AI Vs. Machine Learning - Which is Spookier? | Cordelia Labs

First and foremost, Happy Halloween!

Here at Cordelia Labs, we embrace all types of Halloween celebrations like dressing up like zombies or carving scary pumpkins to ward off ghosts. We also like candy, but who doesn’t like candy?

What we don’t like is being creeped out. You know, the Michael Myers lurking in the shadows type scary or whatever Freddy Kruger did on Elm Street. We don’t like being stalked or killed for that matter. But we Digital Media buyers do all sorts of things like stalking and creeping people out all the time.

We hate the use of the word creepy when it comes to Media Marketing but let’s face it, AI and Machine Learning is creepy. The question is, which is creepier?

Do AI and Machine Learning work together?

Yes, AI and Machine Learning often work together. Machine Learning is a subset of AI that focuses on enabling machines to learn from data and make predictions or decisions without being explicitly programmed. AI, on the other hand, encompasses a broader range of techniques and approaches to simulate human intelligence in machines.

Machine Learning is a crucial component of AI, providing the algorithms and methods that allow machines to learn and adapt, making AI systems more intelligent and capable.

A great real life example of AI and Machine Learning working together is the Tesla. While it is not a fully autonomous vehicle, it is one that has the ability to simulate human intelligence and decision-making capabilities (AI) This would be cars that can park and drive without human interaction. All the while the “machine” is learning. It’s analyzing and learning from vast amounts of data, such as sensor inputs and historical driving patterns.

By combining AI and Machine Learning, autonomous vehicles can navigate and make real-time decisions on the road, adapting to changing conditions and improving their performance over time. Basically, we will get to a place where it’s normal for cars to drive themselves. Talk about the scaries!

How is AI used in Digital Media?

AI, or artificial intelligence, in Digital Media refers to the use of AI technologies and techniques to enhance and optimize various aspects of digital media production, distribution, and consumption. AI in digital media can involve the use of Machine Learning algorithms, natural language processing, computer vision, and other.

The best uses are as follows;

1. Content creation: AI can generate or assist in generating digital media content such as articles, videos, and images. This can involve automated writing, video editing, or image creation based on predefined templates or patterns.
2. Content recommendation and personalization: AI algorithms can analyze user preferences, behavior, and historical data to provide personalized recommendations for digital media content. This can help improve user engagement and satisfaction.
3. Data analytics: AI can analyze large amounts of data generated by digital media platforms to extract insights, identify trends, and make data-driven decisions. This can help media companies optimize their content strategies, marketing campaigns, and audience targeting.

We believe pretty strongly that copywriting with the help of AI is faster, provides ongoing optimization and removes human bias (among many other benefits). For those reasons, we fully use AI to assist in our copywriting here at Cordelia Labs.

Overall, the ability to to improve efficiency, enhance user experiences and enable new capabilities in the creation, distribution, and consumption of digital media content. Work smarter, not harder.

So if AI is creating ads, what does Machine Learning do?

Machine Learning in Paid Media refers to the use of algorithms and techniques to optimize and improve advertising campaigns. It involves using historical data and real-time feedback to make predictions and automate decision-making processes in order to maximize the effectiveness and efficiency of advertising efforts.

We specifically use Machine Learning in retargeting. This is where the creepiness factor comes in. Imagine you’re beginning to research a beautiful, beach-filled vacation, and to do so, you visit multiple websites to learn more about this amazing trip. But you’re not quite ready to book; no, you’re just gathering ideas and piecing the vacation together.

Fast forward a few hours, and while you’re reading your local news, those beaches are following you around. That is retargeting. Your profile was captured and we will bug you until you pull the trigger. We kindly refer to it as reminder messaging. Does it work? Yes. Is it creepy? Also yes.

Here is a general overview of how machine learning works in paid media:

1. Data collection: Machine learning algorithms require a large amount of data to learn from. In the context of paid media, data is collected from various sources such as ad platforms, website analytics, customer databases, and third-party data providers. This data includes information about ad performance, user behavior, demographics, and other relevant variables.
2. Data preprocessing: Once the data is collected, it needs to be cleaned and preprocessed to remove any inconsistencies, errors, or missing values. This step is crucial to ensure the accuracy and reliability of the machine learning models.
3. Feature engineering: Feature engineering involves selecting and transforming the relevant variables from the collected data to create meaningful features for the machine learning models. This process helps to improve the predictive power of the models by capturing the most important information.
4. Model training: In this step, machine learning models are trained using the preprocessed data. Various algorithms, such as regression, decision trees, random forests, or neural networks, can be used depending on the specific objectives and requirements of the advertising campaign. The models learn from the historical data to identify patterns and relationships between the input features and the desired output, such as click-through rates or conversion rates.
5. Model evaluation and optimization: Once the models are trained, they are evaluated using validation data to assess their performance and accuracy. If the models are not performing well, they can be fine-tuned and optimized by adjusting various parameters or using different algorithms. This iterative process helps to improve the models’ predictive capabilities.
6. Prediction and automation: After the models are trained and optimized, they can be used to make predictions on new, unseen data. In the context of paid media, these predictions can be used to automate various aspects of advertising campaigns, such as bidding strategies, ad targeting, budget allocation, and creative optimization. By leveraging machine learning, advertisers can make data-driven decisions in real-time and optimize their campaigns for better performance and ROI.

Overall, machine learning in paid media enables advertisers to leverage data and automation to optimize their advertising efforts, improve targeting, and deliver more personalized and relevant ads to their target audience.

At the end of the day, the machines are getting smarter and learning more of our habits. While it is on the creepier side, from a media perspective it works.

The Human is the common thread for both AI and Machine Learning.

As we said, these two work together and Machine Learning is an important part of AI. But at the end of the day, they both need the human factor.

Sure, AI can write copy, but if a human doesn’t review, edit and add their own voice and tone to it, then it’s just words. The same goes for Machine Learning. It can provide valuable data and information after a campaign runs. But if a human doesn’t review it, then it’s just numbers.

The biggest benefits to using one or the other is time. Both AI and Machine Learning do save time, which we all know is precious. Below are a few other benefits:

1. Automation: AI and Machine Learning algorithms can automate repetitive tasks, thus saving time and reducing human effort. This can lead to increased productivity and efficiency.
2. Improved Decision Making: AI algorithms can analyze large datasets and provide insights that can aid in better decision making. They can identify patterns and trends that might not be apparent to humans, leading to more informed and accurate decisions.
3. Personalization: AI can learn from user behavior and preferences to provide personalized experiences. This can be seen in recommendation systems used by streaming platforms, e-commerce websites, and social media platforms.
4. Enhanced Customer Service: AI-powered chatbots and virtual assistants can provide instant and accurate responses to customer queries, improving customer service and satisfaction.
5. Predictive Analytics: Machine Learning algorithms can analyze historical data to make predictions and forecasts. This can be beneficial in various fields, such as finance, healthcare, and marketing, by identifying potential risks, trends, and opportunities.
6. Fraud Detection: AI algorithms can detect anomalies and patterns in large datasets, helping in fraud detection and prevention. This is particularly useful in financial institutions and cybersecurity.

Overall, AI and Machine Learning has the potential to revolutionize the way we work by improving efficiency, accuracy, and decision-making capabilities.

Don’t be scared, we can protect you.

While our team is not in the market to creep you out, we are there to help Contact us today. We use both AI and Machine Learning in various different ways, but honestly only to better the campaign we have in market and to help you achieve your goals. We are HUMAN and do what we need to do to make both work in your favor. We promise there is nothing creepy about that!