or a phenomenon. For example, a model can be used to predict the price of a house based on its size, location, and other features. To create a model, we need to train it on a dataset, which is a collection of examples with known inputs and outputs. During the training process, the model adjusts its internal parameters to minimize the difference between its predictions and the actual outputs in the training dataset.

• According to a recent report by Gartner, the global AI industry is expected to reach a value of $557 billion by 2026, up from $37 billion in 2020.
• A survey by Deloitte found that 55% of businesses have already implemented some form of AI or plan to do so in the near future, and 73% believe that AI will be a "business advantage" within the next three years.
• The number of active chatbots on Facebook Messenger has grown from 33,000 in 2016 to over 300,000 in 2021.
• Google's DeepMind AI program, AlphaGo, was trained on thousands of historical Go games and played itself millions of times before defeating the world champion Lee Sedol in 2016.

Once the model has been trained, it can be used to make predictions on new, unseen data. For example, we can use the house price model to predict the price of a new house that has not been included in the training dataset. This is called inference, and it is the core of many ML applications, such as recommendation systems, fraud detection, and image recognition.

AI and ML are already being used in many industries, from healthcare to finance to retail. They have the potential to transform the way we live and work, but they also raise ethical and social issues, such as bias, privacy, and job displacement.

In the next article in this series, we'll dive deeper into the ethical considerations of AI and ML, and explore how we can ensure that these technologies are developed and used responsibly.

Did you know that in 1997, an AI program called Deep Blue defeated world chess champion Garry Kasparov in a six-game match? It was the first time a computer had beaten a reigning world champion under tournament conditions, and it demonstrated the potential of AI to excel at complex tasks that were previously thought to be the exclusive domain of human intelligence.