Large language models (LLMs) like GPT-4 have captivated business leaders with the promise of enhanced decision-making, streamlined operations, and new innovation. Companies such as Zendesk and Slack have started using LLMs to advance customer support, improving satisfaction and reducing costs. Meanwhile, Goldman Sachs and GitHub are employing a similar AI to assist developers with code writing. Likewise, the company Unilever is using LLMs to help it respond to messages from customers, generate product listings, and even minimize food waste. Yet, off the shelf, LLMs don’t offer the plug-and-play solution companies might be hoping for. When confronted with an organization’s unique context, they often underperform.
How Data Collaboration Platforms Can Help Companies Build Better AI
These platforms can be used to address data quality, problematic bias, and privacy concerns.
January 26, 2024, Updated February 01, 2024
Summary.
There are three immediate challenges for companies that want to train fine-tuned AI models: 1) they require extensive, high-quality data — a scarce resource for many enterprises, 2) third-party AI models can include problematic biases, and 3) training fine-tuned models with users’ personal data may result in privacy violations. However, data collaboration platforms can help address these challenges. They can provide a privacy-preserving training space on high-quality, abundant data, ensuring compliance with privacy laws and unleashing the full potential of fine-tuned models.
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Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Excel in a world that's being continually transformed by technology.