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In the presentation titled "Dreaming of Intelligent Machines," given by Paolo "Nusco" Perrotta at the Paris.rb Conf 2020, the evolution of artificial intelligence (AI) is explored, particularly contrasting the Symbolist and Connectionist approaches. The discussion begins with modern technologies like self-driving cars and digital assistants, tracing their roots back to the mid-20th century.

- **The Symbolist Approach**: Symbolists, led by Marvin Minsky, believed that thinking is about manipulating symbols. They focused on programming machines to handle simple shapes and concepts, evolving towards intelligent behavior through cumulative learning and better programming languages like Lisp.

- **The Connectionist Approach**: In contrast, the Connectionists aimed to replicate the human brain's neural architecture, using models like the perceptron. Instead of a ground-up strategy, they explored a network of interconnected inputs and outputs. Over time, the perceptron became significant in image recognition tasks.

- **Training the Perceptron**: The perceptron learns through a training phase, classifying examples (like images of squares) by adjusting weights assigned to the input features. The speaker emphasizes the importance of the training algorithm in enhancing the perceptron's predictive abilities.

- **Criticism from Minsky**: Minsky criticized Connectionism, showcasing the perceptron’s limitations and predicting its inability to tackle non-linear data, which led to a decline in interest and funding for neural networks after the publication of his book.

- **Revival of Connectionism**: Despite the setback, researchers continued working on neural networks, eventually developing the backpropagation algorithm in the 1970s, which allowed effective training of multi-layer networks. Advancements in computer hardware and the explosion of big data further revitalized interest in neural networks.

- **The 2012 Breakthrough**: A pivotal moment came during the 2012 ImageNet competition, where a deep neural network named AlexNet outperformed all previous entries, cementing belief in the feasibility of neural networks for complex AI tasks.

- **Conclusion and Ethical Considerations**: While the advancements in AI have the potential to transform many industries, they also raise critical ethical concerns regarding privacy, surveillance, and the implications of autonomous technologies. Perrotta concludes by reflecting on the unpredictable future of AI and the journey that led to current capabilities in machine intelligence, emphasizing that the story of AI is ongoing and complex.

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