AI Foundational Series - Part 1] What Is AI? A Historical and Conceptual Overview

From Turing to Deep Learning – How We Got Here and Where We’re Headed

Introduction: The Inevitable Rise of Intelligence Beyond the Human Mind

Artificial Intelligence (AI) is no longer the future—it is the present. From financial markets to policymaking, climate research to medical breakthroughs, AI is reshaping industries, economies, and societies at an unprecedented scale. But to truly grasp where AI is headed, we should first understand where it came from—its historical foundations, conceptual breakthroughs, failures, and eventual resurgence.

This post unpacks the key moments that have defined AI’s evolution—from Alan Turing’s foundational question (“Can machines think?”) to the rise of deep learning and large-scale AI models like ChatGPT and Google DeepMind’s Gemini.

1. The Birth of AI: Alan Turing’s Vision and the Turing Test (1950s)

In 1950, Alan Turing, the British mathematician and cryptographer who played a pivotal role in breaking Nazi Germany’s Enigma code, published a groundbreaking paper: “Computing Machinery and Intelligence.”

• Instead of asking, “Can machines think?”—a question difficult to define—Turing proposed a practical test: If a machine could carry on a conversation indistinguishable from that of a human, should we not call it “intelligent”?
• This became known as the Turing Test, a challenge that continues to shape AI discourse today.

🔍 Why It Matters: The Turing Test remains one of the most debated ideas in AI, influencing everything from chatbots to natural language processing. Some argue that passing the Turing Test is meaningless—mimicking intelligence is not the same as understanding. Others see it as a benchmark for AI sophistication.

📖 Further Reading:

• Alan Turing: The Enigma – Andrew Hodges
• Turing’s original paper: "Computing Machinery and Intelligence"

2. The Rise of Symbolic AI and Expert Systems (1950s–1970s)

By the late 1950s, AI research had split into two competing schools of thought:

1️⃣ Symbolic AI (Good Old-Fashioned AI - GOFAI)

• Symbolic AI assumed intelligence could be built using explicit rules and logical reasoning—a system of if-then statements, heuristics, and structured knowledge graphs.
• Example: Expert systems (such as MYCIN for medical diagnosis) were built to mimic human decision-making based on programmed rules.

🔍 Why It Failed: While powerful in controlled environments, symbolic AI struggled with real-world complexity. The world is messy, ambiguous, and unpredictable—something hard to encode into rigid rule-based systems.

2️⃣ Statistical AI: The Alternative Approach

• Statistical AI, an early precursor to machine learning, suggested that AI should learn from data, not just follow rules.
• This approach, though in its infancy, would later dominate AI with neural networks, deep learning, and probabilistic reasoning.

📖 Further Reading:

• Artificial Intelligence: A Guide for Thinking Humans – Melanie Mitchell

3. The AI Winters: When Hope Turned to Disillusionment (1970s–1990s)

Despite early optimism, AI research faced two major “winters”—periods of reduced funding and public interest due to overhyped expectations and underwhelming results.

AI Winter #1 (1974–1980)

• The U.S. and U.K. governments withdrew AI funding after early systems failed to deliver on grand promises (e.g., real-time translation, human-like reasoning).

AI Winter #2 (1987–1993)

• The collapse of expert systems and high computing costs led to another AI stagnation.
• The rise of personal computers in the 1990s shifted focus to more practical applications like search engines, databases, and consumer software—putting AI in the shadows.

🔍 Lesson Learned: AI often follows a cycle of hype, disappointment, and rebirth—something we may be experiencing again today with generative AI.

📖 Further Reading:

• AI 2040: Ten Visions for Our Future – Various Authors (YouTube explainer by Kai-Fu Lee)

4. The Deep Learning Revolution: How AI Came Back Stronger (2000s–Present)

AI’s resurgence came from a simple but powerful realization: data and computing power could solve what rules-based programming could not.

Key Breakthroughs That Led to Modern AI:

✅ Machine Learning (1990s–2000s): Instead of hard-coding rules, models learn from data.

✅ Deep Learning (2010s–present): Neural networks mimicking the brain’s layered structure led to rapid improvements in image recognition, language translation, and speech processing.

✅ Big Data & GPUs (2010s–present): AI models became more effective as vast amounts of data and computing power became available.

AI Milestones Since 2010:

• 2011: IBM’s Watson wins Jeopardy!
• 2012: ImageNet competition proves deep learning’s dominance.
• 2016: DeepMind’s AlphaGo defeats the world’s best Go player.
• 2020–Present: OpenAI’s GPT models and Google’s Gemini redefine conversational AI.

🔍 Why It Matters: AI is no longer just an academic pursuit—it is now embedded into daily life, business, finance, and governance.

5. The Future: What’s Next for AI?

AI is evolving at breakneck speed. But where does it go from here?

Short-Term Trends (2024–2030)

✅ AI in Finance & Markets: Algorithmic trading, risk management, and climate modeling.

✅ AI Regulation & Ethics: The rise of AI policy to prevent bias, misinformation, and misuse.

✅ AI-Augmented Professions: AI as a co-pilot for decision-making in law, healthcare, and asset management.

Long-Term Possibilities (Beyond 2030)

🌍 Artificial General Intelligence (AGI): Machines that can think, reason, and learn like humans.

🤖 AI and Consciousness: Can AI ever achieve awareness—or is that uniquely human?

🔄 The Singularity Debate: Will AI surpass human intelligence?

📖 Further Reading:

• Superintelligence – Nick Bostrom
• The Alignment Problem – Brian Christian

🔍 Bottom-Line Summary for Busy Readers

• AI’s roots trace back to Alan Turing’s vision (1950s), but early AI systems failed to scale.
• Symbolic AI (rule-based) dominated for decades but lost to statistical AI (learning from data).
• Two AI winters (1970s–1990s) delayed progress, but deep learning (2000s) led to today’s AI boom.
• The current AI wave is powered by big data, neural networks, and computing advancements.
• Future AI developments will focus on automation, ethical AI, and the potential for AGI.

📚 Additional Resources & Further Reading

*Below list was suggested by AI 'Perplexity'. I asked it to give some resources as if it is putting together an MIT course for AI 101.

Podcasts

1. Mind to Machine: A Brief History of AI
   This Carnegie Mellon University podcast explores AI's evolution from ancient mythology to Alan Turing's groundbreaking work, featuring insights from pioneers like Herbert Simon and Raj Reddy.

1. Cognitive Revolution
   Hosted by Nathan Labenz and Erik Torenberg, this biweekly podcast delves into AI's transformative potential, societal implications, and interviews with leading researchers23.
2. DeepMind: The Podcast
   Hosted by Professor Hannah Fry, this award-winning series examines AI's role in scientific discovery and its pursuit of artificial general intelligence3.
3. Eye on AI
   Craig S. Smith interviews key figures in AI, such as OpenAI’s Ilya Sutskever, discussing ethical considerations and breakthroughs like GPT models3.

Books

1. Artificial Intelligence: A Modern Approach by Stuart J. Russell & Peter Norvig
   A foundational text covering theoretical concepts and real-world applications of AI.
2. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
   Explores AI's societal impact and philosophical implications while tracing its historical arc.
3. Artificial Intelligence: An Illustrated History by Clifford A. Pickover
   Offers a detailed timeline of AI from medieval automata to modern neural networks.
4. Machines of Loving Grace by John Markoff
   Examines the relationship between humans and robots, focusing on finding common ground in the age of automation.
5. A Brief History of Artificial Intelligence by Michael Wooldridge
   Simplifies complex AI concepts while providing an engaging overview of its history.

Interviews

• Yann LeCun on Pioneers of AI
  AI scientist Rana el Kaliouby interviews Yann LeCun, a Turing Award winner, about the history of neural networks and the deep learning revolution.

Father of AI: AI Needs PHYSICS to EVOLVE | prof. Yann LeCun (March 2025)

• Demis Hassabis on The Cognitive Revolution (March 2025)
  Google DeepMind and Anthropic founders, Demis Hassabis and Dario Amodei, are two of the world's foremost leaders in artificial intelligence. Our editor-in-chief, Zanny Minton Beddoes, sat down with them to discuss AI safety, timelines for artificial general intelligence and whether they fear becoming the Oppenheimers of our time, in a conversation for Visionaries Club.