Concept to Functional - Understanding Different Types Of AI!

Explore the different types of AI along with real-life examples and applications to get the gist of each concept.

The concept of AI has always remained a fascinating one. The power to create a machine or code that is autonomous has been a decades-old conversation that has become a reality today. However, the spectrum of achieving it is so large that now there are different types of AI. Also, if we assess the rapid growth of AI which is expected to grow with a CAGR of 27.67% between 2025-2030, it becomes essential for stakeholders too, to understand the difference between functional and conceptual AIs from an implementation perspective.

Exploring the different types of artificial intelligence, in this editorial, we have explained everything in detail. For better clarity, we have defined each type in layman's terms and provided real-life examples to help you understand the concepts.

So, let’s begin.

Types of Artificial Intelligence with Examples: Functionality-Based

With different forms of AI available or at least materialized through theory (basically in concept), it is essential to understand which AIs are functional. Stating this, these AIs are part of our world and are getting integrated into our IT infrastructure to achieve automation and better performance.

Narrow AI

Often referred to as “Weak AI” or “Narrow AI,” artificial narrow intelligence (ANI) is great for performing a single task. Unlike the different types of AI, this one is specific in nature and doesn’t try to mimic human intelligence. Instead, it is completely focused on a single task. The majority of AI-integrated systems today make use of this type of AI.

Examples of Artificial Narrow Intelligence:

• Facial Recognition System: This system is used by smartphone companies and other known businesses like Google and Facebook to detect people in images.
• Chatbots: Some of the most common examples of AI chatbots or virtual assistants that use ANI are Siri, Google Assistant, Alexa, etc.
• Self-driving Vehicles: Tesla Cars are one of the most popular examples of ANI. Furthermore, this particular technology is also being used in drones, boats, factory robots, etc.
• Predictive Maintenance: ANI is used to predict the systems that can fail or which might prolong. Some common examples are building management, manufacturing monitoring, oil analysis, etc.
• Recommendation engine: The common examples of this application are streaming platforms like Netflix, Amazon Prime, and Hulu.

Limitations of Artificial Narrow Intelligence

• Narrow AI can only perform simple problems and can not be included in complex reasoning.
• These systems require large datasets to perform accurately.
• It can be difficult to interpret narrow AI.

Reactive AI

Reactive AI is a subset of artificial intelligence. It reacts and responds to inputs from the sensors and takes actions based on them. Unlike other types of artificial intelligence, it uses rule-based principles. 

Reactive AI doesn’t have any memory. So, it can not learn from its past experiences and only responds as it has been trained. Some of the different approaches its AI model takes to learn are:

• Condition-Action Rules: This approach works on If-Else conditions. If the conditions are fulfilled, then the step is taken; otherwise, it is reverted.
• Finite State Machines: This approach works on determining the state of the agent. The state of the agent changes at each level to determine the best action possible whenever a condition is fulfilled.
• Fuzzy Approaches: It is the combination of the above two approaches. As per this, condition, action, and states are nothing but boolean quantities of “0 or 1” or “Yes or No.”
• Connectionist Approaches: In this, multiple units are connected in an artificial neural network. Each unit is subjected to an abstract activity that induces a behavior into another unit. The unit that induces an activity is known as an activity inducer. 

Examples of Reactive AI:

A common example that is often given in the realm of reactive AI is IBM’s Deep Blue. This system defeated the chess grandmaster Gary Kasparov. The team behind the development of Deep Blue doubled the number of chess chips used for enhancement. 

This gave the reactive AI the capability to predict 100 to 200 million moves per single move made by Gary Kasparov. This became a defining moment of AI and is often included within the history of AI development with golden words.

Below is the famous video where Gary Kasparov resigns from IBM’s Deep Blue during the gameplay:

Other examples are:

• AlphaGO, a computer program developed to play the board game GO developed by DeepMind (a company backed by Google)
• Self-driving cars by Tesla
• Spam filters are used for detecting spam emails.

Limited Memory AI

Limited memory AI is an AI type that stores certain previous experiences and data in its memory. Based on that, it derives better insights, predictions, and actions. A limited memory AI uses ML technology to learn and evolve complex tasks. Below, we have mentioned the different approaches limited memory AI takes to train its model.

• Long Short-term Memory: This approach is excellent in terms of creating an artificial neural network. Entire datasets of information are fed to the system, enabling the trained model to predict what comes next.
• Reinforcement Learning: In this method, an AI agent interacts with a problem to train the model. For every right decision, it receives a reward. Contrarily, the AI is penalized for every wrong decision, eventually training it to figure out the correct decision by itself. Since it has limited memory, the AI model has limited capability to remember past experiences.
• E-GAN (Evolutionary Generative Adversarial Network): The methodology of training works on the principle of mutation. This technique helps an AI model to evolve with every cycle and become better with time.

Examples of Limited Memory AI:

• Autonomous Vehicles (Tesla Autopilot, Waymo Driver, etc.)
• Recommendation Engines (Netflix Recommendation, Amazon Product Recommendation, etc.)
• Prediction Models (Weather Forecasting, Financial Market Prediction, etc.)

Types of Artificial Intelligence with Examples: In Concept

The AI models mentioned below are concepts that researchers are constantly striving hard to develop. Each of these AI types mimics human intelligence in some shape or form, so achieving them in reality would be the breakthrough of the millennia. So, let’s take a dive into the sections below to understand them.

Self-Aware AI

"Philosophers haven't really settled on a definition of consciousness yet, but if we mean self-awareness and these kinds of things, I think there's a possibility that AI could one day be," says the CEO of DeepMind (backed by Google) as stated in a Futurism article.

Out of all the different types of AI, a Self-aware AI would be aware of its own existence. But, if we see through the filters of reality, it is a far-fetched hypothetical concept that isn’t possible with what we have today. 

On the contrary, there are research projects by OpenAI (the creators of ChatGPT), such as the AI project in Consciousness and Self Awareness Lab at the University of Sussex, Project Alexandria by Allen Institute for Artificial Intelligence, etc., that are working to achieve it. To create Self-Aware AI, some of the key components that would be used are:

• Consciousness: The machine needs to be aware of its own consciousness. Being conscious means having the capability to introspect queries, responses, thoughts, beliefs, etc., and derive an answer from them. 
• Self-Identity: The striking feature of human consciousness is to self-identify itself. If we look at our own reflection, we are able to understand that the person standing is me. In fact, even if we are standing with a bunch of people, the story is the same.
• Capability of Expressing Emotions and Motivation: Showing and understanding emotions and feeling motivated to do something is another key aspect that makes us human. The capability to feel emotions is what often distinguishes us from machines. It is important to take this into consideration because, the majority of the time, humans take actions or reach an understanding through emotions. Also, often these emotions are the motivation that compels us to achieve a task.

Potential Challenges With Self-Aware AI:

• It could lose control
• It can have a superiority complex
• It could malfunction and become dangerous
• It can be used for malicious purposes as it can be manipulated just like humans

Artificial General Intelligence

Artificial general intelligence is a hypothetical concept that aims to achieve the consciousness level of the human mind. In terms of sentience and capability, it is much closer to self-aware AI. 

Unlike the different categories of AI available, this type of artificial intelligence won’t be dependent on rule-based principles. Instead, it can learn from its own environment and design its own way of understanding, just like humans. 

Right now, this AI type doesn’t exist. However, we have weaker AI-based tools like Watson, ChatGPT, Bing AI, etc., that fractionally mimic the capability to handle complicated tasks.

Benefits of General Artificial Intelligence

• Capability to solve complex problems without human intervention.
• High level of automation across industries.
• It can help humans by providing answers to complex queries like tackling climate change, finding a cure for cancer, etc.

Artificial Superintelligence

Artificial Superintelligence is on much higher levels of artificial intelligence achieved. Nick Bostrom of the University of Oxford defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.” As of now, it is a hypothetical concept. However, it has been an inspiration for multiple sci-fi movies. 

Most Popular AI Superintelligence Examples:

• Skynet (Terminator)
• Ultron (Marvel)
• HAL 9000 (2001: Space Odessey)

Theory of Mind AI

Theory of Mind AI follows the principle of the Theory of Mind, which is used in psychology itself. As per psychology, the theory of mind deals with attributing different mental states such as thoughts, beliefs, intent, perception, etc. 

It is already in development and has huge potential to transform the way we interact with computers. Various organizations and institutions like MIT, Stanford, Google, Facebook, OpenAI, etc., are trying to develop one using different forms of AI or, to be precise, subfields, such as natural language processing, machine learning, and computer vision. However, even if it gets developed, it may not be available for widespread use, as of now.

The intention behind the creation of this AI is to understand the meaning behind human language and not simply mimic it. But, the development of the Theory of Mind is still full of challenges because of the following constraints:

• Data: It would require tons of data to learn, which can be difficult and expensive to collect.
• Complexity: Creating human-like behavior via an algorithm is a complex task and isn’t possible as of today.
• Interpretability: For TOM (Theory of Mind) AI to truly behave like a human, it needs to understand humans at a subconscious level. This becomes almost impossible in today’s time, considering there isn’t any technology available to comprehend this task.

Potential Applications of Theory of Mind

• Virtual Assistants: It can be used for creating virtual assistants that are as intuitive as a human. These virtual assistants will surpass the capabilities of ChatGPT by tenfold. The application can also be great for psychological support.
• Robotic Interactions: It will help in creating robots that will have almost human-like interaction capabilities.
• Remote Education via AI: The only thing in which remote education via presentations lags behind is access to an intuitive teacher. However, an AI-based one can be tweaked or updated to make it more interesting.

Subfields of Artificial Intelligence

Aside from the main types of AI, there are several subfields that are actively used to develop AI-based products and services. So, let’s learn about them.

Artificial Robotic Intelligence

AI in robotics was a pipedream that became a reality. The combination of AI and robotics creates artificial robotic intelligence. This discipline of AI is used to create autonomous robots integrating advanced algorithms and machine learning that can take up repetitive and labor-intensive tasks.

AI-enabled robots can analyze lots of data, recognize patterns, and even improve their processing over time. In fact, they are even capable of interpreting and understanding visual elements of the surroundings using Computer Vision.

Examples of Artificial Robotic Intelligence:

• The da Vinci Surgical System
• Amazon Astro
• iRobot Roomba

Computer Vision

Computer vision is a field of artificial intelligence that is used to enable computers to “see” and interpret images. These systems are developed by feeding the AI model with tonnes of images of different animals, articles, faces, etc. To identify and classify objects, the technology uses both supervised and unsupervised learning. Based on it, the computer vision AI model is capable of making decisions or taking any action.

Real-Life Examples of Computer Vision:

• Cruise Origin
• Apple Face ID
• CADx Systems
• Instagram
• Amazon Go

Expert Systems

Expert systems are created to mimic the decision-making ability of humans. It uses AI to solve complicated problems by reasoning through knowledge or symbolic forms over which it is trained by a human trainer. The core aim of an expert system is to capture and replicate human expertise.

Further on, they are provided with structured rules around the skill, decision trees, or semantic networks in the knowledge base. Once done, the system itself applies its knowledge to solve problems and, with the help of a UI facilitates interaction for input and output.

Real-Life Examples of Expert Systems:

• Mycin
• PUFF
• XCON (R1)

Generative AI

To give you an initial idea, ChatGPT is a generative AI. And, if for some reason you haven’t used it, then it is a type of AI that can be used to create multiple forms of content. For example, text, images, music, and even code. In fact, it is often the first choice today if any vendor chooses to integrate AI into an app, software, or web app.

To achieve a generative AI model, complex algorithms along with massive datasets are fed to the system for generating new content. Plus, it also uses neural networks like Generative Adversarial Networks (GANs) to generate synthetic data for training and Variational Autoencoders (VAEs) to create a more structured and interpretable approach for data generation for effective training.

Real-Life Examples of Generative AI:’

• Bard
• DALL-E 2
• AIVA
• Github Copilot
• Midjourney

Multimodal AI

Multimodal AI is capable of processing and understanding information from a plethora of sources simultaneously. Some of the sources they can read are text, images, audio, and sensor data. 

These are created by feeding a lot of data types to the AI model and aligning it with different data modalities. To fuse the data they use techniques like Early Fusion, Late Fusion, and Intermediate Fusion.

Real-Life Examples of Multimodal AI:

• Siri
• Google Assistant
• Alexa
• Twitter
• YouTube

Deep Learning

Deep Learning is a subfield of machine learning which is a subfield of AI. It uses artificial neural networks of multiple layers that enable it to learn complex patterns in data. Neural networks are inspired by the structuring and the functioning of the human brain hence called neural networks. 

A deep learning model is created by providing the initial data that can comprise any format. A hidden layer then performs complex computations on the data to extract features and learn to create the final model.

Real-Life Examples of Deep Learning:

• Google Photos
• Google Translate
• Netflix
• Amazon Alexa
• ChatGPT

Natural Language Processing 

Natural language processing is a field of AI that enables a computing device to understand, interpret, and generate human responses in text. 

It works by breaking down words and sentences into smaller units called tokens. Then, part of speech tagging is conducted over it to figure out nouns, verbs, adjectives, etc. Now, these are identified and classified using a technique called Named Entity Recognition, and the emotional tone of the sentence is figured out through sentiment analysis.

After the previous process, syntax, semantics, and discourse of the content are analyzed. Based on this, the text is summarized and translated, and relevant text is generated.

Real-Life Examples of NLP:

• Google Translate
• Siri
• ChatGPT
• Grammarly
• Semantria

Large Language Models (LLM)

Large Language Model is an AI with the capability to understand, generate, and manipulate human language. These are built over deep learning architectures and transformer models. 

The dataset over which LLMs are trained is enormous, and with the help of transformer architectures, the AI model is able to form relationships between words, even those that are far apart. When LLM models mature, they are capable of generating text, translating languages, writing a variety of content, and, most importantly, answering all your questions.

Real-Life Examples of LLM:

• GPT-4 by OpenAI
• Bard by Google
• BERT by Google

Strategic Considerations for Adoption of AI and Its Subfields

Aside from understanding the different kinds of AI, it is essential for established companies and startups to consider strategic considerations. This would enable a smooth transition to an AI infrastructure and would help in the following ways:

• Automation of work
• Optimization of workflow
• Enhancement of  operational efficiency
• Improvement in customer experience.
• Developing innovative business models, etc.

Strategic considerations for businesses involve the integration of AI for impact (in multiple ways). They could help a company potentially align business objectives and address implementation challenges. 

Below, we have mentioned some strategic considerations to guide businesses in their AI adoption journey.

1. Identify High-Impact Use Cases

It is essential to focus on the potential benefits of artificial intelligence and what it can do for your company. This could range from automating tasks to enabling advanced analytics for decision-making.

For example, Salesforce uses AI with its CRM platform, ‘Einstein,’ to analyze sales data, predict customer behaviors, and even automate repetitive tasks using the tool. And, it has significantly improved the efficiency of sales and customer service.

2. Data Quality and Accessibility

The quality of the data fed remains a significant barrier in terms of application.

Almost all types of AI models require high-quality and relevant data, whether B2B or B2C. This data is known as labeled data, which is used to train AI models. Based on this data, different types of AI make decisions and provide insights. 

For example, Siemens uses its AI model to analyze data from their IoT platform ‘MindSphere.’ By doing this, they optimize operations and maintenance and reduce energy consumption for their clients.

3. Skills and Talent Acquisition

The history of AI goes back centuries if we start to include the literature that mentions similar entities. However, there are currently several artificial intelligence companies, and one needs to select the best. 

Investing in AI talent or partnering with providers who understand its ropes is essential. This is because AI development and deployment require specialized skills. In fact, Gartner predicts that by 2028, about 75% of Enterprise Software Engineers will be using AI code assistants.

A great example is the partnership between ‘IBM’ and ‘Box.’ Box used Watson AI (an IBM product) to analyze and classify the documents stored in their tool. With this integration, they enhanced content management and collaboration for businesses.

4. Scalability and Integration

One of the most important aspects of any further IT development today is scalability. User data is expanding every year, so it becomes essential to upgrade to systems that are scalable in nature. This norm should be followed with AI solutions because most of these systems use customer data to provide insights, predictive analytics, etc.

For example, KONE is a company that specializes in elevators and escalators. They utilized IBM Watson’s AI to predict maintenance needs and optimize service schedules. 

5. Strategic Partnerships

There are several AI development companies out there that are using different types of AI models for different types of work. Therefore, it is important to share technology and expertise to grow further. Forming strategic partnerships with AI technology will help accelerate development and find solutions faster. 

For example, GE Healthcare partnered with NVIDIA to accelerate its processes. Processes like improving diagnostic accuracy, medical imaging data, and patient care.

6. Measuring ROIs

Implementing AI and checking its efficacy post-integration are two different things. It is important to establish clear metrics to measure its return on investment. 

This would help bridge gaps, focus on improvements, improve customer satisfaction, and boost revenue. In fact, PwC has decided to contribute up to $15.7 trillion by 2030 to improve productivity and personalization.

Challenges and Opportunities Associated with AI Adoption for Business Applications

AI adoption for businesses presents exciting opportunities and significant challenges. Let’s take a look at both:

Challenges

• Several types of AI technologies require massive amounts of high-quality labeled data (training data), which is not readily available.
• Finding and retaining talented professionals who can build and understand models is difficult and expensive.
• Implementing different types of AI models can be costly and doesn’t always present a clear picture of ROI.
• There are ethical AI considerations as AI systems are susceptible to bias and can lead to discriminatory or unfair outcomes.
• Introducing AI requires organizational changes and potential resistance from the employees.

Opportunities

• AI systems can help automate repetitive tasks, analyze vast amounts of data, and optimize processes.
• AI-powered chatbots can provide personalized recommendations and predictive maintenance.
• AI systems can help businesses develop new products and services, discover new markets, and help stay ahead of the competition.
• AI can analyze complex data to produce actionable insights and recommendations for better business decisions.
• Early adopters will gain a sufficient competitive edge to stay ahead and improve their operations and offerings.

Future Trends of AI 

The future of AI is fascinating. This is especially true with the latest addition to the AI realm, i.e., Generative AI. However, the future holds more, and several trends will see more light this year. Therefore, here are the trends to know about this year from a business perspective:

1. Hyper-Personalized Interactions

One of the key advantages of AI is the hyper-personalization it offers for consumers. This hyper-personalization involves analyzing buying patterns, competitor intelligence, and social media. 

Using different types of artificial intelligence, we can tailor offers, pricing of the productions, and even the communication style. In fact, here are some stats to showcase the rising interest in the hyper-personalization trend:

• Around 59% of companies were able to increase their market share by utilizing AI (Chatbots, Dynamic Territory Modeling, etc.), as opposed to 32% who shrank.
• About 63% of marketers claim that including personalization can improve conversion rates.
• Personalization can reduce acquisition costs by 50%.
• 80% of buyers are likely to do business with companies that personalize their experience.

2. Democratization of AI

AI won't be reserved for tech giants anymore. User-friendly, cloud-based AI tools will empower SMEs to leverage AI for tasks like predictive analytics, sales forecasting, and risk assessment. This will level the playing field and create new opportunities for innovative startups.

We have multiple open-source AI tools like TensorFlow, Keras, Scikit-learn, etc., that are catapulting the growth of AI systems. Furthermore, there are several user-friendly cloud-based AI tools that can empower SMEs. This will make AI services accessible to SMEs without actually adopting in-house AI systems for the infrastructure. 

The utilization of cloud-based AI systems will be done for tasks like predictive analytics, sales forecasting, and risk assessment. Further on, democratization will even level the playing field and create opportunities for startups with innovative ideas.

Here are some factors that show the increasing interest and imminent need for the democratization of AI:

• The AI market is expected to grow to $243.70 billion by the end of the year 2025.
• The rapid integration of AI, especially Generative AI, is expected to compound the CAGR by 29.0% between 2024-2028.
• As per a survey by Statista, around 50% of respondents reported using Generative AI with limits, while 70% responded with at-scale usage.

3. AI-Powered Collaboration and Negotiation

The most impressive thing about any AI system is that it can simplify and automate any workflow. AI will facilitate smoother collaboration between teams and across organizations. Several intelligent assistants are capable of managing schedules, translating languages, and summarizing complex documents. 

In fact, there are several AI-driven negotiation tools that can analyze data and suggest optimal strategies. These tools can ensure that the outcome is a win-win, saving time and resources. Some examples are Pactum, CatalystGo, Zoom.ai, etc.

4. The Rise of “Industry Clouds” with Embedded AI

Industry Clouds are specialized cloud computing platforms designed to cater to specific needs and challenges. They are different from traditional general-purpose cloud platforms in several ways:

• They offer focused functionalities with pre-built tools, applications, and tailored services.
• These cloud systems have deep knowledge and experience in the industries they serve.
• These cloud systems make use of industry-specific compliance and security standards. This helps businesses mitigate risks and manage sensitive data.
• Industry cloud systems can help in research and tailored development as per the industry’s requirements.

The industry-specific cloud platforms will help integrate AI capabilities directly to the end customer. This can help spread the power of AI that enables streamlined workflows, improved efficiency, and acceleration in overall research.

5. AI-Driven Talent Management and Reskilling

With AI taking up all the mundane, repetitive tasks and automating them, the shift will focus on human creativity, problem-solving, and strategic thinking. AI has the potential to manage talent, identify potential candidates, personalized training, and a lot more. 

The integration of AI in talent management will ensure that the companies recruit the right candidates for the right job. Furthermore, AI-based skilling will increase the understanding of concepts within candidates.

To further showcase the grip of this trend in the current market, here are some stats:

• In 2024, around 65.71% of companies tried recruiting data scientists, while 57.14% tried to recruit data engineers.
• A BCG publication states that 89% of executives believe Gen AI and AI skills are among the top three techs.
• In a survey by Gartner, 76% of HR (Human Resource) leaders said that if they don’t use Generative AI then they’ll be lagging behind in comparison to organizations who are adopting it.

6. Recommendations for Decision-Makers

Being a decision-maker can be overwhelming. The involvement of AI in our work is relatively very new. However, as a decision-maker, one needs to ensure that their decisions don’t hamper the company’s growth.

Therefore, here are some basic tips for successful AI adoption:

• Focus on specific use cases with a clear vision of potential ROI.
• Make investments toward data quality and infrastructure.
• Build a team with the necessary AI experience and expertise or partner with any AI development company.
• Be proactive while considering transparency in AI implementation.
• Communicate the benefits of using AI to the employees and how it will help them.
• Monitor and evaluate AI initiatives.

Regulatory Compliance and Ethics - AI Landscape

In this year, AI ethics and regulatory compliance will become increasingly important. AI technologies are affecting multiple aspects of our personal and professional lives. This rapid advancement and adoption of AI technologies prompt government bodies to make more comprehensive regulatory frameworks. Therefore, it is important for businesses to consider AI ethics and stay aligned with regulatory compliances.

Some attributes related to this aspect are:

• Different regions have adopted different approaches to AI regulation. For example, the European Union categorizes these types of AI systems according to their risk levels.
• Additional layers of AI-related regulations are being added in certain industries such as healthcare, finance, etc.
• Organizations like IEEE, OECD, and other national bodies have published guidelines for AI.
• There is a growing emphasis on developing and implementing AI audit frameworks.
• Data protection laws like GDPR in Europe, CCPA in California, etc., have set standards for AI systems for processing personal data, consent, transparency, and the right to explanation.
• Increasing demand for AI systems to be more explainable, enabling developers and regulators to understand AI models.
• There is a push towards international cooperation and harmonization of AI regulations.

A great example of this would be the AI-based compliance tool developed by Accenture. Banks can use this tool which can help automate the monitoring of transactions.

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Conclusion

From its inception, there have been several iterations in the types of AI. However, the things that fascinate people around the topic of AI are still hypothetical. Multiple companies like OpenAI, DeepMind, etc., are constantly trying to blur the boundaries between AI and human intelligence. And trying to implement these types of AIs for business applications, customer personalizations, behavior analysis, etc. However, it would be interesting to see artificial general intelligence, theory of Mind AI, etc., becoming a reality.