{"id":7543,"date":"2022-06-30T10:44:47","date_gmt":"2022-06-30T17:44:47","guid":{"rendered":"https:\/\/www.springboard.com\/?p=7543"},"modified":"2023-08-18T07:47:56","modified_gmt":"2023-08-18T14:47:56","slug":"artificial-intelligence-vs-machine-learning-vs-deep-learning","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/artificial-intelligence-vs-machine-learning-vs-deep-learning\/","title":{"rendered":"AI vs. Machine Learning vs. Deep Learning: What\u2019s the Difference?"},"content":{"rendered":"\n

From movies to sci-fi novels, the fields of artificial intelligence (AI), machine learning (ML), and deep learning (DL) have all captured the public\u2019s imagination. But it\u2019s easy to forget that these distinct fields all have specific purposes in the world of computer science and software engineering. <\/p>\n\n\n\n

What further confuses things is that the terms are often misused, even by corporations claiming to employ these disciplines. One study that looked at the work of 2,830 European companies claiming to use AI and ML in their software found that 40% of them didn\u2019t use those technologies at all. Often, these companies were banking on the public\u2019s fascination with these terms to drum up investor and consumer interest. <\/p>\n\n\n\n

While some companies might be able to get away with playing fast and loose with those terms, software engineers cannot. So if you aspire to work in the software industry and actually use technologies like machine learning, deep learning, and AI, then you need to know what they really are. That\u2019s exactly what we\u2019re going to break down in this post. <\/p>\n\n\n\n

Is There a Difference Between AI, Machine Learning, and Deep Learning?<\/h2>\n\n\n\n
\"Is
Source: Microsoft Docs<\/a><\/figcaption><\/figure>\n\n\n\n

The effort to turn computers into \u201cintelligent machines\u201d goes back to the 1950s. Pioneers in the field wanted to turn computers from devices that could only execute commands to ones that could store procedures and be able to do some level of decision-making themselves.<\/p>\n\n\n\n

One could say that artificial intelligence, machine learning, and deep learning are technologies that emerged to make that happen. But there are important distinctions between the three of them. Let\u2019s break those down.  <\/p>\n\n\n\n

Getting To Know the Terms: AI vs. ML vs. Dl<\/h2>\n\n\n\n

Let\u2019s look at what these three terms mean so we can begin to tease out the differences between them. <\/p>\n\n\n\n

What Is AI?<\/h3>\n\n\n\n
\"What
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Artificial intelligence is simply a system\u2019s ability to correctly interpret data, learn from it, and then use those learnings to achieve specific goals and complete tasks through adaptation. In general terms, AI is great at automating routine and repetitive tasks. In other words, it\u2019s great at optimizing. <\/p>\n\n\n\n

We use the term artificial intelligence because it manifests itself in ways that remind us of our own human intelligence. That means that AI seems to be able to produce programs that can learn from data and make adaptations that are not hard-coded into the program. <\/p>\n\n\n\n

As you can see, AI has a pretty broad definition. That is why fields like machine learning are considered a sub-field of AI. <\/p>\n\n\n\n

Related Read: How Will Artificial Intelligence Affect Our Lives in the Future?<\/a><\/em><\/p>\n\n\n\n

What Is Machine Learning? <\/h3>\n\n\n\n
\"What
Source: Bismart<\/a><\/figcaption><\/figure>\n\n\n\n

Machine learning<\/a> is a system of algorithms that receives inputs, produces outputs, then checks the outputs and adjusts the system\u2019s original algorithms to produce even better outputs. The algorithms used in machine learning are ones that have been around for a long time like linear regression and classification algorithms. <\/p>\n\n\n\n

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When it comes to machine learning, it isn\u2019t enough to have an algorithm. You also need to have data that it can \u201clearn\u201d from. The greater the volume of data available to the algorithm, the better it will be at producing accurate results using both supervised and unsupervised learning methods<\/a>. <\/p>\n\n\n\n

Related Read: Future Scope of Machine Learning: What\u2019s to Come for Work & Business<\/a><\/em><\/p>\n\n\n\n

What Is Deep Learning?<\/h3>\n\n\n\n
\"What<\/figure>\n\n\n\n

Machine learning checks the outputs of its algorithms and adjusts the underlying algorithms to get better at solving problems. Deep learning links (or layers) machine learning algorithms in such a way that the output layer of one algorithm is received as inputs by another. <\/p>\n\n\n\n

Deep learning is considered a subset of machine learning because of that. It achieves the ability to link the results of machine learning algorithms by using a framework called a neural network<\/a> or deep learning network. These neural networks are used to develop deep learning models that have powerful learning capabilities<\/p>\n\n\n\n

Related Read: How Deep Learning Revolutionized NLP<\/a><\/em><\/p>\n\n\n\n

How Do They Work?<\/h2>\n\n\n\n

Now let\u2019s take a look at what this all looks like in action. <\/p>\n\n\n\n

How Does AI Work?<\/h3>\n\n\n\n
\"ai
Source: Atlearner<\/a> <\/figcaption><\/figure>\n\n\n\n

Let\u2019s take a look at an example that most of us might be familiar with to look at how AI works. <\/p>\n\n\n\n

Amazon Prime\u2019s Warehouses used to be powered by people. But because of the predictable nature of this work, it could be optimized and handed over to robots that run on artificial intelligence. <\/p>\n\n\n\n

The robots that were developed were built using pre-existing data about how items are stored in warehouses, how orders come in, and what needs to be done to pick and pack items for a particular order. That\u2019s how AI works, by using pre-existing data and adapting learnings from it to automate specific tasks. <\/p>\n\n\n\n

What AI is not great at is creating and thinking outside the box. Things that fall outside of this core competency include: creativity, imagination, holistic viewing of something, arts, motor skills, rhythm, non-verbal communication and cues, musicality, feelings, visualization, and empathy. When the robot carrying the yellow bin filled with products you bought gets stuck, it doesn\u2019t know how to solve that problem. This requires creativity and a holistic approach\u2014a job meant for people. Workers in new Amazon distribution centers now help robots do their jobs. <\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Melanie<\/a>

Melanie Hanna<\/p>

Data Scientist at Farmer's Fridge<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Isabel<\/a>

Isabel Van Zijl<\/p>

Lead Data Analyst at Kinship<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Garrick<\/a>

Garrick Chu<\/p>

Contract Data Engineer at Meta<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

How Does Machine Learning Work?<\/h3>\n\n\n\n
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One example of machine learning in action is an organization called Crisis Text Line, which uses machine learning to figure out which words<\/a>, when typed in a text message, are the most likely to predict suicide. To isolate words, it employs a machine learning technique called entity extraction. Then it uses natural language processing and sentiment analysis to figure out that the word \u201cibuprofen\u201d is 14 times more likely to predict suicide than the actual word \u201csuicide,\u201d and that the crying face emoji is 11 times more likely to predict that the person is in crisis.<\/p>\n\n\n\n

This example illustrates how machine learning models work. If you have a dataset where certain patterns present themselves, you can then use machine learning algorithms<\/a> to study those patterns and initiate a learning process about the connections within that data. The more complicated the problem we attempt to solve with machine learning, the more sophisticated the algorithms become. <\/p>\n\n\n\n

How Does Deep Learning Work?<\/h3>\n\n\n\n
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Source: Tech Target<\/a><\/figcaption><\/figure>\n\n\n\n

Deep learning is the use of machine learning algorithms that use a nested hierarchy of simple concepts to represent more abstract and complex concepts. For example, a company called Dialpad uses deep learning loaded with natural language processing and entity extraction to automatically transcribe massive amounts of phone call data. It then uses sentiment analysis\u2014a deep learning technique\u2014to discern whether the sentiment of the conversation is positive or negative, in real-time. This gives people using Dialpad an opportunity to respond to negative sentiments with more empathy and data.<\/p>\n\n\n\n

How Are They Used?<\/h2>\n\n\n\n

Businesses use AI, ML, and DL for different purposes. Let\u2019s take a look at what determines which use cases they\u2019re applicable for. <\/p>\n\n\n\n

AI<\/h3>\n\n\n\n

Ai is used in a very wide variety of applications<\/a>. Companies take advantage of AI wherever there is an opportunity to automate a repeated process. It is especially popular for building robots that work in e-commerce warehouses, self-driving cars, and tools that automatically parse large texts like legal documents. <\/p>\n\n\n\n

Machine Learning<\/h3>\n\n\n\n

Many pieces of software become more efficient when they\u2019re able to learn from pre-existing data. Businesses use machine learning to learn from data at a rate that humans never could. <\/p>\n\n\n\n

Internet search engines use machine learning algorithms to connect keywords to internet pages, Similarly, the technology is also used to learn what spam is and filter it out of email. <\/p>\n\n\n\n

Deep Learning<\/h3>\n\n\n\n

Companies use deep learning whenever there is a large volume of data points available in such a way that what you learn from one data point can inform inferences from other ones. <\/p>\n\n\n\n

The most common technology that underlies any natural language processing software is deep learning. Consider any device that processes audio files or live audio, like when you interact with Siri. There are deep learning networks within the software that process what you say progressively and connect them to specific outputs. <\/p>\n\n\n\n

Deep learning is also used in fraud detection software for insurance companies and computer vision tools. <\/p>\n\n\n\n

AI vs. Machine Learning vs. Deep Learning Examples<\/h2>\n\n\n\n

Let\u2019s take a look at a few different examples so we can get a better understanding of the applications of each of these technologies. <\/p>\n\n\n\n

Examples of AI<\/h3>\n\n\n\n

Self-driving cars are a great example of what becomes possible with artificial intelligence. <\/p>\n\n\n\n

A self-driving car is basically a machine that learns how to drive like human beings do (and maybe even better). It might not be what some refer to as true machine intelligence because it still requires some inputs from humans. But it does do a pretty great job of mimicking human intelligence by using image recognition to maneuver through roads and make key decisions. <\/p>\n\n\n\n

Some other examples of AI are: <\/p>\n\n\n\n