Artificial Intelligence (AI) VS. Machine Learning (ML) — Crucial Differences
Artificial Intelligence (AI) and Machine Learning (ML) are two hot topics right now, and often seem to be used interchangeably.
Recently, a report was released regarding companies falsely claiming to use artificial intelligence in their products and services. According to the Verge, almost half of the European startups incorrectly represent AI. Last year, TechTalks also found numerous instances of companies misusing machine learning and advanced artificial intelligence, gathering millions of user data to improve user experience on their products and services.
The public and media need more clarity on what genuinely artificial intelligence is and what exactly machine learning is. At times, these terms are incorrectly used as synonyms. In other cases, they are portrayed as distinct, parallel advancements, while some are taking advantage of the trend to create hype and excitement to generate sales and revenue.
Below are some main differences between AI and machine learning.
What is Artificial Intelligence (AI)?
Artificial intelligence is vast in scope. According to Andrew Moore, a former-Dean of the School of Computer Science at Carnegie Mellon University, describes Artificial Intelligence as the engineering & science of making computers behave intelligently in ways previously thought to require human intelligence.
AI can be broadly defined in a single sentence; however, this indicates how broad and abstract the field is. Fifty years ago, a program that played chess was seen as a type of AI because game strategies and theory were thought to be beyond the capabilities of a computer. Playing chess has become commonplace — it’s available on almost every computer’s Operating System. Hence “until recently” is something that changes over time.
Zachary Lipton, an Assistant Professor and Researcher at CMU, explains that “AI” is an “aspirational, moving target” based on human capabilities, but machines do not. While AI is typically associated with Machine Learning, it also encompasses other technological advancements. For instance, Deep Blue, the AI that defeated the world’s chess champion in 1997, used the tree search algorithm to evaluate millions of moves in each turn.
Today, AI is synonymous with Human-AI interactive devices like Google Home, Alexa, and Siri. These machines are powered by machine learning video prediction systems that drive services like Netflix, Amazon, and YouTube. This technology has become indispensable for enhancing our everyday lives & professional productivity.
AI continuously evolves, and its meaning can vary day-to-day depending on technological breakthroughs. Many AI advancements may be considered obsolete within the next few decades. To stay ahead of the curve, professionals in the field must stay current on technological breakthroughs and applications.
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What is Machine Learning (ML)?
Machine learning (ML) is an artificial intelligence branch that enables computers to learn by analyzing large datasets. As Tom M. Mitchell, computer scientist and machine learning pioneer, stated: “Machine learning is the study of computer algorithms that allow computer programs to improve through experience automatically.” ML is one of several strategies to achieve AI, allowing us to explore patterns and nuances found within data sets ranging from small to large.
By using machine learning models and providing them with many songs you enjoy, along with their corresponding audio statistics such as dance-ability, instrumentality, tempo & genre, you can create a recommender system with high accuracy to suggest you with music in the future that will suit your tastes. Companies such as Netflix and Spotify are already using this technology.
Machine learning can potentially automate and speed up data analysis in various ways, such as with x-ray images. An ML algorithm can learn to detect patterns in pictures with similar indications with a sufficiently large dataset of x-rays and descriptions and labels for each image. When new images are loaded, the model compares its parameters with the existing examples to determine how likely they contain a specific indication. This process may even be automated if an appropriate ML algorithm is used.
Supervised learning is one kind of machine learning algorithm in which predictive models are created to map relationships between the target output and input features. It allows for predictions of new values based on what has been learned from earlier datasets.
Unsupervised learning, another type of machine learning, enables pattern detection & descriptive modeling by working with data that has not been labeled. Unlike supervised learning, unsupervised algorithms are used with unlabeled data, meaning no predetermined set of categories or labels. It’s a powerful tool that can help uncover hidden insights to aid decision-making.
Reinforcement learning, the third popular type of machine learning, uses observations from its environment to make decisions to maximize reward or minimize risk. It’s an iterative process in which the machine learning algorithm (called “the agent”) learns from its own experience. A great example of reinforcement learning is computers reaching a super-human level and beating humans at various computer games.
Machine learning is a powerful technique, and its complex applications, such as deep learning and multiple neural networks, offer exciting possibilities. Observing the inner workings of ML can be challenging for some people; however, it is simply Computational Learning Theory at work. Deep learning and neural networks prove that complex computations can be achieved efficiently. However, they do not mimic the same processes as our brains.
Why do tech companies tend to use AI and ML interchangeably?
The term “artificial intelligence” was coined in 1956 by researchers including Allen Newell and Herbert A. Simon. The AI industry has seen many ups and downs over the years. There was a lot of buzz about the industry in its initial stages, and many scientists agreed that human-level AI was just around the corner. However, lofty expectations that were not met caused a loss of enthusiasm for the discipline leading to what is known as AI winter, marked by reduced investment & diminished public interest.
Alternative terms were adopted as organizations sought to distance themselves from the often-overhyped association with AI. For instance, IBM categorized Deep Blue as a supercomputer but did not acknowledge its use of AI — disregarding what was happening behind the scenes.
In recent years, there’s been an increase in the use and popularity of terms like big data, predictive analytics, & machine learning. 2012 was a pivotal year, as it saw significant advances in these areas and their implementation in various industries. Because of these technological advances, organizations embraced the new terminology & began to brand their products with “machine learning” or “deep learning.”
Thanks to advancements in deep learning, computing is revolutionized. Tasks that had previously been impossible with classic rule-based programming suddenly became achievable. Deep neural networks have enabled us to take giant steps forward in facial recognition, speech recognition, natural language processing, and image classification. This significant progress was acknowledged by awarding three of the most important deep learning pioneers with a Turing Award in March 2019. Deep neural networks have become a critical part of modern computing.
As a result, we have witnessed recent advancements in AI technology. Compared to more traditional software, the capabilities of deep learning almost appear like “magic.” Hiring demand for machine learning and deep learning engineers has increased significantly, and salaries are often competitive regardless of employer type — the public or private sector. This field is highly sought after.
