Hierarchical Temporal Memory (HTM) - Revision history

BPeat at 00:57, 29 April 2024

2024-04-29T00:57:04Z

BPeat at 03:17, 2 March 2024

2024-03-02T03:17:04Z

BPeat at 13:54, 13 September 2023

2023-09-13T13:54:32Z

BPeat at 22:36, 16 August 2023

2023-08-16T22:36:25Z

BPeat at 02:15, 12 July 2023

2023-07-12T02:15:25Z

BPeat at 18:00, 2 July 2023

2023-07-02T18:00:07Z

BPeat at 17:59, 2 July 2023

2023-07-02T17:59:31Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T22:16:36Z

Text replacement - "http:" to "https:"

BPeat at 00:23, 20 August 2020

2020-08-20T00:23:43Z

BPeat at 00:20, 20 August 2020

2020-08-20T00:20:11Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Hierarchical+time+history+Temporal+future+Memory+HTM+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Neuromorphic Computing#Temporal Computing | Temporal Computing]]
 * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]

@@ Line 29: / Line 29: @@
 * [https://numenta.com/resources/biological-and-machine-intelligence/ Biological and Machine Intelligence (BAMI)]
-HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [https://en.wikipedia.org/wiki/Hierarchical_temporal_memory Wikipedia]
+Hierarchical Temporal [[Memory]] (HTM) are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [https://en.wikipedia.org/wiki/Hierarchical_temporal_memory Wikipedia]
 https://upload.wikimedia.org/wikipedia/commons/0/05/Neuron_comparison.png

@@ Line 20: / Line 20: @@
 * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]
 * [[Hierarchical Cluster Analysis (HCA)]]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [https://www.analyticsvidhya.com/blog/2018/05/alternative-deep-learning-hierarchical-temporal-memory-htm-unsupervised-learning/ An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava]
 * [https://www.youtube.com/user/OfficialNumenta Numenta HTM School]

@@ Line 20: / Line 20: @@
 * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]
 * [[Hierarchical Cluster Analysis (HCA)]]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [https://www.analyticsvidhya.com/blog/2018/05/alternative-deep-learning-hierarchical-temporal-memory-htm-unsupervised-learning/ An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava]
 * [https://www.youtube.com/user/OfficialNumenta Numenta HTM School]

@@ Line 20: / Line 20: @@
 * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]
 * [[Hierarchical Cluster Analysis (HCA)]]
-* [[Clustering]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 * [https://www.analyticsvidhya.com/blog/2018/05/alternative-deep-learning-hierarchical-temporal-memory-htm-unsupervised-learning/ An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava]
 * [https://www.youtube.com/user/OfficialNumenta Numenta HTM School]
@@ Line 28: / Line 28: @@
 * [https://numenta.org/ Numenta.org]
 * [https://numenta.com/resources/biological-and-machine-intelligence/ Biological and Machine Intelligence (BAMI)]
 HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [https://en.wikipedia.org/wiki/Hierarchical_temporal_memory Wikipedia]

@@ 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=Hierarchical+time+history+future+Temporal+Memory+HTM YouTube search...]

@@ 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=Hierarchical+time+history+future+Temporal+Memory+HTM YouTube search...]
+[https://www.youtube.com/results?search_query=Hierarchical+time+history+future+Temporal+Memory+HTM YouTube search...]
-[http://www.google.com/search?q=Hierarchical+time+history+Temporal+future+Memory+HTM+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Hierarchical+time+history+Temporal+future+Memory+HTM+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Neuromorphic Computing#Temporal Computing | Temporal Computing]]
@@ Line 13: / Line 13: @@
 * [[Capabilities]]
 * [[Clustering]]
-* [http://www.analyticsvidhya.com/blog/2018/05/alternative-deep-learning-hierarchical-temporal-memory-htm-unsupervised-learning/ An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava]
+* [https://www.analyticsvidhya.com/blog/2018/05/alternative-deep-learning-hierarchical-temporal-memory-htm-unsupervised-learning/ An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava]
-* [http://www.youtube.com/user/OfficialNumenta Numenta HTM School]
+* [https://www.youtube.com/user/OfficialNumenta Numenta HTM School]
-* [http://www.slideshare.net/ibobak/hierarchical-temporal-memory-for-realtime-anomaly-detection Hierarchical Temporal Memory for Real-time Anomaly Detection | Ihor Bobak]
+* [https://www.slideshare.net/ibobak/hierarchical-temporal-memory-for-realtime-anomaly-detection Hierarchical Temporal Memory for Real-time Anomaly Detection | Ihor Bobak]
-* [http://numenta.com/ Numentra - Where Neuroscience Meets Machine Intelligence]
+* [https://numenta.com/ Numentra - Where Neuroscience Meets Machine Intelligence]
-* [http://www.youtube.com/user/OfficialNumenta Hierarchical Temporal Memory (HTM) School | Matt Taylor - Numentra]
+* [https://www.youtube.com/user/OfficialNumenta Hierarchical Temporal Memory (HTM) School | Matt Taylor - Numentra]
-* [http://numenta.org/ Numenta.org]
+* [https://numenta.org/ Numenta.org]
-* [http://numenta.com/resources/biological-and-machine-intelligence/ Biological and Machine Intelligence (BAMI)]
+* [https://numenta.com/resources/biological-and-machine-intelligence/ Biological and Machine Intelligence (BAMI)]
 * [[Anomaly Detection]]
-HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [http://en.wikipedia.org/wiki/Hierarchical_temporal_memory Wikipedia]
+HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [https://en.wikipedia.org/wiki/Hierarchical_temporal_memory Wikipedia]
-http://upload.wikimedia.org/wikipedia/commons/0/05/Neuron_comparison.png
+https://upload.wikimedia.org/wikipedia/commons/0/05/Neuron_comparison.png
 <youtube>-h-cz7yY-G8</youtube>

@@ Line 27: / Line 27: @@
 <youtube>-h-cz7yY-G8</youtube>
 <youtube>pX7DVm50qLY</youtube>
 <youtube>lzJd_a6y6-E</youtube>
 <youtube>gXP-63sZM_o</youtube>
-<youtube>ZFazR5yqesk</youtube>
+<youtube>tg_m_LxxRwM</youtube>
 <youtube>u-BIsnN-xxM</youtube>
 = Study of the Brain and Development of Intelligent Machines | Jeff Hawkins =
@@ Line 60: / Line 58: @@
 <youtube>1mq8c2Orgso</youtube>
 <youtube>A8sHMcCk0lU</youtube>