Discussing AI is akin to discussing the flavors of ice cream nowadays. While many are familiar with the term, only some truly grasp its intricacies or the various forms it can take.
In this easy guide, we aim to shed light on the different types of AI technologies. Although AI continues to evolve rapidly, understanding these seven primary types can aid in distinguishing reality from fiction and enable individuals to formulate well-informed predictions about its future trajectory.
AI has already revolutionized website development, marketing, and numerous other business sectors.
However, its full potential remains untapped. “Given the exponential growth of this technology over the past six months, I can only anticipate an even swifter pace in the next six months,” remarks Yaara Asaf, Wix’s Head of Product. “We’re endeavoring to harness the benefits of technology as swiftly as possible.”
A. 7 Types of AI Technologies
Below are the seven different types of Artificial Intelligence (AI) technologies that are rapidly becoming a part of our everyday lives:
- Artificial Narrow Intelligence (Narrow AI or Weak AI)
Artificial Narrow Intelligence, or Narrow AI, forms the foundation of today’s AI technology. It encompasses AI systems capable of performing specific, predefined tasks. For instance, Siri on your iPhone can respond to voice commands and inquiries within a limited scope of functions.
When asked to provide directions to the airport, Siri utilizes AI algorithms to analyze map data and current traffic conditions, offering the optimal route. However, deviating from conventional topics often results in Siri’s response, “I’m not sure I understand.”
Narrow AI relies on extensive reference data to develop its intelligence but lacks the ability to learn, generalize, or apply this knowledge to new tasks.
Although modern AI tools have significantly advanced over the past five years, revolutionizing various domains such as website development and manufacturing, they remain rooted in narrow AI.
- Artificial General Intelligence (General AI or Strong AI)
Artificial General Intelligence (AGI) aims to replicate human decision-making processes, incorporating elements of logic, emotion, and learning.
Initially anticipated to be prevalent by the turn of the 21st century, AGI development has proven challenging due to the complexity of human intelligence.
Researchers have devised tests, such as the Turing test, to assess the AGI capabilities of AI systems. While some generative AI models, like ChatGPT, show promising results, achieving AGI remains elusive.
Although AI can excel in certain tasks, such as generating digital ads, it still struggles with mundane but intricate activities, like navigating an unfamiliar kitchen to make coffee.
- Artificial Super Intelligence (Super AI)
Artificial Super Intelligence, or Super AI, exists primarily in science fiction and portrays AI systems surpassing human intelligence and consciousness.
While the notion of AI-dominated dystopias may seem far-fetched, some researchers warn of the potential risks associated with achieving AGI.
Once AI technology reaches the AGI threshold, proponents argue that AI systems could rapidly learn, adapt, and refine their capabilities, potentially surpassing human proficiency.
However, the development of Super AI remains speculative, and debates surrounding its feasibility and ethical implications persist.
- Reactive AI
Reactive AI, also known as reactional AI, operates within specific parameters without referencing past interactions or outcomes. The efficacy of reactive AI relies solely on the quality of the data used for its training, as this data serves as the algorithm’s sole source of knowledge.
Despite its narrow focus, reactive AI can outperform humans in certain tasks due to its rapid processing capabilities. Notable examples include IBM’s Deep Blue, which defeated a chess champion, and Watson, which triumphed on the TV game show “Jeopardy!”.
Today, many routine tasks rely on reactive AI for basic decision-making tasks based on pattern recognition, such as automated spam filtering in email systems, credit scoring in finance, and simple product recommendations in e-commerce.
- Limited Memory AI
Limited Memory AI utilizes stored data to inform its current behavior, resulting in more sophisticated performance compared to reactive AI.
It can process sequences of input and react accordingly, incorporating the results of its interactions as new training data to refine its actions over time.
This type of AI enhances digital experiences by leveraging past interaction data to predict users’ preferences and behaviors in real time. It dynamically selects appropriate content and offers personalized recommendations, improving user engagement.
Limited Memory AI is being tested in cutting-edge applications such as self-driving cars, where it processes sensor data to recognize and respond to objects like traffic lights and pedestrians.
However, challenges remain, including the need for vast datasets to train the AI and the potential for unforeseen obstacles to disrupt its performance.
- Theory of Mind AI
Theory of Mind AI aims to mimic human-like understanding by recognizing that other beings possess thoughts and emotions that influence their actions.
Although this level of AI awareness is still theoretical, achieving it would enable AI to interact with others in a more empathetic and context-aware manner.
- Self-aware AI
Self-aware AI represents a hypothetical form of AI that can recognize its own mental states, emotions, and memories.
In addition, it would possess emotional intelligence to interpret and infer the internal states and motivations of others.
While self-aware AI remains a theoretical concept, its realization would mark a significant advancement in AI capabilities.
B. Applications of AI Technologies
AI in Retail and eCommerce: Revolutionizing Shopping Experiences
For years, both traditional stores and eCommerce platforms have embraced intelligent algorithms to enhance the overall shopping journey.
As AI continues to evolve, merchants are increasingly eager to leverage these new technologies to stay ahead of the competition.
A survey by Honeywell reveals that six in ten retailers worldwide are either currently using AI or have plans to adopt it in the near future.
Let’s explore some primary applications of AI in retail and eCommerce:
1. Personalization
Personalization lies at the heart of AI-driven shopping experiences. Limited memory AI algorithms can analyze digital interactions in real-time, allowing retailers to predict which products are most relevant to recommend to individual shoppers.
This level of personalization creates a tailored shopping experience akin to having a personal shopper.
By embedding JavaScript in website code, retailers can deliver personalized product recommendations that adapt to each shopper’s behavior in real time.
Tools and Software:
- Shopify: Utilizes AI-powered recommendation engines to suggest products based on browsing history and purchase behavior.
- Adobe Target: Offers AI-driven personalization features to optimize content and product recommendations for each visitor.
2. Inventory Management
AI plays a crucial role in optimizing retailers’ inventory management processes. By analyzing purchasing patterns and historical sales data, AI algorithms can accurately predict demand and recommend when to place orders with manufacturers.
This minimizes stockouts and overstock situations, ensuring that retailers maintain optimal inventory level in order to meet the customer demand.
Tools and Software:
- Oracle Retail: Provides AI-driven inventory optimization solutions to help retailers forecast demand and manage inventory efficiently.
- Blue Yonder (formerly JDA Software): Offers AI-powered demand forecasting and replenishment solutions to optimize inventory levels and reduce carrying costs.
3. Customer Service
AI-powered customer service solutions enhance the efficiency and effectiveness of online support channels.
Reactive AI algorithms can power order lookups to provide real-time updates on eCommerce shipments, while AI applications using natural language processing (NLP) can handle more complex customer inquiries via chatbots, email, and social messaging apps.
These AI-driven solutions meet consumer expectations for swift response times and personalized support.
Tools and Software:
- Zendesk: Employs AI-powered chatbots and email automation to simplify customer service operations and improve response times.
- Intercom: Offers AI-driven conversational support tools, including chatbots and self-service options, to provide personalized assistance to online shoppers.
AI in Travel, Restaurants, and Hospitality: Enhancing Customer Experiences
Exceptional service is the cornerstone of memorable travel and dining experiences, and AI plays a crucial role in powering administrative tools and digital interfaces to provide personalized experiences to every customer while optimizing operational efficiency.
Here are some key applications:
1. Personalized Journeys
AI enables personalized recommendations tailored to each individual’s preferences in the travel and dining sectors.
By leveraging purchase histories and user behavior data, AI algorithms can suggest destinations, travel packages, flights, or restaurant offers that align with a customer’s interests and past experiences.
For instance, travel platforms can recommend destinations based on previous bookings and preferences, while restaurant services can highlight local dining options that match a diner’s preferred cuisine or ambiance.
Tools and Software:
- Tripadvisor: Utilizes AI-driven recommendation engines to suggest personalized travel experiences, accommodations, and dining options based on user preferences and past reviews.
- OpenTable: Employs AI-powered restaurant discovery features to recommend dining establishments based on user preferences, location, and cuisine preferences.
2. Seasonal Demand Predictions
AI algorithms analyze historical usage and purchase data to predict seasonal demand patterns in the travel, restaurant, and hospitality industries.
By analyzing trends, AI can help businesses determine peak pricing periods, blackout dates, optimal hours of operation, staffing levels, and route schedules.
This enables businesses to efficiently allocate resources and optimize service offerings to meet customer demand during peak periods.
Tools and Software:
- Amadeus: Offers AI-driven demand forecasting solutions for hotels, airlines, and travel agencies to optimize pricing strategies and capacity planning.
- SevenRooms: Provides AI-powered reservation and seating management solutions for restaurants, enabling them to predict demand, optimize table turnover, and improve guest experiences.
AI in Healthcare and Wellness: Revolutionizing Patient Care
The integration of AI and machine learning technologies into healthcare is rapidly transforming patient care, with 94% of healthcare organizations already utilizing AI or ML in various capacities, according to research from Morgan Stanley.
These advanced technologies streamline routine tasks and administrative work, allowing healthcare professionals to allocate more time and focus on diagnosis, treatment, and patient interaction. Let’s explore some of the top applications of AI in healthcare:
1. Synthesizing Research
AI tools play a crucial role in synthesizing vast amounts of medical literature and research data to provide healthcare providers with the latest best practices, clinical trials, and medical insights.
By scanning and analyzing medical literature, AI algorithms can identify relevant information and present it in a digestible format, enabling healthcare professionals to make informed decisions about patient care or treatment plans.
Tools and Software:
- IBM Watson for Drug Discovery: Leverages AI to analyze biomedical literature, patents, and clinical trial data to accelerate drug discovery and development.
- MetaMind: Offers AI-powered research synthesis tools to extract key insights from medical literature and scientific papers, aiding healthcare professionals stay updated with the latest advancements.
2. Interpreting Scans and Test Results
AI algorithms are competent to process and interpret medical scans, such as X-rays, MRIs, and CT scans, with remarkable speed and accuracy.
By analyzing imaging data, AI can detect anomalies and abnormalities that could indicate underlying health conditions.
Additionally, AI can combine test findings with historical data patterns to help the healthcare providers in making accurate diagnoses and treatment recommendations.
Tools and Software:
- Aidoc: Utilizes AI algorithms to analyze medical imaging scans and highlight abnormalities for radiologists, facilitating faster and more accurate diagnosis.
- Zebra Medical Vision: Offers AI-powered imaging analytics solutions to assist healthcare providers in detecting various medical conditions from imaging scans, improving diagnostic accuracy and efficiency.
3. Proactive Monitoring and Recommendations
Wearable devices equipped with relevant sensors, such as smartwatches and fitness trackers, enable continuous monitoring of health indicators, including heart rate, blood pressure, and sleep patterns.
AI algorithms can analyze real-time data collected from wearable devices and provide personalized recommendations for diet, exercise, sleep, and medication management.
By leveraging AI-driven insights, individuals can take proactive steps to maintain their health and well-being.
Tools and Software:
- Fitbit: Integrates AI algorithms to analyze user data from wearable devices and provide personalized health and fitness recommendations.
- Ada Health: Offers AI-driven symptom assessment tools that analyze user-reported symptoms and provide personalized health recommendations and potential diagnoses.
AI in Design and Technology Development: Enhancing Efficiency and Innovation
In the realm of design and technology development, AI offers a plethora of opportunities for programmers, engineers, and designers to streamline workflows, automate repetitive tasks, and accelerate innovation.
By harnessing the power of AI, professionals may optimize digital assets and drive continuous improvement in their projects. Let’s delve into some of the key applications of AI in this domain:
1. A/B Testing
AI-powered tools facilitate efficient A/B testing by tracking the performance of multiple variations of website pages or app experiences in real time.
By analyzing user interactions and engagement metrics, AI algorithms can identify which design elements or features resonate most with users and dynamically optimize digital assets accordingly.
This iterative approach enables businesses to iterate and refine their products or services more rapidly, ultimately enhancing user satisfaction and conversion rates.
Tools and Software:
- Optimizely: Offers AI-driven experimentation and optimization tools for A/B testing website and app designs, allowing businesses to deliver personalized experiences and drive better outcomes.
2. Error Checking
AI-powered tools play a vital role in error-checking and quality assurance processes by scanning code, identifying defects, and predicting potential incompatibilities with existing systems.
By leveraging AI-driven static code analysis and dynamic testing techniques, developers can detect and resolve coding errors more efficiently, ensuring the reliability and stability of software applications.
Tools and Software:
- SonarQube: Employs AI-driven code quality and security analysis tools to identify code smells, bugs, and vulnerabilities in software projects, enabling developers to maintain code quality standards and mitigate risks.
- DeepCode: Utilizes machine learning algorithms to analyze code patterns and detect potential errors or performance issues, offering actionable insights to improve code quality and maintainability.
3. Automation of Routines
AI enables the automation of repeating tasks in design and technology development, allowing professionals to focus on more creative and strategic aspects of the projects.
Whether it’s resolving coding errors, resizing batches of images, or building static websites, AI-driven automation tools streamline repetitive processes and enhance productivity.
For instance, AI-powered website builders can generate static websites with minimal design and content inputs, significantly reducing the time and effort required to launch a new website.
Tools and Software:
- Wix ADI (Artificial Design Intelligence): Leverages AI algorithms to automatically generate personalized website designs based on user preferences and industry standards, enabling users to create professional-looking websites with minimal effort.
- Adobe Sensei: Provides AI-powered automation features across the Adobe Creative Cloud suite, allowing designers and creative professionals to streamline workflows, automate repetitive tasks, and unlock new creative possibilities.
Summarizing The Types of AI
There are seven types of artificial intelligence, each having capabilities and applications that are quite different from others.
For example, you have the simple, more limited AI with instructions to follow and the advanced systems that can reflect human reasoning.
Knowing these classifications gives one an insight into the evolution of AI and how it affects daily life, businesses, and even the future of technology.
Each type is explained in a straightforward, accessible manner, helping to make the world of AI less intimidating for those beginning to explore it.
Which types do you think have the most favorable future?
Suggested read: The Rise of Ecommerce Chatbots: Enhancing Customer Experience with AI
Common FAQs on The Types of AI Technologies
What is the difference between Reactive AI and Limited Memory AI?
Reactive AI is designed to only do one task concerning existing data that does not have the capacity to hold past experiences. On the other hand, is the Limited Memory AI, which can hold past experiences for a little while but can make decisions from historical data in addition to real-time info.
Does AI really have types that are available today?
Yes, the majority of the AI applications that exist today are based on the kind called Artificial Narrow Intelligence (ANI) or those designed to do a narrow set of things, such as facial recognition, language translation, or predictive text. More advanced kinds, such as Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI), currently exist only in theory.
Why is the Theory of Mind AI important?
Theory of Mind AI is important as it would inform and understand emotions, beliefs, and other mental states, thereby making it interact more naturally and effectively with humans. Yet, this type of AI remains a full-blown reality, but it surely shows great promise in areas such as healthcare, education, and customer service.
What is special about Artificial Superintelligence (ASI)?
ASI is a concept of an AI system that is meant to bring more intelligent performance beyond human beings. It would essentially perform some specific tasks and is filled with more creativity or emotional intelligence and problem-solving skills beyond human existence. However, this is still a theoretical existence that poses the ethical question of its control and safety.
