If you’re a business-minded person concerned with boosting your company’s bottom line, there’s a good chance you’ve heard artificial intelligence (AI), machine learning (ML), and deep learning (DL) mentioned as technologies that can make a real difference for you, either now or in the not-too-distant future. And you may know that robotic process automation (RPA) is one key way you can put these tools to work for your business – saving time, cutting costs, and reducing human error.
But while recent innovations and trends have increased awareness of the potential of AI, ML, and DL, many people still don’t understand the differences between the three terms. Why does that matter? Because understanding what these technologies are – and what they are not – can help you understand what they can offer your business, especially when it comes to RPA.
So how do the three terms relate to each other? Generally, artificial intelligence includes the entire category of technologies that imitate the human brain, whereas machine learning is a specific type of AI and deep learning is a type of machine learning. For instance, when robots like Kryon’s interpret images and text using deep learning, that is a sophisticated example of both machine learning and artificial intelligence.
What’s Artificial Intelligence?
Broadly speaking, artificial intelligence includes any technology that mimics a human brain’s cognitive processes. This can include the decision-making processes behind physical actions, as in the computers that run self-driving cars. It can also include the more cutting-edge capabilities of today’s RPA solutions.
Although it is often defined in very general terms, the term “artificial intelligence” refers only to processing technologies that imitate human capabilities – not to the classical functions of personal computers and calculators. Still, there isn’t universal consensus about which systems are complex enough to be considered examples of AI.
One type of AI that clearly makes the cut – one of the more advanced categories of artificial intelligence – is machine learning.
A Closer Look at Machine Learning
How can a computer system be programmed to carry out tasks through artificial intelligence? Theoretically, people could create extensive coding laying out the full set of instructions that the computer will follow. But the emergence of a different approach – machine learning – is one of the key factors that has allowed artificial intelligence to grow into a rapidly developing field. In the machine learning model, people create a set of algorithms that a computer will follow in order to “learn” how to carry out the desired tasks.
If a computer system relies on machine learning, it is initially programmed to perform (or attempt to perform) one or more tasks. For example, a robot can be programmed to interpret speech. Then, after the robot has attempted the task – in this case, after each attempt to interpret a spoken word or sentence – it is informed (or it determines) whether it has performed successfully or made a mistake.
Over time, the system can gather a large – in some cases, enormous – amount of data showing when it has performed tasks correctly and when it has failed. As it accumulates data, it makes adjustments to its operating instructions. As a result, the system becomes increasingly accurate and reliable as it works.
What’s So Deep About Deep Learning?
Machine learning has seen significant advances in recent years, and deep learning is one of the most important reasons for this progress. Deep learning is a type of machine learning in which multiple pieces of input information are interpreted using a system consisting of multiple processing layers, which are designed to mimic (to some extent) the neural networks of a human brain.
As tech journalist Michael Copeland explains, this multi-layered – or “deep” – approach, combined with huge amounts of data and other advances in computing technology, has allowed for the creation of machine learning systems with vastly improved abilities to process information, get feedback, and learn. Today, through deep learning, computer systems can perform “intelligent” tasks far more accurately than their predecessors could even in the recent past.
What else is important for business-oriented people to know about deep learning? As some of the most advanced types of artificial intelligence, deep learning solutions are not only for high-tech companies. Today, they can help a wide variety of businesses looking to increase their efficiency. For example, an insurance company, financial institution, or telecom company can streamline back-office tasks by using robots to process paperwork. With an RPA system like Kryon, applications of artificial intelligence – for example, the ability to “read” text and images and interpret natural language – are constantly increasing the versatility of robots. This versatility, in turn, can save valuable worktime, cut costs, and reduce the chances of expensive human errors.
Bringing It All Together
For those without technical backgrounds, the terms “artificial intelligence,” “machine learning,” and “deep learning” can sometimes sound like buzzwords that only IT professionals, engineers, or leaders of high-tech companies really need to understand. But because AI, ML, and DL are incorporated into today’s most sophisticated RPA solutions, you can now use these technologies to increase efficiency, prevent human error, and cut costs within your business – even if that business tends to think of itself as low-tech.
So – even if your business is not focused primarily on technology – AI, ML, and DL may be relevant for your bottom line. And if you’re considering using automation to raise that bottom line, keep in mind: In the era of deep learning, artificial intelligence can be a powerful factor enabling an RPA solution to automate complex processes efficiently and reliably.