Machine Learning vs. AI: What’s the Difference?
Every time Netflix recommends a new binge-worthy show for you, or Amazon suggests a related product, or Google helps you find the name of that one actor that was on the tip of your tongue, you’re experiencing machine learning at work.
All of these real-world applications use a subset of artificial intelligence technology to find patterns, solve problems, and accomplish tasks.
But although machine learning, deep learning, and artificial intelligence (AI) are related, the differences between them can be confusing.
In this post, we’ll break down the differences in these exciting technologies in plain language, and explore how they’re relevant to your business.
Definitions: AI vs. Machine Learning vs. Deep Learning
Let’s start with some definitions:
Artificial Intelligence (AI)
Artificial intelligence is the study of how to build programs that can solve problems in a similar way to humans; it’s about replicating human problem-solving and intelligence in machines.
When working to develop AI, scientists quickly realized that teaching an AI every single thing it needed to know to perform its intended function was a non-starter. Instead, teaching the AI how to learn was the path forward. And so machine learning was born.
Machine Learning (ML)
Machine learning is a subset of AI concerned with helping intelligent systems learn and improve over time without being programmed to do so, to essentially learn for themselves.
Deep Learning (DL)
Deep learning is a subset of machine learning which analyzes and sifts through information with a structure designed to mimic the neural structure of the human brain, called a neural network. One example is comparing data to find common patterns, something that comes naturally to humans and is a key element of how we solve problems.
How AI and Machine Learning Are Different
What’s the difference between AI and machine learning? Here are some key differences to understand:
|Definition||The study of how to build programs that can solve problems in a similar way to humans||A subset of AI concerned with helping intelligent systems improve over time without explicit programming|
|Objective||Building systems that can think like humans and solve various complex problems||Enabling machines to learn and become more accurate over time at performing the specific tasks they are trained to do|
|Categories||Narrow, General, and Super||Supervised, Unsupervised, Semi-supervised, and Reinforcement|
|Common Applications||Voice assistants like Siri and Alexa, online chatbots, human-like robots||Automatic suggestions for shows, music, products, new Facebook friends based on users’ previous behaviors; Google Search algorithms|
3 Levels of Artificial Intelligence
Both AI and machine learning are popular buzzwords, but when companies claim to use these technologies, it can be difficult to know how they really come into play.
To better understand real-world applications of AI, it’s helpful to first understand the three different levels of artificial intelligence:
This is a type of artificial intelligence that’s trained to do one task extremely well. Narrow AI can perform its one, specific task more efficiently than a human.
A well-known example is Deep Blue, the chess-playing computer built by IBM in the nineties, that was so good it beat reigning chess champion Garry Kasparov.
But if you were to ask Deep Blue to do anything other than play chess – like recommend new music as Spotify does – it wouldn’t know what to do. By the same token, the music recommendation AI from Spotify wouldn’t be very good at chess.
Narrow AI exists today and is used broadly across many applications. When businesses refer to using AI technology in their software offerings, this is usually what they mean.
General AI refers to artificial intelligence on par with human intelligence. In other words, an AI that can perform any cognitive task that a human can perform, just as well as a human can perform it. General AI is where most of the exciting research is taking place today, but the average expert prediction puts this breakthrough no earlier than 2060.
Also called Strong AI or superintelligence, this type of artificial intelligence is superior to human intelligence. In other words, an AI that can perform any cognitive task better than a human. While this type of AI is still currently in the realm of science fiction, experts believe it will eventually happen.
4 Main Types of Machine Learning
So how do machines really manage to learn? There are four main ways machine learning happens:
In supervised machine learning, a human helps the machine through the training process and has a clear end goal or output in mind. A common example is computer vision, where AIs are taught how to “read” elements of an image. The programmer knows what the images contain and is teaching the AI to recognize the key elements in order to be able to distinguish similar patterns.
You may have even helped train an AI in computer vision the last time you were asked to complete a CAPTCHA with images, like the example below:
Computer vision can also be used to parse data elements in images, such as receipts or claims forms, to populate that data into systems like case management CRMs to automate processing.
In unsupervised machine learning, the AI is left to look for patterns independently with no human help. This is helpful when trying to process very large sets of numerical data where the programmer doesn’t know what they’re trying to find, but thinks there may be patterns there to detect.
For example, consider setting an AI loose to analyze a huge list of financial transactions. As the AI identifies common patterns, it will become apparent to the AI which transactions are anomalies, and therefore possibly fraudulent.
As you may have guessed, semi-supervised machine learning is a combination of supervised and unsupervised. The programmer knows the end goal but leaves the AI to its own devices to figure out the patterns.
This machine learning model most closely replicates how humans learn: by trial and error. As we learn something new, we experience both negative and positive feedback that builds our experience of how to handle similar situations in the future.
For example, a child learns that when they run too fast, they fall down and skin their knee (although it may take several repetitions for this message to sink in!). Self-driving cars and robots are often trained using reinforcement learning.
The Impact of AI and Machine Learning to Your Business
Twenty years ago, AI was merely a novelty – used to beat the world’s greatest chess player as a demonstration of what the science could do, but not very pragmatic for businesses to use.
As AI and machine learning technologies have become more sophisticated, they have also become more practical for businesses. As mentioned at the start of the article, we now come into contact with applications of these technologies every day.
But what does this mean for your business?
AI in the Enterprise: Practical Applications
Because narrow AI is best at performing one specific task extremely well (better, faster, and more accurately than a human), applications of this tech are well-suited to automating repetitive yet inefficient processes. Here are some examples of how different teams could leverage today’s applications of AI to make a real impact on their operations:
Social media monitoring and social listening are vital to understanding conversations taking place around your brand, but it’s impossible for your team to read every single tweet or post that might relate to your brand – the sheer volume of social media chatter makes this unscalable.
AIs can be trained to process the deluge of information provided on social media channels, sifting through posts and media to identify trends, analyzing sentiment, finding new sources of information, surfacing actionable posts and comments, and even alerting teams to possible brand crisis situations.
Much of today’s practical AI technology centers on analyzing language (natural language processing, or NLP) and learning what decisions to make in certain situations (machine learning). Both of these skill sets are incredibly valuable in customer care.
For example, email automation tools process an inbound customer email, analyze the request to determine what it’s about, and generate a reply based on its knowledge of how human agents have responded to similar questions in the past. After an initial training period, the AI can be “let loose” to manage customer email backlogs far faster and more efficiently than human agents. Continuing to learn over time allows the technology to become more accurate.
Another classic customer care example is the chatbot. To be effective for customer care, these conversational interfaces need to be able to understand the nuances of human language, correctly categorize the content of the query, and decide on the best way to answer. Well-designed, well-trained AI chatbots can be a huge boon to customer care teams, enabling them to focus on more complex cases and to support additional communication channels more easily.
Voice of the Customer (VoC) efforts can leverage AI, too. One of the richest forms of customer survey feedback is the free-text response. However, they are extremely time-consuming for humans to analyze. AI can be used to analyze the text, cluster related topics, and surface key insights that might merit an action.
But with all these examples, the human element is still vitally important. Today’s AI can automate tedious, time-consuming processes to free up your team’s time to focus on things that narrow AI can’t do: make complex, nuanced strategic decisions.
Astute uses AI, including machine learning and natural language processing, across our product suite – everything from analyzing how human agents have responded to customers in the past to auto-create email replies, to learning from every customer interaction to improve self-service chatbot responses and more.
Astute Bot is the foundational platform for many of Astute’s AI-driven technologies. Learn more about Astute Bot to see how it could impact your business.