Bias and Variances - Revision history

BPeat at 02:44, 6 March 2024

2024-03-06T02:44:23Z

BPeat at 11:28, 12 July 2023

2023-07-12T11:28:46Z

BPeat at 01:15, 11 July 2023

2023-07-11T01:15:04Z

BPeat at 01:14, 11 July 2023

2023-07-11T01:14:09Z

BPeat at 17:18, 30 June 2023

2023-06-30T17:18:26Z

BPeat at 01:14, 2 May 2023

2023-05-02T01:14:30Z

BPeat at 20:33, 16 April 2023

2023-04-16T20:33:34Z

BPeat at 16:00, 26 March 2023

2023-03-26T16:00:22Z

BPeat at 12:00, 5 March 2023

2023-03-05T12:00:29Z

BPeat at 00:36, 29 January 2023

2023-01-29T00:36:28Z

@@ Line 29: / Line 29: @@
 ** [[Explainable / Interpretable AI]]
 ** [[Algorithm Administration#Model Monitoring|Model Monitoring]]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Overfitting Challenge]]

@@ Line 22: / Line 22: @@
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
-* [[Activation Functions]]
+* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 ** [[Evaluation - Measures#Accuracy|Accuracy]]

@@ Line 23: / Line 23: @@
 * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [[Activation Functions]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 ** [[Evaluation - Measures#Accuracy|Accuracy]]

@@ Line 22: / Line 22: @@
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [[AI Governance]] / [[Algorithm Administration]]
 ** [[Algorithms]]
-* [[Evaluation]]   ... Prompts for assessing AI projects
+* [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
-** [[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]]
 ** [[Explainable / Interpretable AI]]
 ** [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Overfitting Challenge]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=ai+Data+Bias+Variances YouTube]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[https://www.youtube.com/results?search_query=Bias+Variance+artificial+intelligence+deep+learning Youtube search...]
+[https://www.youtube.com/results?search_query=ai+Data+Bias+Variances YouTube]
-[https://www.google.com/search?q=Bias+Variance+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.quora.com/search?q=ai%20Data%20Bias%20Variances ... Quora]
 * [[Evaluation]]   ... Prompts for assessing AI projects
 ** [[Evaluation - Measures]]
@@ Line 13: / Line 20: @@
 *** [[Evaluation - Measures#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]]
 *** [[Evaluation - Measures#Specificity|Specificity]]
 ** [[Explainable / Interpretable AI]]
 ** [[Train, Validate, and Test]]
@@ Line 19: / Line 25: @@
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Overfitting Challenge]]
 * [https://becominghuman.ai/a-basic-recipe-for-machine-learning-2fbebd3549f5 A Basic Recipe for Machine Learning | Hafidz Zulkifli]
 * [https://en.wikipedia.org/wiki/Inductive_bias Inductive Bias | Wikipedia]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Bias+Variance+artificial+intelligence+deep+learning Youtube search...]
+[https://www.youtube.com/results?search_query=Bias+Variance+artificial+intelligence+deep+learning Youtube search...]
-[http://www.google.com/search?q=Bias+Variance+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Bias+Variance+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Evaluation]]
@@ Line 17: / Line 17: @@
 ** [[Train, Validate, and Test]]
 ** [[Algorithm Administration#Model Monitoring|Model Monitoring]]
-* [http://becominghuman.ai/a-basic-recipe-for-machine-learning-2fbebd3549f5 A Basic Recipe for Machine Learning | Hafidz Zulkifli]
+* [https://becominghuman.ai/a-basic-recipe-for-machine-learning-2fbebd3549f5 A Basic Recipe for Machine Learning | Hafidz Zulkifli]
-* [http://en.wikipedia.org/wiki/Inductive_bias Inductive Bias | Wikipedia]
+* [https://en.wikipedia.org/wiki/Inductive_bias Inductive Bias | Wikipedia]
 * [[Overfitting Challenge]]
 * [[Data Science]]
@@ Line 24: / Line 24: @@
 Two terms of <b>bias</b> in Artificial Intelligence:
-# <b>Node Bias</b> - The activation of a node in a neural network is determined by the following: output = activation function (dot_product(weights, inputs) + bias)  This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data. Hence changes to the weights alter the steepness of the sigmoid curve, whilst the bias offsets it, shifting the entire curve so it fits better. Note also how the bias only influences the output values, it doesn’t interact with the actual input data.   [http://medium.com/deeper-learning/glossary-of-deep-learning-bias-cf49d9c895e2 Glossary of Deep Learning: Bias | Jaron Collis - Deeper Learning]
+# <b>Node Bias</b> - The activation of a node in a neural network is determined by the following: output = activation function (dot_product(weights, inputs) + bias)  This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data. Hence changes to the weights alter the steepness of the sigmoid curve, whilst the bias offsets it, shifting the entire curve so it fits better. Note also how the bias only influences the output values, it doesn’t interact with the actual input data.   [https://medium.com/deeper-learning/glossary-of-deep-learning-bias-cf49d9c895e2 Glossary of Deep Learning: Bias | Jaron Collis - Deeper Learning]
-# <b>Model Bias</b> - Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. [http://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai#:~:text=Bias%20can%20creep%20into%20algorithms,or%20sexual%20orientation%20are%20removed.&text=Bias%20is%20all%20of%20our%20responsibility. What Do We Do About the Biases in AI? | J. Manyika, J. Silberg, and B. Presten - Harvard Business Review]
+# <b>Model Bias</b> - Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. [https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai#:~:text=Bias%20can%20creep%20into%20algorithms,or%20sexual%20orientation%20are%20removed.&text=Bias%20is%20all%20of%20our%20responsibility. What Do We Do About the Biases in AI? | J. Manyika, J. Silberg, and B. Presten - Harvard Business Review]
 = Node Bias =
-http://cdn-images-1.medium.com/max/800/1*RvLVTYBiRXogAr6Ap5jgLA.png
+https://cdn-images-1.medium.com/max/800/1*RvLVTYBiRXogAr6Ap5jgLA.png
-http://cdn-images-1.medium.com/max/800/0*ml9CpRkTA8fY9AFT.png
+https://cdn-images-1.medium.com/max/800/0*ml9CpRkTA8fY9AFT.png
@@ Line 40: / Line 40: @@
 <youtube>SjQyLhQIXSM</youtube>
 <b>Bias/Variance (C2W1L02)
-</b><br>[[Creatives#Andrew Ng|Andrew Ng]]  Deeplearning.ai   Take the Deep Learning Specialization: http://bit.ly/3amgU4n  Check out all our courses: http://www.deeplearning.ai
+</b><br>[[Creatives#Andrew Ng|Andrew Ng]]  Deeplearning.ai   Take the Deep Learning Specialization: https://bit.ly/3amgU4n  Check out all our courses: https://www.deeplearning.ai
 |}
 |<!-- M -->
@@ Line 57: / Line 57: @@
 <youtube>C1N_PDHuJ6Q</youtube>
 <b>Basic Recipe for Machine Learning (C2W1L03)
-</b><br>[[Creatives#Andrew Ng|Andrew Ng]]  Deeplearning.ai  Take the Deep Learning Specialization: http://bit.ly/3cpg1K9   Check out all our courses: http://www.deeplearning.ai
+</b><br>[[Creatives#Andrew Ng|Andrew Ng]]  Deeplearning.ai  Take the Deep Learning Specialization: https://bit.ly/3cpg1K9   Check out all our courses: https://www.deeplearning.ai
 |}
 |<!-- M -->
@@ Line 87: / Line 87: @@
-* [http://ai.googleblog.com/2017/08/exploring-and-visualizing-open-global.html Exploring and Visualizing an Open Global Dataset | Reena Jana and Josh Lovejoy]
+* [https://ai.googleblog.com/2017/08/exploring-and-visualizing-open-global.html Exploring and Visualizing an Open Global Dataset | Reena Jana and Josh Lovejoy]
-http://4.bp.blogspot.com/-KbGvGbxn8X4/WZ9MwE2GdOI/AAAAAAAAB-Y/zqkSZzRgBmcTnrxWE2_Zt4mBn7bng3poACLcBGAs/s640/chair-world-light-1000.gif
+https://4.bp.blogspot.com/-KbGvGbxn8X4/WZ9MwE2GdOI/AAAAAAAAB-Y/zqkSZzRgBmcTnrxWE2_Zt4mBn7bng3poACLcBGAs/s640/chair-world-light-1000.gif
 = Model Bias =
 * [[Ethics]]
 * [[Privacy]]
-* [http://developer.ibm.com/articles/machine-learning-and-bias/ Machine learning and bias | IBM] [http://aif360.mybluemix.net/?_ga=2.258555210.1353694320.1576870371-739966746.1575208440 AI Fairness 360 Open Source Toolkit] extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. Containing over 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms
+* [https://developer.ibm.com/articles/machine-learning-and-bias/ Machine learning and bias | IBM] [https://aif360.mybluemix.net/?_ga=2.258555210.1353694320.1576870371-739966746.1575208440 AI Fairness 360 Open Source Toolkit] extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. Containing over 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms
-* [http://www.usatoday.com/story/tech/2020/07/22/uber-lyft-algorithms-discriminate-charge-more-non-white-areas/5481950002/ Was your Uber, Lyft fare high because of algorithm bias? | Coral Murphy - USA TODAY]
+* [https://www.usatoday.com/story/tech/2020/07/22/uber-lyft-algorithms-discriminate-charge-more-non-white-areas/5481950002/ Was your Uber, Lyft fare high because of algorithm bias? | Coral Murphy - USA TODAY]
-* [http://research.google.com/bigpicture/attacking-discrimination-in-ml/ Attacking discrimination with smarter machine learning |] [[Creatives#Martin Wattenberg |Martin Wattenberg]], [[Creatives#Fernanda Viegas |Fernanda Viegas]], and Moritz Hardt
+* [https://research.google.com/bigpicture/attacking-discrimination-in-ml/ Attacking discrimination with smarter machine learning |] [[Creatives#Martin Wattenberg |Martin Wattenberg]], [[Creatives#Fernanda Viegas |Fernanda Viegas]], and Moritz Hardt
-* [http://www.technologyreview.com/s/612876/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/ This is how AI bias really happens—and why it’s so hard to fix | Karen Hao]
+* [https://www.technologyreview.com/s/612876/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/ This is how AI bias really happens—and why it’s so hard to fix | Karen Hao]
 {|<!-- T -->
@@ Line 105: / Line 105: @@
 <youtube>PWCtoVt1CJM</youtube>
 <b>How To Stop Artificial Intelligence From Marginalizing Communities? | Timnit Gebru | TEDxCollegePark
-</b><br>Timnit Gebru, Stanford Alum and Co-Founder of Black in AI, shares remarkable insights to show how artificial intelligence is influencing thinking and decision-making in ways we didn't imagine and must counter before it further marginalizes people.  Timnit works at Microsoft, New York in the Fairness Accountability Transparency and [[Ethics]] (FATE) Group where her team works on the complex social implications of AI, machine learning, data science, large-scale experimentation, and increasing automation. She previously worked at Stanford’s Artificial Intelligence Lab where she received her PhD, and is Co-Founder of Black in AI, an organization that aims to foster collaborations and discuss initiatives to increase the presence of Black people in the field of Artificial Intelligence. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://www.ted.com/tedx
+</b><br>Timnit Gebru, Stanford Alum and Co-Founder of Black in AI, shares remarkable insights to show how artificial intelligence is influencing thinking and decision-making in ways we didn't imagine and must counter before it further marginalizes people.  Timnit works at Microsoft, New York in the Fairness Accountability Transparency and [[Ethics]] (FATE) Group where her team works on the complex social implications of AI, machine learning, data science, large-scale experimentation, and increasing automation. She previously worked at Stanford’s Artificial Intelligence Lab where she received her PhD, and is Co-Founder of Black in AI, an organization that aims to foster collaborations and discuss initiatives to increase the presence of Black people in the field of Artificial Intelligence. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
 |}
 |<!-- M -->