For years, artificial intelligence (AI) and machine learning (ML) have been hyped as the next generation of technology not just in advertising but all industries. Confusingly, these terms are often used interchangeably. In mobile advertising, you might hear the use of AI, ML or both — to describe smart bidding, optimization algorithms and everything in between.
To clear up this confusion, we’re breaking down the differences between AI and machine learning. We’re also giving examples of each technology in action to cut through the mobile marketing jargon.
Artificial Intelligence and Machine Learning
First thing’s first, to clear up the basics: both AI and ML are computer science terms. While artificial intelligence is an umbrella term for any “smart” process, machine learning is a specific application of it.
Artificial intelligence (AI) is the ability of a computer or machine to imitate human behavior. AI enables machines to undertake human-like tasks that use human-like intelligence, such as thinking, reasoning, learning from experience and making decisions. As put by American Computer Scientist, John McCarthy: “AI is the science and engineering of making intelligent machines.”
In the above definition, intelligence is defined as the ability to perform human tasks exceptionally well but not better than humans. In other words, computers or machines that have AI capabilities have not reached the truly emotional level of human understanding yet.
A small misconception with AI is that it is a system. More accurately, AI is implemented in a system rather than describing the system itself. So, while smart bidding has AI capabilities — in other words, smart bidding allows machines to make human-like decisions on the best way to bid on ad space — it is not artificial intelligence, in and of itself.
Examples of AI
A great example of artificial intelligence in action are personal assistant technologies like Google Home by Google, Siri by Apple, Alexa by Amazon, and Cortana by Microsoft. These systems use human-like intelligence to search information, schedule meetings and send communications.
Additionally, increasingly more brands use chatbots to process customer service requests and automate booking and messaging. Chatbots use artificial intelligence to engage in human-like conversations and processes.
In mobile advertising, AI can optimize many different processes using human-like intelligence. As mentioned, AI optimizes smart bidding algorithms that use human-like intelligence to bid more efficiently. AI also enables predictive analytics that underpin processes like personalization, fraud detection, marketing performance management, and reporting.
As mentioned above, machine learning (ML) is an application of AI. It enables the automatic processing of data without explicit programming. It does this through artificial neural networks that process data in a similar way that neurons process information in the human brain.
Massive amounts of data are fed into neural networks which trains the system to collate and classify the data. Over time, a machine will iterate based on this data so that the data trains the model itself. The system evaluates performance and win rates and feeds that information back into the systemic model to make better predictions. This is the process of machine learning.
The iterative aspect of ML is perhaps its most marked feature. American Computer Scientist, Arthur Samuel, defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed.” In other words, ML programs machines to learn on their own using data.
ML has been an extremely valuable tool to mobile marketers as it helps analyze huge data sets on user behaviors, purchases, and preferences. As more is learned about each user, performance marketing campaigns can get better at predicting the right products, the right ads, and the right bids to serve, enhancing the user experience overall.
Examples of ML
One example of ML is email spam technology. Most email service providers use machine learning tools to automatically catalog spam emails and phishing messages. These spam filtering tools also learn over time. The system identifies more rules as it processes more emails.
In mobile advertising, a great example of machine learning technology is personalization. Ecommerce apps like Amazon use machine learning to give recommendations based on users’ historical buying data. In this way, retargeted ads leverage machine learning technology. For example, if you spend an hour browsing for a certain pair of shoes on an ecommerce app, you might see an in-app ad displaying those shoes while browsing your mobile news app later that day.
ML also powers product recommendations. These are suggestions of subsequent purchases based on what you have favorited, purchased, added to your cart and other browsing behavior.
The Difference Between AI and Machine Learning
To conclude, AI undertakes tasks that require human intelligence; while ML is an application of artificial intelligence that enables systems to learn over time using data.
This means that all machine learning is artificial intelligence, but not all artificial intelligence is machine learning.
Applying these concepts to mobile marketing: smart bidding uses artificial intelligence to bid more efficiently using human-like intelligence. But, the process that enables the “learning from experience” aspect of that human-like intelligence is machine learning. ML allows smart bidding processes to learn by feeding more and more data on win rates, conversion rates, etc. This allows the bidding mechanism to learn and improve over time.
Takeaways on the Differences Between AI and Machine Learning
- Artificial intelligence (AI) is the ability of a computer or machine to imitate human behavior.
- The use of personal assistants like Siri and chatbots are examples of artificial intelligence in today’s marketplace.
- In mobile marketing, smart bidding uses AI to bid more efficiently using human-like intelligence.
- Machine learning (ML) enables the automatic processing of data without explicit programming. Results data on processes is consistently fed back into the model to make better predictions. This allows a machine to learn over time.
- Personalization and product recommendations are key examples of ML in today’s marketplace.
- In mobile marketing, the use of ML allows mobile marketers to analyze huge data sets on user behaviors, purchases, and preferences and scale their performance marketing campaigns by feeding that data back into their bidding and optimization models.
- The Difference between AI and machine learning: While all machine learning is artificial intelligence, not all artificial intelligence is machine learning. AI undertakes tasks that require human intelligence while ML is an application of artificial intelligence that enables systems to iterate over time using data.
Want to learn more mobile growth terms?
For more information about key topics in the app marketing space like artificial intelligence and machine learning, read our App Growth Glossary here.