A lot of people in today’s world tend to confuse the concepts of “Artificial Intelligence” and “Data Science”, treating the former as a buzzword for the latter. However, while they are related, they are often used for different purposes. Data science is fundamentally the use of statistical tools and analysis to give meaning to a large set of data. AI itself may then use the patterns that are found in the data to create machines capable of performing tasks that would require cognitive input to do.
While the concept of data science has existed for a very long time, it is the use of AI to enhance and implement what is derived from data which has seen immense improvement over the past few years. All neural networks and machine learning algorithms built require a training dataset. This dataset is broken down by the network until patterns are found which could be used to relate certain traits of the data to an expected output. For example, in a regression model for housing prices as a function of various factors (location, square footage, amenities, view, surrounding area, etc), patterns would be found using data analysis to teach the model how to analyze. The weightage of certain factors can be calculated using statistical analysis, but the idea that these patterns could be fed into a program to automate a task such as predicting housing prices is very much artificial intelligence.
Although this was a very simplified example, this is fundamentally what many of the largest companies in the world do in regards to creating models. For example, ChatGPT is a large-scale language model that uses a prompt to predict what order of words to generate to address said prompt. Examples such as these showcase how AI amplifies the capabilities of data science, making it more efficient and impactful across various domains.
Data For Earth 