demonstration
Beyond Market Intelligence keeps demonstration in one place: 4 stories so far. The section currently leads with “See how attackers can trick AI spreadsheets into ignoring your instructions”, “How Frontier AI is quietly reshaping robot learning from demonstration”, and “Exploring how one small edit can unravel a chess AI's deepest strategy”. Attackers can trick AI spreadsheets into ignoring your instructions by exploiting how these models process context. Roboticists in Learning-from-Demonstrations and Behavioral Cloning are asking a timely question: are frontier LLMs reshaping their field, or is it charting its own course? Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every demonstration story on Beyond Market Intelligence, newest first.
See how attackers can trick AI spreadsheets into ignoring your instructions
Attackers can trick AI spreadsheets into ignoring your instructions by exploiting how these models process context. It's a reminder that even intelligent tools have blind spots. Patience is key when testing these systems, they can be slow, but rushing opens the door to manipulation. For deeper insight into how language models handle context, our piece on "How language models learn to copy context with hash tables" explores the underlying mechanics. Understanding these vulnerabilities helps you stay ahead of bad actors.
How Frontier AI is quietly reshaping robot learning from demonstration
Roboticists in Learning-from-Demonstrations and Behavioral Cloning are asking a timely question: are frontier LLMs reshaping their field, or is it charting its own course? The answer is nuanced. These models are influencing how demonstrations are encoded and generalized, yet core LfD principles remain independent. ViTs and VLAs are creeping into pipelines, offering new perceptual grounding. For a deeper look at how distributed systems underpin such training, our guide, *Unlock LLM Training: A Practical Guide to Distributed Algorithms*, is worth exploring.
Exploring how one small edit can unravel a chess AI's deepest strategy
One head. Out of 128. Remove it, and a chess transformer stops seeing Morphy's legendary queen sacrifice entirely. That's the kind of surgical fragility worth pausing over, not because it breaks the model, but because it reveals how much meaning concentrates in a single parameter. The Chessformer Lens demo makes that visible, and the GitHub notebooks let you verify it yourself. For anyone curious about interpretability, this is a hands-on invitation to explore.
From Demos to Production: Building AI That Handles the Unexpected
A team wraps a few LangChain calls in a loop, adds tools, and calls it an agent. The demo impresses. Then production hits an unexpected input, and the workflow breaks. That is not agency; that is automation wearing a costume. The real difference is adaptability, and confusing the two costs you in downtime. For a deeper look at how agents truly learn, check out our piece on editing context instead of weights.