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Artificial Intelligence (AI) vs. Machine Learning (ML) - Understanding the Differences

Here at SpotlightIQ, we are big believers in utilizing Artificial Intelligence (AI) in campaign work and the company's day-to-day operations. Machine Learning (ML) is another term important to advertisers.  It was built into many advertising platforms years ago. More recently, AI advancements have given these platforms even more power. While these terms are used interchangeably, they are not synonymous. However, stepping back and understanding the differences between AI and ML is essential. This piece aims to demystify them, exploring their definitions, differences, and intricate relationships. Here are our thoughts.

What is Artificial Intelligence (AI)?

At its core, AI refers to the simulation of human intelligence in machines. It's a broad field of study encompassing everything from robotic process automation to actual robotics. The main goal of AI is to create systems capable of executing tasks that normally require the human brain. These tasks include problem-solving, recognizing speech, translating languages, and decision-making.

AI is categorized into two primary types: Narrow AI and General AI. Narrow AI, also known as Weak AI, focuses on one task. There is no self-awareness or genuine intelligence behind the task it performs. Most AI today, from chatbots to virtual assistants, falls under this category. On the other hand, General AI, which is still a theoretical concept, refers to systems that can understand, learn, and apply knowledge in different contexts, much like a human being.

What is Machine Learning (ML)?

Machine Learning is a subset of AI and arguably its most successful application. ML is the science of getting computers to learn and act like humans. It improves their autonomous learning by feeding them data and information through observations and real-world interactions.

ML algorithms use statistical techniques to enable machines to improve tasks with experience. The key here is that these machines are not specifically programmed to perform a specific task; instead, they are trained using large amounts of data that allow them to learn how to complete the task.

The Differences Between AI and ML

While ML is a subset of AI, the distinction between the two can be nuanced. Here are key points that highlight their differences:

  • Scope and Definition: AI includes logic, decision trees, and a range of other strategies. In contrast, ML focuses on developing algorithms and statistical models that enable machines to improve tasks through experience and data.

  • Goal and Functionality: AI aims to simulate natural intelligence to solve complex problems. In contrast, the primary goal of ML is to allow machines to learn from data so they can give accurate predictions and decisions based on that data.

  • Flexibility and Application: AI encompasses a broader range of applications and is not limited to learning. It includes anything from a simple computer program playing chess to a complex computational system managing a self-driving car. ML primarily focuses on developing systems that can learn from and adapt to new data without human intervention.

The Relationship Between AI and ML

While different, AI and ML are deeply interconnected. ML is often viewed as the current state-of-the-art in AI. It's the primary means we expect to achieve intelligent behavior in machines. Without ML, AI might remain a far-fetched concept, limited to science fiction. ML provides the necessary means for machines to learn, adapt, and evolve.

The Future of AI and ML

The future of AI and ML is auspicious and continually evolving. We are already seeing AI and ML impact various sectors, including healthcare, finance, transportation, and more. The potential for these technologies to transform our world is immense, with possibilities ranging from personalized healthcare to more efficient intelligent cities.

However, this future also comes with challenges. Ethical concerns, such as privacy, security, and the potential for job displacement, are part of the ongoing conversation about AI and ML. Balancing the benefits of these technologies with these concerns will be a critical focus in the years to come.

Wrapping Up

While AI and ML are closely linked and often used interchangeably, they are distinct fields with different scopes, goals, and methodologies. AI is the wider concept of machines being able to intelligently carry out tasks the way humans do. ML is an application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves.

As humans continue to advance in these fields, understanding the nuances between AI and ML becomes increasingly essential, not only for those working in the field but also for anyone interested in the impacts of these technologies on our future.

By leveraging AI and ML, we at SpotlightIQ focus on building solutions that free us up to do the complex things humans are best at. We aim to replace redundant and menial tasks with AI, freeing people to do more. It's crucial to understand nuance before taking a giant leap forward.

What to learn more? Contact us to discover how SpotlightIQ can immediately impact CTV campaigns.


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