Marketing Via AI

Great insights for business leaders as the value and necessity of preparing for #AI resources continues to become more relevant across industries.

Reducing AI Hallucinations: A Crucial Priority As business leaders increasingly look to adopt AI, we need to make evaluating and mitigating risks like AI "hallucinations" a top priority. Hallucinations refer to factual inaccuracies or unverified speculative content generated by AI systems. Researchers recently surveyed 32 techniques to diminish hallucinations in large language models (LLMs), the backbone of many AI apps. Here are three leading approaches: 1. Retrieval-Augmented Generation (RAG): Enhances LLMs by retrieving and conditioning responses on authoritative external knowledge bases. Effective at improving accuracy but can be resource intensive to implement at scale. 2. Chain-of-Reasoning (CoR): LLMs verify their own responses through automatically generated questioning. Promising for boosting accuracy with additional computation cost. 3. Knowledge Retrieval: Leverages model output values to validate content against known facts. Easier to integrate but dependent on efficiency of retrieval systems. Testing required, but focusing on hallucination mitigation abilities alongside practical implementation considerations allows us to create safer, more responsible AI. Reference: A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://lnkd.in/eDqzVjkk My evaluation criteria: - Effectiveness in Reducing Hallucinations: How well does the method mitigate inaccuracies? - Integration with Existing Systems: Can the method be easily integrated into your current technological infrastructure? - Scalability: Is the method suitable for your operation's scale? - Ease of Implementation: Consider the learning curve and resource requirements. - Cost: Evaluate both upfront and ongoing costs. - Performance Metrics: Look at speed, accuracy, and other relevant performance indicators. Results: Retrieval-Augmented Generation (RAG): A technique to enhance the responses of Large Language Models by incorporating information from external authoritative sources. - Effectiveness: High. It enhances response accuracy by leveraging external, authoritative knowledge bases. - Integration: Moderate. It may require adjustments to integrate with existing LLM infrastructures but utilizes pre-trained models for easier integration. - Scalability: High. Suitable for large-scale operations due to its robust external knowledge base. - Ease of Implementation: Moderate. Involves understanding and implementing seq2seq models and Dense Passage Retrieval. - Cost: Moderate to High. While using pre-trained models reduces initial costs, integrating and maintaining a dense vector index can be resource-intensive. - Performance Metrics: Good. Offers improved accuracy and relevance, although speed might be impacted due to external data retrieval.

With OpenAI's GPT Store live, there are now millions of possible paths to completing tasks or gaining new insights. You can find Marketing Via AI's first contribution to the GPT Store in our Value Proposition GPT. If you have 30min, I'd love for you to give a try! Our GPT is intended to provide serious marketers with a comprehensive exercise that can draw from internal company materials and/or your own expertise to create specific, targeted messaging. We built this GPT by harnessing decades' worth of knowledge management materials (knowledge graphs, definitions, elicitation exercises) and prompting the GPT to carefully consider new inputs and how they relate to the overall creation of marketing messages. Since yesterday's launch of the GPT Store and through our own internal use, we've seen over 40 value proposition exercises logged. I hope it can be helpful! Try it here: https://lnkd.in/g8bMN4Vi

Revolutionizing Adult Learning: How GPTs Enhance Andragogy in Corporate Learning and Development Imagine a corporate training session where every participant is deeply engaged, drawing from their own experiences, and finding immediate applications for what they learn. How? Enter GPTs - AI-powered assistants transforming adult learning. - https://lnkd.in/e4nu64gF Andragogy, or adult learning, has evolved. Let's explore how GPTs (Generative Pre-trained Transformers) are making a groundbreaking impact: Personalizing Learning with GPTs: - Customized Learning Journeys: GPTs analyze individual preferences to tailor learning experiences, promoting self-directed learning. - Dynamic Content Adaptation: These AI systems adjust content to fit the learner’s current needs and goals, ensuring relevance and applicability. Leveraging Adult Experience in Learning: - Experience Integration: GPTs encourage learners to incorporate their own experiences, enhancing the relatability and depth of the learning process. - Real-Time Scenario-Based Learning: They offer scenarios and challenges rooted in real-world contexts, aligning with adult learners' preference for practical, applicable knowledge. Problem-Solving and Engagement: - Interactive Problem-Solving: GPTs present real-life challenges, encouraging learners to apply problem-solving skills, crucial in adult learning. - Continuous Feedback Loop: With real-time feedback, these AI tools keep learners on track and motivated, adapting to their evolving learning curve.