Across
- 1. The first artificial neuron model that could learn simple patterns.
- 3. One of the earliest programming languages.
- 5. Early approach using rules and symbols instead of data-driven learning.
- 7. Controls how much a neuron “fires.”
- 10. Settings that guide how a model learns.
- 16. The challenge of keeping AI’s goals human-friendly.
- 17. Strength of a neuron’s output.
- 20. Time when AI funding and optimism ran cold.
- 21. Layered networks that power modern AI systems.
- 22. The data used to check an AI’s learning.
- 24. Trick image that confuses an AI.
- 25. Central idea in Hofstadter and Mitchell’s theory of intelligence.
- 26. The horse that “solved” math problems by reading cues.
- 27. The 1956 workshop that officially launched the field of AI.
- 28. Early program that tried to model human reasoning.
Down
- 2. Idea that true intelligence needs a body.
- 4. Learning from rewards and punishments.
- 6. The examples used to teach an AI.
- 8. How neural networks adjust their connections.
- 9. A period of renewed optimism and funding for AI after years of stagnation, often tied to breakthroughs in deep learning.
- 11. Models inspired by how the brain processes vision.
- 12. Company known for creating AlphaGo.
- 13. The point when AI surpasses human intelligence.
- 14. IBM’s computer that beat chess champion Garry Kasparov.
- 15. Massive labeled image dataset that revolutionized computer vision and helped spark the deep learning boom.
- 18. An early, cross-disciplinary field linking biology and engineering, emphasizing feedback loops that inspired later AI research.
- 19. The 2012 network that started the deep learning boom.
- 23. Hofstadter’s idea for self-awareness.
- 26. Hofstadter’s computer model of analogy-making.