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LLMs Are Not Black Magic At All • Preben Thorø • GOTO 2024
Discover how large language models work without magic, delving into their neural networks, training data, and algorithms. Learn about the cognitive parallels between human brains and LLMs.
- Neural networks are not black magic, they’re based on principles of pattern recognition and the human brain’s processing of information.
- The human brain can be thought of as a 20-watt computer with a low clock frequency, processing information with neural networks.
- The brain’s process of recognizing objects is similar to how neural networks work, using layers of neurons to recognize patterns.
- GPT-1, GPT-2, and GPT-3 are examples of large language models (LLMs) that have been trained on large datasets to generate text.
- LLMs are not magical, but rather the result of complex algorithms and statistical models.
- The quality of an LLM’s output is dependent on the quality of its training data and the complexity of its architecture.
- GPT-3 is 10 times bigger than GPT-2 and has 175 billion parameters.
- The human brain has an estimated 100 billion neurons, whereas GPT-3 has only 100 million parameters.
- Neural networks are not restricted to just recognizing shapes and objects, but can also be used for tasks such as language processing and text generation.
- The brain’s process of recognizing objects is based on principles such as proximity, similarity, and closure, which are also used in neural networks.
- Gestalt psychology’s principles, such as proximity and similarity, are used to explain how the brain recognizes objects.
- The mind is capable of making connections between seemingly unrelated information to form new concepts and ideas.
- LLMs can be used for tasks such as language translation, text summarization, and generating sentences that are grammatically and semantically correct.
- GPT-3 can generate text that is indistinguishable from human-created text, but it’s still limited by the quality of its training data and architecture.
- The future of LLMs is uncertain, but it’s likely to continue to improve with advances in AI and machine learning.