Comparing Artificial Intelligence vs Machine Learning: What's the Difference?

 

What is machine learning? Machine learning, or ML, is the ability of computer programs to learn without being explicitly programmed. The term has become popular in recent years because it can be used to describe many modern applications of artificial intelligence (AI), including natural language processing and speech recognition. ML is often implemented as a component of AI systems that anticipate user needs and make decisions on their behalf. Machine Learning vs Artificial Intelligence: What’s the difference? Machine learning is one sub-category of AI, which can be defined broadly as using

 

What is machine learning?

Machine learning, or ML, is the ability of computer programs to learn without being explicitly programmed. The term has become popular in recent years because it can be used to describe many modern applications of artificial intelligence (AI), including natural language processing and speech recognition. ML is often implemented as a component of AI systems that anticipate user needs and make decisions on their behalf. While the term ML has been in use since the 1950s, the field has gained in popularity as it became easier to apply ML to real-world problems. ML, when implemented correctly, can be used to improve online marketing, recommender systems, business intelligence (BI), self-driving cars and financial services.

 

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What is artificial intelligence?

Artificial intelligence, or AI, is the creation of systems, typically computerized, that exhibit traits such as perception, reasoning, learning, and self-consciousness. Most famously, AI was pioneered by Alan Turing in the 1940s. Turing proposed the “eureka” moment was when an intelligent computer had finally achieved human-like reasoning through an insight. What is machine learning? Machine learning, or ML, is the ability of computer programs to learn without being explicitly programmed. The term has become popular in recent years because it can be used to describe many modern applications of artificial intelligence (AI), including natural language processing and speech recognition.

 

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How does AI differ from ML?

AI systems are based on learning and optimization. ML is based on observing patterns and using a learning algorithm to make decisions. As a result, ML systems can analyze large amounts of data much faster than a human and respond in a human-like way. ML is often used for pattern recognition and prediction, whereas AI systems are often used to automate a process or make an automated decision. AI systems can make predictions, while ML systems usually respond to data. How do AI and ML differ from each other? Machine learning is the ability of a machine to learn without being explicitly programmed. Like ML, AI is considered the subset of AI where ML is used. In most situations, the two terms are used interchangeably.

 

How do computers learn?

The modern era of computing first began in the early 1960s, with the computer program Pong, developed by Atari in 1972. This was the first program to be capable of simple one-off actions, and in particular, of completing specific tasks. It paved the way for the invention of multi-tasking computers and now-ubiquitous apps such as Google, Facebook, Amazon and Instagram. In the 1980s, this progressed into more complicated programs like Lotus 1-2-3, which contained a few hundred features for performing repetitive actions. These programs would first be shown a list of tasks, and then given a data set from which to draw a solution. Read More: The Future of Artificial Intelligence is now: Here’s how AI is transforming our lives As the home computer transformed the technological landscape.

 

Supervised Machine Learning

When a machine is tasked with finding a particular object in an image, it's called a "Supervised Machine Learning" (SM) system. In this type of system, each frame is labeled with a probability associated with whether that particular object can be found in that particular image. The first picture below shows an example of a Supervised Machine Learning system, in this case, the Intel Adaptive Multi-View Transform (AMVT). The system takes a photo of a broken window and uses labeled data to find out the likelihood that this picture contains a window. Image Courtesy of Intel Supervised Machine Learning systems can be quite complex in terms of both their architecture and input data.

 

Unsupervised Machine Learning

Supervised Machine Learning The artificial intelligence space is filled with various sub-areas. As they gain popularity, these categories are often subbed as machine learning, deep learning, artificial intelligence and so forth. While one may hear different terms being used in different contexts, they are all very similar and all fall under the umbrella of artificial intelligence. So what exactly are these terms, how are they related, and what are the differences? Let’s take a look. Unsupervised Machine Learning The most widely used subcategory of artificial intelligence today is unsupervised machine learning. Unsupervised machine learning is also referred to as machine learning that deals with unstructured data.

 

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The future of AI and ML

Machine learning remains an area of intense research and development across many different industries, including industries such as automotive, healthcare, and finance. Additionally, ML has seen rapid adoption across sectors such as agriculture, robotics, and the internet of things (IoT). It’s not surprising, therefore, that a growing number of leading technology companies are betting on ML and its potential to help them stand out in an increasingly crowded marketplace. The future of AI and ML Machine learning remains an area of intense research and development across many different industries, including industries such as automotive, healthcare, and finance. Additionally, ML has seen rapid adoption across sectors such as agriculture, robotics, and the internet of things (IoT).

 

Conclusion

As machine learning and AI continue to become more commonplace in a number of different industries, it’s helpful to have a better understanding of the differences between these technologies. By understanding where each one falls in the spectrum of AI applications, you can better identify how you can leverage this technology in your company.

 

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