Hallucinations are an eternal problem in generative AI in particular, and while it’s true that large language models (LLMs) require vast amounts of data, the quality of that information will affect the quality of the output.

What can businesses do to ensure they’re using AI both effectively and responsibly?

In this episode of the ITPro Podcast, Jane and Ross are joined by Amanda Stent, head of AI strategy and research in the office of the CTO at Bloomberg, to examine what responsible AI is, how organizations can use it, and what has been achieved at Bloomberg.

Highlights

"The (Bloomberg) terminal gives users access to more than 17,000 news providers, not just Bloomberg News, more than 1000 research brokers, more than 400 million documents from companies themselves, and billions and billions and billions of ticks – that's prices – every day for equities, bonds, commodities, derivatives, any kind of financial instrument you can think of. So, in that context, accuracy is paramount. If we hallucinate or do something that's otherwise incorrect, markets may move, and that might be bad."

"We have guardrails that we run on every input to and output from a Gen AI system ... (which) are specific to financial services. For example, we don't want users to be injecting code into our systems. That's a generic guardrail, and we also are in the business of offering financial information, but not financial advice. So if you say. What's a buy case for IBM? We should give it to you. If you say, 'Should I buy IBM?' we should say, 'Nope, I can't answer that question because that's not something we're in the business of doing', and that's a finance-specific guardrail."

"We have analysts using our AI systems to ... help them write their research reports more quickly, more easily to cover more companies, to understand the context of a company with its sector and its industry, to write on-demand reports for clients instead of a monthly or a quarterly report. We have portfolio managers doing the same thing, writing on-demand reports for clients using AI instead of quarterly reports. So these are some of the new ways in which people are using AI. But to me, traditionally it was about efficiency and signal generation. And today I think it's about effectiveness. So it's helping people become more effective in how they use AI."

Highlights

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