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A non-techie's guide to the 7 types of AI

seven types of AI

Talking about AI is almost as common as talking about your favorite ice cream flavor these days. Everyone seems to know what AI is—but few actually know how it works or all of its different flavors.

In this blog, we’ll introduce you to the full spectrum of artificial intelligence. While AI is a rapidly evolving field and is only getting more advanced by the day, familiarizing yourself with these seven main types of AI can help you better discern fact from fiction and to make your own informed predictions about the future.

AI is already transforming website development, marketing and many other facets of business. But AI’s full potential has yet to be seen. “Based on the previous six months of how fast this technology grew, I can only imagine that the next six months will go even faster,” says Wix’s Head of Product Yaara Asaf. “We’re trying to collect the fruits that technology gives us as fast as we can.”

Try AI tools for copywriting, web design and more in Wix’s free website builder.

Keep reading to leave the seven main types of AI:

Types of AI (by capability)

Perhaps the best way to understand AI today is by viewing it from two different lenses. The first entails classifying AI by capability, i.e., the degree to which it can mimic human thought processes. Pioneering AI researchers developed a hierarchy of AI for describing and testing their projects.

three types of AI by capability

01. Artificial narrow intelligence (a.k.a. “narrow AI” or “weak AI”)

This is the type of AI that today’s technology is based on. It encompasses AI that can complete a specific set of predetermined tasks. For example, Siri on your iPhone can respond to voice commands and questions, based on a focused set of functions. If you ask Siri to direct you to the airport, an AI algorithm combines roadmap data and current traffic conditions to calculate the best route. But straying too far from conventional topics leads to a dead end; if you’ve ever asked Siri an offbeat or complicated question, then you’re probably familiar with the phrase “I’m not sure I understand.”

Narrow AI needs a large pool of reference data in order to develop its intelligence, and it can’t learn, expand upon or interpret this information and apply it to new tasks. Today’s AI-powered tools are more sophisticated than we could have dreamed of even five years ago; they’ve fundamentally changed how to make a website and how manufacturing facilities operate (as examples). But they’re all still based on narrow AI.

02. Artificial general intelligence (a.k.a. “general AI” or “strong AI”)

Artificial general intelligence (AGI) describes AI that’s capable of mimicking human decision-making and incorporating logic, emotion and learning. Although researchers initially thought AGI would be widespread before the beginning of the 21st century, human intelligence has turned out to be hard to recreate.

Developers have devised a number of tests to determine whether their creations meet the standard for AGI. The most famous of them is the Turing test, which compares responses to questions given by a computer and a human to see if the tester can tell the difference.

Some types of generative AI like ChatGPT, can, according to some, now meet the Turing threshold. For example, when a small group of ad executives tried comparing machine- versus human-generated digital ads, they could only guess which were written by AI with 57% accuracy.

But AI is still unable to pass other AGI tests (which involve mundane but complex tasks, like making a cup of coffee in an unfamiliar kitchen) and some claim the Turing test to be outdated. By and large, AGI is still considered a goal rather than reality.

03. Artificial super intelligence (a.k.a. “super AI”)

Currently the stuff of science fiction, super AI surpasses human intelligence and consciousness, giving machines the upper hand. While robots taking over the world sounds far-fetched, some researchers believe that once technology meets the AGI threshold, AI tools will quickly be able to learn, adapt and perfect their functions, ultimately surpassing human skills.

Types of AI (by functionality)

Another way to classify AI tools is to consider the type of work it can perform. Researcher Arend Hintze defined four types of AI, including two that are still aspirational at this stage.

four types of AI by functionality

04. Reactive AI

Reactive, or reactional, AI functions within narrow parameters, without referencing past interactions, responses or results. The data used to train reactive AI is all-important, since it’s the sole source of the algorithm’s knowledge.

Despite its limited focus, reactive AI can beat humans at specific tasks, thanks to its high-powered processing speed. The most famous examples of reactive AI are IBM’s Deep Blue, which beat a chess champion, and Watson, which won the TV game show “Jeopardy!”.

Many everyday tasks now rely on reactive AI for simple decision-making based on pattern recognition, such as automated spam filters for email, credit scoring mechanisms in finance and simple eCommerce product recommendations.

05. Limited memory AI

Limited memory AI uses stored data to inform current behavior, making its performance more sophisticated than reactive AI. Limited memory AI can process sequences of input and react accordingly. Additionally, it can use the results of its interactions as new training data, “learning” and refining its actions over time.

Limited memory AI improves digital experiences by using past interaction data to predict what current website visitors or app users want to do or see next. It picks the right dynamic content for the situation in real time, recommending products you didn’t know you needed or generating the correct chatbot responses to customer service questions.

Self-driving cars are testing the cutting edge of limited memory AI. They’re trained to process data from sensors and recognize objects like traffic lights and buses. When a pedestrian steps into a crosswalk, the car uses limited memory AI to perceive, react and apply the brakes.

There are constraints, though: Vast amounts of data are still required to train limited memory AI to perform straightforward tasks. Its knowledge expands when results are fed back to itself. The dataset grows, but the processing mechanism stays the same—unless humans reprogram it. That’s why self-driving cars have been flummoxed by seemingly-minor but unanticipated obstacles, such as San Francisco’s ever-present fog or protestors armed with traffic cones.

06. Theory of mind AI

In psychology, the term “theory of mind” refers to the understanding that other beings have thoughts and emotions that affect their actions. In order to approach human intelligence, AI needs to develop this awareness of others, and be able to interact in ways that take into account others’ knowledge and experiences. AI with a theory of mind doesn’t yet exist.

07. Self-aware AI

Another as-yet hypothetical version of AI, self-aware AI can (in theory) recognize its own mental states, emotions and memories. In addition, self-aware AI would be able to apply its own emotional intelligence to interpret and guess at others’ unstated motivations and internal states.

Top applications of AI: how you can use AI today or in the near future

The road ahead for AI is still long and uncertain, but AI is already reshaping how work gets done. Tasks that were previously repetitive, time-consuming and impractical to implement at scale are now within reach, thanks to AI’s processing speed and predictive capabilities.

For example, by combining AI with its decades’ worth of knowledge in web design, Wix has been redefining the way websites and digital experiences are created. Wix’s AI-powered tools let you build an entire website from scratch, in addition to simplifying tedious tasks like writing site copy, designing images and more.

illustration of Wix's AI site builder

Sign up for Wix today to see its AI tools in action.

Check out the other cool applications of AI across a variety of industries.

AI in retail and eCommerce

For years, stores and eCommerce sites have been high-profile users of intelligent algorithms to perfect shopping experiences, and as AI evolves, merchants are eager to implement new technologies. Six in 10 retailers globally are using or plan to adopt AI in the coming year, according to a survey by Honeywell. Primary uses include:

  • Personalization: Limited memory AI can process digital interactions in real time and predict which products are most relevant to recommend and which offers will resonate with individual shoppers, scaling up the experience of having a personal shopper. Javascript in the website code enables personalized product recommendations that adapt to behavior in real time.

  • Inventory management: Guessing how much of which items to stock can be a headache for store owners, but AI can analyze purchasing patterns and accurately predict when to place orders with manufacturers.

  • Customer service: Reactive AI can power order lookups to resolve questions about the status of eCommerce shipments, while AI applications using natural language processing can respond to more complex online customer service queries via chatbots, email and social messaging apps, meeting consumer expectations for swift response times.

AI in travel, restaurants and hospitality

Premier service is the hallmark of standout travel and dining experiences, and AI helps power administrative tools and digital interfaces to offer individualized experiences to every customer while maximizing efficiency behind the scenes. Applications include:

  • Personalized journeys: AI can leverage purchase histories to recommend destinations, travel packages or flights that align with individual preferences. Restaurant services can surface offers from local spots and highlight top-rated properties that offer the exact ambience or cuisine that a diner is looking for.

  • Seasonal demand predictions: By analyzing historical usage and purchase data, AI algorithms can help determine peak pricing periods, blackout dates, hours of operation, staffing levels and route schedules.

AI in healthcare and wellness

Already, 94% of healthcare organizations use AI or ML in some form, according to research from Morgan Stanley, and there are dozens of potential applications. Most automate routines and administrative work so that human caregivers can focus on diagnosis, treatment and connection with patients. Top healthcare uses for AI include:

  • Synthesizing research: AI tools can scan medical literature and surface the latest best practices and clinical trials for care providers to consider.

  • Interpreting scans and test results: AI can process imaging scans quickly and spot anomalies, and can combine test findings with historic data patterns to suggest potential diagnoses.

  • Proactive monitoring and recommendations: Wearable devices that monitor health indicators such as blood pressure can connect with AI tools that process the live data and make timely recommendations about diet, sleep, exercise and medications.

AI in design and technology development

Programmers, engineers and designers can leverage AI to automate routine tasks and run tests at scale, allowing them to quickly optimize digital assets. Uses include:

  • A/B testing: AI tools can track performance of multiple variations of website pages or app experiences and fine-tune elements based on live results, enabling businesses to run test cycles more quickly.

  • Error checking: AI tools can scan code, spot defects and predict incompatibilities with existing systems.

  • Automation of routines: Whether they’re looking to resolve coding errors or resize batches of images, digital creatives and programmers can outsource manual tasks to AI. Automation can even be applied to the task of building a static website with just a few initial design and content inputs.

AI in finance, legal and professional services

Business and legal offices stand to make huge gains in productivity thanks to AI. Machines can be tasked with many administrative details, while predictive modeling can guide forecasting. Four in five CFOs predict they’ll increase spending on AI through 2024, according to Gartner, which found that two-thirds of finance leaders believe autonomous AI will perform their functions within this decade. Among the ways AI can boost back-office efficiencies:

  • Fraud detection and security: Using pattern detection to spot anomalies, AI-powered tools can flag potentially fraudulent transactions, scam insurance claims and data breaches. IBM found that AI helps speed containment of data breaches by as many as 100 days.

  • Automation of templated documents, filings and reports: AI can assemble text and charts for reports and pro forma documents, and generate insurance claims paperwork. Just don’t expect AI to write complex briefs; attorneys who tried found that ChatGPT “hallucinated” imaginary cases to support its argument.

  • Real-time quotes and pricing: Insurance policies and property rental contracts can adapt to changing conditions, such as a surge in market demand or changes in customers’ usage.

  • Forecasting: Using past transactions and data about current conditions, AI can forecast trends and predict budget needs and earnings.

AI in manufacturing

“Smart” manufacturing is widespread already, and AI is poised to make even more gains in efficiency in the coming years as robots, “internet of things”-equipped machinery and digital tools collaborate to optimize production. Advances include:

  • Robots and cobots: Robotic machinery equipped with AI-powered computational strength can handle repetitive, rules-based tasks. Cobots, or collaborative robots, work alongside humans, with the capability to sense motion and avoid obstacles as well as complete physically-taxing chores.

  • Predictive maintenance: Machinery equipped with sensors can transmit its operational status, which AI-powered tools can monitor for signs of wear and tear. Predictive forecasting can help manufacturers budget for upgrades, repairs and replacements.

  • Quality control: Sensors can monitor manufacturing output and flag inconsistencies and defects.

  • Sustainable supply chain: Consumer demand is growing for sustainable products, and AI can help companies meet expectations by monitoring carbon emissions and wastewater usage and surfacing high-performing sustainable suppliers around the globe.

  • Demand forecasting: Based on prior purchase patterns and current economic conditions, AI models can predict when manufacturers need to step up production, helping them align staffing and materials costs with anticipated orders.

AI in logistics and transportation

AI’s ability to analyze the movement of goods around the globe, sense patterns and predict demand can improve efficiency by 15% and service levels by 65%, McKinsey found. AI helps companies streamline operations on several fronts:

  • 24/7 automation: Robots and autonomous forklifts can keep warehouse operations running around the clock. On the roads, companies are experimenting with self-driving technology for freight trucking to help keep goods moving overnight.

  • Staffing and operational forecasting: AI can forecast peak periods of demand so that transportation and logistics companies can staff and supply warehouses appropriately, aligning expenditures with anticipated income.

  • Reducing environmental impact: Using AI, transport and logistics companies can analyze trip data and optimize routes to reduce miles traveled and overall carbon emissions.

AI in entertainment, sports and culture

As the recent Hollywood writers’ strike showed, there are unresolved ethical questions about the use of AI to generate scripts, songs and visuals—especially when mimicking well-known artists’ existing work. But entertainment and sports brands are already putting AI to use in less controversial ways to better serve their audiences and professionals. Among them:

  • Bespoke experiences for fans: In response to a query, AI can parse, select and assemble highlight reels showing a season’s worth of standout touchdowns or ovation-worthy performances, giving audiences a tailored view on demand.

  • Training and performance monitoring: Using “smart” sensors in athletic equipment and apparel in tandem with health monitors, AI can help teams train better and prevent injuries.

  • Enhanced officiating: Major League Baseball is experimenting with using AI to help call balls and strikes based on pattern recognition. In the future, officials will be able to not only consult instant replay, but also consult instant AI analysis about whether a play was fair or foul.

  • Production automation: Video and audio editing tasks and engineering of soundtrack scores can be performed and optimized by AI, cutting production time. Automated translation can draft subtitles for videos and films.

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