The article further addresses concerns from a leadership perspective, including cost management, security and governance, and copyright and ethics. Agarwal suggests that a workload placement strategy and usage-based subscription model could help manage costs. She also stresses the need for cybersecurity and data governance in a distributed data environment. Lastly, she touches on the ethical issues surrounding generative AI models, emphasizing the need for unbiased AI algorithms and transparency about data sources used in training models.
Key takeaways:
- Large Language Models (LLMs) like GPT-3 and BERT are transforming computing, storage, and networking, but their size and complexity can lead to significant processing and computing requirements.
- Costs, security, governance, and copyright are key concerns when using LLMs. It's important to consider where data is stored and used, how it's managed, and where models are trained to optimize costs.
- Generative AI models raise ethical concerns about copyright, intellectual property, and data bias. Organizations must ensure their AI algorithms are unbiased and do not infringe on intellectual property rights.
- There is a need for flexibility in AI infrastructure to cater to different industry requirements and use cases. This flexibility is crucial for the democratization of AI.