and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input. Sequential Data Processing in Parallel: Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets. This parallelism is achieved through the self -attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part. Capturing Long -Range Dependencies: Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self -attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence. This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation. Applications in LLMs: In the context of GPT -4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models. Reference: Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long -range dependencies, which are essential for effective language understanding and generation. Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
What does "fine-tuning" refer to in the context of OCI Generative AI service?
D. Fairness Explanation: Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision -making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically. What is the benefit of using embedding models in OCI Generative AI service?
Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages. Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively . What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
➢ TOTAL QUESTIONS:300 What is the key feature of Recurrent Neural Networks (RNNs)?
