- Mar 19, 2025
- 4 min read
Hello, AI Enthusiasts.
Halfway through—are you thriving, surviving, or letting AI do the work?
With AI churning out text, images, and even code, spotting machine-made content is getting tricky. Some models are so good, even AI struggles to tell the difference. Researchers think cryptographic watermarking might be the key—can AI ever be truly traceable?
AI is stepping into instructional design, helping professors stress-test assignments before students even see them. Smarter courses, fewer grading nightmares.
Nvidia’s latest AI chip drop signals big changes ahead—get ready for Blackwell Ultra and Rubin.
Midweek madness over. Now let’s see if AI breaks the internet by Friday.
Here's another crazy day in AI:
A deeper look at cryptographic watermarks for AI content
Using AI to stress-test assignments and improve course design
Nvidia reveals next-gen AI hardware with Blackwell Ultra and Rubin
Some AI tools to try out
TODAY'S FEATURED ITEM: Cryptography Meets AI Content
Image Credit: Wowza (created with Ideogram)
How can we trust what we see online when AI-generated content becomes indistinguishable from human-created work?
As AI-generated content becomes more common—text, images, videos, and even code—it’s getting harder to distinguish what’s human-made from what’s machine-created. With AI models producing increasingly realistic outputs, the need for a reliable way to verify content origins is more pressing than ever. Researchers are exploring cryptographic watermarks as a possible solution, embedding hidden markers within AI-generated material to help trace where it came from.
In a recent Cloudflare Blog post, Research Engineers Teresa Brooks-Mejia and Christopher Patton take a closer look at how cryptographic watermarks work, their potential role in AI transparency, and the challenges involved in making them practical. Unlike traditional watermarking techniques, which often rely on metadata, cryptographic watermarks are embedded directly into the content itself. This approach could make them more resistant to tampering and removal, but it also raises new technical and security questions.
It reveals several important points:
Why content verification matters – As AI tools become more advanced, being able to confirm whether something was generated by a machine or a human could help prevent misinformation and maintain trust.
How cryptographic watermarks work – These watermarks are embedded into the content itself rather than stored as separate metadata, making them more difficult to remove.
Challenges in making them effective – A watermark must be detectable when needed, but not affect the quality of the content or be easy to forge.
The role of cryptography – Researchers are testing techniques like pseudorandom codes to create watermarks that are both subtle and secure.
What’s next – While promising, these methods are still being developed and will need further refinement before they can be widely implemented.
Watermarking AI-generated content is an intriguing idea, but it comes with trade-offs. A watermark needs to be subtle enough that it doesn’t interfere with the content itself, yet strong enough to resist manipulation. It also raises questions about enforcement—who decides which content needs a watermark, and how would these systems be adopted across different platforms? These are not easy challenges to solve, especially as AI continues to evolve.
Beyond just identifying AI-generated content, this research points to a broader issue: as AI becomes more integrated into digital creation, we need tools to help navigate questions of authenticity and provenance. Whether cryptographic watermarks become a standard feature remains uncertain, but the need for transparency in an AI-driven world is clear. The conversation about trust in digital content is just beginning, and how we approach it will shape the way we interact with AI-generated material in the years to come.
Read the full article here.
OTHER INTERESTING AI HIGHLIGHTS:
Using AI to Stress-Test Assignments and Improve Course Design
/Nathan Pritts, PhD on Faculty Focus
AI is emerging as a valuable tool for educators—not just students. Nathan Pritts, a leader in higher education, explores how faculty can use AI to stress-test assignments, simulating a range of student responses to identify potential pitfalls, misinterpretations, or areas needing clarification before students even engage with the material. By analyzing prompts and predicting student challenges, AI helps refine instructional design, saving faculty time while improving student outcomes. While human expertise remains essential, AI offers a powerful preemptive approach to course development.
Read more here.
Nvidia Reveals Next-Gen AI Hardware with Blackwell Ultra and Rubin
/Kif Leswing on CNBC
Nvidia has introduced two major AI hardware innovations—Blackwell Ultra and the upcoming Rubin chip family—at its annual GTC conference. Blackwell Ultra, shipping later this year, is designed to boost AI inference speeds and efficiency, while Rubin, expected in 2026, will introduce Nvidia’s first custom CPU alongside next-gen GPUs. As Nvidia moves to an annual chip release cycle, the industry is watching closely to see how these new processors will shape the future of AI workloads, cloud computing, and enterprise adoption.
Read more here.
SOME AI TOOLS TO TRY OUT:
Zoom AI Companion – AI agents for meeting productivity and more.
Kintsugi – Automates sales tax tracking, calculation, and filing.
Aha – AI-powered influencer marketing, from discovery to optimization.
That’s a wrap on today’s Almost Daily craziness.
Catch us almost every day—almost! 😉
EXCITING NEWS:
The Another Crazy Day in AI newsletter is on LinkedIn!!!
Leveraging AI for Enhanced Content: As part of our commitment to exploring new technologies, we used AI to help curate and refine our newsletters. This enriches our content and keeps us at the forefront of digital innovation, ensuring you stay informed with the latest trends and developments.
