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The Rise Of AI: How Enterprises Harness The Power Of Large Language Models

Sep 11, 2023 - forbes.com
Large Language Models (LLMs) are AI systems that can generate and understand natural language, with capabilities such as conversational dialogue, contextual question answering, summarization, translation, and sentiment analysis. Major companies like IQVIA, Anthropic, and SAP are piloting LLMs to increase productivity, boost efficiency, and improve customer satisfaction. However, the use of LLMs comes with risks such as spreading harmful content, amplifying biases, exposing sensitive data, and unpredictable behaviors, which can be mitigated through responsible development and adoption.

The article recommends that business leaders should start with a narrowly defined pilot, develop or leverage pre-trained LLMs fine-tuned for their industry, establish responsible AI reviews, upskill staff in complementary capabilities, and continuously monitor LLMs for accuracy and unintended behaviors. Businesses that adopt LLMs responsibly can revolutionize customer experience, increase productivity by 30% or more, automate repetitive tasks, create new revenue opportunities, and gain a competitive advantage.

Key takeaways:

  • Large Language Models (LLMs) are AI systems that can generate and understand natural language, with applications in dialogue, question answering, summarization, translation, and sentiment analysis.
  • Major companies like IQVIA, Anthropic, and SAP are piloting LLMs to increase productivity, boost efficiency and improve customer satisfaction.
  • While LLMs offer significant opportunities, they also come with risks such as spreading harmful content, amplifying biases, exposing sensitive data, and unpredictable behaviors. Responsible development and adoption are essential to mitigate these risks.
  • Businesses can use LLMs to revolutionize customer experience, increase productivity by 30% or more, automate repetitive tasks, create new revenue opportunities, and gain a competitive advantage if adopted responsibly.
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