As a consultant I can’t tell you how many times I have seen bad decisions (the what) drive by irrational human factors that drive the context (the why). Corporate politics , emotional risk aversion to short term negative implications ,and ignorance to important variables are just some of the examples of bad (sometimes long term) strategic decisions being made. What happens to “context” when people are documenting and saying one thing to keep their job ls, but quietly muttering dissenting
disgust to themselves. It will be interesting to see how these human factors get resolved in context graphs.
Really enjoyed the discussion on context graphs, especially the point that decision rationale emerges from observing recurring patterns rather than retrospective extraction. The idea that embedded operators could be the key to building useful context at scale feels like an important missing layer in the current debate. In areas like legal, this decision rationale can be used to reduce algorithmic bias and ensure transparency. This can provide insight for courts on how exactly these AI Agents operate and where do they're embedded biases emerge from.
Hi Chris-important to note that foundation cap plagiarized from my work. I have reported to Substack. This is an established domain of research and practice.
Not just my research but the research of EU commissioned Cefriel group, the perks project, and peer reviewed scholarly articles based upon uses cases involving Siemens and BOSCH, all cited in my work.
And they lifted exact sentences from my writing, not just the thesis and framework I presented.
I have reported the violations to Substack and asked foundation cap and their authors to add attribution and corrections but met by silence.
Not sure why data professionals and the like are falling over the context graph content which is written by VCs with zero experience in data. They stripped the research, prior art and the years of founded work produced by the established knowledge management domain.
As a consultant I can’t tell you how many times I have seen bad decisions (the what) drive by irrational human factors that drive the context (the why). Corporate politics , emotional risk aversion to short term negative implications ,and ignorance to important variables are just some of the examples of bad (sometimes long term) strategic decisions being made. What happens to “context” when people are documenting and saying one thing to keep their job ls, but quietly muttering dissenting
disgust to themselves. It will be interesting to see how these human factors get resolved in context graphs.
Agree. I’m haunted by those bad decisions and when I start to see patterns, I build much stronger convictions about what not to do.
Really enjoyed the discussion on context graphs, especially the point that decision rationale emerges from observing recurring patterns rather than retrospective extraction. The idea that embedded operators could be the key to building useful context at scale feels like an important missing layer in the current debate. In areas like legal, this decision rationale can be used to reduce algorithmic bias and ensure transparency. This can provide insight for courts on how exactly these AI Agents operate and where do they're embedded biases emerge from.
Your quoted text is actually plagiarized directly from my text almost verbatim.
Hi Chris-important to note that foundation cap plagiarized from my work. I have reported to Substack. This is an established domain of research and practice.
https://substack.com/@jessicatalisman/note/c-208499397?r=ee6qm&utm_source=notes-share-action&utm_medium=web
https://jessicatalisman.substack.com/p/the-trillion-dollar-rebranding?r=ee6qm
Thanks, Jessica, for bringing this to my attention. I wasn't aware of your research in this area and I appreciate your efforts to build a foundation.
Not just my research but the research of EU commissioned Cefriel group, the perks project, and peer reviewed scholarly articles based upon uses cases involving Siemens and BOSCH, all cited in my work.
And they lifted exact sentences from my writing, not just the thesis and framework I presented.
I have reported the violations to Substack and asked foundation cap and their authors to add attribution and corrections but met by silence.
Not sure why data professionals and the like are falling over the context graph content which is written by VCs with zero experience in data. They stripped the research, prior art and the years of founded work produced by the established knowledge management domain.