- GPS and IoT
- Agile and Quantum
- Blockchain and 5G
- Cloud and Data
Ans: (d) Cloud and Data
Option (d) came out as correct because cloud provides computational power to generative AI while data acts as a source of learning for the AI model.
Let’s have a look at why other options are not correct and how cloud and data together will help large-scale generative AI in business.
How does the Cloud help in scalability?
Generative AI models require multiple resources for their functioning such as massive computing power, distribution of processing capacity, and on-demand scalability. This all is provided by cloud computing.
For example, platforms like AWS, Azure, or Google Cloud have made high-performance GPUs and TPUs available on demand. Businesses can acquire these resources and start using them without worrying about setting up infrastructure.
Cloud also helps in enhancing collaboration among various groups and this makes remote contribution possible.
Why is Data crucial for Generative AI?
Any AI model lives upon data for training and functioning. Without a data input, a model will produce ambiguous outputs. Data helps AI models in learning patterns, structures, and relationships.
Businesses should build a scalable, secure, and accessible data storage infrastructure as this is a crucial element for fueling generative AI integration into them.
Let’s understand this whole point through a real-world analogy:
Suppose there is a music concert where a generative AI model is a singer. The management which puts all things into place and organizes the concert would be ‘cloud’. An efficient management means that you’ll have a smoother experience at the concert.
The music knowledge and the lyrics the singer has are his training data. The better the training data is, the more enjoyable the concert becomes.
Why are other options not correct?
- GPS and IoT: These might become useful in some cases but are not a foundational technology when it comes to running generative AI models. GPS can act as an additional data source while IoT can help in scalability, but their usability varies across businesses.
- Agile and Quantum: Here, agile refers to a project management methodology. This has nothing to do with running AI models. Quantum computing is something that is still in the research phase.
- Blockchain and 5G: For securing the data and making its retrieval faster, blockchain technology can be used. But again, this is extended infrastructure and is not the backbone of AI models. Similarly, 5G can enhance connectivity and speed but their use depends on businesses’ choice.