mlsanityData to 14 Oct 2026

AI Generations: From AI 1.0 to AI 4.0

arXiv 2502.11312 ↗First posted 16 Feb 2025cs.AIcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and now a speculative AI 4.0 (Conscious AI). Each of these AI generations is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 ushered in breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly seventy years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data, have spurred each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the profound ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI transformative potential benefits society as a whole.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 25 of its 74 references that are in the map. First posted on arXiv 16 Feb 2025.

Builds on

25 of 74 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023AutoAgents: A Framework for Automatic Agent Generation29 Sep

Other references · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2020Scaling Laws for Neural Language Modelscited by 6,586
  6. 2017On Calibration of Modern Neural Networkscited by 4,960
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  10. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  11. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  12. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
12 more
  1. 2016End to End Learning for Self-Driving Carscited by 1,009
  2. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
  3. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  5. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  6. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
  7. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 111
  8. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 104
  9. 2024Towards evaluations-based safety cases for AI schemingcited by 33
  10. 2023Towards Evaluating AI Systems for Moral Status Using Self-Reportscited by 22
  11. 2022Annotated History of Modern AI and Deep Learningcited by 20
  12. 2024Machine Consciousness as Pseudoscience: The Myth of Conscious Machinescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Generative Artificial Intelligence for Internet of Things Computing: A Systematic Surveycited by 3
  2. 2026Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systemscited by 0

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