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Different Types of AI Thinking Models and Their Applications

Info Setronica July 15th, 2025

AI systems don’t all “think” the same way. Some are built to process language, others to recognize images, and others to play strategic games – each using completely different approaches under the hood.

Last time, we discussed how AI thinking models differ from classical ones. In this article, we’ll explore six major categories of AI thinking models that represent different approaches to creating intelligent systems. We’ll look at how each type works, what makes it unique, where it excels, and where it falls short.

1. Large language models (LLMs)

These are perhaps the most visible AI thinking models in our daily lives – systems that can generate human-like text, engage in conversations, write code, and reason about a wide range of topics.

🛠️ How they work: LLMs are trained on vast text collections from books, websites, and other sources. They learn patterns in language that allow them to predict what words should come next in a sequence. This simple-sounding objective leads to surprisingly sophisticated capabilities.

Strengths

Limitations

Can perform a wide range of language tasks without task-specific training
Can confidently present incorrect information (“hallucinations”)
Contain vast implicit knowledge absorbed from training data
Struggle with multistep logical reasoning
Can maintain coherence across long passages
Knowledge limited to training data available before their training cutoff date
Demonstrate capabilities not explicitly programmed, like basic reasoning
Limited in how much information they can consider at once

🧠 Examples: GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google), Llama 2 (Meta), and Mistral.

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Carrefour (European retailer) employs Mistral’s models to provide personalized shopping recommendations and customer service on their digital platforms.

Meta uses Llama 2 to analyze and organize vast amounts of internal research data, making it more accessible to teams across the company.

2. Reinforcement learning models

These models learn through trial and error, receiving feedback in the form of rewards or penalties based on their actions.

🛠️ How they work: Unlike models trained on static datasets, reinforcement learning models interact with environments – real or simulated – and improve their strategies over time based on the outcomes of their actions.

Strengths

Limitations

Can discover novel, sometimes counterintuitive solutions
Often require enormous numbers of attempts to learn effectively
Can improve through ongoing interaction
Performance heavily dependent on how rewards are defined
Can adjust to changing conditions and requirements
Often struggle to apply learning from one environment to another
Optimize directly for desired outcomes
Can get stuck in suboptimal strategies

Examples: AlphaGo and AlphaZero (DeepMind), OpenAI Five (played Dota 2), TD-Gammon (backgammon), and reinforcement learning systems for data center cooling optimization.

Siemens applies reinforcement learning principles from TD-Gammon to industrial control systems for more efficient factory operations.

Alibaba uses DeepMind-inspired reinforcement learning for logistics route optimization, reducing delivery times and costs.

3. Multimodal models

These versatile systems can process and generate multiple types of information – text, images, audio, and sometimes even video – allowing them to understand the world more holistically.

🛠️ How they work: Multimodal models combine specialized neural networks for different data types and learn to translate between these different forms of information.

Strengths

Limitations

Can analyze relationships between different types of information
More complex to train and deploy than single-modality models
Can accept one type of data and output another
Need diverse paired data across modalities
Better aligned with how humans process information across senses
Often require more computing resources
Can use one modality to disambiguate another
Capabilities often stronger in some modalities than others

🧠 Examples: DALL-E (OpenAI), Midjourney, Stable Diffusion, GPT-4V (Vision), Gemini, and Claude Opus.

Adobe employs DALL-E (OpenAI) to help their design teams rapidly prototype visual concepts for software interfaces and marketing materials.

Stability AI (creators of Stable Diffusion) partners with Getty Images to offer AI image generation that respects copyright and licensing.

4. Neural-symbolic models

These hybrid approaches combine the pattern-recognition strengths of neural networks with the logical precision of symbolic AI.

🛠️ How they work: Neural-symbolic models use neural networks to process raw data but incorporate explicit symbols and rules to handle logic, reasoning, and knowledge representation.

Strengths

Limitations

Often more transparent in their reasoning than pure neural approaches
Combining the two paradigms seamlessly is technically difficult
Can follow explicit logical constraints consistently
Symbolic components can be hard to scale to very complex domains
Can learn from less data when domain knowledge is available
Require expertise in both neural and symbolic approaches
Symbolic components can sometimes be formally verified for correctness
Often built for specific applications rather than general use

🧠Examples: DeepMind’s AlphaGeometry, IBM’s Neuro-Symbolic Concept Learner, MIT-IBM Watson AI Lab’s Neuro-Symbolic AI projects, and various domain-specific expert systems.

Bosch applies neural-symbolic approaches similar to DeepMind’s work to create more interpretable AI systems for industrial automation.

ServiceNow incorporates MIT-IBM Watson AI Lab’s neural-symbolic techniques to improve their IT service management with more transparent reasoning.

5. Bayesian models

These probabilistic models explicitly represent uncertainty and update their beliefs as new evidence arrives.

🛠️ How they work: Bayesian models use probability theory to represent knowledge and make predictions, systematically updating their confidence in different hypotheses as they find new information.

Strengths

Limitations

Explicitly model confidence levels in their predictions
Can be computationally expensive for complex problems
Update beliefs in a mathematically coherent way
Results can be sensitive to initial assumptions
Can incorporate domain expertise through prior distributions
Traditional implementations struggle with very high-dimensional problems
Often learn effectively from smaller datasets
Require significant expertise to implement properly

🧠 Examples: Spam filters, recommendation systems with uncertainty estimation, Bayesian neural networks, and probabilistic programming languages like Stan, PyMC, and Pyro.

Netflix applies Bayesian recommendation systems internally to predict which content they should license or develop based on viewer preferences.

Uber employs Bayesian neural networks to estimate arrival times, providing users with confidence intervals rather than point estimates.

6. Cognitive architectures

These comprehensive frameworks attempt to model human-like thinking more directly by incorporating multiple cognitive processes.

🛠️ How they work: Cognitive architectures integrate various subsystems – perception, attention, memory, reasoning, planning – into unified models of cognition.

Strengths

Limitations

Handle multiple aspects of cognition in a unified framework
Extremely complex to design, implement, and validate
Often based on psychological and neuroscience research
Can require significant computational resources
Help understand human cognition as well as build AI
More common in research than commercial products
Model both quick reactions and long-term learning
Difficult to evaluate objectively

🧠 Examples: ACT-R, Soar, CLARION, SIGMA, and ICARUS – all primarily used in cognitive science research and specialized applications.

Boeing uses Soar cognitive architecture to simulate pilot decision-making for flight system development and safety testing.

Honeywell uses ICARUS to improve control system interfaces by modeling operator attention and decision processes.

Real-world applications of AI thinking models

Let’s look at how specific AI approaches are creating tangible value across different industries today.

1. Healthcare diagnostics and treatment planning

❓ Problem: Medical professionals face overwhelming amounts of patient data and must stay current with rapidly evolving research while making life-critical decisions.

💡 AI solutions in action:

  • RadNet uses convolutional neural networks to analyze mammograms and detect breast cancer at early stages, sometimes outperforming human radiologists in initial screenings.
  • IBM Watson for Genomics combines natural language processing with knowledge graphs to analyze a patient’s genetic profile against thousands of research papers. It identifies potential treatment options for rare cancers in minutes instead of weeks.
  • Tempus integrates multiple AI thinking approaches to analyze clinical and molecular data, helping oncologists develop personalized treatment plans based on patients with similar genetic profiles and treatment responses.
  • Reinforcement learning models optimize radiation therapy planning, determining precise radiation dosage and beam angles that maximize tumor impact while minimizing damage to healthy tissue.

2. Financial analysis and risk assessment

❓ Problem: Financial institutions must evaluate risk across millions of transactions while detecting fraud, predicting market changes, and optimizing investment strategies.

💡 AI solutions in action:

  • J.P. Morgan’s COIN (Contract Intelligence) uses natural language processing to review complex loan agreements in seconds, rather than the 360,000 hours annually it previously took human lawyers.
  • Bayesian networks power credit scoring systems that assess loan default risk while quantifying the uncertainty in their predictions – crucial for responsible lending decisions.
  • Two Sigma uses reinforcement learning to develop trading strategies that adapt to changing market conditions in real-time.
  • Neural-symbolic systems help detect money laundering by combining pattern recognition (unusual transaction sequences) with rule-based compliance requirements (regulatory frameworks).

3. Scientific research and hypothesis generation

❓ Problem: Scientists face an explosion of research data and complex problems that require identifying subtle patterns across vast datasets.

💡 AI solutions in action:

  • AlphaFold by DeepMind revolutionized protein structure prediction using deep learning, solving a 50-year-old biology challenge and accelerating drug discovery efforts worldwide.
  • Semantic Scholar uses natural language processing to analyze scientific literature, identifying connections between research papers that authors themselves might not be aware of.
  • Bayesian optimization models help materials scientists design new compounds with specific properties while minimizing expensive physical experiments.
  • MIT’s Robot Scientist combines multiple AI thinking approaches to automate the scientific process itself – formulating hypotheses, designing experiments, and interpreting results in cycles of discovery.

4. Business intelligence and strategic decision support

❓ Problem: Business leaders must make strategic decisions based on incomplete information across complex, interconnected systems with many stakeholders.

💡 AI solutions in action:

  • Palantir Foundry integrates diverse data sources and uses multiple AI approaches to identify patterns relevant to strategic decisions, from supply chain optimization to product development.
  • Cognitive architectures power some enterprise decision support systems, modeling different stakeholders’ perspectives and simulating potential outcomes of strategic choices.
  • Tableau’s Ask Data uses natural language processing to allow non-technical business users to analyze complex datasets through conversational queries.
  • Multimodal models help retail businesses integrate visual data (store layouts, product images), text (reviews, descriptions), and numerical data (sales figures, inventory) to optimize merchandising strategies.

5. Educational systems and personalized learning

❓ Problem: Traditional education uses a one-size-fits-all approach, despite enormous variation in how individual students learn best.

💡 AI solutions in action:

  • Carnegie Learning’s MATHia uses a cognitive tutoring architecture to model each student’s understanding of mathematical concepts, adapting instruction to address specific misconceptions.
  • Duolingo’s BIRDBRAIN reinforcement learning system optimizes lesson sequencing to maximize long-term language learning, balancing review of difficult material with introduction of new concepts.
  • Squirrel AI in China uses Bayesian knowledge tracing to build detailed models of student knowledge across thousands of fine-grained concepts, personalizing learning pathways at a remarkably detailed level.
  • Multimodal systems power tools like Photomath that can interpret a student’s handwritten math problem from an image and provide step-by-step guidance tailored to their level.

6. Creative domains

❓ Problem: Creative professionals need tools that augment their capabilities, provide inspiration, and handle technical aspects while preserving their creative vision.

💡 AI solutions in action:

  • DALL-E, Midjourney, and Stable Diffusion use diffusion models (a type of generative AI) to create images from text descriptions, enabling artists to explore visual concepts rapidly.
  • Google’s MusicLM transforms text descriptions into music, generating compositions in specific styles or moods that musicians can use as starting points.
  • GitHub Copilot uses large language models to suggest code completions, helping programmers focus on higher-level design while the AI handles routine implementation details.
  • Runway’s Gen-2 combines multiple AI thinking approaches to generate and edit video content, allowing filmmakers to prototype visual ideas without expensive shoots.

7. Complex systems modeling and prediction

❓ Problem: Many crucial systems – from climate to traffic to economies – involve countless interacting variables that make prediction and management extremely difficult.

💡 AI solutions in action:

  • DeepMind’s precipitation nowcasting models use specialized neural networks to predict rainfall with greater accuracy than traditional methods, helping communities prepare for potential flooding.
  • Waymo’s autonomous driving system combines multiple AI approaches – computer vision for perception, reinforcement learning for decision-making, Bayesian models for handling uncertainty – to navigate complex traffic environments.
  • AlphaStar mastered the complex strategy game StarCraft II using a combination of supervised learning and reinforcement learning, demonstrating the ability to manage complex systems with many moving parts.
  • Digital twins powered by multiple AI approaches model physical systems from manufacturing plants to urban infrastructure, allowing operators to simulate different scenarios before making changes to real-world systems.

Conclusion

As these technologies continue to evolve, the distinction between different AI approaches may become less visible to end users. However, understanding the underlying models remains crucial for organizations implementing AI solutions. The right approach – or combination of approaches – can mean the difference between transformative success and expensive disappointment.

For business leaders, the message is clear: rather than asking “How can we use AI?” the better question is “Which type of AI thinking model best addresses our specific challenges?” By starting with the problem rather than the technology, you can make more informed decisions about which AI capabilities to develop or deploy.

Ready to implement the right AI approach for your business challenges? Our team of AI specialists can help you find solutions that deliver real results. Contact us today via the form below to schedule a consultation.

Chapters

  • 1. Large language models (LLMs)
  • 2. Reinforcement learning models
  • 3. Multimodal models
  • 4. Neural-symbolic models
  • 5. Bayesian models
  • 6. Cognitive architectures
  • Real-world applications of AI thinking models
  • Conclusion

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