A brief history of AI: how we got here and where we are going
How does symbolic AI differ from machine learning?
Symbolic AI, also known as Good Old-Fashioned AI (GOFAI), relies on human-defined rules and logic to perform tasks, making it suitable for domains with well-defined knowledge25. Machine learning, on the other hand, involves algorithms learning rules through data correlation and pattern recognition. Symbolic AI is more transparent and requires less data, while machine learning excels at handling complex tasks like image recognition and natural language processing.
What is the Turing test's basic premise?
The Turing test's basic premise is to determine a machine's ability to exhibit human-like intelligence in conversation. A human judge engages in a conversation with both a human and a machine, without knowing which is which. If the judge cannot reliably tell which participant is the human and which is the machine, then the machine is said to have passed the Turing Test.
What role do generative-adversarial networks play in AI?
Generative-adversarial networks (GANs) are a type of deep learning model used in AI for generating new, realistic data samples. They consist of two neural networks, a generator and a discriminator, that compete against each other. The generator creates synthetic data, while the discriminator evaluates its authenticity. Through repeated training, GANs can produce high-quality data that closely resembles the original distribution, making them useful in various applications, such as image synthesis, video prediction, and data augmentation.