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Procedural knowledge (i.e., knowledge-how) is different from descriptive knowledge (i.e., knowledge-that) in that it can be directly applied to a task. [2] [4] For instance, the procedural knowledge one uses to solve problems differs from the declarative knowledge one possesses about problem solving because this knowledge is formed by doing. [5]
The adaptive control of thought model assumes a distinction between declarative knowledge, knowledge that is conscious and consists of facts, [2] and procedural knowledge, knowledge of how an activity is done. [3] [4] In this model, skill acquisition is seen as a progression from declarative to procedural knowledge. [4]
Implicit knowledge usually refers to knowledge acquired unconsciously and intuitively through meaningful exposure to and use of language, resembling the knowledge of a first language. On the other hand, explicit knowledge involves conscious understanding of grammatical rules and structures, primarily acquired through formal education and learning.
These two forms of knowledge have been the subject of extensive debate among linguists, language teachers, and researchers seeking to understand how best to facilitate language learning. The debate touches on how each type of knowledge is acquired, how they interact, and the degree to which explicit instruction can foster implicit knowledge.
Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. [ 12 ] In 2015, a team at Google released DeepDream , a program that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia .
CLIP has been used in various domains beyond its original purpose: Image Featurizer: CLIP's image encoder can be adapted as a pre-trained image featurizer. This can then be fed into other AI models. [1] Text-to-Image Generation: Models like Stable Diffusion use CLIP's text encoder to transform text prompts into embeddings for image generation. [3]
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ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: declarative and procedural. Within the ACT-R code, declarative knowledge is represented in the form of chunks, i.e. vector representations of individual properties, each of them accessible from a labelled slot.