📈Investment in AI is growing, with over 70% of companies planning to increase their investment in 2023.
🚀Fine-tuning and reinforcement learning from human feedback are key to improving generative AI performance.
🔍Enterprises are shifting from experimentation to implementation of generative AI.
💡Base models often require fine-tuning with specialized data for specific enterprise tasks.
🌍Generative AI has the potential to disrupt various industries, including finance, retail, and media.
What is fine-tuning in generative AI?
—Fine-tuning involves training base models with domain-specific data to improve performance on specific tasks.
How does reinforcement learning from human feedback work?
—Reinforcement learning from human feedback incorporates human preferences into model outputs, improving interactivity and accuracy.
What are the challenges of fine-tuning models?
—Acquiring training data and managing ML infrastructure are key challenges in fine-tuning large language models.
Which industries are adopting generative AI?
—Generative AI is being adopted across industries, including finance, retail, and media.
Why is investment in AI increasing?
—Companies recognize the potential of generative AI to transform their business models and gain a competitive edge.
00:03Introduction to the 2023 Zeitgeist report and the importance of generative AI in the enterprise.
02:59The growth of investment in AI and the shift from experimentation to implementation of generative AI.
04:57The role of fine-tuning and reinforcement learning from human feedback in improving generative AI performance.
08:59Challenges faced in fine-tuning models, including acquiring training data and managing ML infrastructure.
11:27Generative AI's potential to disrupt various industries, such as finance, retail, and media.
