Artificial intelligence (AI) and machine learning (ML) dominate today’s lists of required qualifications, novel technologies used in production, and promising degrees to earn. While the interest in this field is peaking, the confusion surrounding it is also on the rise. One of the points of misinformation lies in the very meaning of AI and ML: while these terms are highly interconnected, they are not interchangeable.
Learning the differences between artificial intelligence and machine learning in 2020 is useful not only for aspiring data engineers but for marketers, entrepreneurs, and the general population heading deeper into the digital age.
What is Machine Learning?
By scientific definition, machine learning is “the study of computer algorithms that allow computer programs to automatically improve through experience.” Essentially, ML is an approach that strives to eliminate the need for humans to program each piece of functionality by hand and instead make a computer teach itself to accomplish a task. Contrary to popular belief, machine learning doesn’t necessarily strive to make machines intelligent or capable of creativity. It is merely a tool that allows us to process data more efficiently and with less personal intervention.
Machine learning operates large datasets and tries to find patterns in the information. Enormous amounts of high-quality data are crucial to successfully apply machine learning algorithms and obtain accurate predictions. Most frequently, companies already possess this data in abundance, and data science specialists, together with a team of ML engineers, are employed to leverage this data and compile insights. For more information about machine learning services to aid the development of your software, visit this website.
To understand the differences between ML and AI, it’s necessary to first grasp the basics of how machine learning accomplishes the fascinating results we see daily. Fundamentally, ML algorithms can be divided into two categories: supervised and unsupervised learning.
Supervised learning works with labeled datasets. It means that before using a model to make predictions, you need to train it on sets that already have both factors and results. For instance, to predict the price for a particular house in your local area, a supervised model first needs to process a list of house characteristics and known corresponding prices.
Unsupervised learning works with unlabeled datasets. Most frequently, these algorithms are used to classify objects and find patterns in datasets. Outside the scientific field, unsupervised learning can be used for customer clustering to find groups with similar characteristics within your target audience.
What is Artificial Intelligence?
Artificial intelligence is a scientific field and an endeavor to enable machines to perform specific tasks more or as effectively as humans do. According to Andrew Moore, the former Dean of the School of Computer Science at Carnegie Mellon University, “artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”
These tasks are centered around creativity, interpretation of context, the deciphering of human emotions in audio or visual form, and others. For instance, computer vision, one of the fastest-growing areas in AI, aims to bring computers closer to humans in their ability to comprehend visual data. Artificial intelligence trained to work with pictures is currently able to perform a number of spectacular tasks, including distinguishing cancer cells from healthy ones and determining the position of other vehicles on the road for a driverless car’s navigation system.
How Does ML and AI Differ and Connect?
In its essence, machine learning can be treated as a special case of application of AI. The whole approach is built around the idea that we should simply give machines the relevant data and reap the rewards without putting manual effort into processing this information. ML, in this case, is the result of the attempt to enrich computers with enhanced human capabilities.
No artificial intelligence algorithm is possible without machine learning. All subfields of AI, from computer vision to natural language processing, depend on enabling a computer to work with the given data independently to build reusable models.
The scope to which machine learning is used, however, doesn’t always qualify to be labeled as AI. For instance, a simple prediction model computing the forecast for your customers’ satisfaction with a new feature in your mobile app can use machine learning, but, at the same time, it doesn’t require human intelligence and hence doesn’t present an instance of AI.
ML vs. AI: Recap
The main difference between machine learning and artificial intelligence lies in the scope. ML is a practical approach and an idea of how to work with data effectively, while AI is a concept of what we would like to achieve in terms of computers’ capabilities. Artificial intelligence uses machine learning, among other technologies, to empower machines with human vision, interpretation possibilities, and creativity. Machine learning and artificial intelligence, both separate and intersecting in some cases, greatly improve numerous fields from medicine and education to art, finance, space exploration, and social wellness.