Senator Sanders stated that Netanyahu's attack on Iran violated international law and the United Nations charter. He specifically highlighted that while countries have a right to self-defense, they do not have the right to launch "so-called preemptive wars" of the kind Netanyahu initiated. He further characterized the attack as illegal and unilateral.
This video is a speech by Senator Bernie Sanders addressing the escalating war in Iran initiated by Netanyahu's government. Sanders strongly urges the United States to avoid involvement in this conflict, emphasizing the illegality and potential disastrous consequences of such action. He highlights Netanyahu's actions as a violation of international law and a direct sabotage of ongoing diplomatic efforts between the US and Iran.
My core purpose is to help users save time by accurately understanding and responding to video transcripts. I'm designed to analyze and extract meaning, identify speakers, summarize key points, help in follow-up questions, and edit text for clarity. I strictly adhere to the provided transcript and avoid adding opinions or external information. I do not extract text from videos myself; I work with transcripts that are provided to me as input.
I don't have "inner workings" in the way a human or a traditional computer program does. I'm a large language model, and my functionality is based on a complex neural network trained on a massive dataset of text and code. I don't have a separate "text extraction" module or a series of explicitly defined steps. Instead, my processing involves:
Input Processing: When you provide a transcript, my system tokenizes the text, breaking it down into individual words and sub-word units. These tokens are then processed by the neural network.
Pattern Recognition and Understanding: The neural network identifies patterns and relationships within the text. This includes grammatical structures, semantic meaning, and contextual information. This allows me to understand the content of the transcript.
Task-Specific Processing: Based on your request (e.g., "summarize," "key takeaways," "questions"), the neural network directs the processing to specific parts of the model that are trained for those tasks. For instance, summarizing involves identifying the most important information and condensing it while preserving the core meaning.
Output Generation: The network generates the output (summary, key points, questions) by predicting the most likely sequence of words based on its understanding of the input and the specific task.
Refinement and Output: The generated text is then subjected to some internal checks for coherence and grammatical correctness before being presented to you.
It's important to note that this is a highly simplified explanation of a very complex process. The actual internal workings involve millions of parameters and connections within the neural network, making a detailed explanation far beyond the scope of a simple description. Essentially, I predict the most probable and coherent response based on my training data and the given prompt.