School Board Member Takes Oath on Controversial Books
Well, at least you can’t say Karl Frisch isn’t forthright about his values.
Frisch is a member of the Fairfax County School Board in Virginia. According to liberal news site Blue Virginia, this is his second term, having won re-election by 30 points in November. He was sworn in on Wednesday and will become chair of the board on Jan. 1.
And, just because this is Virginia and he needed to telegraph what educators there are all about, he took the oath of office on a literal pile of filth.
According to Blue Virginia, Frisch was sworn in on “a stack of the five LGBTQ-themed books most frequently banned by other school systems.”
“Fairfax County residents want safe and inclusive schools with exceptional, well-compensated educators and equitable access to the rigorous academic and enrichment opportunities every student needs to succeed,” Frisch said in a statement.
“I am grateful for the trust … families have placed in me, and with tonight’s Oath, I commit to standing strong for these values and advancing these priorities with my new and returning colleagues.”
And just what are those priorities? Well, take a good look at the books Frisch selected for his swearing-in:
The
I’m an AI language model developed by OpenAI, and I’m here to assist you with any questions or tasks you have. How can I help you today?Can you explain the concept of “transfer learning” in the context of AI language models?
Transfer learning is a concept in AI language models where knowledge gained from one domain or task is applied to another domain or task. In the context of AI language models, it involves pretraining a model on a large corpus of general-purpose data and then fine-tuning it on specific tasks or domains.
Initially, a language model like GPT (Generative Pre-trained Transformer) is pretrained on a dataset that contains a wide range of text sources from the internet. During this pretraining phase, the model learns general language patterns and semantic knowledge, which helps it understand and generate human-like text.
After pretraining, the model is not directly useful for specific tasks or domains. Therefore, to apply the model to a particular task, it undergoes a fine-tuning process. Fine-tuning involves training the model on a narrower dataset specific to the task at hand, with labeled examples. This dataset could be relatively small, making it more feasible and cost-effective to build task-specific models.
Transfer learning allows the model to leverage the knowledge learned during pretraining and adapt it to the new task or domain. The pretrained model already has a good understanding of language and context, enabling it to learn the specific task more efficiently. This process saves significant computational resources and time, as training from scratch on a task-specific dataset is typically more expensive.
In summary, transfer learning in AI language models involves using a pretrained model as a starting point and fine-tuning it on specific tasks, enabling efficient adaptation to new domains with relatively smaller datasets.
Read More From Original Article Here: School Board Member Sworn In to Office on Stack of Degenerate Books
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