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Four paradigms for Ethical Trusted AGI today

Artificial General Intelligence (AGI) has immense potential to deliver value at scale, but obstacles hinder its widespread adoption. Eighty percent of data projects fail, and over 90% face trust issues around models, translation, or data quality. Here are four approaches to overcome these challenges and unlock the power of AGI:

Define the Problem, Then the Solution

Like AlphaZero learning game rules or ImageNet's image classification, AGI excels with clear goals. Instead of chasing vague promises of transformation, focus on specific business problems and how AGI can solve them.

Prioritize Data Credibility

AGI depends on reliable data. Invest in collecting, validating, and structuring data for actionable insights. Graph databases can bridge the gap between data and knowledge, while rethinking ERP systems could revolutionize their potential.

Build a Data-Driven Culture

Success hinges on data literacy and trust in AI outputs. Encourage data use across the organization, decentralize ownership of data projects, and demonstrate proven returns to build this culture.

Embrace a Closed-Loop, Reasoned System

AGI needs a feedback loop for continuous improvement, similar to what enabled leaps in speech and image recognition. Closed-loop, reasoned systems promote trust and accountability.

Four new paradigms needed for AGI to be trusted and deliver on value promises
Four paradigms to reinvent AGI for commercial applications


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