Additionally, there are a huge number
Posted: Sun Feb 09, 2025 7:46 am
Another factor is cost. According to Ilkka Turunen, CTO of Sonatype, most models with API access are charged per token sent and received. This means that costs can quickly add up due to widespread use. “The calculations for these requests are not always straightforward and require a deep understanding of the payload,” he adds.
of intellectual property issues surrounding model training that still need to be addressed.
“Jurisdictions like the EU and Japan have recently clarified that their laws allow for the extraction of text and data in the context of training AI models, and there is a longer list of countries that have similar regimes,” says Owen Larter, director of public policy at Microsoft’s Office of Responsible AI.
And in the US, The New York Times recently filed a singapore mobile database against Microsoft and OpenAI, the developer of ChatGPT, alleging that the two companies illegally used work from its sites to create AI products that compete with the publication and threaten its news service.
Using domain-specific or proprietary data can help your LLM stand out from the competition.
The proprietary nature of LLM means that the models run within an organization’s own IT infrastructure, without relying on any external connections. “By hosting these models within your own secure IT infrastructure, you can protect your corporate knowledge and data,” says Oliver King-Smith, CEO of smartR AI.
However, he notes that private models need buy-in from all stakeholders in an organization, and encourages IT leaders considering deploying private LLMs to conduct a risk assessment before implementing them.
“When deploying them, companies should have clearly defined policies for their use,” adds King-Smith. “As with any other critical IT resource, access control for key personnel must be ensured, especially if they are working with sensitive information.”
For example, companies that need to comply with standards such as ITAR, GDPR and HIPPA should check that their LLMs are compliant. As examples of accidental abuse, King-Smith cites cases where lawyers have been caught preparing cases in ChatGPT, a clear breach of attorney-client privilege.
of intellectual property issues surrounding model training that still need to be addressed.
“Jurisdictions like the EU and Japan have recently clarified that their laws allow for the extraction of text and data in the context of training AI models, and there is a longer list of countries that have similar regimes,” says Owen Larter, director of public policy at Microsoft’s Office of Responsible AI.
And in the US, The New York Times recently filed a singapore mobile database against Microsoft and OpenAI, the developer of ChatGPT, alleging that the two companies illegally used work from its sites to create AI products that compete with the publication and threaten its news service.
Using domain-specific or proprietary data can help your LLM stand out from the competition.
The proprietary nature of LLM means that the models run within an organization’s own IT infrastructure, without relying on any external connections. “By hosting these models within your own secure IT infrastructure, you can protect your corporate knowledge and data,” says Oliver King-Smith, CEO of smartR AI.
However, he notes that private models need buy-in from all stakeholders in an organization, and encourages IT leaders considering deploying private LLMs to conduct a risk assessment before implementing them.
“When deploying them, companies should have clearly defined policies for their use,” adds King-Smith. “As with any other critical IT resource, access control for key personnel must be ensured, especially if they are working with sensitive information.”
For example, companies that need to comply with standards such as ITAR, GDPR and HIPPA should check that their LLMs are compliant. As examples of accidental abuse, King-Smith cites cases where lawyers have been caught preparing cases in ChatGPT, a clear breach of attorney-client privilege.