Great Minds Think Alike
Introduction to Attention and Neural Networks
In our last blog, we talked about using machine learning to expedite the data labeling and data training process. In this blog, we’ll go into more detail about what that looks like, how it is done, and what types of use cases it can benefit most.
Don’t Let Complexity Scare You
Big, complex data presents a problem when it comes to training your automation to understand and interpret that information. There is too much room left for error, not to mention the possibility your system isn’t living up to its potential in terms of productivity and output. This means this data will have to be trained.
One way to train this data is to do so manually. People from your team can look at the data, break it into sections, and label each set from there. As we know, this is a time-consuming and tedious process that discourages the use of automation for complicated data, even if that’s where automation is the most useful.
The alternative is to introduce machine learning to train the data. With Neural Networks and Attention Models, machine learning approaches data in the same way your team would, but much faster and more efficiently.
What Are Neural Networks?
Neural Networks are learning algorithms modeled after the human brain which are used to translate a data input into the desired output. The biggest advantage of Neural Networks used in machine learning is that it is far more accurate than human-created counterparts, and does not need to be programmed with specific rules in order to know what to expect from data inputs.
However, Neural Networks could never replace human input. Neural Networks still need to be trained with labeled data, just much smaller amounts of it. However, the more examples it is given, the more accurate the output will be. Still, this gives you and your team much more flexibility when it comes to capacity, and saves time and cost in the future.
What Are Attention Models?
If Neural Networks are like our brain, attention is like our thought process. In other words, attention is how we get from point A to B, or, more specifically, how we solve problems along the way. Attention Models go about this problem solving by breaking down large tasks into smaller ones that are solved consecutively.
Attention Models are a part of Neural Networks that allow for the interpretation of very complex data inputs. Attention Models focus on specific aspects of the data, one at a time. This approach is similar to how humans problem-solve, thereby resulting in accurately categorized data even when the data set is highly complex.
Neural Network Use Cases
Neural Networks and Attention Models are great for training big data. That includes images and video. What’s more, neural networks do not require explicit programming to understand those different inputs.
Neural Networks are already being used today for pattern recognition, self-driving vehicles, facial recognition, and cancer research. When our computers think the way we do, the opportunities for application are endless.
The Future of Neural Networks
It is evident that we have made a lot of progress with Neural Networks and Attention Models. The shift in the market towards digital transformation has punctuated the need for these technologies.
However, it is important to realize that a lot of the capabilities that are demonstrated by these technologies are mostly in the academic setting. But soon, we will start seeing more and more tangible use cases for this technology.
Neural Networks excel in sorting through vast quantities of data. This provides countless possibilities in sectors such as marketing research, among others.
In the meantime, the conversation is swirling around the ultimate goal of enabling this technology to mirror human decision-making skills. That is the direction we are going, but when will that happen and what does it really look like? We will leave this question open for debate in our follow-up piece.
To learn more about the automation technologies that can streamline your business right now, contact us.