In the study of AI, many different phrases and names get thrown around. Some even have similar definitions, and almost seem designed to confuse those new to the area–especially because the types of AI all have some overlap.
In particular, some common areas mentioned are ML and predictive analytics. But what do they all mean? Where do they overlap and how are they different?
In this article we’ll cover:
What is artificial intelligence (AI)?
Firstly, let’s get on the same page about AI. A simple definition of AI is a wide branch of computer science concerned with creating systems and machines that can perform tasks that would otherwise be too complex for a machine. It does this by processing and analyzing data, which allows it to understand and learn from past data points through specifically designed AI algorithms.
Despite the hype and fear that the phrase “artificial intelligence” can inspire, it’s already used around the world for tasks from mundane to incredible. Some common examples of AI include:
- Smart assistants like Alexa and Siri
- Self-driving cars
- Social media monitoring
- Search engines like Google and Bing
- And many more
What is machine learning?
ML is a subset of AI that uses statistical techniques and data to extract algorithms and models for learning. It applies generalizations from its learning to new situations and tasks and doesn’t involve direct human programming. The systems built through machine learning can learn from their own experiences and historical data.
Examples of machine learning include:
- Speech and image recognition
- Medical mapping and diagnosis
- Automated stock trading
- Online chatbots
What is predictive analytics?
A common tool in data science, predictive analytics interprets historical data in order to make predictions about the future. It does that using techniques like data mining, modeling, machine learning, artificial intelligence, and statistics. It’s often used as a tool to help identify upcoming risks and opportunities for organizations.
Some examples of predictive analytics in action include:
- Weather forecasting
- Amazon’s recommendations and similar items
- Modeling of flu trends
- Insurance risk assessments
Artificial intelligence versus machine learning versus predictive analytics: Similarities
ML and predictive analytics are both sub-areas within the broader category of AI, and utilize it in their operations. ML, in particular, is a subset of AI that’s concerned with enabling machines to make accurate predictions through self-guided classification.
Predictive analytics uses ML and AI as tools to parse data and predict possible outcomes.
AI versus ML versus predictive analytics: Key Differences
Now there are some specific differences that set AI, ML, and predictive analytics apart. These range from uses and industries to the fundamentals of how each works. Below, we’ve broken down the key differences between each in a direct comparison.
Artificial intelligence vs machine learning
AI and ML have a lot of overlap since ML is a subset of AI. However, there are some key differences, beyond just the fact that AI is a broader term than ML. For example, the goal of AI is to create computer systems that can imitate the human brain. The goal is to create intelligence that is artificial -- hence the name. On the other hand, ML is much more focused on training machines to perform certain tasks and learn while doing that. AI tends to focus on solving broad and complex problems, whereas ML focuses on streamlining a certain task to maximize performance.
Artificial intelligence vs predictive analytics
The most glaring difference between AI and predictive analytics is that AI can be autonomous and learn on its own. On the other hand, predictive analytics often relies on human interaction to help query data, identify trends, and test assumptions, though it can also use ML in certain circumstances. Because of this, AI has a much broader scope of applications than predictive analytics.
Machine learning vs predictive analytics
Much like AI, a big difference between ML and predictive analytics is that ML can be autonomous. It’s also worth noting that ML has much broader applications than just predictive analytics. It has applications such as error detection and reporting, pattern recognition, etc. Additionally, predictive analytics can utilize ML to achieve its goal of predicting data, but that’s not the only technique it uses.
Use cases
Sometimes semantic differences can be hard to understand without real-life examples. We’ve compiled a list of use cases for each of our three terms to aid in further understanding.
Artificial intelligence
AI has a myriad of applications across industries and verticals, some of which we’ve already mentioned above. Here are three more examples of how they can be used in specific industries.
Healthcare
The healthcare field utilizes AI in many ways to help medical staff understand risks, streamline procedures, and overall improve patient outcomes. Here are just some ways a healthcare professional may use AI in their day-to-day work:
- Prioritization and triage based on real-time patient data, which tells medical staff who needs the most urgent care at any given moment.
- Early diagnosis based on medical history and statistical data, which can give doctors an accurate assessment of the likelihood of a specific patient developing a disease later in their life.
- Medical imaging to allow professionals to easily visualize areas of the body, aiding in quicker and more accurate diagnoses.
Customer service
Using AI, representatives can equip themselves to best support customers. This can make it an invaluable tool for people in such a fast-paced industry. Some of the ways companies use AI to support customer service include:
- Smart call routing leans on AI to route incoming calls to the correct agents for each expertise. This lowers wait times and ensures everyone’s concerns are addressed by those best able to help them.
- Online chatbots which can interact with customers and solve common disputes are run off of simple AI. These can help with things like forgotten passwords, answer common questions, and more.
- Smart Analytics provided to managers or heads of departments that are automatically parsed and presented as a whole, which allows for faster interpretation and implementation.
Finance
For a long time, the financial sector had roles that could only be done by humans with specialized degrees and training. But now, AI can help to fulfill those roles, as well as provide complete accuracy and speed while doing them. Some of the ways AI is used in finance include:
- Fraud detection helps to catch fraud as soon as it happens using patterns of data that establish norms of financial behavior, then monitor for anything out of the ordinary.
- Expense management systems use AI to help quickly and accurately categorize expenses to help with tracking, future projections, and reimbursement.
- Automated billing allows money to flow out and in quickly and easily, saving time from chasing after invoices and categorizing expenses.
Machine learning
Like AI, the applications of ML are both numerous and varied. These applications tend to be more specific and prescriptive. Here are three example uses of ML:
Social media moderation
Many of the major social media platforms utilize ML to help in their moderation process. This helps to flag and identify posts that violate community standards. Of course, these programs can sometimes be incorrect in their classification, which is where the support of a manual review team comes into play.
Email automation
Anyone who sends emails should know about this application. Your email provider uses ML to detect spam and filter it out of your inbox.
Facial recognition
This is perhaps the most obvious application of ML. The algorithm behind this program recognizes specific patterns in facial features and assigns them to a name. Facebook did this for a while with its photo recognition. Many phones, laptops, and tablets use this feature to unlock the device without a passcode.
Predictive analytics
One of the best tools a business can have is the ability to project possibilities of their future. This can help to prevent poor decisions, support smart ones, and overall inform the business leaders of the best path to take. Some common business applications for predictive analytics include:
Predictive maintenance
Imagine knowing what part of your car would break down next. You could preemptively fix or replace it and save yourself a headache. That’s what predictive maintenance is all about. Preventing pricey repairs through predictive maintenance is an effective strategy for increasing revenue.
Risk modeling
Risk is a part of any business. It can come in the form of equipment breaking, bad deals, price fluctuations, and many other things. Risk modeling is a form of predictive analytics that takes in a wide range of data points collected over time and uses those to identify possible areas of risk. These data trends equip businesses with the data needed to mitigate and take informed risks.
Next best action
The next best action use of predictive analytics takes in data points around customer behavior (such as buying patterns, consumer behavior, social media presence, etc). Using that data, it provides insights on the best way to interact with your customers, as well as the time and channels to use.
Business benefits
As you can now see, there are many areas of overlap between ML, AI, and predictive analytics. Likewise, there are many differences and different business applications for each. Utilizing a mix of AI, ML, and predictive analytics will equip any business with the ability to make informed decisions, streamline your operations, and better serve your customers. In particular, the role of AI, ML, and predictive analytics in helping businesses make informed decisions through clear analytics and future predictions is critical. Learn how Tableau provides our customers with transparent data through AI-powered analytics.