Discover the Science Behind AI
- Describe what machine learning is.
- Articulate the different flavors of machine learning.
- Understand how to bring AI into your business.
- Visual perception
- Speech recognition
- Sentiment analysis
The brains behind AI include modeling techniques such as machine learning, deep learning, and natural language processing. Let’s start with machine learning.
Machine learning is the core driver of AI. It’s the process of using algorithms to tell you something interesting about your data without writing code specific to the problem you’re trying to solve. Said differently, it’s a way to have computers learn from data with minimal programming. Instead of writing code, you feed a machine data and it builds its own logical function based on this data.
So how does this work in practice? Well, it all starts with “training data.” That’s a set of data you give to your data model to help it learn. The more data you feed your model, the stronger it gets.
When you feed training data to your machine learning model, that data is defined by a set of attributes and characteristics. It’s up to the model to determine how to make sense of all these attributes.
So how does the model decide what attributes are most important to build the best model? The algorithm “weights” the different features of your model to determine the best set of attributes, that when combined into an equation, solve a specific problem.
Finally, ever heard the phrase, “Garbage in, garbage out”? That’s especially important here, when you’re preparing your training data set. The better quality your data, the better your model.
We just learned that typical applications of machine learning involve selecting the best features to produce the most optimal model. We also learned that an algorithm can only be as good as the data that goes into training it.
For these very reasons, the performance of machine learning algorithms can weaken when key information is buried in unstructured data. That’s where deep learning comes into play.
- Uses complex algorithms to perform tasks in domains where it actually learns the domain with little or no human supervision.
- Learns how to learn. For example, consumer apps like Google use deep learning to power facial recognition in photos.
Natural Language Processing (NLP)
- Can perform sentiment analysis, where algorithms look for patterns in social media postings to understand how customers feel about a specific brand or product.
- Handles speech recognition, providing a text summary derived from “listening” to an audio clip of a human speaking.
- Conducts question answering, typically handling those questions with a specific answer (for example, What is the square root of 4?), but also exploring how to handle more complex and open-ended questions.
Now that you know more about the science behind AI, let’s talk about different ways to integrate AI into your business.
The first step is understanding where it makes sense to initially deploy AI. Organizations have tons of data, so establishing quick wins and building trust within your organization with a focused approach is key.
- What’s the likelihood to open an email based on its subject line?
- What’s the likelihood to act on special offers once an email is opened?
One option is to partner closely with companies that offer point solutions to specific machine learning problems across various domains. This option is great if you want to test the waters and start small before carrying out any large-scale implementation. However, keep in mind that you’ll end up paying another vendor to execute your AI strategy. AI could become an integral part of your business strategy over time, so consider finding a more cost-effective approach that doesn’t involve exporting your data to another platform.
If you want to keep things in-house, you could hire your own internal team of AI experts. This approach is great if AI is a long-term part of your business strategy; however, it takes time to hire great talent in a fiercely competitive market. It’s also expensive to not only hire but also retain premium AI talent.
You could adopt a hybrid approach where you partner with companies that offer point solutions and hire AI experts over time. This is a great approach if you want to start testing AI within your business for a few targeted use cases while building out teams of experts that can replicate results in other areas of your business.
The Ultimate Goal
The ultimate goal is to seamlessly embed AI into your existing business processes and applications. This is the path of least resistance, because you’re using the same technology that you already use every day, just with smarter features that adjust to your preferences over time. Your users can experience the benefits of AI without having to adopt new technology.