The AI Information Chain

A common mistake we've observed is equating generative AI with the entirety of artificial intelligence. This misconception can obscure the broader and richer landscape of AI technologies that have been in use for years.

The AI Information Chain

To clarify, we've developed a framework outlining the AI information chain, which encompasses three critical components: Data, Logic, and Learning.

Data

• Sources of Information: AI systems draw from various sources such as data tables, algorithm outputs, documents, the web, and media (pictures and videos). Generative AI plays a role here, but it's only one piece of the puzzle.
• Access Methods: These include APIs, SFTP, RAG (retrieval-augmented generation), and NLP (natural language processing). Effective AI implementation requires integrating these methods to create a seamless data pipeline that feeds into algorithms continuously.

Logic

• Outcomes: AI algorithms process inputs to make decisions. This involves analyzing states, actions, and context to derive meaningful insights. Generative AI contributes by generating vectors and embeddings that can be used for decision-making.
• Decision Methods: Techniques such as reinforcement learning, gradient descent, and tree-based methods are employed. Generative AI, primarily using vectors, fits into this logic framework but doesn't encompass all decision methods.

Learning

• Outcomes: The results produced by AI include classifications, similarity scores, generated content (text, images), and more. Generative AI excels at producing new content but is part of a broader set of outcomes from various AI methods.
• Learning Methods: AI systems learn from outcomes through reward functions, learning curves, and human-in-the-loop feedback. This continuous learning loop is essential for maintaining and improving AI performance.

Understanding Generative AI's Role

Generative AI's ability to create content from prompts has undoubtedly revolutionized user interaction with AI. It's accessible, user-friendly, and provides immediate, tangible results. However, it's a subset of AI techniques. Traditional AI methods, such as reinforcement learning and gradient descent, continue to be vital for tasks that require deep computational analysis and probabilistic reasoning.

For instance, creating a reinforcement learning agent requires specialized tools and expertise, which is not as straightforward as generating content with a large language model (LLM). The entry barrier for generative AI is lower, making it easier for broader applications. However, this ease of use can lead to misapplications, where generative AI is deployed for tasks better suited to other AI methods.

The Broader Implications

Ignoring the broader AI spectrum and focusing solely on generative AI can lead to significant operational risks. For example, while generative AI can generate impressive outputs, it may not be the best fit for tasks requiring high computational accuracy or complex decision-making based on probabilistic models. Misapplying generative AI in these scenarios can result in failures and inefficiencies.

At OnCorps, we emphasize a balanced approach to AI. Our AI solutions integrate generative AI with traditional AI methods to leverage the strengths of each. This holistic approach ensures we provide robust, reliable, and effective AI-driven insights and automation for our clients.

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