AI vs Machine Learning vs Deep Learning: How are They Different?

Digital transformation is accompanied by concepts and terms that often have their applications and meanings confused. After all, do you know the difference between Artificial Intelligence, Machine Learning, and Deep Learning? All three are very important for the future of your company, but also quite different.

AI vs Machine Learning vs Deep Learning: How are They Different?

With so many advances in technology and societal changes, it is easy to get lost in different concepts and their applicability. 

Here’s a great example: Artificial Intelligence (AI) is often used in science fiction storytelling and is a well-known term, but do you know what it actually offers? 

Have you ever stopped to think about the differences between “AI vs Machine Learning vs Deep Learning”?

Each of these concepts serves a purpose and can be implemented differently.

To make it easier, we have written this article to explain these terms and their applicability in everyday life at a company. 

We will talk about these differences and the following topics:

    What is Artificial Intelligence, and How Does it Work?

    AI refers to the development of systems and machines capable of thinking and acting independently, without the need for direct human participation.

    It deals with simpler operations, such as extracting data from a spreadsheet, and more complex processes, such as automating machinery.

    This kind of technology allows a machine not only to perform tasks but to interact with its surroundings. 

    The concept appeared in the 1950s and has always been a subject that has attracted society’s curiosity, especially with the presence of the term in science fiction movies.

    AI has become a reality in different companies and segments. Processes are streamlined, decisions are more precise, and the entire work environment benefits from it.

    A common system can analyze data and point out errors, while AI is capable of interpreting scenarios and situations. It can, for example, identify a fraud attempt in e-commerce. 

    In short, it is a way to simulate the functioning of the human brain in machines and systems, interpreting information and data to use in day-to-day work.

    How can it be applied?

    Do you want to know what the possible applications of AI are in your Marketing plan or other areas of your company? 

    Check out some examples:

    Predictive analysis

    The use of data is fundamental to the success of any company today, and one of the most efficient ways to do this is through predictive analysis.

    By studying data and metrics, it is possible to define a trend. A store, for example, can better evaluate its stock with the use of AI, preventing an item from running out or piling up.

    Automated conversation

    Another area that benefits from AI is Customer Service

    Instead of having a team make a basic contact with the user, a chatbot can answer simple questions or even direct the conversation to an employee specializing in a particular topic. 

    The idea is to optimize the steps to offer a more fluid customer experience.

    Performance monitoring

    Managers can also enjoy the benefits of AI when it comes to productivity. 

    A system can help identify who is performing well and who needs to improve.

    The details can be very useful, for example, during remote work, where it is not as simple to closely monitor the performance of each professional on your team.

    Personal assistant

    AI has also been gaining space as a personal assistant. 

    One example is Amazon’s Alexa which can identify the user’s voice commands and perform specific tasks.

    Apple’s Siri is another example of AI as a personal assistant tool. 

    In a busy routine, the function can help remember appointments, answer questions, or even send an email.

    How Does Machine Learning Work?

    Machine Learning is a fundamental process for AI to work. 

    This technology involves the ability of a robot or system to learn from the data and information it processes.

    Without human participation, it is possible to identify behavioral patterns and even make intelligent decisions. 

    This concept is essential for the humanization process of AI.

    Have you ever talked to a chatbot and realized that it had previous information, for example, on your purchases on that e-commerce site?

    Machine Learning, no doubt, was a part of that system.

    As the name indicates, through this technology, a machine can learn and evolve, offering a more humanized experience, mimicking the thinking process of a human. 

    This artificial thinking, therefore, enables a series of new opportunities and alternatives.

    With Machine Learning, a company will need less human participation in many processes, as the system itself can reason and understand how to make the best decision.

    It can be used in different contexts, such as making a CRM system more efficient or automating the machinery in a factory.

    How can it be applied?

    One of the great benefits of Machine Learning is the possibility of applying it in different areas of a company, regardless of segment or size. 

    Keep on reading to find out the main alternatives:

    Humanized chatbots

    When using chat on a website, over 86% of consumers prefer to talk to humans, according to a Forbes survey

    One way to overcome this problem is to invest in Machine Learning since it can understand patterns in user behavior and even change the tone of voice, recommendations, or suggested procedures.

    More accurate reports

    One of the great advantages of Digital Transformation is that it facilitates bureaucratic tasks, such as creating reports and spreadsheets. 

    Machine Learning can help turn raw data into information and consequently generate valuable insights for your team.

    This way, Marketing or Sales professionals can have more precise details about segmenting a campaign or closing a sale.

    Recommendation system

    Another benefit of Machine Learning is recommendations. 

    An e-commerce company, for example, can run segmented campaigns according to the user’s behavior on its pages.

    This way, the consumer receives more precise recommendations related to their interests, increasing the chances of them making a purchase. 

    In times of fierce competition, this can be a relevant differential.

    More segmented content

    Machine Learning can also be useful for your Inbound Marketing strategy. 

    Besides good copywriters, counting on valuable insights about what can be interesting for your readers is crucial.

    The machine can identify subjects and even content formats, such as interactive materials, that can cause a greater impact on your target audience.

    What is Deep Learning, and Where Does it Apply?

    Deep Learning is an even more advanced process of Machine Learning. 

    Its capacity is so high that it can reach levels of unsupervised learning, that is, without human participation in any process. 

    All this is possible because of a system that simulates the functioning of the human brain at very high levels.

    Deep Learning is, therefore, an evolution of Machine Learning, thanks to its more profound layers of algorithms. 

    With this structure, the machine can recognize objects, understand voice commands, translate languages, and even make decisions. 

    It does not even need human supervision for it to continue learning and developing.

    Deep Learning is the most efficient technology when it comes to Big Data, for example. After all, it is not so simple to interpret so much information, but it is fundamental.

    This can be the solution to extract valuable data from the most diverse sources, such as social networks, systems, search engines — in short, to filter what is most relevant for a company’s planning.

    The reason for such a high capacity is the high level of artificial neural networks used, reproducing the human brain in a very similar way and allowing a non-linear approach when interpreting data and information.

    How can it be applied?

    What is the applicability of Deep Learning in a company? Check out some suggestions!

    Fraud detection

    As the system can identify behavior, it can detect fraud in a financial transaction or even authentication to access a system. 

    Deep Learning works in a non-linear way, so the technology relates to different scenarios and behaviors to understand that a particular action is not appropriate and may signal a problem.

    Unsupervised automation

    Google and Uber use Deep Learning to allow cars to be controlled by AI. 

    It is a process that is still under development but has already significantly advanced.

    This is all thanks to the high learning capacity of the system, which can react to everyday situations in traffic. 

    Without any supervision, the car is able to carry a passenger without any problems.

    Face recognition

    Many smartphones already offer facial recognition systems. 

    This process seems relatively simple and is directly related to Deep Learning. 

    The technology can identify details to be able to determine and differentiate facial expressions, ensuring the highest security for users.

    AI vs Machine Learning vs Deep Learning: What are the Main Differences?

    Now that you know more about AI, Machine Learning and Deep Learning, it might be easier to understand the differences between them.

    In short, the last two technologies are part of the universe of the first. 

    They are evolutions of the process, making a system even more capable of taking decisions without human interference.

    AI, therefore, is an early stage of artificial reasoning, where a machine can make its own decisions but is not highly capable. 

    Machine and Deep Learning are even more complex stages in which systems and machines have greater autonomy, increasing the capacity of reasoning and, consequently, of decision making.

    Between Machine Learning and Deep Learning, the names are a good indicator of their differences. 

    As the term “deep” indicates, Deep Learning encompasses an even more complex and advanced Machine Learning. 

    The system creates an artificial neural network from an algorithm layer, allowing it to make its own decisions without human participation.

    In summary, the three technologies differ in logic and algorithm, allowing them to have different objectives and applicability within a company. 

    But they complement each other, representing varying degrees of capability. The use of each varies according to the demands.

    Wrap Up: Moving Forward with your Newly-obtained AI Knowledge

    More than merely knowing the answer to the “AI vs Machine Learning vs Deep Learning” question, it is important to comprehend how to integrate these technologies to get your strategies to perform better.

    Understanding the differentials of each concept, it is possible to make more comprehensive and complete use of them within your organization, leveraging different areas and sectors to achieve better results.

    Besides the technologies used, it is important to count on relevant information and data to get where you want to go. 

    Now that you know that, how about watching our recorded webinar on the role of AI in Marketing?

    The special guest is Paul Roetzer, founder and CEO of PR 20/20 and the Marketing Artificial Intelligence Institute!

    Understanding the role of AI in marketing

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