clean up

README.md

@@ -62,23 +62,24 @@ CK consists of several ongoing sub-projects:
 
 ### Motivation behind CK and CM projects
 
-* ACM REP'23 keynote about MLCommons CM: [ [slides](https://doi.org/10.5281/zenodo.8105339) ] 
+* ACM REP'23 keynote about the MLCommons CM automation framework: [ [slides](https://doi.org/10.5281/zenodo.8105339) ] 
 * ACM TechTalk'21 about automating research projects: [ [YouTube](https://www.youtube.com/watch?v=7zpeIVwICa4) ] [ [slides](https://learning.acm.org/binaries/content/assets/leaning-center/webinar-slides/2021/grigorifursin_techtalk_slides.pdf) ]
 
 ### Documentation
 
 **We plan to rewrite and simplify the CM documentation and tutorials based on user feedback in Q2 2024 - please stay tuned for more details**.
 
-* [News](docs/news.md)
-* [Getting Started Guide and FAQ](docs/getting-started.md)
+* [CM Getting Started Guide and FAQ](docs/getting-started.md)
   * [Common CM interface to run MLPerf inference benchmarks](docs/mlperf/inference)
   * [Common CM interface to re-run experiments from ML and Systems papers including MICRO'23 and the Student Cluster Competition @ SuperComputing'23](docs/tutorials/common-interface-to-reproduce-research-projects.md)
   * [CM automation recipes for MLOps and DevOps](cm-mlops/script)
   * [Other CM tutorials](docs/tutorials)
 * [Full documentation](docs/README.md)
+* [CM development tasks](docs/taskforce.md#current-tasks)
 * [CM and CK history](docs/history.md)
 
 
+
 ### Acknowledgments
 
 This open-source technology is being developed by the [MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md)

docs/README.md

@@ -4,12 +4,14 @@
 
 Collective Mind (CM) is a lightweight, non-intrusive and technology-agnostic workflow automation framework 
 being developed by the [MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md)
-based on the feedback from the [the community, MLCommons members and individual contributors](CONTRIBUTING.md).
+based on the feedback from the [the community, MLCommons members and individual contributors](../CONTRIBUTING.md).
 
 The goal is to provide a common, simple and human-readable interface to help users encode their knowledge
 about how to build, run and customize diverse AI/ML apps, benchmarks and research projects across 
 continuously changing models, datasets, software and hardware from different vendors in a unified and automated way.
 
+You can find on-going development tasks [here](https://github.com/mlcommons/ck/blob/dev/docs/taskforce.md#current-tasks).
+
 * [Getting Started Guide and FAQ](getting-started.md)
 * [Introduction](introduction-cm.md)
 * [CM installation and customization](installation.md)

docs/history.md

@@ -17,16 +17,14 @@ The archive of this discontinued framework is available in the [/ck directory](.
 
 # MLCommons Collective Mind (CM)
 
-Grigori donated the CK technology to [MLCommons](https://mlcommons.org) in 2022 to benefit everyone and help establish
-the [MLCommons Task Force on Automation and Reproducibility](taskforce.md) co-led with [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh).
+Grigori donated the CK technology to [MLCommons](https://mlcommons.org) in 2022 to benefit everyone,
+prototyped a new version of CK called [Collective Mind (CM)](https://github.com/mlcommons/ck/tree/master/cm) 
+with [portable and reusable automation recipes for universal benchmarking and optimization of applications
+and systems (CM scripts)](https://access.cknowledge.org/playground/?action=scripts),
+and helped establish the [MLCommons Task Force on Automation and Reproducibility](taskforce.md)
+co-led with Arjun Suresh.
 
-This collaborative engineering effort resulted in the new version of CK called Collective Mind (CM)
-with [simple, portable and ready-to-use automation recipes](list_of_scripts.md) for MLOps and DevOps 
-developed by scratch with great help, feedback and contributions 
-from [the community](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md).
+We continue extending CM to support different MLCommons projects to modularize and unify benchmarking
+of ML/AI systems as a collaborative engineering effort based on [user feedback](../CONTRIBUTING.md).
 
-CM is now officially supported, developed and maintained by [MLCommons.org](https://mlcommons.org)
-with the help from [cKnowledge.org](https://cKnowledge.org) and [cTuning.org](https://cTuning.org).
-
-Please go to this [GitHub page](https://github.com/mlcommons/ck) to learn more about this project
-and check [the slides from ACM REP'23 keynote about CM](https://doi.org/10.5281/zenodo.8105339).
+You can learn more about the CM concept and motivation from the [keynote at ACM REP'23](https://doi.org/10.5281/zenodo.8105339).