Source: rtinsights.com
In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge.
Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.
Defining AI and What It Means for Your Company
AI is an umbrella term referring to any technology that can evaluate and make decisions based on large volumes of data input. This takes several forms, which can make it difficult for companies to start the implementation process.
While 83% of businesses claim AI is a strategic priority, only 23% say they have successfully incorporated it into processes and product/service offerings. This is likely to change soon. The AI market is expected to surpass $190 billion by 2025, and by next year, spending will reach $57.6 billion.
Why does this matter? Consider the sweeping benefits of AI. PwC illustrates what many technologists have long described as mass automation of the US workforce. As many as 38% of US jobs could be partially or fully automated by the early 2030s, boosting overall labor productivity by 40%. Companies that do not invest will be at a significant disadvantage against those that do. Most businesses know this, with 84% saying AI will help them gain a competitive advantage.
We’re past the point of value recognition. Now is the time for companies to invest, but where and how. Many executives are concerned that their managers don’t understand AI, and 93% of automation specialists don’t feel prepared to use smarter technologies. A big part of this is understanding what is available and how to match it to your business case.
Machine Learning vs. Deep Learning
Much as AI refers to several forms of technology (including machine learning and deep learning), deep learning is itself a subset of machine learning. The main difference between the two is the type of data fed to the system.
In the case of Machine Learning, structured data that has a single, direct input for each field is utilized. Think of an excel sheet with predetermined values selected for each entry. The data is clean, it’s easy to work with, and there are no nuances to it. This, of course, leads to limitations in what an algorithm can do with that data.
Deep Learning, on the other hand, works with unstructured data, for which there are no set, recognizable answers. These are the message and text fields on your forms. The transcripts of a chat conversation on your website. The wealth of emails, conversations, and other “messy” data is captured every day from billions of users.
So, which is best for your application? It truly does depend on what you are attempting to do. Deep learning certainly sounds more robust, but remember that it works with a messier data set, and for some applications, clarity is key.
Machine learning is best when you have massive volumes of structured data that would take years for a human operator to process. It can be immensely efficient at classifying information, predicting outcomes based on previous behavior and performance, and organizing information together based on key variables.
Deep learning is better for volumes of data that a human mind cannot even fathom. Think of the healthcare industry, for example, where unstructured data in the form of medical notes, exam results, and patient feedback is massive in scope. Or transaction and conversation data for a major bank or retailer – the volume alone makes deep learning a valuable resource. The added depth even more so.
Choosing the Right Algorithm for Your Organization
At this stage, you are still at the earliest stages of the process – determining what general methodologies will work best. Other considerations that must be made to select the algorithm best able to support your efforts, however, include:
- Know the data you are working with: Is your data structured or unstructured? Has it been visualized to identify outliers and show the spread of data? Have you evaluated correlations to find the strongest relationships?
- Clean your data to work with it: Most models will be impacted to some degree by missing data (some significantly more so), along with outliers.
- Augmenting your data: Raw data is rarely ready for modeling. Several steps are needed to make the data easier to work with.
- Determine your problem and how you want to fix it: First, map out the input you have – the data that will be input to the machine. Then map out what you’d like to get back. Are you trying to retrieve a number? A class? A group of inputs? This is the issue you’re trying to solve and will determine which algorithm makes the most sense.
There are several tools already available to leverage for your efforts based on the results of the four steps above. This is where you will evaluate the type of model you should use, whether it meets your business goals, and how accurate and actionable it is in context.
Deciding How Best to Utilize AI for Your Company
Once you know what’s needed, how do you implement it? Who do you hire, what kind of training is needed for existing staff? Who will spearhead the initiative?
For larger organizations, the first step is to establish a data science team, led by a CIO or CISO with more than just a passing knowledge of AI applications. For smaller companies, a data scientist who can lead the initiative may be enough. These individuals will be responsible for evaluating your core needs and determining which combination of algorithms and support systems will help create value for your organization.
Most importantly, you don’t want to over-invest. Deep learning is capable of incredible things, but if you are working with mostly structured data for straightforward purposes, Machine Learning can be a much more viable and affordable application, especially as a smaller organization with limited resources.