The Practical Differences Between AI VS Machine Learning
The legendary showdown between chess grandmaster Kasparov and chess-playing computer Deep Blue left us all with a new reality. Computers can outsmart humans.
It’s been more than two decades since then. Artificial intelligence (AI) has advanced in leaps and bounds – and there is a whole mess of new concepts that are transitioning from fiction to reality.
Neural nets, deep learning, computer vision. What’s the difference? What do we mean when talking about AI vs machine learning?
Read on for a crash course.
The Differences: AI vs Machine Learning
Artificial Intelligence (AI) and machine learning are both terms that are used interchangeably. In practice, though, there’s a difference.
AI is an umbrella term with multiple categories. Machine learning is a particular branch of AI and the one that has the most practical applications today.
What Is AI?
The idea behind AI is to simulate human intelligence. The types of AI being explored today have multiple branches and use cases:
- Natural language processing
- Image analysis
- Accessibility enhancement
Natural language processing (NLP) is one of the types of artificial intelligence that specializes in human language.
NLP is capable of understanding the nuances of text, speech, and recorded audio. Human language isn’t flawless – we like to “um” and “ah” and speak in a blundering fashion at times.
IBM’s Watson – a computer that famously beat reigning competitors on Jeopardy in 2011 – is an example of the power of NLP to out-perform humans.
Image analysis is a branch of AI that frequently uses machine learning to achieve its aims. Otherwise known as “computer vision”, it has a myriad of applications in law enforcement and risk analysis.
You can find out more information at SentiSight.ai about image analysis.
AI is also seeing increasing application in making our lives easier. From smartphones that learn how to save power to autonomous vehicles and the self-driving sector, it will become more common in our daily lives.
What Is Machine Learning?
Machine learning is in use today. It’s what filters spam out of your inbox, it’s what lets Google and Bing understand the context of a search, and it’s what Facebook uses to highlight faces in grainy images.
Machine learning refers to complex algorithms that can recognize and learn from patterns in data sets. The bigger the volume of data, the more accurate the result.
The most powerful application of machine learning is in generating predictions from large data sets.
You may have heard of the types of machine learning:
- Supervised learning
- Unsupervised learning
These outline the strategy used to allow an algorithm to learn. With supervised learning, an algorithm is given a set of data and the desired result. It then recognizes patterns and makes predictions.
Those “training” the algorithm verify the predictions. The training is complete when the algorithm reaches a high level of accuracy.
With unsupervised learning, the algorithm is often more complex and generates observations and predictions without user input.
Practical Uses of AI
Let’s talk practicality. In website optimization and business, AI is already a powerful tool, even if you’re not aware you’re using it.
Search engines and social media employ AI-driven, trained algorithms to do some fancy stuff under the hood. You can see it yourself in action: Toss any loosely connected jumble of words at Google and it will figure out what you want.
That’s just for starters. AI has a wide variety of potential applications:
- Analyzing massive data sets to forecast sales outcomes
- Generating recommendations for website optimization
- Targeting ads at appropriate audiences
- Publishing content at the best time for effect
- Reactive cybersecurity
- Reducing costs through automation
One thing is clear: AI solutions are must-haves for businesses that plan ahead.
Embrace the Power of AI
Programming your own neural network takes a lot of skill, but understanding what makes AI vs machine learning so powerful doesn’t. It’s already helping thousands of businesses to succeed across the globe.
It’s changing the way we do business.
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