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Defining Machine Learning and Deep Learning to Understand AI

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Today, it’s possible to have one-to-one marketing, sales, and customer service conversations at scale thanks to AI-powered chatbots.

These chatbots help brands engage with every visitor, buyer, and customer in a personalized way, increasing engagement, conversions, and sales. Thanks to powerful AI, these chatbots can even learn over time to better predict the information and responses humans want. 

No matter what type of marketing or sales you do, chances are that AI-powered conversations are a big way you can make your current marketing smarter, starting today.

But to fully leverage the opportunity, you’ll want to understand the differences between machine learning and deep learning, two AI technologies that power the world’s most sophisticated chatbots — and plenty of other cutting-edge AI marketing tools.

What is machine learning?

Machine learning is a type of AI. With machine learning, AI systems process data, then make predictions based on that data. That prediction could be as simple as what movie you might want to watch next based on your Netflix history. Or, it could be as complex as forecasting what you’re going to say next as you’re typing an email in Gmail in real time. (Although machine learning is a complex subject, there are plenty of online learning materials about it to get up to speed quickly.)

What makes machine learning appealing, however, isn’t just the ability to make predictions based on data. AI systems that use machine learning can also learn to make better predictions over time as they learn from more and more data.

Sometimes, these systems learn based on training from human programmers. Others learn on their own. Often, systems learn from a mix of supervised learning from humans and unsupervised learning on their own.

Here’s a simple example of how a combination of supervised and unsupervised learning might work with a chatbot that uses machine learning:

  1. You provide the chatbot with dozens or hundreds of past chat conversations. 
  2. You mark which conversations were helpful and which were not.
  3. The chatbot analyzes this data, then predicts which responses will be helpful moving forward.
  4. As the chatbot engages with customers, it gets better at predicting which responses will be helpful, then incorporates more of these into its responses moving forward.

The result? A chatbot that both responds in a personalized way based on past conversations and improves its responses the more conversations it has, all thanks to machine learning.

What is deep learning?

Deep learning is a subset of machine learning. It uses layers of nodes to perform complex cognitive tasks like recognizing images, identifying objects in videos, and recognizing speech.

Deep learning takes raw data and runs it through these layers to better manipulate and extract insight from the data. The “deeper” you go in a deep learning model, the further you’ve progressed through a neural network that may consist of many, many layers of nodes.

More sophisticated chatbots may use deep learning to perform advanced natural language processing and natural language generation tasks like dynamically understanding and responding to complex conversation topics.

How can marketers use machine learning and deep learning?

So, how do you get started applying this knowledge?

Demand generation with AI-powered chatbots is one big way to start actually using AI, machine learning, and deep learning in your marketing to improve results.

To get started, Drift created a guide to conversational marketing that teaches demand generation marketers how to drive more leads, book more meetings, and generate more revenue.

Click below to get it.

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