There used to be a clear, technical separation between terms like AI (Artificial Intelligence) and ML (Machine Learning ) – until recently, these technologies remained largely theoretical. As they became practical in the real world and then turned into products, marketers stepped in.
The widespread misuse of AI/ML in marketing has begun to confuse what these words mean.
What is artificial intelligence?
There is an automatic connection between AI and science fiction. When people think of AI, they usually think of The Terminator, Star Trek, etc. They are a very specific form of AI known as AGI – artificial general intelligence (also known as strong AI) – a form of digital consciousness that can match or exceed human performance on any number of metrics. AGI is just as capable of solving mathematical equations as it is of conducting a human conversation, or composing a sonnet.
There is currently no working example of AGI, and the likelihood of creating one is still low. Attempts to create AGI currently revolve around the idea of scanning and modeling the human brain, and then replicating the human brain in croatia telephone number data software. This is a sort of top-down approach—humans are the only example of working consciousness, so to create other sentient systems,
it makes sense to start from the point of view of our brains and try to copy them.
If you take a bottom-up how to increase organic traffic – 10 seo tips approach, you get what’s called narrow or weak AI. This is the kind of AI you see every day—AI that excels at one specific task. AI powers apps that help you find music to listen to, tag friends in photos on social media, can help protect against malware, and more.
This type of narrow AI does just one thing, but it does it much faster and better than a human. Imagine scanning a million orders a day to make sure there’s no counterfeiting—you’ll get bored quickly and start making mistakes. The AI can process those orders in the blink of an eye and catch more errors and suspicious activity than a trained human observer ever could.
What is machine learning?
Machine learning and AI are not the same thing – BUT, if you want to create narrow AI in a simple way, machine learning is increasingly the only option.
Machine learning is when you do something wrong and then you finally get it right. Here’s a layman’s explanation of how it works.
Let’s say you’re writing an image recognition program to find pictures of cute dogs. First, you give the program some idea of what a dog looks like. Then you show it a data set of images—some with dogs, some without. You tell your software to pick out the dogs. Most likely, the software will get most of it wrong. That’s fine. You tell the program which pictures are correct, and then you repeat with different data sets until the software confidently picks out dogs.
This example illustrates the main principle of machine
learning’s advantages: At no point do you have to get into the intricacies of software and code it to recognize dogs. Instead, the conduit china machine “codes itself,” generating mathematical models to find dogs, then improving them as it is trained on additional data.
These are the basics of how it works.
By using machine learning, you save time and effort in creating narrow AI. Instead of manually creating a complex and branching decision tree, the decision tree grows on its own and increases its usefulness every time it encounters new data and classifies it. Machine learning significantly increases the effectiveness of data scientists by reducing the effort involved in creating models and categorizing data.
artificial intelligence machine learning
AI/ML for better performance
The difference between machine learning and AI is that machine learning is one of the – but not the only – precursors to creating narrow AI. Specifically, machine learning is the best and fastest way to create a narrow AI model to categorize data, detect fraud, recognize images, or predict the future (among other things).
While marketing has distorted the meaning of machine learning and AI in many ways, the upside to commercialized technology is that it’s now easier than ever to use and build machine learning models—assuming you’re working with a company that sells a genuine product.