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Found 6 papers, 0 papers with code
Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life.
Methodological reflections for AI alignment research using human feedback
The field of artificial intelligence (AI) alignment aims to investigate whether AI technologies align with human interests and values and function in a safe and ethical manner.
Thinking Fast and Slow in Large Language Models
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life.
Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards.
Compositionality as Learning Bias in Generative RNNs solves the Omniglot Challenge
One aspect of learning to learn concerns the development of compositional knowledge structures that can be flexibly recombined in a semantically meaningful manner to analogically solve related problems.
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
In the past, the character challenge was only met by complex algorithms that were provided with stochastic primitives.
