INTHEBLACK October/November 2024 - Magazine - Page 37
Companies like KPMG
Australia and Spark in New
Zealand have developed
customised large language
models (LLMs) to improve
efficiency and productivity.
LLMs can be hugely beneficials
for organisations who get them
right, but robust guardrails are
required to mitigate risk factors
and ensure efficacy.
Data quality and volume,
in-house skills and a clear
AI governance framework
should be in place before
making any technology live.
The lure
of LLMs
Large language models (LLMs) are beginning to revolutionise
how we work. Like any form of artificial intelligence, LLMs need
clear governance and risk management to be effective.
Words Susan Muldowney
In March 2023, KPMG Australia
welcomed a new team member to the fold.
Dubbed “KymChat”, this new addition
is a customised version of ChatGPT, the
powerful large language model (LLM)
chatbot released in 2022 by artificial
intelligence (AI) research laboratory
OpenAI.
KPMG Australia is among a growing
number of organisations to create their
own LLM. Other organisations exploring
their options include Westpac, Australian
construction company John Holland and
New Zealand telco Spark.
Meanwhile, customised LLMs look set to
take off in Singapore, with the government
last year announcing the “AI Trailblazers”
joint venture with Google Cloud. The
program is designed to help organisations
to identify real-world challenges that can
be addressed with generative AI, build
prototypes and bring them to production.
LLMS EXPLAINED
An LLM is an advanced form of generative
AI (GenAI) that can understand and
generate human-like text. It is considered
GenAI because it can create new content. An
LLM works by analysing large amounts of
text data, such as books, articles and websites,
to learn language patterns. This allows it to
predict and generate relevant content based
on the input it receives. This means it can
help with a range of tasks, from writing
reports and answering questions to engaging
in conversation.
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