Someone just referenced this article I wrote in '15 about #ai https://
bit.ly/42mA3zd So much has changed in the technology, but little in how we approach it. My examples would have changed, but not the "how to raise AI" advice…almost 8 yrs later!
@jeggers
-
How to Raise AI: Timeless Principles Beyond Technology
By
–
-
Organizing People and Processes for AI: Banker Talk
By
–
Dang, @robmay
, perfect timing. I'm giving a talk this morning to bankers on organizing (as in people and process) for #ai. Just added your head/subhead as my first slide https://
bit.ly/3ZY6OAQ Fits perfectly with my talk. -
Boston Startup Community Shows Strong Support Network
By
–
Dang, so sorry I missed this due to travel. #bostonStartup scene supporting each other. #bostonStrong Thx @theinnocrew
! -
Equal Pay Day: Women earn 8% less than male colleagues
By
–
Current affairs for women: today is Equal Pay Day "[controlling] for 'everything we could find reliable data on' found that women still earn about 8% less than their male colleagues for the same job." Even worse for women of color. https://
n.pr/3Te3tuZ #feetOffOurNecks -
Validating Tech Solutions Through Lived Experience and Expertise
By
–
"The surest cure for BS is exposing it to the scrutiny of people who truly, deeply understand a problem through years of lived experience." +100! Plus "the problem" isn't crypto or #ai but what those techs are trying to solve in that use case. Thx @EthanZ for a def recommend read
-
AI Code Generation: Beyond Syntax Errors to Production Quality
By
–
Rec'd read: "AI may turn out to be bttr at producing code than writing academic papers, as determining whether a program runs w/o syntax errors is far less subjective" https://
bit.ly/41vgF2C Beyond syntax: Is it prod-ready code fitting the use case? Was the generation legal? 1/2 -
Tech Giants Shifting Away From Expert Bias in Algorithms
By
–
Yes, and the big tech companies seem to be moving away from that (page rank for example was an "expert" bias), but the pendulum could swing back.
-
LLMs Beyond Statistics: Rethinking AI Learning Paradigms
By
–
It needs to learn in a new way beyond statistics. LLMs rely on stats. So the path that they are going down isn't one that is likely to bear fruit.
-
Teaching Bears Statistics: AI Training Data and Misinformation Risks
By
–
But who is teaching the bear? Right now, it is statistics…. and well, lies, damn lies, and ….
-
AI Technology Accuracy Issues: Confident Wrong Answers
By
–
The tech will be interesting. But we need to make sure the tech is generating correct results. It's getting basics wrong, tho answering confidently: See https://
bit.ly/3K6MxEe + the image, 2 of 3 of the co's listed for my leadership roles & both of the orgs are wrong. 3/4