How Does AI Work: Everything You Need To Know

What is artificial intelligence (AI)?

Artificial intelligence is a machine’s ability to process, compute, analyze, and produce data. In this sense, we could say that any computer is potentially an artificial intelligence. However, this is not the case. 

We could also say that machines create, but it is not exactly that. The main principle of AI is emulating intellect abilities similar to human thinking but with the capacity to perform highly analytical tasks (and work with huge amounts of data at once). 

The term is broad and could mean a variety of different things, but it essentially always refers to a machine’s intelligence. 

Can machines think?

Seeing machines as things that can “think” is a pretty recent idea. This concept  became popular in 1950 when Alan Turing wrote one of the first pieces exploring the question. In his article, Turing wanted to answer the question “Can machines think?” and stated that instead of thinking, machines actually mimic thinking. Hold on to this idea, because this might be a good point to use when thinking about AI.

Turing proposed that machines don’t necessarily need to have the same thought processes as humans; rather, they can mimic the results of thinking. By applying rules, logic, and computation, machines can handle information and generate responses that seem intelligent. He thought of the human brain as a complex machine as well, implying that intelligence — whether it’s natural or artificial — can arise from systems that are capable of learning, reasoning, and solving problems.

Today, AI solutions are so well developed that we can actually be tricked into questioning if some texts are written by humans or AI. Here at Selzy blog, we have a quiz to test your ability to detect it, the “AI or Human Writing Quiz.”

How does artificial intelligence work exactly?

Artificial intelligence works by looking at massive amounts of data for patterns, much like how we humans learn from our experiences. At its heart, AI depends on algorithms — these clever sets of rules and instructions that empower machines to analyze information, discover connections, and cope with challenges quickly and effectively.

For example, AI can interpret visual data like photos or videos to identify objects, people, or patterns. This technology is applied in facial recognition, self-driving cars, and medical imaging, allowing machines to “see” and interpret visual content.

In the following sections, we explain different areas and processes within the AI field. It is important to remember that none of these study fields stand on their own. Think of them as interconnected fields that work together in AI solutions.

Machine learning (ML)

Machine learning is an exciting aspect of AI. In ML, systems are “trained” using data. For a long time, ML was a synonym for self-teaching systems. As they process more and more information, they can adapt and improve their functions. It’s wonderful to see how they evolve with each interaction, growing smarter and more capable every step of the way.

Machine learning in your inbox

The more common use of ML is very present in our digital lives. Inside the most used email services (such as Gmail, Outlook, Yahoo, or Proton) are many automation processes that learn patterns and apply filters accordingly. So, behind your spam folder, which usually fills in automatically, are scripts built on the concept of machine learning. These scripts learn from data collected from various email accounts and identify common traits of spam messages. 

So, with email campaigns, marketers must adhere to best practices. A system’s decision to flag a sender as a spammer can have an impact on other email providers, which means that once any email provider tags a sender as a spammer, other email providers learn and replicate it. This shows how important it is to keep a good reputation when doing email marketing.

Neural networks

Neural networks are systems that replicate the way organic brain neurons communicate with each other. It is considered a model for machine learning and the Nobel Prize of Physics of 2024 was awarded to the scientists who created the concept, John Hopfield and Geoffrey Hinton. 

The trick behind neural networks is creating a system of layers between data input and data output. This means that scripts can cross different paths of analysis and give a variety of possibilities for answers.

Neural networks in translation tools

Neural networks play a significant role in automatic translation tools such as Google Translate and DeepL. They understand the meanings of phrases in one language and translate them into corresponding phrases in another language, taking into account the context. This functionality is enabled by the code’s architecture, which is based on the neural network model.

Large language model (LLM)

A popular term in relation to AI tools for writing is the Large Language Model (LLM). LLM is a neural network that connects words with their meanings to help create text. In practice, LLMs utilize clever neural network models to forge links between words and different uses for them. Each word stored in their memory is tied to various potential matches, which combine to form coherent text sense.

The main example of LLM today is ChatGPT.

The downside of LLM’s popularity is its use in education, for example. Many educators have a hard time dealing with texts generated by AI. But we already have ways of detecting AI use in texts, even though they need to be in constant improvement.

Processing database

AI needs huge databases to be efficient. Processing Database is a term used to describe a particular way to manage databases; instead of using external systems to manipulate or analyze them, these processes are performed in the place where the data is collected.

Processing database in Google Maps

For example, applications that use geographical information process their data in real time, showing directions and suggesting better routes. Google Maps has the ability to perform these tasks thanks to the integration of data processing with other AI methods, like machine learning. To provide users with accurate and updated information, Google Maps processes large amounts of collected data combined with users’ preferences, including their location and usual routes.

So how does AI learn?

AI basically learns from human knowledge. 

It collects data produced by people, from several sources, and merges them as if they were a single database. Then this huge pool of information is analyzed and manipulated using the techniques and processes described in the previous paragraphs.

The main functionality of AI is the possibility to adjust results according to real-time feedback. It stores users’ reactions to the results. So, it learns from the static data and the feedback it gets from the users. 

What types of artificial intelligence are there?

All methods and techniques described above were employed in different systems and solutions decades before the popularization of AI. But now they are incorporated into the AI jargon, as AI has become an entire field of study. 

This makes it necessary to address different types of AI according to their applications.

Reactive machines

Reactive machines have a limited scope of operation. They are designed to handle a limited number of tasks, making them effective for specific purposes. We call them reactive because they can only bounce back quick requests.

Reactive machine on Instagram

The real-time filters people use on Instagram, Snapchat, and TikTok videos employ a reactive machine behind the scenes. When we click on the video filter, it immediately recognizes faces, for example, and applies its layers to the video on the points it identifies as elements of a human face.

Limited memory machines

Memory AI is used for predictions. It has the capacity to learn and train for new tasks by storing knowledge, including its own results as a source for new requests. This ability enables it to adapt and improve its predictions over time, making it highly capable of managing new requests. With this, Memory AI can respond not only to direct inquiries but also foresee future needs, delivering a more intelligent and efficient solution in dynamic environments.

Limited memory machines on TikTok

TikTok is famous for learning from users’ behavior. It predicts what a user might like based on what the user has liked before, how much time was spent on specific types of content and how is that user engagement trend. With this feature, Tiktok personalizes its “For you” tab.

Theory of mind

There are AI solutions that mimic the field of Theory of Mind in psychology. They are trained with information that drives responses to human emotions, recognizing patterns of thinking and feeling. They also try to interpret social situations and individual motivations in social contexts. 

This particular type of AI is a long-time ongoing research and its applications are still limited.

Theory of mind AI in chatbots

Some automated chat solutions utilize the Theory of Mind to communicate effectively with humans. In customer service, these systems can analyze a user’s responses and react appropriately. Many are designed to align with a brand’s voice, adjusting their tone and style of communication to reflect the company’s values.

Self-awareness

Self-aware AI refers to the theoretical phase in which machines have self-awareness. Commonly known as the singularity point in AI, self-aware AI marks a progression beyond the theory of mind and is considered one of the ultimate objectives in AI research. It is believed that upon achieving self-aware AI, systems can be autonomous.

There is no real-life example of AI self-awareness today.

Strong AI vs weak AI: key differences

Strong AI and Weak AI represent two different levels of artificial intelligence capabilities. 

Strong AI, often referred to as Artificial General Intelligence (AGI), is designed to emulate human intelligence by applying knowledge across diverse tasks with exceptional speed and efficiency. While self-aware AI is still a theoretical concept, we are getting closer every day. Currently, advancements like Large Language Models, like ChatGPT-4, represent our most advanced approach to AGI.  

In contrast, Weak AI, or Artificial Narrow Intelligence (ANI), is made for specific tasks. It excels in its designated functions but doesn’t have the ability to generalize its knowledge beyond what it’s programmed for. A good example of ANI is the reactive machine described here before.

Industries using AI

As mentioned before, several technologies that we now refer to as AI have actually been around for quite a while. Today, numerous industries are embracing AI, although many of those technologies existed before. It’s just that over time, they’ve evolved and earned the title of AI. Plus, there have been remarkable advancements in technologies, thanks to the growing power of computers. This lets companies invest even more in smart solutions for everyday tasks.

Let’s take a look at how different industries are using AI.

Marketing

• Email template builders. For example, you can use AI to create beautiful designs for your email campaigns in Selzy’s AI-powered email builder. 
• Email assistant. Marketing campaigns can be designed by AI now. Try out Selzy’s AI email assistant.
• Analytics. Thanks to tools like HubSpot AI, marketers can access a comprehensive arsenal of statistics and predict trends more efficiently.
• Customer behavior. Some marketing tools, like SPD, improved their data about customers and now, based on processing data, can predict how they behave. This helps to enhance strategies for marketing campaigns, for example.

Healthcare

• Wearable technologies. AI can support doctors in providing highly accurate data inputs about patients, collecting this data from wearables, like smartwatches.
• Disease prevention. With large databases in healthcare, tools like MedLM help doctors and patients to predict possible conditions, based on age and medical history. 

Education

• Language learning. Apps like Duolingo allow people to learn new languages with adaptive content contextualized by user behavior. 
• Tutoring help. Teachers can make the best of AI with tools like Khanmigo, that help prepare classes and optimize teaching workflows by type of classes, age of student, etc.

Business

• Decision-making. With predictive model AIs, managers can access data analysis that provides insights for future projects. Platforms like GiniMachine support this type of service.
• Meeting scheduling. Booking meetings can be challenging in big teams, but now there are AI tools that optimize this by analyzing users’ availability, like SchedulerAI.

Entertainment

• Personalized suggestions. Big entertainment platforms like Netflix personalize suggestions based on their customers’ preferences by using AI methods.
• Curated content. Thanks to AI, media services like Spotify offer curated content, like tailored playlists, that are automatically generated according to customers’ data. 

Transportation and Logistics

• Routing. Fleet managers use AI, such as RouteQ, to optimize routes by geographical data, using weather conditions and real-time traffic updates.
• Scheduling. Platforms like Upper use AI to optimize fleet scheduling, improving delivery time and cost-effectiveness in logistics.

Finance

• Investment strategy. With large data from investment marketing, many finance management systems, such as Mercer, are offering AI integrations to help investors trace strategies for future endeavors. 
• Detection of fraudulent transactions. AI detection systems for finance institutions, like banks, are becoming common and its use is almost expected. These solutions are natively implemented into the banking systems.

To sum up

AI is opening up space for more accurate decisions and managing large amounts of data. In robotics, scientists and companies are leveraging AI’s ability to process vast amounts of data to create innovative robots. This involves combining advanced hardware and software in ways never seen before. The field of science is also benefiting from AI by optimizing research processes and predicting outcomes. Utilizing AI can feel like having access to many human minds simultaneously, but it is important to remember that AI is just a tool that mimics human intelligence. So, keep this in mind when using any AI tool, and don’t be shy about trying out new ways to improve your work with it.  

In email marketing, for example, there are tons of possibilities to use AI to enhance customer experiences and develop strategies that cover spots that, alone, we would not see — or that we would take a long time to note.

For more information, read our article on the best AI content detectors.