AI Edition - It's AI Time - 230 [01-12-2024]

AI Edition - It's AI Time - 230

  1. Generative AI Risks and Governance: Organizations are increasingly focusing on addressing the risks of generative AI, particularly inaccuracy and intellectual property (IP) infringement. While these risks are gaining attention, cybersecurity remains a consistent concern. However, efforts to mitigate labor displacement risks have declined compared to previous years. Some organizations are moving to establish better AI governance frameworks, though widespread adoption is still limited.

  2. Smaller, More Efficient Models: The AI industry is leaning towards smaller models due to rising GPU costs and hardware shortages. Techniques like Low Rank Adaptation (LoRA) and Quantization are becoming popular, allowing efficient fine-tuning and deployment of AI models, particularly for resource-constrained scenarios.

  3. AI Applications Across Industries: Companies are leveraging generative AI to enhance productivity and revenue, particularly in areas like supply chain management and marketing. Most businesses report that generative AI implementation takes just a few months, with many using off-the-shelf models while customizing them for specific needs.

  4. Focus on Explainable AI: As AI models grow more complex, explainability is becoming a critical priority. Ensuring transparency in AI decision-making processes is vital for trust and reliability in applications like finance, healthcare, and legal sectors.

  5. Customized Local Models: There is a growing trend toward developing localized AI models to address specific needs in sensitive industries. These models can run on modest hardware, reducing reliance on third-party systems and safeguarding proprietary data.

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