How Artificial Intelligence Will Transform Business


Today, artificial intelligence is a household name (and sometimes even a household presence – hi, Alexa!).

While artificial intelligence's acceptance in mainstream society is a new phenomenon, it is not a new concept. The modern field of artificial intelligence came into existence in 1956, but it took decades of work to make significant progress toward developing an artificial intelligence system and making it a technological reality.


In business, artificial intelligence has a wide range of uses. In fact, most of us interact with artificial intelligence in some form or another on a daily basis. From the mundane to the breathtaking, artificial intelligence is already disrupting virtually every business process in every industry. As artificial intelligence technologies proliferate, they are becoming an imperative for businesses that want to maintain a competitive edge.


What is artificial intelligence?

Before examining how artificial intelligence technologies are impacting the business world, it's important to define the term. "Artificial intelligence" is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving. Calling specific applications "artificial intelligence" is like calling a 2013 Honda Accord a "vehicle" – it's technically correct, but it doesn't cover any of the specifics. To understand what type of artificial intelligence is predominant in business, we have to dig deeper.


Machine learning

Machine learning is one of the most common types of artificial intelligence in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of artificial intelligence are algorithms that appear to "learn" over time, getting better at what they do the more often they do it. Feed a machine learning algorithm more data and its modeling should improve.


Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the internet of things – into a digestible context for humans.


For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it's too much data for a human to ever sift through, and even if they could, they would likely miss most of the patterns. Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine learning algorithm can catch it and notify decision-makers that it's time to dispatch a preventive maintenance team.


But machine learning is also a relatively broad category. The development of artificial neural networks, an interconnected web of artificial intelligence "nodes," has given rise to what is known as "deep learning."


Deep learning

Deep learning is an even more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. It can do this by analyzing a wide range of factors at once. For example, for self-driving cars to work, several factors must be identified, analyzed and responded to at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds. All this information is calculated side by side to help a self-driving car make decisions like when to change lanes.


Deep learning has a great deal of promise in business and is likely to be more commonly used soon. Older machine learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received. This makes deep learning models far more scalable and detailed; you could even say deep learning models are far more independent.


Artificial intelligence and business today

Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.


"Artificial intelligence is kind of the second coming of software," said Amir Husain, founder and CEO of machine learning company SparkCognition. "It's a form of software that makes decisions on its own, that's able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software."


Those traits make artificial intelligence highly valuable throughout many industries, whether it's simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs.