The 6 Types of AI Models

By Dr. Luke Soon

Abstract

Artificial Intelligence (AI) has diversified into multiple model families, each serving unique functions across industries. This article examines six foundational AI model types—Machine Learning, Deep Learning, Generative Models, Hybrid Models, Natural Language Processing (NLP), and Computer Vision. Drawing on industry white papers, academic literature, and real-world applications, we map how these models are used, the risks they pose, and where research is headed.

1. Machine Learning Models

Key Use Cases

Finance: Credit scoring and fraud detection (FICO, 2022 white paper on Responsible AI in Lending). Healthcare: Predictive analytics for patient readmission risk (NIH Clinical AI guidelines, 2021). Manufacturing: Predictive maintenance using anomaly detection (PwC, AI in Industrial IoT, 2020).

Research & White Papers

DARPA’s Explainable AI (XAI) Initiative (2019): Stressing interpretable models in high-stakes domains. OECD AI Principles (2019): Noting the ongoing reliance on classical ML for regulated industries requiring traceability.

Insight: ML remains indispensable where interpretability and compliance are essential, often outperforming deep learning in smaller, structured datasets.

2. Deep Learning Models

Key Use Cases

Healthcare Imaging: Radiology diagnostics using CNNs (Stanford CheXNet study, 2017). Autonomous Driving: Tesla and Waymo’s perception stacks rely heavily on CNN + RNN pipelines (Waymo Safety Report, 2021). Voice Assistants: RNNs and Transformers for ASR (Automatic Speech Recognition) underpin Siri, Alexa, Google Assistant.

Research & White Papers

LeCun, Bengio, Hinton (Nature, 2015): Pioneering review establishing deep learning’s rise. MIT CSAIL Report (2021): Emphasising efficient neural architectures for resource-limited environments.

Insight: Deep learning dominates unstructured data problems, but sustainability concerns (energy, compute intensity) are driving research into green AI and few-shot learning.

3. Generative Models

Key Use Cases

Creative Industries: Image synthesis via MidJourney/DALL·E; fashion design prototyping. Software Development: GitHub Copilot and AlphaCode accelerating programming workflows. Media & Marketing: Synthetic ad copy, hyper-personalised campaigns (McKinsey Generative AI & Productivity, 2023). Drug Discovery: Generative chemistry (Insilico Medicine, Nature Biotechnology, 2020).

Research & White Papers

Stanford HAI AI Index (2023): Generative AI is the fastest-growing subfield in publication and funding. EU AI Act draft (2023): Identifies generative models as “foundation models” requiring special governance.

Insight: Generative AI is a double-edged sword: capable of extraordinary creativity, but raising urgent concerns on deepfakes, IP rights, and safety alignment.

4. Hybrid Models

Key Use Cases

Search + Generation: Retrieval-Augmented Generation (RAG) improves accuracy in enterprise search (Meta AI, 2021 RAG white paper). Healthcare Decision Support: Combining symbolic reasoning (clinical guidelines) with neural networks (patient data). Financial Compliance: Rule-based checks integrated with NLP models for regulatory reporting (PwC Responsible AI for Financial Services, 2022).

Research & White Papers

IBM Research (2020): Hybrid neuro-symbolic AI enhances reasoning transparency. Alan Turing Institute (2021): Position paper on hybrid models for trustworthy AI governance.

Insight: Hybrids will power enterprise adoption, balancing scalability with compliance, auditability, and governance.

5. Natural Language Processing (NLP) Models

Key Use Cases

Customer Service: Virtual agents (banks, airlines) leveraging GPT-class models. Legal & Policy: Contract summarisation and e-discovery (EY LawTech, 2022). Education: Personalised tutors (Duolingo’s GPT-4 integration, 2023).

Research & White Papers

BERT (Devlin et al., 2019): Groundbreaking in bidirectional contextual language representation. Stanford CRFM (2023): “Foundation Models in Society” report, highlighting risks in NLP (bias, misinformation). Singapore IMDA’s AI Verify (2023): One of the first sandboxes testing LLM governance frameworks.

Insight: NLP is maturing into agentic reasoning, with governance frameworks focusing on bias, misinformation, and explainability.

6. Computer Vision Models

Key Use Cases

Healthcare: Detecting tumours via MRI and CT scans (Nature Medicine, 2020). Security: Facial recognition in surveillance (controversial use, see EDRi’s Ban Biometric Mass Surveillance paper, 2021). Retail: Amazon Go cashierless stores powered by YOLO and EfficientNet-like architectures. Agriculture: Crop monitoring with drone-based vision.

Research & White Papers

Fei-Fei Li (ImageNet, 2009): Benchmark dataset that catalysed modern computer vision. Google Research (2022): Vision Transformers (ViTs) outperform CNNs on large datasets.

Insight: CV is moving toward multimodal integration (e.g., CLIP, Flamingo), enabling perception + language synergy for embodied agents.

Cross-Cutting Themes

Energy Efficiency: White papers from the Partnership on AI (2022) emphasise carbon-conscious AI design. Trust & Governance: Singapore’s AI Verify (2023), UK’s Alan Turing Institute assurance frameworks (2021), and the OECD AI Principles converge on ethical deployment. Embodied Intelligence: Combining vision, language, and action into unified Agentic AI systems (MIT Sloan’s Superhuman Workforce, 2025).

Conclusion

The six model classes represent not just technical categories but paradigms of intelligence. Their convergence is shaping a new generation of AI systems—creative, multimodal, and agentic. Yet, white papers from regulatory, academic, and industry bodies warn that alignment, governance, and sustainability must remain central.

The next decade will be defined not by which model dominates, but by how we integrate, govern, and humanise AI to serve collective human experience.