These platforms can be used to address data quality, problematic bias, and privacy concerns.
January 26, 2024
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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.
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.
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José Parra-Moyano is a professor of Digital Strategy at the International Institute for Management Development (IMD Business School) in Switzerland. His research focuses on the management and economics of data and privacy, with a special focus on how organizations can use data analysis techniques and AI to increase their competitiveness. He is an award-wining teacher, whose research has been published in top tier academic journals.
KS
Karl Schmedders is a professor of Finance at the International Institute for Management Development (IMD Business School) in Switzerland, where he teaches and researches on strategy relevant topics, finance, decision making, and game theory. He holds a Ph.D. in Operations Research from Stanford University. His research focuses on quantitative methods in economics and finance. He has published numerous research articles in top tier international academic journals.
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Alex (Sandy) Pentland is the Toshiba Professor of Media Arts and Sciences with the Media Lab, Sloan School of Management, and College of Computing at MIT. Sandy directs MIT’s Connection Science and Human Dynamics research laboratories, advises the OECD, UN, and previously AT&T, Google, and American Bar Association, and co-led the World Economic Forum Personal Data initiatives.
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