Explainable / Interpretable AI - Revision history

BPeat at 14:27, 28 May 2025

2025-05-28T14:27:42Z

BPeat: /* Weirder Than You Thought */

2025-03-29T11:06:29Z

‎Weirder Than You Thought

BPeat: /* Trust */

2025-03-29T11:02:54Z

‎Trust

BPeat: /* Domains */

2024-05-27T10:27:57Z

‎Domains

BPeat at 20:28, 28 April 2024

2024-04-28T20:28:16Z

BPeat at 01:31, 27 April 2024

2024-04-27T01:31:27Z

BPeat at 10:33, 8 March 2024

2024-03-08T10:33:54Z

BPeat at 17:17, 8 September 2023

2023-09-08T17:17:36Z

BPeat: /* Explainable Artificial Intelligence (XAI) */

2023-08-01T20:17:32Z

‎Explainable Artificial Intelligence (XAI)

BPeat at 00:19, 11 July 2023

2023-07-11T00:19:42Z

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
+|keywords=ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 <!-- Google tag (gtag.js) -->
@@ Line 36: / Line 36: @@
 * [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Causation vs. Correlation]] ... [[Autocorrelation]] ...[[Convolution vs. Cross-Correlation (Autocorrelation)]]
-* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s ... Generative Pre-trained Transformer ([[GPT-4]]) ... [[GPT-5]]
+* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[RETRO]] | [[Google | DeepMind]] ... see what the AI has learned by examining the database rather than by studying the [[Neural Network]]
 * [[Creatives]] ... [[History of Artificial Intelligence (AI)]] ... [[Neural Network#Neural Network History|Neural Network History]] ... [[Rewriting Past, Shape our Future]] ... [[Archaeology]] ... [[Paleontology]]

@@ Line 162: / Line 162: @@
 == <span id="Weirder Than You Thought"></span>Weirder Than You Thought ==
 * [https://www.anthropic.com/research/tracing-thoughts-language-model Tracing the thoughts of a large language model | ][[Anthropic]]
 * [https://www.pcgamer.com/software/ai/anthropic-has-developed-an-ai-brain-scanner-to-understand-how-llms-work-and-it-turns-out-the-reason-why-chatbots-are-terrible-at-simple-math-and-hallucinate-is-weirder-than-you-thought/ Anthropic has developed an AI 'brain scanner' to understand how LLMs work and it turns out the reason why chatbots are terrible at simple math and hallucinate is weirder than you thought | Jeremy Laird - PC GAMER]
@@ Line 167: / Line 168: @@
 <youtube>Bj9BD2D3DzA</youtube>
 = <span id="Trust"></span>Trust =

@@ Line 159: / Line 159: @@
 |}
 |}<!-- B -->
 = <span id="Trust"></span>Trust =

← Older revision		Revision as of 10:27, 27 May 2024
Line 261:		Line 261:
	safety, and accountability. Ideally, these aspects of AI should be considered early in the design process and tested during the [[development]] and use of AI technologies. AI standards and related tools, along with AI risk management strategies, can help to address this limitation and spur innovation. ... It is important for those participating in AI standards [[development]] to be aware of, and to act consistently with, U.S. government policies and principles, including those that address societal and ethical issues, governance, and [[privacy]]. While there is broad agreement that these issues must factor into AI standards, it is not clear how that should be done and whether there is yet sufficient scientific and technical basis to develop those standards provisions. [https://www.nist.gov/news-events/news/2019/08/plan-outlines-priorities-federal-agency-engagement-ai-standards-development Plan Outlines Priorities for Federal Agency Engagement in AI Standards Development;]		safety, and accountability. Ideally, these aspects of AI should be considered early in the design process and tested during the [[development]] and use of AI technologies. AI standards and related tools, along with AI risk management strategies, can help to address this limitation and spur innovation. ... It is important for those participating in AI standards [[development]] to be aware of, and to act consistently with, U.S. government policies and principles, including those that address societal and ethical issues, governance, and [[privacy]]. While there is broad agreement that these issues must factor into AI standards, it is not clear how that should be done and whether there is yet sufficient scientific and technical basis to develop those standards provisions. [https://www.nist.gov/news-events/news/2019/08/plan-outlines-priorities-federal-agency-engagement-ai-standards-development Plan Outlines Priorities for Federal Agency Engagement in AI Standards Development;]

−	~~<youtube>aWl56ohVylQ</youtube>~~
	<youtube>YSsYXAn_L00</youtube>		<youtube>YSsYXAn_L00</youtube>

@@ Line 34: / Line 34: @@
 ** [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s ... Generative Pre-trained Transformer ([[GPT-4]]) ... [[GPT-5]]
 * [[RETRO]] | [[Google | DeepMind]] ... see what the AI has learned by examining the database rather than by studying the [[Neural Network]]

@@ Line 38: / Line 38: @@
 * [[RETRO]] | [[Google | DeepMind]] ... see what the AI has learned by examining the database rather than by studying the [[Neural Network]]
 * [[Creatives]] ... [[History of Artificial Intelligence (AI)]] ... [[Neural Network#Neural Network History|Neural Network History]] ... [[Rewriting Past, Shape our Future]] ... [[Archaeology]] ... [[Paleontology]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [https://towardsdatascience.com/python-libraries-for-interpretable-machine-learning-c476a08ed2c7 Python Libraries for Interpretable Machine Learning | Rebecca Vickery - Towards Data Science]
 * Tools:

@@ Line 37: / Line 37: @@
 * [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s ... Generative Pre-trained Transformer ([[GPT-4]]) ... [[GPT-5]]
 * [[RETRO]] | [[Google | DeepMind]] ... see what the AI has learned by examining the database rather than by studying the [[Neural Network]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [https://towardsdatascience.com/python-libraries-for-interpretable-machine-learning-c476a08ed2c7 Python Libraries for Interpretable Machine Learning | Rebecca Vickery - Towards Data Science]
@@ Line 63: / Line 64: @@
 ** <b>Explanation Accuracy</b>: The explanation correctly reflects the system’s process for generating the output.
 ** <b>Knowledge Limits</b>: The system only operates under conditions for which it was designed or when the system reaches a sufficient confidence in its output.
-* https://www.linkedin.com/pulse/collection-recommendable-papers-articles-explainable-ai-murat-durmus/ A collection of recommendable papers and articles on Explainable AI (XAI) | Murat Durmus - LinkedIn]
+* [https://www.linkedin.com/pulse/collection-recommendable-papers-articles-explainable-ai-murat-durmus/ A collection of recommendable papers and articles on Explainable AI (XAI) | Murat Durmus - LinkedIn]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=Explainable+Interpretable+AI&qft=interval%3d%228%22 ...Bing News]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
 * [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]

@@ Line 67: / Line 67: @@
 = Explainable Artificial Intelligence (XAI) =
 AI system produces results with an account of the path the system took to derive the solution/prediction - transparency of interpretation, rationale and justification. 'If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. That’s crucial. We humans are able to project ourselves into situations that are very different from our day-to-day experience. Machines are not, because they don’t have these causal models. We can hand-craft them but that’s not enough. We need machines that can discover causal models. To some extend it’s never going to be perfect. We don’t have a perfect causal model of the reality, that’s why we make a lot of mistakes. But we are much better off at doing this than other animals.' [https://www.technologyreview.com/s/612434/one-of-the-fathers-of-ai-is-worried-about-its-future/  Yoshua Benjio]
 <youtube>OvId1rhhBJk</youtube>

@@ Line 26: / Line 26: @@
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]]
-** [[Evaluation - Measures]]
+** [[Evaluation - Measures#Accuracy|Accuracy]]
-*** [[Evaluation - Measures#Accuracy|Accuracy]]
+** [[Evaluation - Measures#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]]
-*** [[Evaluation - Measures#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]]
+** [[Evaluation - Measures#Specificity|Specificity]]
 ** [[Benchmarks]]
 ** [[Bias and Variances]]