Artificial intelligence is actually a term that encompasses a host of technology and tools. Here’s a closer look at some of the more important ones.
Artificial Intelligence Technologies- Artificial Intelligence. Everybody wants it. Everybody knows they need to invest in pilots and initial projects. Yet getting those projects into production is hard, and most companies still aren’t in with both feet.
If you aren’t hands on with the projects yourself, you may have heard a lot of different terminology. You may be wondering what it all means. Is AI the same as machine learning? Is machine learning the same as deep learning? Do you need them all? Sometimes the first steps of understanding whether a technology is a fit for your organization’s challenges and problems is understanding the basic terminology behind that technology.
Let’s start with a basic definition of artificial intelligence. The term means a lot of things to a lot of different people, from robots coming to take your jobs to the digital assistants in your mobile phone and home — Alexa, Siri, and the rest. But those who work with AI know that it is actually a term that encompasses a collection of technologies that include machine learning, natural language processing, computer vision, and more.
Artificial intelligence can also be divided into narrow AI and general AI. Narrow AI is the kind we run into today — AI suited for a narrow task. This could include recommendation engines, navigation apps, or chatbots. These are AIs designed for specific tasks. Artificial general intelligence is about a machine performing any task that a human can perform, and this technology is still really aspirational.
With AI hype everywhere today, it’s time to break down some of the more common terms and technologies that make up AI, and a few of the bigger tools that make it easier to do AI. Take a look through the terms and technologies you need to know — some components that make up AI, and a few of the tools to make them work.
Machine learning may be the first step for many organizations that are adding AI-related technologies to their IT portfolio. automates the process of creating algorithms by using data to “train” them rather than human software developers writing code. Basically, what you are doing is showing the algorithm examples, in the form of data. The famous example is a cat. What is a picture of a cat and what is not a picture of a cat. Can you train a machine to recognize a cat by showing it examples of both cat and not cat? By “looking” at all these examples, the machine learns to recognize the difference.
Organizations are getting more adept at machine learning, and there are plenty of use cases to consider. For instance, NewYork-Presbyterian Hospital was using machine learning for cybersecurity and then recognized that it could use the same techniques to help it fight the opioid crisis.