Data and AI
Artificial intelligence and the science of data analysis are two indispensable concepts for understanding the digital and technological revolution we’re currently living in. Both sciences share an intention to take advantage of the amount of data generated every day and process it efficiently.
While these terms are closely related, there are small differences that make them independent fields with their own characteristics and particularities. Let’s see what each of them involves.
What is data science?
Data science is the branch responsible for extracting, visualizing, and analyzing the value of unstructured data by using fields such as statistics, machine learning algorithms, and scientific methods. This data is collected on websites, smartphones, sensors, and other systems and devices on which users generate and share information.
The analysis of information carried out by data scientists is key to discovering patterns and developing strategies and solutions for decision-making in everyday life.
To collect and analyze data, data science uses:
- Data mining, which consists of searching for patterns, correlations, and anomalies in large groups of visible data in order to analyze them and predict results.
- Inferential statistics, which is based on the information from the sample data to draw conclusions and make deductions.
What is artificial intelligence?
Artificial intelligence (AI for short) makes it possible for machines to be capable of performing tasks and actions that require human intelligence automatically, such as text interpretation or speech recognition, pattern identification, learning based on data analysis, etc.
Generally speaking, AI uses multiple pieces of software to create algorithms that allow programs to respond and reason as humans do, generating analysis solutions.
What is the difference between data science and artificial intelligence?
The distinguishing feature of these two branches is that data analysis involves analyzing, predicting, and visualizing data, as its name implies; it is an exercise prior to data processing, while artificial intelligence directly consists of implementing data.
These differences can also be highlighted:
- Data science uses statistical techniques, while artificial intelligence designs algorithms.
- Data science is based on data analysis, while artificial intelligence is based on machine learning.
- Data science comes from the need to find trends and patterns in data and extracts the most useful ones, while artificial intelligence handles data, autonomously displacing human participation in some way so that the machine operates independently.
What other concepts are related to these?
- Machine learning: a branch that makes it possible for computers to be able to identify, predict, and offer artificial intelligence applications, allowing the imitation of human processes and decision-making based on algorithms.
- Deep learning: a variant of machine learning that enables solving more complex problems. Siri or Alexa are examples of applications that use DL to serve user requests.
How do data and artificial intelligence support the Sustainable Development Goals?
The need for accessible, open data that artificial intelligence and new technologies are based on is fundamental for carrying out projects that support achieving the Sustainable Development Goals (SDGs). This way, companies, governments, and non-governmental organizations will be able to work together to generate, share, and implement solutions based on data analysis.
Here, we’ll tell you how we’re incorporating data science and artificial intelligence in our projects at Ferrovial.