9. Connectionism Flashcards
Q: Why did AI progress start to stall in the late 1970s and 1980s?
A: The progress in AI slowed down because simulating complex processes, such as object recognition in the visual system, required increasingly longer computation times. This computational intensity was unexpected and puzzling given that signal propagation in computers is much faster than in the human brain.
Q: What characterized the period of significant progress and optimism in AI from the 1940s until the late 1970s?
A: This period saw impressive advancements in AI, with programs successfully solving complex arithmetic and algebra problems, proving theorems, and even playing checkers and chess. There was great enthusiasm for the potential of AI to achieve human-like intelligence.
Q: How did the robot “Shakey” illustrate the limitations of early AI systems?
A: Shakey, a robot equipped with a TV camera and wheeled base, faced several limitations. Its tasks were slow to complete, it required a detailed map of its environment, and it could not generalize tasks like humans. These issues highlighted the difficulties in modeling perception and adaptability in AI.
Q: What did the Churchlands propose as reasons for the failure of traditional AI architectures?
A: The Churchlands suggested several reasons, including that symbol-manipulating machines were not brainlike, the brain’s neurons operate in parallel while computers are sequential, neurons process signals analogously rather than digitally, and the nervous system’s plasticity allows for learning and adaptability, unlike the rigid, fault-intolerant von Neumann machines.
Q: What are connectionist models and how do they differ from traditional AI?
A: Connectionist models, also known as neural networks, are inspired by the structure and functioning of the human brain. They process information in parallel, operate on analog principles, learn and adapt through experience, exhibit fault tolerance, and store information in a distributed manner across the network, unlike traditional AI which relies on sequential processing and symbol manipulation.
Q: How do connectionist models handle parallel processing?
A: Connectionist models simulate the brain’s parallel processing by allowing numerous interconnected nodes to work simultaneously, enabling the system to process information in a more efficient and brain-like manner compared to traditional sequential AI architectures.
Q: What is the significance of analog computation in connectionist models?
A: Analog computation in connectionist models allows for more nuanced and continuous representations of information, closely mimicking the way neurons transmit signals in the brain, leading to more accurate and flexible data processing.
Q: How do connectionist models achieve learning and adaptability?
A: Connectionist models adjust the strength of connections between nodes based on experience using methods like backpropagation and gradient descent. This learning process involves continuously updating the model to minimize errors and improve performance, similar to how the brain learns from experience.
Q: Why are connectionist models considered fault-tolerant?
A: Connectionist models are fault-tolerant because the failure of individual nodes does not cause the entire system to fail. Information is distributed across the network, making it resilient to damage and able to continue functioning despite some loss of nodes, akin to the brain’s ability to withstand neuronal loss.
Q: What does distributed representation mean in the context of connectionist models?
A: Distributed representation means that information is stored across the entire network rather than in discrete, symbolic units. This allows for more flexible and robust data representation and retrieval, similar to how the brain stores and retrieves information through associative memory.
Q: How do connectionist models represent a paradigm shift from traditional AI?
A: Connectionist models represent a paradigm shift by focusing on neural networks and their capabilities to process information, learn, and adapt, rather than relying on traditional symbol-manipulating computational processes. This shift changes the questions asked, the experiments designed, and the interpretation of results in AI research.
Q: What is the main difference between computationalism and connectionism in AI research?
A: Computationalism focuses on understanding the range of behavior possible through internal computation, asking which computational processes underlie behavior. In contrast, connectionism looks at the range of behavior possible through neural networks, asking which neural networks underlie behavior and how they process information.
Q: What is the “Luminous Room” thought experiment and its purpose?
A: The “Luminous Room” thought experiment illustrates that intuition should not constrain empirical research. It involves a man pumping a magnet in a dark room to produce electromagnetic waves, which we know to be light, though not visible to the human eye due to low frequency. This demonstrates that just because something is not immediately intuitive or observable does not mean it is impossible, emphasizing the importance of empirical evidence over intuition.
Q: How does Searle respond to the “Luminous Room” analogy?
A: Searle argues that the analogy fails because electromagnetic radiation is a causal story with physical effects, unlike formal symbols in a computer program, which have no causal power. He emphasizes that the analogy does not apply to his Chinese Room argument because formal symbols do not produce understanding or consciousness.
Q: What critique do the Churchlands have of Searle’s certainty about syntax and semantics?
A: The Churchlands argue that Searle’s certainty that syntax is sufficient for semantics is unfounded. They claim that Searle begs the question and that our lack of imagination should not limit the potential of connectionist models to achieve understanding or consciousness through different mechanisms than those imagined by Searle.
