ETEC 511 IP2

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Alan Turing was a mathematician who was famous for breaking Nazi ciphers during WWII (“Alan Turing”, 2020). He also designed the Turing test, which questions the ability of a machine to exhibit intelligent behavior like a human (“Turing, 1950). Turing thought that intelligence could be identified in machines if the machines could behave like humans, and if humans could not tell the difference.

Alan Turing, a pioneering mathematician and computer scientist, contributed to AI by proposing the Turing Test, which gauges a machine's intelligence based on its ability to mimic human conversation. He believed intelligence could be identified when a machine's responses were indistinguishable from a human's, emphasizing the importance of natural language understanding in AI.

John McCarthy was a professor of computer science at Stanford University that coined the term “artificial intelligence.” His idea of intelligence is that it is “the computational part of the ability to achieve goals in the world” (“McCarthy, 2007). This means that a computer can be intelligent as long as it can help achieve goals in the world.

John McCarthy, a computer scientist, introduced the term "artificial intelligence" and developed the LISP programming language, a key tool in early AI research. He believed intelligence could be identified through problem-solving and logical reasoning.

Herb Simon was an economist who was also one of the founding fathers of artificial intelligence. He believed that machines could be used to replicate “cold cognition” behavior such as reasoning, and pioneered a theory for the simulation of human problem solving (Wikipedia contributors, 2023). To him, intelligence is the ability to solve problems.

Herb Simon, a Nobel laureate in economics, co-founded AI and contributed by creating computer programs that simulated human decision-making. He believed intelligence could be identified by modeling human problem-solving processes.

Marvin Minsky was a mathematician and computer scientist who built one of the first neural-network learning machines (BBC, 2016). His work was in computational linguistics, mathematics and robotics (BBC, 2016). To him, intelligence in machines represents the “human capacity for commonsense reasoning (BBC, 2016).

Marvin Minsky, a prominent AI researcher, co-founded AI and worked on neural networks and robotics. He thought intelligence could be identified through the development of cognitive architectures and understanding how the mind works.

Timnit Gebru is a leader in AI ethics research. She identified biases within large language models, as well as the costs and opportunity costs of developing such models. Her idea of intelligence is that the meaning should be understood, and that current AI models simply manipulate language instead of understanding it.

Timnit Gebru, an AI ethicist and researcher, advocated for ethical AI and fairness. She emphasized that AI's intelligence could be identified by its alignment with ethical values and addressing societal biases and injustices.

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Machine languages only have zeros and ones. There are only 2 meanings. Unlike human language, there are no synonyms, allegories, analogies, historical or cultural references (Harris, 2018). Machine language also can’t evolve and develop the way human language does. Whereas new words and slang can be formed in human language, all machine language has to be predefined. Machine language has to be perfect with no room for error, whereas human language can be imperfect but still don’t “disrupt the message we’re trying to communicate” (Harris 2018). Examples include accents, mispronunciation, typos, jargon and argot.

Machine (programming) languages and human (natural) languages differ fundamentally in their purpose, structure, and usage. Machine languages are designed for computers to understand and execute instructions. They are low-level, consisting of binary code or assembly language, and lack the nuance, ambiguity, and expressiveness of human languages.

In contrast, human languages are used for communication between people and convey complex ideas, emotions, and culture. They are highly flexible, rich in context, and can express abstract concepts. Human languages often rely on context, tone, and cultural nuances, while machine languages demand precise and unambiguous instructions for computers to follow. Overall, machine languages prioritize computational efficiency, while human languages prioritize communication and expression.

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Human intelligence and artificial intelligence are often compared using each’s ability to complete specific tasks. However, the ability to complete tasks is heavily influenced by prior knowledge and experience. In the case of machines, this ability can be “bought” with training data (Chollet, 2019). This masks the AI’s ability to learn with what it has learned. An AI’s ability to learn is much more representative of machine intelligence than the level of learning it reaches. This is different from human intelligence in that human intelligence is usually measured not by a human’s ability to learn, but by the achievements of that human after they have learned.

Machine intelligence, or artificial intelligence (AI), differs from human intelligence in several key ways. First, AI operates based on algorithms and data, while human intelligence incorporates emotions, consciousness, and subjective experiences. Human intelligence is highly adaptable and capable of generalizing knowledge to diverse situations, whereas AI often specializes in narrow tasks.

Human intelligence understands context, empathy, and abstract reasoning, qualities AI struggles to replicate fully. Additionally, humans possess common-sense reasoning, creativity, and moral judgment, aspects that AI typically lacks. AI can process vast amounts of data quickly and perform repetitive tasks efficiently, but it doesn't possess the depth and breadth of human intelligence.

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Humans have the ability to recognize bias in their learning, consider multiple perspectives, and seek out new data and perspectives. Machines learn only based on the data that is provided to them. This means machines are susceptible to the biases of the humans designing them as well as their dataset selection decisions. Human learning is also shaped by recency, context, and intensity of the learning experience. Meanwhile, a machine is trained on datasets and is unable to assign chronological significance to the data. A machine may not be able to recognize the constantly changing preferences of humans unless it is being fed that data.

Machine learning differs from human learning in several key aspects. Machine learning involves algorithms that use data to improve their performance on specific tasks. It relies on pattern recognition and statistical inference but lacks human-like understanding, consciousness, and creativity. Human learning is a complex, multi-modal process involving sensory perception, reasoning, emotional engagement, and social interactions. Humans can transfer knowledge across diverse domains and adapt to new situations flexibly, while machine learning models are typically specialized for specific tasks and require large datasets.

Human learning is driven by curiosity, imagination, and intrinsic motivation, qualities that machine learning currently cannot replicate.