All of the acronymns
It’s almost harder to understand all the acronyms that surround artificial intelligence (AI) than the underlying technology of AI vs. machine learning vs. deep learning. Couple that with the different disciplines of AI as well as application domains, and it’s easy for the average person to tune out and move on. That’s why it's a good idea to first look at how each can be clearly defined when comparing the science behind complex technologies like machine learning vs. AI or NLP vs. machine learning.
Below we attempt to explain the important parts of artificial intelligence and how they fit together. At Sonix, we are specifically focused on automatic speech recognition so we explain the key technologies with that in mind. The insights we provide regarding AI vs. ML vs. DL applications connect directly to the work we perform for our clients.
First let’s start with some of the most commonly used acronyms and their definitions:
- Artificial Intelligence (AI) -the broad discipline of creating intelligent machines
- Machine Learning (ML) -refers to systems that can learn from experience
- Deep Learning (DL) -refers to systems that learn from experience on large data sets
- Artificial Neural Networks (ANN) -refers to models of human neural networks that are designed to help computers learn
- Natural Language Processing (NLP) -refers to systems that can understand language
- Automated Speech Recognition (ASR) -refers to the use of computer hardware and software-based techniques to identify and process human voice
Artificial intelligence (AI) is the overarching discipline that covers anything related to making machines smart. Whether it’s a robot, a refrigerator, a car, or a software application, if you are making them smart, then it’s AI. Machine Learning (ML) is commonly used alongside AI, but they are not the same thing. ML is a subset of AI. ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets. Most AI work now involves ML because intelligent behavior requires considerable knowledge, and learning is the easiest way to get that knowledge. The image below captures the relationship between machine learning vs. AI vs. DL.
There are many techniques and approaches to ML. One of those approaches is artificial neural networks (ANN), sometimes just called neural networks. A good example of this is Amazon’s recommendation engine. Amazon uses artificial neural networks to generate recommendations for its customers. Amazon suggests products by showing you “customers who viewed this item also viewed” and “customers who bought this item also bought”. Amazon assimilates data from all its users browsing experiences and uses that information to make effective product recommendations.
At Sonix, we convert audio to text using machines. The principle underlying technologies are automated speech recognition (ASR) and natural language processing (NLP). ASR is the processing of speech to text, whereas NLP is the processing of the text to understand the meaning. Because humans speak with colloquialisms and abbreviations, it takes extensive computer analysis of natural language to drive accurate outputs.
ASR and NLP fall under AI. ML and NLP have some overlap as ML is often used for NLP tasks. ASR also overlaps with ML. It has historically been a driving force behind many machine-learning techniques. When comparing AI vs. machine learning, it is crucial to understand the overlaps and differences within the diagram.
In summary, DL is a subset of ML, and both are subsets of AI. ASR & NLP are fall under AI and overlap with ML & DL. It's amazing how they are all intertwined. It’s not as much about machine learning vs. AI but more about how these relatively new technologies can create and improve methods for solving high-level problems in real-time.
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