OpenMined - Revision history

BPeat at 03:03, 6 December 2023

2023-12-06T03:03:57Z

BPeat at 23:07, 27 November 2021

2021-11-27T23:07:11Z

BPeat at 13:37, 27 November 2021

2021-11-27T13:37:49Z

BPeat at 03:17, 27 September 2020

2020-09-27T03:17:17Z

BPeat at 21:39, 26 September 2020

2020-09-26T21:39:21Z

BPeat at 15:55, 26 September 2020

2020-09-26T15:55:22Z

BPeat at 21:42, 14 September 2020

2020-09-14T21:42:21Z

BPeat at 01:46, 20 August 2020

2020-08-20T01:46:05Z

BPeat: /* Andrew Trask */

2020-08-20T01:30:29Z

‎Andrew Trask

BPeat at 01:26, 20 August 2020

2020-08-20T01:26:23Z

@@ Line 13: / Line 13: @@
 * [http://www.openmined.org Openmined]
 ** [http://github.com/OpenMined/PySyft PySyft] a A library for encrypted, [[privacy]] preserving deep learning
-** [http://github.com/OpenMined/syft.js syft.js] a client-side microlibrary for running PySyft operations in [[Javascript]]
+** [http://github.com/OpenMined/syft.js syft.js] a client-side microlibrary for running PySyft operations in [[JavaScript]]
 * [http://arxiv.org/abs/1811.04017 A generic framework for privacy preserving deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
 * [[Machine Learning as a Service (MLaaS)]]

@@ Line 19: / Line 19: @@
 * [[Decentralized: Federated & Distributed]] Learning
 * [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption]
-* [http://www.coindesk.com/information/ethereum-smart-contracts-work Blockchain Smart Contracts]
+* [http://www.coindesk.com/information/ethereum-smart-contracts-work Blockchain Smart Contracts]   ...[[Blockchain]]
 * [http://ipfs.io/docs/install Interplanetary File System (IPFS)] - a framework that aims to replace the centralized architecture of the HTTP protocol of the internet with a more distributed approach
 * [http://www.ethereum.org/ Ethereum] is a global, open-source platform for decentralized applications
@@ Line 26: / Line 26: @@
 * [[Privacy]] in Data Science
-Conceptually, federated learning proposes a mechanism to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. OpenMined can be considered an implementation of a [[federated]] learning architecture which powers decentralization using [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] smart contracts...[http://towardsdatascience.com/technology-fridays-openmined-powers-federated-ai-using-the-blockchain-d124e6560dd OpenMined Powers Federated AI Using the [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] | Jesus Rodriguez - Towards Data Science]
+Conceptually, federated learning proposes a mechanism to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. OpenMined can be considered an implementation of a [[federated]] learning architecture which powers decentralization using [[Blockchain]] smart contracts...[http://towardsdatascience.com/technology-fridays-openmined-powers-federated-ai-using-the-blockchain-d124e6560dd OpenMined Powers Federated AI Using the [[Blockchain]] | Jesus Rodriguez - Towards Data Science]
 Specifically, the OpenMined architecture is based on four fundamental building blocks:
 # Capsule — A PGP server to generate public and private keys in order to guarantee the integrity of the different components of a Sonar neural network.
-# Sonar — The heart of the OpenMined platform, Sonar is a federated learning server running on the [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] that control the execution of the different parts of a deep learning appliucation. This library communicates with the Capsule to generate PGP keys and deliver the final, trained results back to the Data Scientist. It also communicates with miners, collecting Gradients and distributing Bounty accordingly.
+# Sonar — The heart of the OpenMined platform, Sonar is a federated learning server running on the [[Blockchain]] that control the execution of the different parts of a deep learning appliucation. This library communicates with the Capsule to generate PGP keys and deliver the final, trained results back to the Data Scientist. It also communicates with miners, collecting Gradients and distributing Bounty accordingly.
 # Mine — This component hosts the individual data repositories for specific users. Mines regularly interact with Sonar to detect new neural nets to contribute to. The more data that is uploaded to a mine, the more relevant it becomes to Sonar.
 # Syft — The library containing Neural Networks that can be trained in an encrypted state (so that Miners can’t steal the neural networks that they download to train).
-OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user [[privacy]], and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
+OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [[Blockchain]] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user [[privacy]], and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
 <youtube>HaulMYiB4EQ</youtube>

@@ Line 26: / Line 26: @@
 * [[Privacy]] in Data Science
-Conceptually, federated learning proposes a mechanism to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. OpenMined can be considered an implementation of a [[federated]] learning architecture which powers decentralization using blockchain smart contracts...[http://towardsdatascience.com/technology-fridays-openmined-powers-federated-ai-using-the-blockchain-d124e6560dd OpenMined Powers Federated AI Using the Blockchain | Jesus Rodriguez - Towards Data Science]
+Conceptually, federated learning proposes a mechanism to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. OpenMined can be considered an implementation of a [[federated]] learning architecture which powers decentralization using [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] smart contracts...[http://towardsdatascience.com/technology-fridays-openmined-powers-federated-ai-using-the-blockchain-d124e6560dd OpenMined Powers Federated AI Using the [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] | Jesus Rodriguez - Towards Data Science]
 Specifically, the OpenMined architecture is based on four fundamental building blocks:
 # Capsule — A PGP server to generate public and private keys in order to guarantee the integrity of the different components of a Sonar neural network.
-# Sonar — The heart of the OpenMined platform, Sonar is a federated learning server running on the blockchain that control the execution of the different parts of a deep learning appliucation. This library communicates with the Capsule to generate PGP keys and deliver the final, trained results back to the Data Scientist. It also communicates with miners, collecting Gradients and distributing Bounty accordingly.
+# Sonar — The heart of the OpenMined platform, Sonar is a federated learning server running on the [[Decentralized: Federated & Distributed#Blockchain|Decentralized: Federated & Distributed: Blockchain]] that control the execution of the different parts of a deep learning appliucation. This library communicates with the Capsule to generate PGP keys and deliver the final, trained results back to the Data Scientist. It also communicates with miners, collecting Gradients and distributing Bounty accordingly.
 # Mine — This component hosts the individual data repositories for specific users. Mines regularly interact with Sonar to detect new neural nets to contribute to. The more data that is uploaded to a mine, the more relevant it becomes to Sonar.
 # Syft — The library containing Neural Networks that can be trained in an encrypted state (so that Miners can’t steal the neural networks that they download to train).

@@ Line 12: / Line 12: @@
 *** [[Cybersecurity]]
 * [http://www.openmined.org Openmined]
-** [http://github.com/OpenMined/PySyft PySyft] a A library for encrypted, [[Decentralized: Federated & Distributed#Privacy Preserving|privacy preserving]] deep learning
+** [http://github.com/OpenMined/PySyft PySyft] a A library for encrypted, [[privacy]] preserving deep learning
 ** [http://github.com/OpenMined/syft.js syft.js] a client-side microlibrary for running PySyft operations in [[Javascript]]
-* [http://arxiv.org/abs/1811.04017 A generic framework for [[Decentralized: Federated & Distributed#Privacy Preserving|privacy preserving]] deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
+* [http://arxiv.org/abs/1811.04017 A generic framework for privacy preserving deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
 * [[Machine Learning as a Service (MLaaS)]]
 * [[Watch me Build a Cybersecurity Startup]] | [[Creatives#Siraj Raval|Siraj Raval]]
@@ Line 24: / Line 24: @@
 ** [http://github.com/ethereumjs/testrpc Ethereum Test RPC | GitHub]
 * [http://medium.com/fondercom/a-new-business-model-decentralized-ownership-7b05adac2df4 A New Business Model: Decentralized Ownership | Fonder Open Network - Medium]
-* [[Privacy in Data Science]]
+* [[Privacy]] in Data Science
 Conceptually, federated learning proposes a mechanism to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. OpenMined can be considered an implementation of a [[federated]] learning architecture which powers decentralization using blockchain smart contracts...[http://towardsdatascience.com/technology-fridays-openmined-powers-federated-ai-using-the-blockchain-d124e6560dd OpenMined Powers Federated AI Using the Blockchain | Jesus Rodriguez - Towards Data Science]
@@ Line 35: / Line 35: @@
 # Syft — The library containing Neural Networks that can be trained in an encrypted state (so that Miners can’t steal the neural networks that they download to train).
-OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user [[Decentralized: Federated & Distributed#Privacy Preserving|privacy]], and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
+OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user [[privacy]], and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
 <youtube>HaulMYiB4EQ</youtube>

@@ Line 12: / Line 12: @@
 *** [[Cybersecurity]]
 * [http://www.openmined.org Openmined]
-** [http://github.com/OpenMined/PySyft PySyft] a A library for encrypted, privacy preserving deep learning
+** [http://github.com/OpenMined/PySyft PySyft] a A library for encrypted, [[Decentralized: Federated & Distributed#Privacy Preserving|privacy preserving]] deep learning
 ** [http://github.com/OpenMined/syft.js syft.js] a client-side microlibrary for running PySyft operations in [[Javascript]]
-* [http://arxiv.org/abs/1811.04017 A generic framework for privacy preserving deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
+* [http://arxiv.org/abs/1811.04017 A generic framework for [[Decentralized: Federated & Distributed#Privacy Preserving|privacy preserving]] deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
 * [[Machine Learning as a Service (MLaaS)]]
 * [[Watch me Build a Cybersecurity Startup]] | [[Creatives#Siraj Raval|Siraj Raval]]
@@ Line 35: / Line 35: @@
 # Syft — The library containing Neural Networks that can be trained in an encrypted state (so that Miners can’t steal the neural networks that they download to train).
-OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user privacy, and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
+OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user [[Decentralized: Federated & Distributed#Privacy Preserving|privacy]], and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
 <youtube>HaulMYiB4EQ</youtube>

@@ Line 16: / Line 16: @@
 * [http://arxiv.org/abs/1811.04017 A generic framework for privacy preserving deep learning | T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, & J. Passerat-Palmbach]
 * [[Machine Learning as a Service (MLaaS)]]
-* [[Watch me Build a Cybersecurity Startup]] | Siraj Raval
+* [[Watch me Build a Cybersecurity Startup]] | [[Creatives#Siraj Raval|Siraj Raval]]
 * [[Federated]] Learning
 * [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption]
@@ Line 48: / Line 48: @@
 <youtube>NJBBE_SN90A</youtube>
-== How Do We Democratize Access to Data?" | Siraj Raval ==
+== How Do We Democratize Access to Data?" | [[Creatives#Siraj Raval|Siraj Raval]] ==
 * [http://github.com/llSourcell/OpenMined_demo OpenMined_demo] - code for this video
 <youtube>HAC6sqq7_-U</youtube>

← Older revision		Revision as of 01:46, 20 August 2020
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	<youtube>HAc3CLQAeME</youtube>		<youtube>HAc3CLQAeME</youtube>

−		+	<img src="http://miro.medium.com/max/1423/1*GK9cCoOLSii191cjAlwsww.png" width="800">
−	http://miro.medium.com/max/1423/1*GK9cCoOLSii191cjAlwsww.png

@@ Line 46: / Line 46: @@
 <youtube>m5p5UFAboqc</youtube>
 <youtube>GJFecMdFy1g</youtube>
 == How Do We Democratize Access to Data?" | Siraj Raval ==

@@ Line 37: / Line 37: @@
 OpenMined a platform that merges [http://www.globalsign.com/en/blog/glossary-of-cryptographic-algorithms/ cryptographic] techniques such as [http://en.wikipedia.org/wiki/Homomorphic_encryption Homomorphic Encryption] and [http://en.wikipedia.org/wiki/Secure_multi-party_computation multi-party computation] and [https://en.wikipedia.org/wiki/Blockchain Blockchain] technology to create the ability to train Machine Learning (ML) models with private user data. OpenMined allows AI companies to develop models, have them trained on user data without compromising user privacy, and incentivize users to train their model. OpenMined is a community focused on building technology for [http://blog.openfuture.io/technical/what-is-the-future-for-decentralized-ownership/ decentralized ownership] of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way.
 <youtube>HaulMYiB4EQ</youtube>
 <youtube>iYP4sYz0jho</youtube>
 <youtube>6ICjrHWJe3w</youtube>
 == Andrew Trask ==