updates

Author: nocomplexity

LLMrisks.md

@@ -0,0 +1,24 @@
+# Risks of using LLMs 
+
+Common advanced LLM powered systems can have severe security risks.
+
+Common security risks are:
+
+*  AI systems can malfunction when exposed to untrustworthy data, and attackers are exploiting this issue.
+
+*    New guidance documents the types of these attacks, along with mitigation approaches.
+
+*    Prompt injection.
+
+*    Leakage of personally identifiable information (PII)
+
+*    Harmful prompts. Relevant when you develop your ‘own’ LLM or LLM powered application.
+
+No foolproof method exists for protecting ML/AI systems from security hacks. This is problematic when ML/AI systems are used for health systems, transport systems or weapons. Misdirection is a common threat.
+
+Using LLMs for health saving systems or software that is used for safety applications (cars, trains, plains):
+
+:::{danger}
+The outcome of LLMs should never be trusted. Despite imense progress on LLMs and their applications:
+Transparency is often absent and outcomes should never ever be trusted without a **SOLID and GOOD** human assessment!
+:::

README.md

@@ -29,12 +29,10 @@ But you can also support the project by [buying a hard copy](https://www.amazon.
 
 If you like your name stated here: This publication is open source. Issues and pull requests are welcome.
 
-All contributors will be added to this list. 
+All contributors will be mentioned in the publication! 
 
 *So get involved in the discussions and make IT better!*
 
-The following people have contributed to this project:
- [name] [OPTIONAL email] [Optional Organization name ]
 
 
 # Licensing
@@ -43,7 +41,7 @@ When you submit text to which you hold the copyright, you agree to license it un
 * Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
 
 
-# Topics covered the Free and Open Machine Learning (version 1.0)
+# Topics covered the Free and Open Machine Learning
 
 * Why Free and Open Machine Learning. 
 

_toc.yml

@@ -1,30 +1,39 @@
 format: jb-book
 root: abstract
 parts:
+
 - caption: Core Concepts
   chapters:
   - file: preface
   - file: introduction
   - file: whyossml
+    sections:
+    - file: ethicalai
+
   - file: whatisml
   - file: openmldefinition  
   - file: ml-business-use
+    sections:
+    - file: businessofai
   - file: architecture
   - file: risks
+    sections:
+    - file: LLMrisks
   - file: nlp
   - file: ml-challenges
+
 - caption: FOSS ML Software
   chapters:
   - file: generatedfiles/overview
     sections:
-    - file: generatedfiles/mlresearchframeworks
     - file: generatedfiles/aiagentframeworks
-    - file: generatedfiles/mlsecurityscanner
     - file: generatedfiles/llmui
-    - file: generatedfiles/nlp
-    - file: generatedfiles/mltools
-    - file: generatedfiles/mlframeworks
     - file: generatedfiles/llms
+    - file: generatedfiles/mlframeworks
+    - file: generatedfiles/mlresearchframeworks
+    - file: generatedfiles/mlsecurityscanner
+    - file: generatedfiles/mltools
+    - file: generatedfiles/nlp
 
   - file: catalogue
     sections:
@@ -46,3 +55,4 @@ parts:
   - file: license
   - url: https://www.amazon.com/Free-Machine-Learning-Maikel-Mardjan/dp/B0863S9LQ5/ref=sr_1_2?qid=1585488090&refinements=p_27%3AMaikel+Mardjan&s=books&sr=1-2&text=Maikel+Mardjan
     title: Support this project:Buy a hardcopy version!
+  - file: sponsors

about.md

@@ -1,33 +1,144 @@
-About
-=====
-
-This publication to fight for real Free and Open Machine learning is
-initially started and created by Maikel Mardjan. Maikel is a hands-on
-practical business IT architect and loves to make simple designs for
-complex IT systems. Maikel has more than 25 years of relevant experience
-on various IT roles in famous (international) companies. Maikel holds
-both a Master (Msc) Business Studies of University of Groningen
-(<https://www.rug.nl/>) and a Master degree (Msc) Electrical
-Engineering, of Delft University of Technology
-(<https://www.tudelft.nl/en/>). Maikel is TOGAF 9 Certified and CISSP
-(Certified Information Systems Security Professional) certified. Maikel
-is also an OWASP member (<https://owasp.org/>) and supporter.
-
-Check <https://nocomplexity.com> for more information about Maikel.
-
-Machine learning is a complex technology. So we need simple and Free and
-Open solutions to create applications so that will solve complex
-problems we humans face. To trust machine learning applications there is
-simply no other option than using fully transparent technologies. So
-Free and Open in the spirit of the Free Software Foundation
-(<https://fsf.org>).
-
-If you or your company is committed to openness make sure to support the
-BM-Support.org Foundation. Supporting this foundation is free! Check
-<https://www.bm-support.org/join/>
-
-This publication would never have reached version 1.0 without your help.
-So I gratefully thank all people who devote time and knowledge to give
-input to this publication. Will we continue this FOSS machine learning
-journey so machine learning technology will stay Free and Open so
-everyone can benefit.
+# About NO|Complexity
+
+## Who is Behind NO|Complexity?
+
+[NO|Complexity.com](https://nocomplexity.com/) is dedicated to solve business IT challenges in a rapidly evolving world. I’m Maikel Mardjan, and I believe that many business IT solutions are overly complex. Simple solutions have many advantages. But creating simple solutions is not simple. Tackling complexity challenges requires an unique combination of expertise, skills and people.
+
+
+With experience across a range of architecture roles—including IT Architect, Security Architect, Business Architect, and Enterprise Architect—I have worked with various organizations to design IT systems that prioritize simplicity. I hold a Master’s degree (MSc) in Business Studies from the University of Groningen and a Master’s degree (MSc) in Electrical Engineering from Delft University of Technology. Additionally, I am TOGAF 9 Certified and hold CISSP (Certified Information Systems Security Professional) certification.
+
+Currently, I lead initiatives at NoComplexity.com, an innovative IT company focused on straightforward solutions. For more insights and updates, you can find me on X (despite my privacy and other concerns with BigTech...) at [@maikelmardjan](https://X.com/maikelmardjan).
+
+## Other NO|Complexity Publications
+
+### Cyber Security 
+
+% Start cards grid
+::::{grid} 3
+:class-container: text-center
+:gutter: 3 
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/securityarchitecture/introduction.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Open Security Reference Architecture**        
+^^^
+Cyber security can still be simple and effective.
+Use this Playbook to create better and faster security solutions for your security use case.
+:::
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/securitybydesign/intro.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Security By Design**        
+^^^
+Security by design is a proven method to develop products that are less vulnerable for cyber security threats.
+Master the topic quickly with this eBook.
+:::
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/securitysolutions/intro.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Open Security Solutions**        
+^^^
+Given the vast array of FOSS cybersecurity products available, this publication offers a handcrafted curated selection.
+:::
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/reports/SimplifySecurity.pdf
+:link-type: url
+{octicon}`book;2em;caption-text` **Simplify Cyber Manifest**        
+^^^
+A manifesto to revolutionize cybersecurity through simplification.
+:::
+
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/simplifysecurity/intro.html#
+:link-type: url
+{octicon}`book;2em;caption-text` **Simplify Security**        
+^^^
+Find open simple cyber solutions that work. Simplify cyber security to accelerate its effectiveness.
+:::
+
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/simplifyprivacy/intro.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Simplify Digital Privacy**        
+^^^
+This digital Playbook is all about protecting *your* digital privacy.
+:::
+
+
+::::
+% End of Cards grid
+
+### Machine Learning - AI and Tools
+
+% Start cards grid
+::::{grid} 3
+:class-container: text-center
+:gutter: 3 
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/fossml/abstract.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Free and Open Machine Learning**        
+^^^
+Freedom to control machine learning and AI technology is not self-evident. This Playbook gives you full control.
+:::
+
+
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/jupyterlab/intro.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Mastering JupyterLab**        
+^^^
+Supercharge your data science journey with Mastering JupyterLab. Use this Playbook to streamline your workflow and boost productivity. 
+:::
+
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/pythonbook/introduction.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Master Python for AI/ML**        
+^^^
+Python programming skills are essential for architects, engineers, and even managers in the IT industry. 
+Become a Python master to do more with AI/ML applications.
+:::
+
+ 
+::::
+% End of Cards grid
+
+
+### General
+
+
+% Start cards grid
+::::{grid} 3
+:class-container: text-center
+:gutter: 3 
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/documents/arplaybook/introduction.html
+:link-type: url
+{octicon}`book;2em;caption-text` **Architecture Playbook**        
+^^^
+A playbook providing practical tools to help create architectures and designs more quickly and effectively. 
+:::
+
+:::{grid-item-card}
+:link: https://nocomplexity.com/simplifyit/
+:link-type: url
+{octicon}`book;2em;caption-text` **Simplify IT**        
+^^^
+This is an eBook with proven scientific tools and frameworks to help you effectively solve complex IT problems and gain a comprehensive understanding of IT systems in a business context.
+:::
+
+
+
+::::
+% End of Cards grid

architecture.md

@@ -1,5 +1,5 @@
-ML Reference Architecture
-=========================
+# ML Reference Architecture
+
 
 When you are going to apply machine learning for your business for real
 you should develop a solid architecture. A good architecture covers all
@@ -67,8 +67,8 @@ good knowledge of machine learning principles and technology also a
 strong and deep knowledge of business and IT architecture and design
 aspects.
 
-The machine learning process
-----------------------------
+## The machine learning process
+
 
 Setting up an architecture for machine learning systems and applications
 requires a good insight in the various processes that play a crucial
@@ -105,8 +105,8 @@ You need to improve your machine learning model after the first test.
 Improving can be done using more training data or by making model
 adjustments.
 
-Architecture Building Blocks for ML
------------------------------------
+## Architecture Building Blocks for ML
+
 
 This reference architecture for machine learning gives guidance for
 developing solution architectures where machine learning systems play a
@@ -185,22 +185,22 @@ developing. Creating principles also makes is easier for third parties
 to inspect designs and solutions and perform risks analysis on the
 design process and the product developed.
 
-#### Example Business principles for Machine Learning applications
+### Example Business principles for Machine Learning applications
 
 In this section some general principles for machine learning
 applications. For your specific machine learning application use the
 principles that apply and make them SMART. So include implications and
 consequences per principle.
 
-#### Collaborate
+### Collaborate
 
 Statement: Collaborate Rationale: Successful creation of ML applications
 require the collaboration of people with different expertises. You need
 e.g. business experts, infrastructure engineers, data engineers and
 innovation experts. Implications: Organisational and culture must allow
 open collaboration.
 
-#### Unfair bias
+### Unfair bias
 
 Statement: Avoid creating or reinforcing unfair bias Rationale: Machine
 learning algorithms and datasets can reflect, reinforce, or reduce
@@ -211,14 +211,14 @@ gender, nationality, income, sexual orientation, ability, and political
 or religious belief. Implications: Be transparent about your data and
 training datasets. Make models reproducible and auditable.
 
-#### Built and test for safety
+### Built and test for safety
 
 Statement: Built and test for safety. Rationale: Use safety and security
 practices to avoid unintended results that create risks of harm. Design
 your machine learning driven systems to be appropriately cautious
 Implications: Perform risk assessments and safety tests.
 
-#### Privacy by design
+### Privacy by design
 
 Statement: Incorporate privacy by design principles. Rationale: Privacy
 by principles is more than being compliant with legal constraints as
@@ -243,8 +243,8 @@ the aspects:
 -   Maintenance (e.g. logging, version control, deployment, scheduling)
 -   Scalability, flexibility and performance
 
-ML Reference Architecture
--------------------------
+## ML Reference Architecture
+
 
 A full stack approach is needed to apply machine learning. A full stack
 approach means that in order to apply machine learning successfully you
@@ -264,7 +264,7 @@ solution.
 
 ![Machine Learning Architecture Building Blocks](/images/ml-reference-architecture.png)
 
-Conceptual overview of machine learning reference architecture
+Conceptual overview of machine learning reference architecture.
 
 Since this simplified machine learning reference architecture is far
 from complete it is recommended to consider e.g. the following questions

businessofai.md

@@ -0,0 +1,24 @@
+# The business of AI
+
+A lot of companies are currently active within the AI / LLM landscape. 
+
+Most AI companies hardly make a profit. Large companies, like Google, Meta(Facebook) or Microsoft, are competing with new startups. But since the release of GPT by [openai.com](https://openai.com/) many new companies have entered the ML arena.
+
+Most companies have a business model based on one of the following options:
+* Selling consultancy
+* Selling hosting
+* Selling Enterprise solutions, including hosting so business can take simple and fast advantage of ML. Ofcourse: often the fast and simple turns out to be a fallacy.
+* Selling training and courses.
+* Selling user profiles since many companies offer a community. User profile information can be very valuable for certain companies. Besides recruiting, also selling additional services to a selected group can be very lucrative.
+Selling a platform and services. Often the ‘free’ tier is to attract developers, but the gold is to sell ML platform services to enterprises and governmental organizations.
+
+:::{danger}
+Truth is: Almost no so called AI company makes profit.
+:::
+
+Hosting cost and development costs for training a LLM are immense. And offering licenses to companies and sell LLM prompts as SAAS services are hardly enough for a healthy revenue. 
+
+The danger is that most AI companies will sell your data as a revenue stream. This besides selling ads injected in prompts.
+
+The companies that make profit are the traditional service companies or product companies that long before LLMs existed already had a healthy business model. For many companies the latest technology options for using AI and LLMs can be profitable. See the section on [business use cases](ml-business-use).
+