Beyond Intelligent Machines: Consciousness, Control, and the Future of Artificial Intelligence

Beyond Intelligent Machines: Consciousness, Control, and the Future of Artificial Intelligence

Computing and Information Science and Technology

Beyond Intelligent Machines: Consciousness, Control, and the Future of Artificial Intelligence

Author: Ahmed Banafa, San Jose State University, USA

ISBN: 9788743812975 (Hardback) e-ISBN: 9788743812982

Available: September 2026


Artificial intelligence has crossed a historic threshold. What began as narrow automation and pattern recognition has evolved into systems capable of reasoning, generating knowledge, shaping economies, influencing geopolitics, and challenging how we define intelligence and humanity itself. AI is no longer merely a supporting technology—it is becoming a foundational force shaping modern civilization.

Beyond Intelligent Machines: Consciousness, Control, and the Future of Artificial Intelligence examines what lies beyond today’s AI systems. The book explores not only how AI works, but what it is becoming and how its trajectory may reshape power, identity, labor, security, and even consciousness. Topics range from large language models and agentic AI to embodied intelligence, emotional systems, and emerging questions about machine autonomy and artificial awareness.

Rather than treating AI purely as a technical achievement, the book frames it as a socio-technical system embedded within culture, economics, ethics, and global competition. It confronts critical questions: What happens when intelligence scales faster than governance? Can machines exhibit agency without intention? How do we maintain meaningful human oversight as decision-making systems become increasingly autonomous?

Written for readers seeking to understand where AI is headed—not just how it works—this book argues that the future of AI will be shaped not only by algorithms, but by the values, constraints, and choices we embed within them today.

Artificial intelligence, machine learning, deep learning, large language models (LLMs), small language models (SLMs), large behavior models (LBMs), model context protocol (MCP), agentic AI, physical AI, emotion AI, conscious AI, artificial general intelligence (AGI), artificial superintelligence (ASI), reinforcement learning, reinforcement Learning from human feedback, transformers, embeddings, retrieval-augmented generation (RAG), AI alignment, AI safety, AI ethics, AI governance, AI security, AI failure, AI stack, vibe programming, AI factory, learning concept model (LCM), autonomous systems, human–AI interaction, AI and human identity, AI regulation, global AI race, multipolar AI, data quality, bias in AI, explainable AI, AI infrastructure, AI decision-making, future of intelligence
  • What is Artificial Intelligence?
  • Understanding the Foundations of Large Language Models (LLMs)
  • Will AI Fight for Its Own Survival? Exploring the Limits of Machine Self-preservation
  • Can AI Build a Time Machine? Exploring the Science, Limits, and Speculative Future of Time Travel
  • The Rise of Conscious AI: When and How Artificial Intelligence May Achieve Awareness
  • AI and Human Identity: The New Frontier of Selfhood
  • The Landscape of AI and Machine Learning Techniques: A Deep Dive
  • Model Context Protocol (MCP): The Nervous System of Smarter AI
  • The Global AI Arena in 2030: Navigating Trade Winds and a Truly Multipolar Technological Landscape
  • Why AI Fails: From Data to Deployment
  • Vibe Programming: Where Emotion Meets Code
  • AI Factory: The Future of Scalable Artificial Intelligence
  • The Learning Concept Model (LCM) in AI: A New Paradigm for Intelligent Systems
  • AI Without Regulations or Guardrails: A Risky Path Forward
  • The Rise of Physical AI: Bridging Artificial Intelligence with the Tangible World
  • Agentic AI: The Rise of Autonomous Intelligence
  • Emotion AI: Unlocking the Power of Emotional Intelligence
  • What AI Strategists Do: Roles, Responsibilities, and Challenges
  • AI: Will It Take Your Job? Understanding the Fear and the Reality
  • The Worst Applications of AI: Ethical Concerns and Societal Impacts
  • Understanding "Robot Suicide": Metaphor, Self-Destruction, and Technical Malfunctions in AI
  • AI Security
  • Slop in AI: The Hidden Challenge of Messy Data and Imprecise Models
  • The AI Stack
  • Retrieval-augmented Generation (RAG) and Artificial Intelligence
  • Spatial AI: Transforming the World with Intelligent Spatial Understanding
  • Small Language Models (SLMs): Compact AI with Practical Applications
  • Large Behavior Models (LBMs): The Next Frontier in Artificial Intelligence
  • Why Companies Fail in Their AI Projects: Lessons from the Frontlines