*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
Machine learning vs AI — while the two may often get misconstrued as the same thing, there are several differences between the two concepts. Despite their differences, machine learning and artificial intelligence (AI) complement each other innovatively across various industries to enhance operations.
Machine learning and AI have revolutionized how businesses run, generally making them more efficient and profitable in today’s tech-driven world. Machine learning and AI can position your business for success, from automation to improving the customer experience and enhancing cybersecurity and fraud detection. Let’s learn more.
Table of Contents
Understanding Artificial Intelligence (AI)
Distinguishing Machine Learning vs AI
Interrelation Between AI and ML
#2. Voice-to-Text and Predictive Text
Understanding Artificial Intelligence (AI)
Artificial intelligence, or AI, is a computer or machine’s ability to mimic human intelligence using hardware, software, machine learning algorithms and large amounts of training data. Data teaches computers how to solve problems, make decisions, and carry out other tasks.
AI emerged in the mid-20th century when the idea that machines could “think” was first introduced to the masses. AI as a concept and its capabilities have evolved dramatically since then. Modern artificial intelligence includes virtual assistants, self-driving cars, and advanced medical diagnostics.
While AI can be complex, it’s certainly not far off. You likely encounter AI every day across several touchpoints, and you may not even realize it. For example, whenever you pick up your phone and unlock it or log into an app with face ID, that’s AI. Apple’s FaceID technology sees in 3D and uses thousands of infrared dots to capture an image of your face. It then stores that image and uses machine learning algorithms to compare every scan of your face it picks up when you use FaceID to the image it has stored.
Several of the platforms and services you interact with daily also use AI. Take Netflix, for example. The streaming platform’s recommendation system is powered by AI and pulls data from your past viewing history to make content recommendations based on the genres, actors, and other characteristics you’ve enjoyed in the past.
There isn’t just one type of artificial intelligence. It’s more of an umbrella term, as there are several different types of AI. Three include:
#1. Narrow AI
Narrow AI is used to perform single tasks. Think facial recognition, chatbots, search engines like Google, and digital voice assistants like Siri and Alexa.
It’s called “narrow” because of its narrow, singular approach. Narrow AI can only carry out tasks it was programmed to do. While narrow AI isn’t as advanced as other forms of AI — as it can’t respond to abstract questions or provide immense detail — it’s still the basis for many machine-learning-enabled systems and more advanced AI tech.
#2. General AI
General AI, also called artificial general intelligence (AGI) or “strong AI,” is a hypothetical type of AI. A machine can learn and think like a human. It’s essentially a term used to explain or describe what the future of AI will or could be like.
Where narrow AI is more hyper-focused on singular tasks, general AI is much broader and adaptable as it follows an unsupervised learning process. It doesn’t need to be labeled and managed like narrow AI does. The tools and programs required to run it are still in development, but general AI is a goalpost for machine learning developers.
#3. Super Intelligent AI
Super intelligent AI, or super AI, is a type of AI that can exceed human intelligence. At this time, conversations about super AI are mostly hypothetical, but it’s largely considered to be the eventual endgame.
This type of highly advanced AI would be able to improve upon itself by learning from input data on its own. This happens at a fast pace until an “intelligence explosion” is reached. An IE creates super-intelligent AI systems. It sounds like something out of a sci-fi movie, but developers and AI experts often refer to it as the future of AI.
Exploring Machine Learning
Machine learning is a branch, or subset, of AI that focuses on the development of computer algorithms and programs that can improve automatically through experience and data usage. Machine learning enables computers to learn from data to make predictions and decisions autonomously, eliminating the need to program them to do so.
These programs and algorithms are designed to improve over time to become more accurate as it processes more data. For example, say you want your machine to recognize images of houses. You’ll feed it thousands of images of houses to train the algorithm to identify patterns and key features. The more images it processes and data it gathers, the more efficient the machine will become.
The ability for computers to learn from data autonomously makes machine learning an extremely valuable and versatile tool, which is in large part why it has emerged as its own field rather than just a subset of AI. It is the driving force behind many technological advancements from self-driving cars to predictive analytics.
There are countless use cases for machine learning across a variety of industries. For example, machine learning analyzes medical images like X-rays and MRIs to help doctors diagnose diseases quickly and with greater accuracy. ML helps improve access to fast, effective healthcare for patients.
Machine learning is also part of the transportation industry. It helps improve vehicle safety and manufacturing optimization. Several car manufacturers use machine learning to improve technology like automatic lane control, adaptive cruise control, and fuel efficiency.
Three concepts fall under the machine learning umbrella:
#1. Supervised Learning
In supervised machine learning, the computer is given a set of training data that has been labeled by humans. The algorithm uses this data to learn how to make predictions based on the patterns it identified from the training data. The earlier example of wanting your computer to learn how to identify images of houses is an example of supervised learning.
Supervised learning is most suitable for classification problems, like determining whether emails are spam or not. Clustering and identifying patterns to create segments, mapping regression trends, and predictive analysis are other examples.
#2. Unsupervised Learning
With unsupervised machine learning, computers identify patterns and gather insights from unlabeled data. UL eliminates the need for humans to label the data for the machine. Unsupervised learning machines don’t need guidance or instruction to operate like supervised learning machines do.
Unsupervised machine learning models are self-learning and use raw, unlabeled data to infer rules and structures based on the insights it gathers independently. Typically, unsupervised learning is best suited for more complex processing tasks.
#3. Reinforcement Learning
Reinforcement learning uses a rewards and punishments system to train models. AI systems, or agents, interact with the model to train it through a trial-and-error approach. The model receives positive or negative feedback and then learns from its mistakes to improve upon itself and become more efficient at mimicking human intelligence.
Reinforcement learning is commonly used in video game AI, robotics, and self-driving car technologies. It can also be used to make financial predictions, control traffic, conduct drug discovery, and assist development in the healthcare industry.
Distinguishing Machine Learning vs AI
In the machine learning vs AI comparison, there are several important differences. However, the two share the same overarching goal: make technology smarter and continue advancing its capabilities for the future.
The specific focus of machine learning vs AI differs slightly. AI primarily focuses on simulating human intelligence in machines and computers, training and empowering them to carry out complex tasks, and making predictions and decisions autonomously with accuracy. As a subset of AI, machine learning focuses more on making computers more intelligent.
Interrelation Between AI and ML
FACT: Intelligent computers use AI to mimic human thinking and complete tasks autonomously and machine learning is the process through which a computer develops its intelligence.
Machine learning and AI are very interconnected. As a subset of AI, machine learning is essentially an application of AI. Artificial intelligence is a much broader concept that covers a slew of tech and approaches across industries and machine learning is just one part of that.
ML helps computers achieve AI through deep learning processes by using mathematical models, statistical algorithms, and data to help the computer learn without interference. Fundamentally, machine learning contributes to the development of AI, as the two work together to advance a computer’s capabilities.
When thinking about machine learning vs AI, the two share a cooperative relationship. Under the AI umbrella, there are several components that contribute to its capabilities, success, and general potential. For instance, machine learning and similar concepts like deep learning and data science.
Some everyday examples of machine learning powering AI systems include:
- Email Spam Filters
- Predictive Text
- Mobile Check Deposits
- Product Recommendations
- Chatbots
#1. Email Spam Filters
To be effective, email spam filters need to continuously learn from examples on how to identify spam messages based on various signal words. It’s not enough to just manually set rules-based filters in your inbox. That’s where machine learning and AI come in.
Email platforms like Gmail rely on machine learning algorithms to filter out potential spam with great success rates. The algorithms use data from email domains, the sender’s location, email text and IP address to determine what is and isn’t spam. It also benefits from interference from you as the user to mark emails as spam if the algorithm misses one, or to correct any emails that were mistakenly filtered as spam.
#2. Voice-to-Text and Predictive Text
Computers can learn to identify and comprehend human language by using natural language processing computers. This tech powers voice-to-text applications like Siri and Cortana, as well as predictive text features found on most smartphones and computers.
With predictive text, supervised machine learning processes train the computers to recognize and predict commonly used words or phrases given the context of a conversation. Predictive text can also be more advanced when unsupervised learning is used. UL allows the computer to pick up on your individual speech pattern and start recommending personalized words and phrases.
#3. Mobile Check Deposits
Most large banks with mobile banking apps allow users to deposit checks on their phone, eliminating the need to go to the bank each time they have a check to cash. It uses handwriting and image recognition to “read” the checks and convert the information to digital text that can be deciphered by the computer.
#4. Product Recommendations
If you’ve shopped online, you’re likely familiar with product recommendations. They fill your homepage or are presented to you at checkout through suggestions like “you might also like.” These are all powered by machine learning algorithms.
These product recommendations are targeted marketing tactics that many retailers rely on, fueled by data gathered on customers’ buying habits, demographics, and other shared characteristics.
#5. Chatbots
AI chatbots have revolutionized the scope of customer service. These programmed algorithms train machines to answer consumer questions and provide quick assistance to customers 24/7.
Chatbots can mimic what a conversation with a human customer service representative feels like through natural language processing. Today’s more advanced AI chatbots can answer complex questions and provide detailed responses, often making it unclear whether you’re talking to a bot or a human.
Conclusion
Machine learning vs AI: despite their subtle differences, they are deeply connected and fuel each other to drive technological advancements across various industries. AI is an umbrella concept covering smart tech, and machine learning is one part of that. While machine learning creates intelligent computers, AI needs intelligent computers to mimic human thinking and complete tasks.
The two work together to advance a computer’s capabilities. Continue exploring and learning about machine learning vs AI to continue advancing and evolving. Don’t get stuck behind the data curve.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with an agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair, Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.
