However, amid the hype surrounding a future filled with cybernetic augmentation and personal servant robots, today’s businesses are harnessing AI in a much less grandiose, but much more impactful, way.
Forrester predicts that by the close of 2017, investment in artificial intelligence (AI) will triple as firms look to tap into complex systems, advanced analytics, and machine learning technology. These investments in AI technology will give businesses the ability to more efficiently capture and manipulate important data, and to forecast events with unparalleled accuracy. In essence, the greatest value inherent in AI has a lot less to do with robots, and more to do with data analytics.
Data Analytics Is the Future of AI.
Data analytics, in one form or another, has been around for nearly as long as business itself. Whenever a merchant would consider customer buying habits and attempt to predict product demand, that was data analytics. Of course, the ability to handle data accurately and in large amounts only became widely possible with the introduction of computational technology during the 20th century.
Since that time, computers have evolved from simplistic adding machines into something much more complex. Today’s advanced analytic tools are capable of gathering data from a wide range of relevant sources. Information related to demographic data, purchase history, support interactions, preferences, and more is automatically captured and evaluated, presenting businesses with refined, easy to digest data conclusions. Data analytics help organizations better understand relevant information. AI takes things a step further.
AI-enhanced analytics not only details the meaning behind the data; it suggests what should come next. Anticipating customer needs and opportunities, resolving support issues before they happen, and creating predictive one-on-one journeys that are personalized to every individual client, AI has the potential to become integral to businesses large and small. That said, in order for AI to be effective, it needs to be smart. That’s why modern AI is built on two very important concepts: machine learning and deep learning.
What Do Machine Learning and Deep Learning Mean for AI?
To understand the concepts that drive modern business AI, first recognize that one is really just a subset of the other — that is to say that deep learning is a type of machine learning.
Machine learning is essentially what it sounds like — the idea that machines can grow their intelligence. This concept is at the heart of nearly all AI theory, as it is generally more feasible to teach a machine how to assimilate information incrementally than it is to preprogram all relevant data into the machine from the beginning. Machine learning makes it possible for AI to detect patterns in structured data. A well-known example of machine learning is predictive recommendations, such as those found on many ecommerce sites and streaming services.
Deep learning also does what its name suggests: it goes deeper. Relying on complex mathematical algorithms, deep learning gives AI the ability to learn without being hand fed the relevant data. Deep-learning-capable AIs can be positioned in domains with little to no human supervision, and can teach themselves tasks from the ground up.
Deep learning is heavily dependant upon having a very broad pool of information from which to draw. Of course, in this age of always-connected digital communication, having access to vast amounts of data is becoming a non issue, particularly in business.
Machine Learning Is Changing the Face of Business.
Business leaders are certainly excited about the analytical aspects of AI and machine learning. In fact, 44% of executives identify the most valuable benefit as being AI’s ability to provide data that can be used to make decisions. But while data analytics is likely the future the AI, machine learning has much to offer other areas of business as well.