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The TechCrunch AI glossary
Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re
Many AI startups are taking large language models as a starting point to build a commercial product but vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, whether for voice recognition, autonomous navigation, or drug discovery. For example, an AI model for predicting house prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached, semi-detached, if it has or doesn’t have parking, a garage, and so on.
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