The history of AI that no one ever told you

The history of AI that no one ever told you

by Matt Wolfe

Formal & Physical Sciences AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Matt Wolfe 👥 1.0M 📅 May 28, 2025 ⏱ 13 min 👁 48K 📄 science communication 🧭 2026-08-28
Available in: English (current) Français

Keywords

Turing testperceptronbackpropagationtransformerGPU

Summary

The video traces the history of artificial intelligence from its rule-based origins in the 1950s to the current deep learning era, highlighting key breakthroughs and the accelerating pace of progress. It begins with Alan Turing’s proposal of the Turing test in 1950 and the Dartmouth conference in 1956, where the term ‘artificial intelligence’ was coined. The rule-based era saw the development of the perceptron and the first chatbot, ELIZA, but was followed by an AI winter due to funding cuts and technical limitations. The machine learning era in the 1980s introduced backpropagation and convolutional neural networks, yet another AI winter occurred. The 1990s brought IBM’s Deep Blue defeating a chess champion and Nvidia’s first GPU, setting the stage for future advancements. The deep learning era, starting in 2006 with deep belief networks and CUDA in 2007, enabled the use of GPUs for AI, leading to rapid progress. Key milestones include IBM Watson, Siri, Google Brain, DeepMind’s AlphaGo, the Transformer architecture, and OpenAI’s GPT series. The video emphasizes the exponential growth of AI, noting that humans struggle to perceive such acceleration, and concludes by questioning whether another AI winter is possible, given the current widespread adoption and AI’s role in advancing itself.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides a valuable synthesis of AI history, connecting technical milestones to the broader narrative of exponential growth. It effectively argues that the pace of AI advancement has accelerated dramatically, particularly since the introduction of GPUs and deep learning. The argumentation is coherent, using a chronological structure to illustrate the compounding nature of progress. However, the video’s value is somewhat limited by its brevity and lack of deep technical detail, which may leave experts wanting more nuance. The claim that humans are no longer the bottleneck is presented without extensive evidence, but it serves as a compelling rhetorical point.

Scientific Rigor, Source Quality, Title Accuracy

The video demonstrates a reasonable level of scientific rigor, accurately referencing well-known milestones and researchers. However, it does not provide explicit citations for its claims, relying instead on general knowledge. The sources linked in the description are primarily promotional (newsletter, social media) and do not serve as academic references. The title accurately reflects the content, which offers a comprehensive overview of AI history. The video’s strength lies in its accessibility and narrative flow, but for a rigorous scientific audience, the lack of detailed sourcing is a notable weakness.

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Title / Content Match

The title accurately reflects the content, which presents a comprehensive timeline of AI history with a focus on lesser-known turning points.

Quality & Reliability

7/10

The video provides a broad historical overview of AI milestones, accurately referencing major events and figures. However, it lacks detailed citations for specific claims and simplifies complex technical concepts, which slightly reduces its reliability for an expert audience.

Chapters

Cited Sources

  • Future Tools — The creator's website for AI tools and news, mentioned in the video description.
  • Future Tools Newsletter — The creator's weekly newsletter, linked in the description.
  • Matt Wolfe on LinkedIn — The creator's LinkedIn profile, linked in the description.
  • Matt Wolfe on Threads — The creator's Threads profile, linked in the description.

Concurring Sources

Dissenting Sources

Contribution & Novelties

The video offers a unique narrative perspective on AI history, emphasizing the exponential growth and the human tendency to overlook gradual changes. It synthesizes well-known milestones into a coherent story, making it accessible to a broad audience. The ‘moving the goalposts’ concept, attributed to John McCarthy, is a notable insight. However, the video does not present new research or original analysis; its value lies in its presentation and synthesis.

Pour aller plus loin :

  • Attention Is All You Need — The seminal paper introducing the Transformer architecture, central to modern AI.
  • Deep Blue (IBM) — The chess-playing computer that defeated Kasparov, a key milestone.
  • AlphaFold — DeepMind’s protein folding prediction system, mentioned in the video.
  • CUDA — The parallel computing platform that enabled GPUs for general-purpose computing, crucial for deep learning.

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Radar Profile

The radar profile shows a balanced performance across all dimensions, with slightly higher scores in information quantity and technical level, reflecting the video's comprehensive yet accessible approach. The lower score in reliability is due to the lack of explicit citations, but overall the video is a solid educational resource.

Reliability 7/10

💬 Très positif. Sur les 30 commentaires analysés, les spectateurs expriment un enthousiasme marqué pour ce format de vidéo-essai, saluant la qualité de la production, la narration et la valeur éducative, avec plusieurs demandes pour plus de contenu de ce type.