Artificial intelligence is back in the news again, and this time it’s for all the right reasons. AI has been a significant buzzword in the technology industry for some time now. More recently, we’ve seen its positive impacts on healthcare, manufacturing, agriculture, and retail. But what exactly is artificial intelligence? How is it different from machine learning? And how does it work to make our lives better? Let’s take a deeper look at this groundbreaking technology and find out.
What is Artificial Intelligence?
Let’s get the basics out of the way. A critical difference between AI and machine learning is that AI is based on artificial intelligence and includes understanding the world beyond what humans can accomplish alone. Machine learning is one way of delivering AI. Still, it can also be used as part of a larger AI solution that includes different machine learning algorithms. Intelligent systems analyze large data sets to solve problems that we don’t have access to or know how to solve. Examples of AI include applications that automatically send electronic payments to employees when they arrive at work or the hospital when a patient is admitted. Modern software systems rely on machine learning.
What Makes Artificial Intelligence Different From Machine Learning?
Machine learning is a type of artificial intelligence that analyzes data to generate predictions. Conventional machine learning systems use features such as text input to generate predictions. Machine learning requires a baseline dataset in which to train an algorithm. Traditionally, this baseline dataset refers to as a training set. There are two types of machine learning systems: supervised learning and unsupervised learning. Learning systems rely on a data set that’s not manually labeled to discover correlations, associations, and patterns within that data. An excellent example of an unsupervised machine learning system is a clustering algorithm, which groups data into similar categories or clusters using unsupervised learning.
How Is AI Developing And Used In Industry?
Algorithms and data-based models often conduct AI research. These algorithms are designed for categorizing data and understanding what it is they’re looking at. They must be able to learn, too—there is no pre-defined knowledge set that they’re expecting to learn from. When you look at how artificial intelligence is developing and used in industry, it’s worth looking at a few essential technology elements. Artificial neural networks are based on the way a human brain works. Neural networks are complex mathematical structures that are capable of recognizing patterns. So while we might not be able to memorize every mathematical equation used in artificial neural networks, we can learn to recognize patterns using them.
How Can You Benefit From Artificial Intelligence?
No matter what industry you work in, there is no doubt that you are using many data every day. The more you work with data, the more data you have access to, and the more you analyze that data, the more you can leverage it to improve the services you offer. Machine learning uses AI to recognize patterns and recognize away when you don’t. In many cases, machine learning can improve what you do, and when it does, you can take immediate advantage. For example, your hospital may be considering changes to how they offer information to patients about what they’re going to be experiencing at the hospital before they get there.
Artificial intelligence in manufacturing, healthcare, and agriculture
In industries like automotive, manufacturing, and agriculture, artificial intelligence replaces human labor as part of the manufacturing process. According to CB Insights, industrial manufacturing companies are planning to replace thousands of workers with robots. At the same time, AI has begun to democratize healthcare by performing diagnoses in medical schools, allowing the ability to identify a dangerous parasite in food products, and placing a chemical attack on a dam. Artificial intelligence and machine learning have revolutionized the manufacturing industry by replacing human labor and improving speed and efficiency. This, of course, has had negative implications for workers. Still, these implications are mitigated by requiring workers to undergo training and pay a salary premium.
Artificial intelligence in retail
There’s an old joke that goes, “I know my Starbucks orders, but I don’t know why I’m ordering them.” This is what makes a good AI personal assistant like Starbucks’s Alexa and Amazon’s Alexa unique. These virtual assistants are constantly learning and growing based on the information that is fed to them. For instance, if you ask Alexa where the best-fried chicken places are in New York. It would ask you questions about New York, your location, and several other factors, which would lead it to create a list of best-fried chicken places in New York. Amazon Echo and Google Home are two examples of companies that have used this technology to make the lives of their customers easier.
How does AI work?
To understand how AI works, we first need to know how computers perceive the world. Computer systems, of which there are many types, are programming to follow specific rules and rules of thumb. Often, these are programs by analyzing lots of past experiences, which means computers learn quickly and perform tasks that humans might consider challenging. Most of the information AI uses stores in data banks, such as databases or high-speed processors. However, that data often doesn’t have enough “humanness” or spontaneity to create value systems. For example, for an AI to help a customer in a call center. It needs to understand the problems and misconceptions the customer might have.
Supervised learning
At its heart, machine learning is simply the name given to a subset of artificial intelligence. All machine learning starts with a piece of raw data that has been labeled as either a “probe” or “model” of some kind. The probe data uses to test the learning algorithms and see if they can accurately learn from them. The model is then created to replace the probe data, using the model to make predictions on new probe data. It’s essentially the opposite of a natural learning system like a child or a spider monkey. A machine learning system can do many things, but a human can’t teach a spider monkey to be a great swimmer. The algorithms we use in machine learning are generally very elaborate.
Unsupervised learning
At its most basic level, artificial intelligence is when a computer learns on its own to perform an algorithmically complex task without being given explicit instructions. It is how software like Siri and Google search know to perform tasks without being explicitly told how. In the early days of artificial intelligence, unsupervised learning was a significant hurdle for researchers. All an unsupervised learning algorithm has to do is analyze data to find associations that seem essential. But are not necessarily the actual trigger for the learning process. For instance, an unsupervised learning algorithm could explore all the links on a website. And identify an increasing volume of links to different articles as the website gets popular.
Reinforcement learning
Artificial intelligence refers to systems that learn independently to adapt their behavior in response to unstructured and structured inputs. The most widely understood application of artificial intelligence is computer programming, especially in intelligent machines. However, machine learning refers to computer algorithms designed to improve their performance with experience instead of explicit programming instructions. Like human brains, artificial intelligence systems typically rely on neural networks to achieve this performance. Neural networks can be considered “deep” because they are hierarchically organized computing elements consisting of various processing elements.