Data Science vs Machine Learning vs. AI: How They Work Together

ai vs ml examples

Three key capabilities of a computer system powered by AI include intentionality, intelligence and adaptability. AI systems use mathematics and logic to accomplish tasks, often encompassing large amounts of data, that otherwise wouldn’t be practical or possible. Within the last decade, the terms artificial intelligence ai vs ml examples (AI) and machine learning (ML) have become buzzwords that are often used interchangeably. While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Artificial Intelligence also has the ability to impact the ability of the individual human, creating a superhuman.

The key aim is to allow the computers learn automatedly without human interference or backing and regulate actions consequently. Reinforcement learning uses trial and error to train algorithms and create models. During the training process, algorithms operate in specific environments and then are provided with feedback following each outcome. Much like how a child learns, the algorithm slowly begins to acquire an understanding of its environment and begins to optimize actions to achieve particular outcomes.

What Does a Machine Learning Engineer Do?

AI/ML—short for artificial intelligence (AI) and machine learning (ML)—represents an important evolution in computer science and data processing that is quickly transforming a vast array of industries. Artificial Intelligence has already occupied several industries, it has spread its wings from medical breakthroughs in cancer and other diseases to climate change research. Humans are able to get efficient solutions to their problems with the help of computers that are inheriting human intelligence. So the future is bright with AI, but it is good to the extent when only humans command machines and not machines start to command humans. So, Artificial Intelligence is a branch of computer science that allows machines or computer programs to learn and perform tasks that require intelligence that is usually performed by humans.

The image below shows concentric circles demonstrating how AI, ML and DL relate to each other. The three technologies are connected in the same way that Russian Dolls are nested; each technology is essentially a subset of the preceding technology. In addition, I have realised that these terms are frequently used interchangeably in social media when, in fact, they are all very different things. How can you use both AI and ML for your business and gain the benefits through them? In order to make things easy for you, here are the applications of AI and ML discussed simultaneously. As we have already discussed, both AI and ML bring plenty to the table with their wide range of functions.

Convolutional Neural Networks

How is the business doing with a particular product or in a geographic region? These are questions that can be answered using the mathematics, statistics and data analytics that are part of the data science process. The main purpose of an ML model is to make accurate predictions or decisions based ai vs ml examples on historical data. ML solutions use vast amounts of semi-structured and structured data to make forecasts and predictions with a high level of accuracy. Machine learning (ML) is considered a subset of AI, whereby a set of algorithms builds models based on sample data, also called training data.