What We Mean When We Say “AI”
The Technology That Is Helping You, Not Replacing You
Could you explain the differences between AI, machine learning, and deep learning, and how they overlap? If not, you’re hardly alone. After attending Programmatic I/O in NYC last month, I saw firsthand just how pervasive the confusion around these terms is—even among industry professionals. Despite their ubiquity, many people don’t fully understand what they mean or how they function. In this blog, I’ll clear up the confusion, break down the distinctions between these terms, and discuss why AI isn’t the threat to your advertising job that you might think it is.
The Problem with “AI”
The overuse of “AI” has led to AI fatigue, where the term is used so frequently—and often inaccurately—that it no longer carries weight. Companies routinely label their technologies as AI, regardless of whether or not they leverage advanced AI capabilities or perform basic data automation.
One of the most significant issues is the confusion between AI, machine learning, and deep learning. These terms are often wrongfully interchanged, making it hard for people to differentiate between them. Without clear definitions, discussions about AI remain surface-level, creating skepticism and leaving people to wonder what information is right, wrong, or flat-out fluff.
Back to Basics: Breaking Down The Terms
Let us take a step back and look at the definitions of each technology and break down what they mean.
AI is the simulation of human intelligence by machines, especially computer systems. It enables machines to perform tasks like learning, problem-solving, decision-making, and pattern recognition, often requiring human-like cognitive functions. A simple real-life example of AI would be your virtual assistants, like Siri or Alexa. When you ask Siri to set a timer, play a song, or send a message, it uses AI to understand your voice, process the request, and provide the right response. AI helps these assistants recognize speech, learn from past interactions to improve over time, and even predict what you might need based on context.
Machine learning, a subset of AI, involves training algorithms to identify patterns and make decisions based on data. It’s like teaching a computer to improve its performance through experience without being explicitly programmed for every possible outcome. For example, when you watch something on Netflix, the platform tracks what you watch, how long you watch it, and what you like or dislike. Machine learning analyzes these patterns and compares them with others to recommend shows or movies you’ll enjoy. As you watch more, the system improves its suggestions by learning from your behavior.
Finally, deep learning, an even more specialized subset of machine learning, utilizes complex neural networks to analyze vast amounts of data. Unlike traditional AI, which often focuses on rule-based tasks, deep learning operates through networks with multiple interconnected layers, allowing the system to learn and improve autonomously. These layers, which include input, hidden, and output, are what make the network “deep.” Deep learning systems can perform complex tasks like speech recognition and natural language processing through this structure. A real-life example is how self-driving cars use deep learning to recognize objects like pedestrians and traffic signs from sensor and camera data. By learning patterns, the system decides when to stop, turn, or accelerate, improving over time as it continuously processes data. Or another real-life example—Cognitiv!
Deep Learning: The Breakout Star
To excel in your job, you rely on intuition and real-world knowledge—something machines lacked just a few years ago. This gap is why traditional machine learning often fell short of its potential in advertising. However, deep learning took the world by storm in 2015 and has been changing the game ever since, enabling machines to acquire and process vast amounts of real-world knowledge in ways that were previously impossible. For the first time, machines can really understand and work with complex data, opening up new possibilities for tasks that used to require a human touch.
Every agency and brand that we work with has unique goals, so we harnessed the power of deep learning to build our Deep Learning Advertising Platform (DLAP) that learns the nuances of current consumers through a myriad of data onboarding options, including website pixel, footfall statistics, CRM data, and offline sales. These algorithms continuously learn and optimize, driving full-funnel marketing performance at scale. The result is highly personalized and impactful targeting.
This sophisticated level of targeting and personalization helps brands reach the right audience within the right context, ultimately driving higher campaign performance and ROI. As the digital advertising landscape becomes increasingly complex and competitive, deep learning’s ability to continuously learn and adapt is setting a new standard for efficiency and effectiveness.
Questions About How It All Works?
The Cognitiv team is here to help—reach out to us anytime. Your curiosity drives innovation, so do not hesitate to ask! You can also explore our resources below for more insights on how deep learning can transform your business.
Resources From Cognitiv
Blogs:
- Contextual Targeting 101: What Every Product Marketer Wants You to Know
- Generative AI’s Genius: Why Chat GPT is Only the Beginning
- Navigating the Cookieless Future: How Advanced AI is Revolutionizing Contextual Targeting in 2024
- Cognitiv Curation: Empowering Marketers with AI-Driven Precision and Cost-Effective Results
Case Studies:
