As artificial intelligence (AI) continues to move into our everyday lives, understanding all of the different facets that create the technology can seem daunting. After enough digging, though, we can begin to uncover the main subsets that form AI. Then, we can take a closer look at AI vs machine learning vs deep learning.
So, is deep learning machine learning?
Today, we're going to answer what constitutes each subset and what is meant by deep learning. After learning the difference between artificial intelligence vs machine learning vs deep learning, you'll be able to understand which one is right for your business and what opportunities each has to offer.
First, we should start with what AI is and how artificial intelligence and deep learning relate to each other.
AI is a broad science covering the concepts behind giving machines the ability to process information and formulate decisions from data like humans, if not in a superior way. This intelligence imbued into machines can be used to calculate predictions, automate processes, and streamline production.
AI is frequently broken down into three main categories that include:
Artificial Narrow Intelligence
Artificial narrow intelligence is the lowest rung of AI computing, as it can only be applied to a predefined and specialized task. This category of AI has been seen for some time, as it's been used to solve chess problems, crawl web pages, or power chatbots.
Artificial General Intelligence
Artificial general intelligence is harder to define as it's still in its infancy, and the nascent technology hasn't been perfected. A general consensus believes that artificial general intelligence should at least match that of a human cognitive function and be able to:
Artificial Super Intelligence
Artificial super intelligence is the strongest form of AI and should demonstrate superior intelligence to humans in creating plans, deductive reasoning, and production. Artificial super intelligence is still in the realm of science fiction. However, the field of AI is growing exponentially every year.
As it currently stands, machine learning and deep learning are still in the artificial narrow intelligence bracket but are starting to break into artificial general intelligence.
Now that we understand the basics of artificial intelligence, it's time to understand the differences between deep learning vs machine learning. At its core, machine learning is a subset of AI that allows a system to learn from data fed and improved on the output figure it produces. This process bypasses the need for a programmer to actively make adjustments to generate a correct result from the system, effectively learning independently.
This learning process starts by being given data and specific instructions to determine if observable patterns can be detected. If patterns and sequences are found, then this information, in some cases, is retained and used to determine the relevance that new data should be affected by it. The system's goal is to learn with minimum, if any, human supervision.
An example of machine translation would be how text is parsed into sequences of keywords or phrases. The system would then use semantic analysis to replicate a humanistic approach to deciphering a text block’s meaning.
Now that we understand one subset of AI, is machine learning and deep learning same?
Comparing deep learning vs machine learning is similar to comparing machine learning to AI. Each is a subset of the other. Deep learning is notable because it relies on artificial neural networks to organize information and produce a usable result. An artificial neural network is a system composed of neurons (nodes) and connections (synapses) to replicate the structure of a human brain.
This system takes in data, processes it through the neurons, and in some programs, will retrace the neurons to correct any errors. These neurons retain memetic data that is applied to a datum, giving it a weighted value. If the neuron adds enough weight to the information that it can surpass a predefined threshold, it is passed along to the next neuron, and the process begins again.
As stated above, the difference between machine learning and deep learning isn't so much what they are but how they're applied.
The early machine learning models could adapt to new tasks given they are continually fed new data under a technician's supervision. For most cases, if a machine learning system encounters an error or produces an inaccurate result, it would have to be manually adjusted. Through deep learning's artificial neural network model, such as recurrent neural networks, it can determine if it produced an inaccurate result and make adjustments through its neurons.
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