Two growing and essential fields in the tech world are artificial intelligence and data analysis, but what are they and how are artificial intelligence engineers and data analysts both similar and different? Let’s dive in:
What is Artificial Intelligence?
Artificial intelligence isn’t anything new; many of your minds may go straight to the recent birth of ChatGPT or even the Terminator films from 40 years ago when you hear the word, but it’s important to keep in mind that it’s not simply the ability for machines to act as if they were human. According to the Merriam Webster dictionary, the artificial intelligence definition is twofold:
a branch of computer science dealing with the simulation of intelligent behavior in computers
the capability of a machine to imitate intelligent human behavior
So what does intelligent human behavior entail? Artificial intelligence refers to software and algorithms that collect and analyze data to solve complex problems that typically require a human-level of intelligence and just like humans, artificial intelligence technology depends on several methods to take in as much information as possible to make the best possible choice.
Some of the most useful methodologies and algorithms that they use include:
Machine learning
Deep learning
Natural language processing
Rule-based systems
Machine vision
Through employing a combination of these methodologies, artificial intelligence is capable of both learning and evaluating the collected data to arrive at the most appropriate and efficient solutions to the problem and as a result, it is a valuable tool in the tech world.
Artificial intelligence as a field of study is growing more as technology becomes more complex and more machines, software, and other programs incorporate AI. There are many job positions that work with AI such as artificial intelligence engineers, machine learning engineers, data engineers, robotics engineers, and software engineers.
Since it’s such an impressive and efficient tool, companies and businesses want to harness its power to perform a variety of functions that support and maximize their reach, efficiency, and potential.
Artificial intelligence examples
There are currently a vast number of technologies and applications that already use AI to complete their tasks such as the following:
Virtual assistant software: have you ever asked Siri a question? Siri, Alexa, Google Assistant, and other virtual assistant programs use algorithms and language processing software to provide users with whatever information they need in an instant.
Chatbots: on many websites, you will immediately encounter a pop up on your screen that asks you if you need any assistance with their product or have any questions. This artificial intelligence chatbot software uses machine-learning techniques and language processing to imitate human behavior when answering your questions.
Image recognition: facial recognition, autonomous images, and object detection rely on the identification and analysis of people, objects, and scenes in images that this type of artificial intelligence provides.
Recommendation systems: that ad on Instagram that’s always exactly the same for months on end wasn’t chosen by you; it was chosen by AI to appeal to you based on all the data of which these social networks can access and leverage. Companies employ this type of AI to choose the products, media, and content that they believe will most interest you.
Financial trading: increasing money flow in your bank account can be quite difficult but AI has the power to analyze data and projections and detect fraud so that your finances will be handled smartly and traded swiftly.
Many more examples of artificial intelligence can be found throughout our modern world and it's important to realize that every day you’re interacting with them on social networks, websites, or applications. Artificial intelligence is all around us!
What is Data Analytics?
Data analytics as a field sounds simple, but it includes several steps that focus on not only gathering and organizing a large amount of information, but also evaluating, confirming, and interpreting all of it. Once information has been collected and analyzed, decisions need to be made based on the story, insights, and patterns that the data is presenting.
Companies, organizations, and other entities can use the insights summarized in the data in a variety of ways such as businesses looking at buyer tendencies to generate feedback for their products or evaluate the reviews that their products receive. The power of data cannot be underestimated and knowing this, businesses can leverage it to help them make important decisions.
Data analysts depend on a few different techniques to complete their analysis:
Regression analysis
Factor analysis
Data visualization
Monte Carlo simulations
Data mining
Time series analysis
Hypothesis testing
Cohort analysis
Employing these and other techniques, data analysts generate insights and patterns that guide businesses to decide the best course of action and as a result, the companies with the best data analytics teams can make the most informed decisions for their future. Companies know that to beat the competition, they must focus on the data and have more accurate predictions; having top notch data analysts makes a huge difference.
What Responsibilities Do Artificial Intelligence Engineers Have?
For artificial intelligence to be created, maintained, and improved upon, AI professionals have to carry out many tasks, designing a network of several algorithms to create the illusion of human-like behavior; the process requires them to have expertise in fields like software development, programming, data science, and data engineering.
Although artificial intelligence engineering is related to these fields, AI engineers seldom write code; they do, however, search for and manipulate data, develop and test machine learning prototypes, and use the API to create and design artificial intelligence software or applications.
Some other responsibilities also may include:
Organizing and providing structure for the development of artificial intelligence systems
Carrying out statistical analysis and interpret the data to improve the decision-making process
Streamlining artificial intelligence systems for data scientists
Coordinating with other teams that use artificial intelligence-based software and technologies
How Do Artificial Intelligence and Data Analysis Differ?
Artificial intelligence has a wider scope that focuses on developing systems that make the final decisions, but data analysis is a subset of artificial intelligence that can only inform others in the decision-making process.
Artificial intelligence uses data analysis as one of its techniques to achieve its human-like behavior and also has access to other of the techniques mentioned previously like machine learning and natural language processing.
Apart from having a wider scope, artificial intelligence also doesn’t require human intervention when the final decision needs to be made; with data analytics, insights, feedback, and patterns are presented to people so that they can make an informed decision.
How Do Data Analysts and Artificial Intelligence Engineers Work Together?
Since data analysts are focused on providing insights into products such as for an artificial intelligence model, they do collaborate with AI engineers on their projects. Their goal is the same: improve the product experience but they complete their objective in different ways.
As we mentioned above, AI engineers focus on creating, maintaining, and improving their AI models and to carry out their projects, they may depend on data analysts for feedback. Data analysts on the other hand, are supporting AI engineers in gathering data on their project and looking at what they can do to assist them in creating the best product possible.
Throughout the artificial intelligence creation process, data analysts provide parameters and guidance to show AI engineers where to go with their product or what modifications to make based on their own data analysis. Data is powerful and AI engineers can use its insight to weave algorithms into a complex network of art.
Although data analysis and artificial intelligence seem to both be complex sciences that depend on various algorithms and computer technologies, they have diverse functions and applications. We have seen that data analysis is an important tool that provides the information, insights, and tendencies yet has its limit unlike artificial intelligence; by using data, artificial intelligence has the power to make a final decision without human intervention.
Both are helping companies and businesses grow more through their distinct roles as company tools to connect more with the users and clients that interact with them. Their significance in the tech world should not be downplayed and if working with either of these two disciplines interests you, Ironhack provides courses to become an expert on either. Now’s your chance to check them out below.
