Category: AI Guardrails
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From Frontier to Framework: What AI Adoption Gets Wrong
In Part 2 of a 4-part series, we explore what marketers get wrong about AI adoption and internal frameworks.
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California Gets Serious About Safe, Trustworthy AI And Leads the Way
California’s SB 53 AI bill helps to deliver AI regulation that combines innovation with responsibility and accountability.
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Elon Musk’s Grok AI: Private Chats Leaked on Google Search
Grok AI has been indexing user chats on Google. More than 370,000 chats are now available for anyone to see online.
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Maximizing AI Performance: Fine-Tuning and RAG Explained
Why you need RAG and fine-tuning. Both rely on additional data to improve model performance, but they use that data differently.
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The Troubling Risks Behind XAI’s Girlfriend & Grok’s Spicy Mode
XAI’s “Companions” are hyper-sexualized AI girlfriends and Grok’s Spicy mode lets boys create sexualized deep fakes of classmates. Here’s a look at these dangerous “features” from Musk.
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AI in Education: New Federal Guidance & Funding Opportunities
The U.S. Department of Education wants your voice on AI in schools. Funding contingent on AI priorities. Comment by August 20!
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Claude 4 Opus Sparks Backlash Over “Whistleblower” Behavior
If Anthropic’s Claude thinks you are using the model to “immoral” reasons, it could alert the media, contact regulators or lock you out. Here is a look at the industry reaction.
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Meta’s Fact-Checking Rollback: Implications for AI & Digital Information Quality
Meta’s decision to abandon fact-checking raises concerns about the quality of future AI training data and the digital information ecosystem.
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Top Six Impactful AI Trends for Marketers in 2025
The six most impactful trends in AI and Marketing for 2025. From digital twins and synthetic emotions to the deregulation of AI and more.
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Trump’s Victory: What It Means for AI Policy and Regulation
A look at the anticipated impact of the Trump administration on AI policies, advancements, and regulations.
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The Future of AI: Scaling Laws and the Path Forward
A growing divide has emerged in the AI community regarding the potential for continued advancement, including the creation of “scaling laws.”