AI isn't just LLMs: My New Framework for Describing AI to Business Leaders

2023-05-09 · Source: Mike Talks AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, medium

Summary

A new framework is proposed to help business leaders understand the diverse landscape of Artificial Intelligence beyond Large Language Models (LLMs). The framework categorizes AI into four distinct periods: "2012 AI" refers to Deep Learning, "2017 AI" encompasses predictive and prescriptive analytics, "2026 AI" represents the current capabilities of LLMs, and "Future AI" addresses speculative, advanced LLM capabilities like Artificial General Intelligence (AGI) or Artificial Super Intelligence (ASI). The author emphasizes that while LLMs currently dominate the "AI" discourse, the "2017 AI" toolkit, including traditional machine learning and analytics, remains highly valuable and should not be overlooked by businesses. This approach aims to clarify the historical evolution and current relevance of various AI tools.

Key takeaway

For Directors of AI/ML or VPs of Engineering evaluating AI investments, recognize that "AI" extends beyond current LLMs. Your teams should continue investing in and utilizing "2017 AI" tools like predictive and prescriptive analytics. These foundational techniques provide significant business value and can establish crucial guardrails for your LLM implementations, making LLMs more accessible and effective. Ignoring these established tools risks missing out on proven capabilities.

Key insights

AI encompasses more than just LLMs, with a historical framework clarifying its diverse applications and continued value.

Principles

• LLMs dominate current "AI" perception.
• "2017 AI" (analytics) remains valuable.
• Distinguish current from future AI capabilities.

Method

The framework categorizes AI by historical periods: 2012 AI (Deep Learning), 2017 AI (Predictive/Prescriptive Analytics), 2026 AI (Current LLMs), and Future AI (speculative capabilities).

In practice

• Use the 4-category AI framework.
• Invest in "2017 AI" tools.
• Apply "2017 AI" for LLM guardrails.

Topics

• AI Frameworks
• Large Language Models
• Deep Learning
• Predictive Analytics
• Prescriptive Analytics
• Business Intelligence

Best for: Director of AI/ML, VP of Engineering/Data, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by Mike Talks AI.