The founding event of artificial intelligence as a field is considered to be the 1956 Dartmouth Workshop in Hanover, New Hampshire.
The proposal listed seven areas of focus for AI: automation of higher-level functions, language models, neural networks, computational efficiency, self-learning, abstraction and generalization from sensor data, and creativity.
These were all revolutionary ideas at the time (and still are), but the one that stands out to me the most is creativity:
“𝘈 𝘧𝘢𝘪𝘳𝘭𝘺 𝘢𝘵𝘵𝘳𝘢𝘤𝘵𝘪𝘷𝘦 𝘢𝘯𝘥 𝘺𝘦𝘵 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘪𝘯𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘤𝘰𝘯𝘫𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘢𝘯𝘥 𝘶𝘯𝘪𝘮𝘢𝘨𝘪𝘯𝘢𝘵𝘪𝘷𝘦 𝘤𝘰𝘮𝘱𝘦𝘵𝘦𝘯𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘭𝘪𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘪𝘯𝘫𝘦𝘤𝘵𝘪𝘰𝘯 𝘰𝘧 𝘢 𝘴𝘰𝘮𝘦 𝘳𝘢𝘯𝘥𝘰𝘮𝘯𝘦𝘴𝘴.”
Today, most generative AI models seem to follow this idea of injecting some randomness. But can a touch of randomness turn 𝘶𝘯𝘪𝘮𝘢𝘨𝘪𝘯𝘢𝘵𝘪𝘷𝘦 𝘤𝘰𝘮𝘱𝘦𝘵𝘦𝘯𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 into 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘪𝘵𝘺? Well, this 𝘤𝘭𝘦𝘢𝘳𝘭𝘺 𝘪𝘴 𝘢𝘯 𝘪𝘯𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘤𝘰𝘯𝘫𝘦𝘤𝘵𝘶𝘳𝘦.
Randomness alone can’t make a model imaginative. Imagination requires an understanding of cause-effect relationships and counterfactual reasoning.
𝘈𝘯 𝘈𝘐 𝘮𝘰𝘥𝘦𝘭 𝘤𝘢𝘯 𝘱𝘦𝘳𝘧𝘦𝘤𝘵𝘭𝘺 𝘳𝘦𝘱𝘳𝘰𝘥𝘶𝘤𝘦 𝘵𝘩𝘦 𝘤𝘰𝘭𝘰𝘳𝘴 𝘰𝘧 𝘢 𝘴𝘶𝘯𝘴𝘦𝘵 𝘪𝘯 𝘱𝘪𝘹𝘦𝘭𝘴, 𝘺𝘦𝘵 𝘪𝘵 𝘸𝘰𝘶𝘭𝘥 𝘧𝘢𝘪𝘭 𝘵𝘰 𝘨𝘳𝘢𝘴𝘱 𝘵𝘩𝘦 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘴𝘱𝘢𝘳𝘬 𝘵𝘩𝘢𝘵 𝘵𝘶𝘳𝘯𝘴 𝘢 𝘮𝘦𝘳𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦 𝘪𝘯𝘵𝘰 𝘢 𝘸𝘰𝘳𝘬 𝘰𝘧 𝘢𝘳𝘵.
That’s why the more exciting potential today seems to lie in creative human input to a model, or in using the output of the model as input to creative human brain.