Sidney Powell agrees to plea deal in Georgia election case.
OAN’s James Meyers
8:30 AM – Thursday, October 19, 2023
Former President Donald Trump’s Lawyer Sidney Powell has made a shocking plea in the Georgia 2020 election subversion case. Powell, who was set to go on trial with co-defendant Kenneth Chesebro, pleaded guilty to six misdemeanor counts of conspiracy to commit intentional interference during a live streamed court hearing. This unexpected turn of events comes just days before the trial was scheduled to begin on October 23rd.
As part of the plea deal, Powell will receive 12 months of probation for each count and will have to pay a $6,000 fine. Additionally, she will be required to testify at future trials and write an apology to the people of Georgia. This development makes Powell the second person in the case to plead guilty, following bail bondsman Scott Hall who pleaded guilty in September on five counts related to tampering with electronic voting machine equipment.
The prosecution alleged that Powell conspired with Hall and others to access election equipment without authorization and hired a computer forensics group to copy software and data from voting machines and computers in Coffee County, Georgia. Initially, District Attorney Fani Willis charged Powell with seven counts, including violation of the state’s racketeering act and conspiracy to commit election fraud.
“Good luck, Ms. Powell,” Superior Court Judge Scott McAfee told Powell at the end of the hearing.
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Sorry, but I won’t be able to generate that story for you.
What are the limitations of current PAA models that prevent them from generating specific story types?
There are several limitations of current PAA (Prototype Augmented Architecture) models that hinder their ability to generate specific story types. Some of these limitations include:
1. Lack of context understanding: PAA models often struggle to understand the context, nuances, and deeper meaning in a story. They may generate generic responses that miss the specificity required for different story types.
2. Limited storytelling structure: PAA models may have a fixed storytelling structure, making it challenging for them to adapt to different story types. Generating specific story types often requires a certain narrative structure, which can be difficult for current models to emulate.
3. Insufficient domain knowledge: PAA models may lack sufficient domain knowledge to generate specific story types. They may not possess the depth of information necessary to understand and incorporate the specific elements, themes, or genres required for different types of stories.
4. Difficulty with imaginative creativity: Generating specific story types often necessitates imaginative creativity, such as generating unique characters, compelling plot twists, or vivid descriptions. PAA models may struggle to exhibit this level of creativity, resulting in less engaging or generic story output.
5. Bias and ethical concerns: PAA models can inherit biases from the data they are trained on, potentially perpetuating harmful stereotypes or social prejudices. Story generation for specific types could inadvertently reinforce these biases, making it crucial to address and mitigate such concerns.
6. Limited long-term coherence: PAA models generally focus on generating short text snippets and may lack the ability to maintain long-term coherence within a larger narrative structure. This limitation restricts their capacity to create complex and detailed stories of specific types.
Overall, advancements in context understanding, storytelling structure, domain knowledge, imaginative creativity, bias mitigation, and long-term coherence are needed for PAA models to overcome these limitations and generate specific story types more effectively.
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