#syntheticdata — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #syntheticdata, aggregated by home.social.
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The preachers of the Silicon Valley Church sell the harvesting of the body as “algorithmic inevitability,” promising immortality. A neat fable. Maybe they’ll keep the data lords alive for 150 years—but half of it will be Alzheimer’s. In the end, the thermodynamic hammer still falls.
#Transhumanizm #DataEngineering #CRISPR #EdgeAI #GenerativeAI #FederatedLearning #MachineLearning #DataScience #AITools #AIAutomation #CloudComputing #SyntheticData #SyntheticData #AntiHarari #MLOps #Longevity -
The preachers of the Silicon Valley Church sell the harvesting of the body as “algorithmic inevitability,” promising immortality. A neat fable. Maybe they’ll keep the data lords alive for 150 years—but half of it will be Alzheimer’s. In the end, the thermodynamic hammer still falls.
#Transhumanizm #DataEngineering #CRISPR #EdgeAI #GenerativeAI #FederatedLearning #MachineLearning #DataScience #AITools #AIAutomation #CloudComputing #SyntheticData #SyntheticData #AntiHarari #MLOps #Longevity -
The preachers of the Silicon Valley Church sell the harvesting of the body as “algorithmic inevitability,” promising immortality. A neat fable. Maybe they’ll keep the data lords alive for 150 years—but half of it will be Alzheimer’s. In the end, the thermodynamic hammer still falls.
#Transhumanizm #DataEngineering #CRISPR #EdgeAI #GenerativeAI #FederatedLearning #MachineLearning #DataScience #AITools #AIAutomation #CloudComputing #SyntheticData #SyntheticData #AntiHarari #MLOps #Longevity -
The preachers of the Silicon Valley Church sell the harvesting of the body as “algorithmic inevitability,” promising immortality. A neat fable. Maybe they’ll keep the data lords alive for 150 years—but half of it will be Alzheimer’s. In the end, the thermodynamic hammer still falls.
#Transhumanizm #DataEngineering #CRISPR #EdgeAI #GenerativeAI #FederatedLearning #MachineLearning #DataScience #AITools #AIAutomation #CloudComputing #SyntheticData #SyntheticData #AntiHarari #MLOps #Longevity -
The preachers of the Silicon Valley Church sell the harvesting of the body as “algorithmic inevitability,” promising immortality. A neat fable. Maybe they’ll keep the data lords alive for 150 years—but half of it will be Alzheimer’s. In the end, the thermodynamic hammer still falls.
#Transhumanizm #DataEngineering #CRISPR #EdgeAI #GenerativeAI #FederatedLearning #MachineLearning #DataScience #AITools #AIAutomation #CloudComputing #SyntheticData #SyntheticData #AntiHarari #MLOps #Longevity -
I'm creating #syntheticdata for teaching in the social sciences & find that #SDG with LLMs isn't for my small-scale use. While there are workflows to combine LLMs & generate more credible output ( https://link.springer.com/chapter/10.1007/978-3-031-93418-6_9 ), general-purpose models often create results that are too diverse & reflexive, even when imitating oral communication. Such data reminds me of journalism scandals à la Stephen Glass. High-quality data in my case is more messy and dull. Just look at YouTube comment sections.
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I'm creating #syntheticdata for teaching in the social sciences & find that #SDG with LLMs isn't for my small-scale use. While there are workflows to combine LLMs & generate more credible output ( https://link.springer.com/chapter/10.1007/978-3-031-93418-6_9 ), general-purpose models often create results that are too diverse & reflexive, even when imitating oral communication. Such data reminds me of journalism scandals à la Stephen Glass. High-quality data in my case is more messy and dull. Just look at YouTube comment sections.
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I'm creating #syntheticdata for teaching in the social sciences & find that #SDG with LLMs isn't for my small-scale use. While there are workflows to combine LLMs & generate more credible output ( https://link.springer.com/chapter/10.1007/978-3-031-93418-6_9 ), general-purpose models often create results that are too diverse & reflexive, even when imitating oral communication. Such data reminds me of journalism scandals à la Stephen Glass. High-quality data in my case is more messy and dull. Just look at YouTube comment sections.
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I'm creating #syntheticdata for teaching in the social sciences & find that #SDG with LLMs isn't for my small-scale use. While there are workflows to combine LLMs & generate more credible output ( https://link.springer.com/chapter/10.1007/978-3-031-93418-6_9 ), general-purpose models often create results that are too diverse & reflexive, even when imitating oral communication. Such data reminds me of journalism scandals à la Stephen Glass. High-quality data in my case is more messy and dull. Just look at YouTube comment sections.
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I'm creating #syntheticdata for teaching in the social sciences & find that #SDG with LLMs isn't for my small-scale use. While there are workflows to combine LLMs & generate more credible output ( https://link.springer.com/chapter/10.1007/978-3-031-93418-6_9 ), general-purpose models often create results that are too diverse & reflexive, even when imitating oral communication. Such data reminds me of journalism scandals à la Stephen Glass. High-quality data in my case is more messy and dull. Just look at YouTube comment sections.
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🚀 NEW on We ❤️ Open Source 🚀
Synthetic data offers a practical path for AI development when privacy, imbalance, and limited edge-case data block progress.
This article walks through how teams generate realistic records, apply differential privacy, and validate usefulness without tracing back to real individuals.
https://allthingsopen.org/articles/synthetic-data-accelerates-ai-development-without-privacy-risk
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🚀 NEW on We ❤️ Open Source 🚀
Synthetic data offers a practical path for AI development when privacy, imbalance, and limited edge-case data block progress.
This article walks through how teams generate realistic records, apply differential privacy, and validate usefulness without tracing back to real individuals.
https://allthingsopen.org/articles/synthetic-data-accelerates-ai-development-without-privacy-risk
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🚀 NEW on We ❤️ Open Source 🚀
Synthetic data offers a practical path for AI development when privacy, imbalance, and limited edge-case data block progress.
This article walks through how teams generate realistic records, apply differential privacy, and validate usefulness without tracing back to real individuals.
https://allthingsopen.org/articles/synthetic-data-accelerates-ai-development-without-privacy-risk
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🚀 NEW on We ❤️ Open Source 🚀
Synthetic data offers a practical path for AI development when privacy, imbalance, and limited edge-case data block progress.
This article walks through how teams generate realistic records, apply differential privacy, and validate usefulness without tracing back to real individuals.
https://allthingsopen.org/articles/synthetic-data-accelerates-ai-development-without-privacy-risk
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🚀 NEW on We ❤️ Open Source 🚀
Synthetic data offers a practical path for AI development when privacy, imbalance, and limited edge-case data block progress.
This article walks through how teams generate realistic records, apply differential privacy, and validate usefulness without tracing back to real individuals.
https://allthingsopen.org/articles/synthetic-data-accelerates-ai-development-without-privacy-risk
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"A recent Axios story on maternal health policy referred to “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think.
But this undermines the very idea of the opinion poll. Public opinion is used to guide policy, politics and social science, and it has value only insofar as it summarizes the beliefs and opinions of actual humans. Using simulations of human opinions in place of the real thing will only worsen our broken information ecosystem, and sow distrust. We should not turn to an artificial society to try to understand our real one."
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"A recent Axios story on maternal health policy referred to “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think.
But this undermines the very idea of the opinion poll. Public opinion is used to guide policy, politics and social science, and it has value only insofar as it summarizes the beliefs and opinions of actual humans. Using simulations of human opinions in place of the real thing will only worsen our broken information ecosystem, and sow distrust. We should not turn to an artificial society to try to understand our real one."
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"A recent Axios story on maternal health policy referred to “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think.
But this undermines the very idea of the opinion poll. Public opinion is used to guide policy, politics and social science, and it has value only insofar as it summarizes the beliefs and opinions of actual humans. Using simulations of human opinions in place of the real thing will only worsen our broken information ecosystem, and sow distrust. We should not turn to an artificial society to try to understand our real one."
-
"A recent Axios story on maternal health policy referred to “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think.
But this undermines the very idea of the opinion poll. Public opinion is used to guide policy, politics and social science, and it has value only insofar as it summarizes the beliefs and opinions of actual humans. Using simulations of human opinions in place of the real thing will only worsen our broken information ecosystem, and sow distrust. We should not turn to an artificial society to try to understand our real one."
-
"A recent Axios story on maternal health policy referred to “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think.
But this undermines the very idea of the opinion poll. Public opinion is used to guide policy, politics and social science, and it has value only insofar as it summarizes the beliefs and opinions of actual humans. Using simulations of human opinions in place of the real thing will only worsen our broken information ecosystem, and sow distrust. We should not turn to an artificial society to try to understand our real one."
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🤯 What if you could train your AI models on INFINITE, PERFECT data... without the privacy headaches or sky-high costs?
Stop dreaming! Synthetic data generation is the game-changer you NEED to know about. We're diving into the BEST tools to unlock its power. ✨
#AI #TechNews #BuildInPublic #SyntheticData #MachineLearning #DataScience
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Oh, look! Another research paper trying to solve the problem of *literally* running out of text by using... *drumroll please*... abstract dynamical systems! Because who needs actual words when you can just invent your own with synthetic data? 😂 It's like trying to teach a dog to speak by showing it modern dance! 💃🕺
https://hanseungwook.github.io/blog/nca-pre-pre-training/ #researchpaper #abstractdynamicalsystems #syntheticdata #humor #innovation #HackerNews #ngated -
Oh, look! Another research paper trying to solve the problem of *literally* running out of text by using... *drumroll please*... abstract dynamical systems! Because who needs actual words when you can just invent your own with synthetic data? 😂 It's like trying to teach a dog to speak by showing it modern dance! 💃🕺
https://hanseungwook.github.io/blog/nca-pre-pre-training/ #researchpaper #abstractdynamicalsystems #syntheticdata #humor #innovation #HackerNews #ngated -
Oh, look! Another research paper trying to solve the problem of *literally* running out of text by using... *drumroll please*... abstract dynamical systems! Because who needs actual words when you can just invent your own with synthetic data? 😂 It's like trying to teach a dog to speak by showing it modern dance! 💃🕺
https://hanseungwook.github.io/blog/nca-pre-pre-training/ #researchpaper #abstractdynamicalsystems #syntheticdata #humor #innovation #HackerNews #ngated -
Oh, look! Another research paper trying to solve the problem of *literally* running out of text by using... *drumroll please*... abstract dynamical systems! Because who needs actual words when you can just invent your own with synthetic data? 😂 It's like trying to teach a dog to speak by showing it modern dance! 💃🕺
https://hanseungwook.github.io/blog/nca-pre-pre-training/ #researchpaper #abstractdynamicalsystems #syntheticdata #humor #innovation #HackerNews #ngated -
Oh, look! Another research paper trying to solve the problem of *literally* running out of text by using... *drumroll please*... abstract dynamical systems! Because who needs actual words when you can just invent your own with synthetic data? 😂 It's like trying to teach a dog to speak by showing it modern dance! 💃🕺
https://hanseungwook.github.io/blog/nca-pre-pre-training/ #researchpaper #abstractdynamicalsystems #syntheticdata #humor #innovation #HackerNews #ngated -
QbitAI Exclusive: Interview with Terence Tao https://www.byteseu.com/1869574/ #AIPopularization #AIXScience #BasicScientificResearch #DataQuality #hallucinations #interpretability #mathematics #ProteinFolding #RichardSutton #SAIRFoundation #ScalingTheScienceOfAI #Science #StandardizedCitations #SyntheticData #TerenceTao #traceability
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🚀 NVIDIA’s new Cosmos Transfer lets developers stream massive synthetic datasets across the Omniverse, scaling physical AI training for robotics and autonomous systems. OpenUSD‑based pipelines mean faster, reproducible simulations. Dive into how this could reshape research and benchmarks. #NVIDIAOmniverse #SyntheticData #PhysicalAI #OpenUSD
🔗 https://aidailypost.com/news/nvidia-cosmos-transfer-enables-scalable-synthetic-data-physical-ai
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🚀 NVIDIA’s new Cosmos Transfer lets developers stream massive synthetic datasets across the Omniverse, scaling physical AI training for robotics and autonomous systems. OpenUSD‑based pipelines mean faster, reproducible simulations. Dive into how this could reshape research and benchmarks. #NVIDIAOmniverse #SyntheticData #PhysicalAI #OpenUSD
🔗 https://aidailypost.com/news/nvidia-cosmos-transfer-enables-scalable-synthetic-data-physical-ai
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🚀 NVIDIA’s new Cosmos Transfer lets developers stream massive synthetic datasets across the Omniverse, scaling physical AI training for robotics and autonomous systems. OpenUSD‑based pipelines mean faster, reproducible simulations. Dive into how this could reshape research and benchmarks. #NVIDIAOmniverse #SyntheticData #PhysicalAI #OpenUSD
🔗 https://aidailypost.com/news/nvidia-cosmos-transfer-enables-scalable-synthetic-data-physical-ai
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NEW BIML Bibliography entry
https://arxiv.org/abs/2404.05090
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, et al
This treatment fails because the models being studied are TOY models too simple to be interesting.
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NEW BIML Bibliography entry
https://arxiv.org/abs/2404.05090
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, et al
This treatment fails because the models being studied are TOY models too simple to be interesting.
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NEW BIML Bibliography entry
https://arxiv.org/abs/2404.05090
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, et al
This treatment fails because the models being studied are TOY models too simple to be interesting.
-
NEW BIML Bibliography entry
https://arxiv.org/abs/2404.05090
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, et al
This treatment fails because the models being studied are TOY models too simple to be interesting.
-
NEW BIML Bibliography entry
https://arxiv.org/abs/2404.05090
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, et al
This treatment fails because the models being studied are TOY models too simple to be interesting.
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Generating Labeled Synthetic Images for Vision AI
Manual annotation of image datasets can slow AI projects. Synthetic data provides pre-labeled, controlled samples for training tasks. By integrating Synthetic Data Generation Services into data pipelines, teams accelerate development while improving model reliability.
Know More: https://www.hitechdigital.com/blog/synthetic-data-train-computer-vision-models
#SyntheticDataGeneration #ComputerVisionData #ImageDataSimulation #AIModelTraining #AIModelOptimization #SyntheticData #SyntheticImageData
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And so the snake starts eating itself... the #Ouroboros!
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And so the snake starts eating itself... the #Ouroboros!
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And so the snake starts eating itself... the #Ouroboros!
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And so the snake starts eating itself... the #Ouroboros!
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And so the snake starts eating itself... the #Ouroboros!
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Synthetic Data and Vision AI Performance
Synthetic datasets allow scalable training and controlled testing environments. This article explains generation techniques and performance benefits. It also discusses when companies outsource data annotation services to refine results.
Know More: https://www.hitechdigital.com/blog/synthetic-data-train-computer-vision-models
#OutsourceDataAnnotationServices #DataAnnotationOutsourcing #DataLabelingAndAnnotationServices #SyntheticData #ComputerVision #BusinessProcessOutsourcing #B2BServices
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https://www.europesays.com/uk/761202/ German synthetic data startup simmetry.ai secures €330k to tackle this key bottleneck holding back AI adoption #AIAdoption #AntonElmiger #ComputerVisionStartups #EU #Europe #GermanResearchCentreForArtificialIntelligence #Germany #HighTechIncubatorAcceleratorProgramme #KaiVonSzadkowski #NBank #SimmetryAi #StefanStiene #SyntheticData
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SyGra Studio eliminates YAML configs with visual workflows drag nodes, monitor token costs, generate multimodal data in real time. AdwaitX breaks down ServiceNow's 2026 synthetic data platform for developers 🔗 #AdwaitX #SyGraStudio #SyntheticData
https://www.adwaitx.com/sygra-studio-visual-synthetic-data-generation/
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Discover how the new NeMo pipelines let you generate realistic product data and Q&A pairs while staying license‑compliant. From synthetic data creation to AI model distillation, the open‑source workflow boosts LLM pipelines and integrates with OpenRouter. Dive in to see the code and start building smarter datasets today! #SyntheticData #NeMoDataDesigner #LLMPipelines #DataLicensing
🔗 https://aidailypost.com/news/generate-realistic-product-data-qa-licensecompliant-nemo-pipelines
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Discover how the new NeMo pipelines let you generate realistic product data and Q&A pairs while staying license‑compliant. From synthetic data creation to AI model distillation, the open‑source workflow boosts LLM pipelines and integrates with OpenRouter. Dive in to see the code and start building smarter datasets today! #SyntheticData #NeMoDataDesigner #LLMPipelines #DataLicensing
🔗 https://aidailypost.com/news/generate-realistic-product-data-qa-licensecompliant-nemo-pipelines
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Discover how the new NeMo pipelines let you generate realistic product data and Q&A pairs while staying license‑compliant. From synthetic data creation to AI model distillation, the open‑source workflow boosts LLM pipelines and integrates with OpenRouter. Dive in to see the code and start building smarter datasets today! #SyntheticData #NeMoDataDesigner #LLMPipelines #DataLicensing
🔗 https://aidailypost.com/news/generate-realistic-product-data-qa-licensecompliant-nemo-pipelines
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🏥 Synthetic Data & Trustworthy Health AI
How can AI learn from health data without violating privacy? At the AI Colloquium, Allan Tucker shares lessons from synthetic health data generation-covering bias, concept drift, and regulation in evolving healthcare systems. 🧬📊⏳
📅 4 Feb 2026 | ⏰ 9:30–10:30
💻 Online via Zoom💬 What role should synthetic data play in medical AI?
#HealthAI #SyntheticData #TrustworthyAI #DataScience #AIColloquium #EthicalAI #OpenScience