- High Performance Computing infrastructure
- Manual data entry systems
- Traditional relational databases only
- Paper-based record keeping
- I don’t know this yet
Answer: High Performance Computing infrastructure
The correct answer is high-performance computing infrastructure because generative AI models require significant computational power, especially during training and inference, and GPUs and TPUs enable this performance.
Answer Explanation:
Generative AI is a type of artificial intelligence technology that can produce a wide range of content, such as text, images, audio, and synthetic data. The present fascination for generative AI has been fuelled by the ease with which new user interfaces can generate high-quality text, images, and videos in seconds.
Generative AI begins with a prompt, which can be text, an image, a video, a design, musical notes, or any other input that the AI system can process. After receiving the request, several AI algorithms return novel content. Essays, problem solutions, and convincing fakes made from a person’s photographs or words are all examples of content.
High Performance Computing infrastructure:
Generative AI models, such as ChatGPT or other large language and vision models, are highly computationally intensive. These models need strong processors, notably Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs), to execute large-scale matrix calculations, which are essential for deep learning algorithms.
These models can take weeks or months to train, requiring sophisticated technology with billions of parameters and terabytes of data. Particularly in real-time applications, even inference—the process of running the model to get results—requires a substantial amount of processing power. Organisations must have on-premise high-performance computing clusters or cloud-based GPU access in order to develop, optimise, and implement generative AI systems.
Manual data entry systems: Manual data entry involves human operators inputting data into systems by hand. This method is time-consuming and inefficient, error-prone, and not scalable for the large volumes of data needed to train generative AI models.
Traditional relational databases only: While traditional databases are excellent for storing structured data, they have limitations. They are not optimized for unstructured data, and they lack the scalability and flexibility.
Paper-based record keeping: Organisations using paper-based systems are far from being AI-ready. Such systems lack digitisation and prevent automated data collection, storage, and analysis.