The allure of Generative AI (GAI) has been captivating, promising transformative change across industries. However, recent insights from Gartner, featured in The Register, call for a sobering reality check, highlighting GAI's current limitations and emphasizing a measured approach.
The Register's Insights: Tempering the Hype
- AI's "Brute Force" Phase: Gartner's Erick Brethenoux observes that GAI is currently in its "brute force" phase, relying heavily on specialized hardware and inefficient programming techniques. He cautions that this reliance is unsustainable, as history has consistently shown the eventual dominance of more efficient and cost-effective solutions.
- The "Recess" Period: The article describes a period from late 2022 to early 2024 as a "recess," where organizations diverted their focus from profit-generating activities to explore GAI's potential. However, many have since returned to established AI methods or adopted "composite AI" approaches, suggesting a growing recognition of GAI's limitations.
- Hype vs. Reality: The disparity between the hype surrounding GAI and its actual use cases is stark. The article notes that GAI currently accounts for only 5% of use cases, despite dominating 90% of the conversation. This discrepancy underscores the need for realistic expectations and a more pragmatic approach to GAI adoption.
- Composite AI as a Solution: The article advocates for "composite AI," combining GAI with established AI techniques like machine learning, knowledge graphs, or rule-based systems. This approach is presented as a more reliable and effective way to leverage GAI's potential while mitigating its risks.
Gartner's Cautions: A Call for Pragmatism
- Limited Applicability: Erick Brethenoux further cautions against overestimating GAI's capabilities, suggesting it's suitable for only around 5% of use cases. This aligns with the growing realization among organizations that GAI often fails to deliver on its promises.
- Hallucinations: The article also raises concerns about GAI's reliability, highlighting the issue of "hallucinations" or factually incorrect GAI outputs. While improvements have been made, the risk of hallucinations remains a significant concern, particularly for mission-critical applications.
Proactive's Composite AI Approach: Beyond Hype, Real Results
At Proactive Technology Management, we embrace GAI's potential but advocate for its judicious use within a broader AI ecosystem. Our fusion development team specializes in crafting composite AI systems that leverage GAI's strengths while mitigating its risks. We echo the sentiment from the experts quoted in the article, who recommended "composite AI as a safer approach, and adopting guardrails that use a non-generative AI technique to check generative results."
Indeed, we do find that composite AI, coupled with guardrails, provides a safer and more effective approach to AI solutions for business. Here's our approach:
- LLM-as-judge Quality Ratchets: We employ Large Language Models (LLMs) as discerning judges, evaluating GAI outputs for quality and accuracy, thus ensuring only reliable and trustworthy information is used.
- Reinforcement Learning through Human Feedback (RLHF): We leverage RLHF to continuously refine our AI models based on human input, fostering ongoing learning and improvement. This enables us to significantly reduce hallucination rates, making LLMs more reliable for broader tasks beyond content generation, such as classification, summarization, and even action item extraction.
- Composite AI Framework: We strategically blend GAI with other AI techniques – machine learning, knowledge graphs, rule-based systems – to create robust, effective solutions. This layered approach provides an additional layer of checks and balances, further minimizing the risk of hallucinations and ensuring the accuracy and reliability of AI outputs.
Case Study: Empowering Faith-Based Outreach
We partnered with a large faith-based organization to revolutionize their market research and content creation.
- Multiclass Classification and Market Segmentation: Machine learning models analyzed data, enabling precise audience segmentation based on demographics, behavior, and engagement.
- Association Rule Mining and Market Basket Analysis: We used market basket analysis to identify content-audience relationships, informing personalized recommendations.
- Generative AI: High-quality, targeted content across various formats was generated, saving time and resources while maintaining a consistent voice, applying LLM-as-judge evaluators to prevent low-quality content from wasting human editors' time.
- Interpretable ML Dashboards: Interactive visualizations empowered leadership with actionable, data-driven insights, enabling easy execution and thumbs up/thumbs down approvals of entire marketing campaigns.
The result? Deeper audience understanding, streamlined content creation, enhanced engagement, and data-driven decision-making.
Proactive's fusion development team exemplifies how to leverage AI's full potential, responsibly and effectively. By integrating GAI into a composite AI framework and employing robust guardrails like LLMs as judges and RLHF, we ensure that GAI's power is harnessed safely and reliably, extending its applicability to a wider range of tasks.
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AI | Integrating Reliable, Scalable Tech Solutions | Driving Efficiency Through Client-centric Approach
1 个月Appreciate you sharing this, Michael.
Software Development | Managed Team | Team extestion | AI/ML Development
2 个月Thanks for sharing Michael, commenting for better reach ??