Pre-compile instructions so all pre-requisites are identified and resolved
Kirk Borne @KirkDBorne #infographic — Explaining supervised and unsupervised #MachineLearning algorithms
Source: https://linkedin.com/posts/opensourcecommunity_confused-between-supervised-and-unsupervised-activity-7100765879373619200-MCzj https://pic.twitter.com/CYCRriGK3e
Replying to @KirkDBorne
Convert this into a form so that it can be absorbed by any AI and used immediately without error. They are going to have to be given the meanings and parameters of the many acronyms you used. You will have to get it OUT of an image. You have pre-compiled it in your brain, so to you it is easy. If AIs cannot easily memorize and use it, the humans with imperfect memories, without pre-requisites, will not be able to. The AIs will have to recursively and blindly search,the humans will have to recursively and blindly search – unless you trace the full background, standardize it and have it ready to link to this one simplified instruction object. Pre-compile instructions so all pre-requisites are identified and resolved
IARPA @IARPAnews A fundamental rethinking of computer architectures that can revitalize performance growth trends in computing capabilities is long overdue. Learn how the AGILE program seeks to seed a new generation of computers with unprecedented pathways: http://bit.ly/3FWSJJG https://pic.twitter.com/S6cgcOPhGu
Replying to @IARPAnews
Bill, you are mistaken in saying “sparse”. The advantages of the approach you aim for here is massively parallel integration of data from many sources to focus on your objective. That only works when data is dense enough to converge using off-the-shelf statistical methods.
Bill, This solicitation status is “Closed”. The teams already chosen. No way to change what they can do.They are not going to share the results or problems, so your fundamental transformation of computer science is unlikely. So why bother to tell anyone else? Sparse = Closed?
Bill, This solicitation status is “Closed”. The teams already chosen. No way to change what they can do.They are not going to share the results or problems, so your fundamental transformation of computer science is unlikely. So why bother to tell anyone else? You are mistaken in saying “sparse”. The advantages of the approach you aim for here is massively parallel integration of data from many sources to focus on your objective. That only works when data is dense enough to converge using off-the-shelf machine learning methods. The “integration” concentrates the information so off-the-shelf methods for dense data will work. But will not work with closed groups. Best wishes.
Nature Microbiology @NatureMicrobiol
Read all about alternatives to antibiotics reviewed in @NatureRevMicro https://nature.com/articles/s41579-023-00993-0
Replying to @NatureMicrobiol and @NatureRevMicro
I think you left out electromagnetics and nanoacoustics, and a few others.
