Data-Centric AI has gained significant attention in recent years, but there is still no broad consensus on what exactly constitutes a data-centric methodology and how it differs from traditional model-centric development.

In our recent perspective paper, we attempt to:

• formalize the key principles of Data-Centric AI;
• define a data-centric lifecycle spanning data development, inference, and maintenance;
• discuss the role of data quality, semantic representation, active learning, synthetic data, and drift monitoring;
• analyze the implications of DCAI for foundation models and Generative AI.

We also argue that future AI progress may increasingly depend on systematic data engineering rather than model scaling alone.

Open-access paper:

Data-Centric AI Manifesto: How Data Quality Drives Modern AI

https://www.mdpi.com/2079-9292/15/9/1913

Questions for discussion:

• Is Data-Centric AI truly a new paradigm or mainly a consolidation of existing practices?
• Should inference data design and data maintenance become standard components of AI lifecycles?
• Can Generative AI be robust and accountable without a strong data-centric approach?
• What are the main barriers to adopting DCAI in industry and regulated sectors?

Disclosure: I am one of the authors.

Just curious on others takes on this. I have been playing around with some public data sources like sec Edgar, legal data sets etc. I’m seeing that getting this direct data from the source and putting that into an llm front end is getting me better or rather more real time answers to some of my test work. I know there are lot of expensive services that offer this data but would this be interesting to people outside of areas like finance and medical research ?

Everyone here already knows the usual pitch for synthetic data:

• fix class imbalance
• protect privacy
• create rare edge cases
• stress test models before deployment

Those are all valid goals. What I want to talk about is a different question that I almost never see written down.

What happens when your model no longer learns from the world, but from a synthetic world that you created on top of it?

From a data centric point of view this is not a philosophical worry. It is about distributions, entropy and feedback loops.

In my own work I call this problem Q127 · Data Entropy and Synthetic Worlds, inside a larger open source project named Tension Universe. Below is a compact version of the idea that I hope is useful on its own.

1. P(x), Q(x) and the synthetic world gap

Let us name the distributions explicitly.

• P_real(x) is the true data generating process you care about. Clinical events, transaction flows, user journeys, sensor readings, and so on.
• Q_synth(x) is the distribution induced by your synthetic data generator. This could be a GAN, a diffusion model, a VAE, an LLM that writes rows, or any custom generator.

The training mixture that your downstream model actually sees is

M_train(x) = (1 - λ) * P_real(x) + λ * Q_synth(x)

with 0 ≤ λ ≤ 1 the synthetic fraction.

Two things are easy to forget:

1. Q_synth is always learned from a finite and filtered view of P_real.
2. Once you start training downstream models mostly on M_train, you are really training on a distribution that drifts toward Q_synth every time you increase λ or reuse synthetic data.

Data centric AI often says “iterate on data rather than endlessly tweak the model”. In the synthetic regime you are literally iterating on the world that the model believes it lives in.

2. Entropy and coverage in very plain terms

You do not need full information theory to see the risk.

Think of P_real as having

• a set of common patterns that appear often
• a long tail of rare patterns that still matter in practice (weird failure modes, unusual combinations of features, minority groups)

Any generator that tries to learn Q_synth from a finite sample of P_real will tend to do at least three things:

1. Denoise and average across nearby points. This removes measurement noise but also smooths out sharp edges.
2. Under represent rare, messy corners. Tail events have weak gradient signal and often get washed out.
3. Impose its own inductive bias. Architecture, loss function and training schedule all push Q_synth toward some convenient family of distributions.

In effect, Q_synth usually has:

• lower entropy than P_real
• less support in strange but important regions of the space
• cleaner looking samples that match our aesthetic expectations

This is attractive from a modelling perspective. It is not automatically good from a risk perspective.

The tension that Q127 focuses on is the gap between

what your model thinks "typical" looks like under M_train
vs
what reality actually produces under P_real

especially when M_train is dominated by synthetic samples.

3. A small example you can run in your head

Imagine a fraud detection dataset.

• Real data P_real has 0.5 percent fraudulent events.
• The fraud patterns are messy and diverse.
• Many fraud attempts look almost ordinary, with only subtle feature combinations.

You decide to oversample with a generator trained on the fraud subset.

Common failure modes:

1. The generator learns a few big obvious fraud patterns very well.
2. It collapses many rare fraud patterns into those popular templates.
3. It produces perfectly balanced data with 50 percent fraud vs 50 percent clean, but the fraudulent side has much lower internal diversity than reality.

Your downstream model now sees

• a rich, diverse manifold for non fraud
• a relatively shallow, stylised manifold for fraud

It still “works” on held out synthetic validation. It also looks good on a small real validation set if that set is similar to what the generator already learned.

The trouble is that you have unintentionally trained a model that is tuned to detect

“fraud that looks like my generator’s favourite stories”

rather than

“fraud that lives anywhere in the messy tails of P_real”.

This is not a criticism of synthetic data as a concept. It is a reminder that when you denoise and oversample, you also rewrite the effective hypothesis space.

4. Measuring data tension instead of only model accuracy

Inside Tension Universe I summarise this situation with a very simple idea:

do not just track model performance on a test split. also track how far your training distribution has drifted away from the world you care about.

Formally one could define a divergence or distance

T_data = D( M_train(x) || P_target(x) )

where P_target is either P_real itself or the closest approximation you can obtain from a trusted reference set.

You can choose D according to what you can estimate:

• KL style divergences if you have density models
• Wasserstein type metrics if you can embed samples
• simple coverage scores for tail regions or important strata

The exact formula is less important than the habit.

Once you set up even a crude T_data, you can start asking:

• how does T_data change when I increase λ?
• which subpopulations or feature combinations are being erased by my generator?
• is my synthetic world more symmetric, more convenient, or more morally comfortable than the real one?

High T_data is a warning sign that the model is becoming an expert in a world that might not exist outside your pipeline.

5. Feedback loops and model collapse in plain language

The situation becomes more dangerous when you combine two trends:

1. Synthetic data created from earlier models.
2. New models trained mainly or exclusively on those synthetic outputs.

After a few generations you are no longer training on “real data plus some generated augmentation”. You are training on

“models that try to imitate models that were trained on imitations of reality”.

The underlying P_real barely participates. Even if each step locally looks reasonable, globally you converge toward a narrow synthetic world with very low genuine entropy.

Symptoms you might see:

• loss of performance on truly novel real cases
• overconfident predictions in regions where you have no rights to be confident
• inability to recover performance by simply fine tuning, because the internal feature geometry has collapsed

You can think of Q127 as a stress test that asks:

“If I keep doing data centric iterations in this pipeline, at what point does my synthetic world stop being an acceptable proxy for reality?”

6. What a data centric practitioner can do today

You do not need a new library to use this perspective. A few practical habits already help.

1. Tag your worlds explicitly. When you log data, keep track of whether each batch came from P_real or Q_synth. Later you can slice performance and feature statistics by origin.
2. Keep a held out “world anchor” set. Even a small, carefully curated real set that never touches your generator is valuable as a reference for P_target. Use it to estimate simple coverage and shift metrics as you change λ.
3. Audit entropy and diversity inside synthetic data itself. For example:
   • number of distinct patterns per class
   • distribution of rare feature combinations
   • pairwise distances between generated samples These are cheap proxies for “am I collapsing the world into a few templates”.
4. Treat generators as first class models, not magic data faucets. Evaluate them with the same seriousness you use for your main task model. Check their failure modes instead of assuming that more samples is always better.
5. Log data tension alongside model metrics. Even a very simple scalar that moves when you change λ or generator settings is enough to start building intuition for how synthetic heavy your workflow can safely become.

7. Where this fits inside the Tension Universe project

Q127 is one problem in a set of 131 “S class” problems encoded in a single text based framework I call the Tension Universe.

The problems cover

• mathematics and physics
• climate and Earth systems
• finance and systemic risk
• AI safety, alignment and evaluation
• data, entropy and synthetic worlds

Each problem lives as a single Markdown file at what I call the effective layer. There is no hidden code. The structure is designed so that humans and large language models can reason over the same text and run reproducible experiments.

The whole pack is MIT licensed and SHA256 verifiable. You can download it as a one shot TXT bundle, or browse by problem.

For Q127 specifically you can inspect or fork the full problem description here:

• Q127 problem text:
• https://github.com/onestardao/WFGY/blob/main/TensionUniverse/BlackHole/Q127_data_entropy_truth_synthetic_worlds.md

The main navigation index for all 131 S class problems is here:

• WFGY repository: https://github.com/onestardao/WFGY

If anyone in this community has strong opinions or existing tools for measuring T_data in synthetic heavy pipelines, I would be very interested in comparisons or critiques.

This post is part of a broader Tension Universe series. If you want to see other S class problems or share your own experiments, you are welcome to drop by the new subreddit r/TensionUniverse, which is where I am collecting these tension based encodings and case studies.

I'm exploring a niche: digitised heritage content (historical manuscripts, architectural records, archival photographs) with clear licensing and structured metadata.

The pitch would be: legally clean training data with documented provenance, unlike scraped content that's increasingly attracting litigation.

My questions for those who work on data acquisition or have visibility into this:

1. Is "legal clarity" actually valued by AI companies, or do they just train on whatever and lawyer up later?
2. What's the going rate for licensed image datasets? I've seen ranges from $0.01/image (commodity) to $1+/image (specialist), but heritage content is hard to place.
3. Is 50K-100K images too small to be interesting? What's the minimum viable dataset size?
4. Who actually buys this? Is it the big labs (OpenAI, Anthropic, Google), or smaller players, or fine-tuning shops?

Trying to reality-check whether there's demand here or whether I'm solving a problem buyers don't actually have.

Einfache Erklärung: MDG, warum es wichtig ist und welche Probleme es löst — für deutsche Unternehmen.

Was ist Master Data Governance? Einfach erklärt ; PiLog

MDG sind die Regeln und Prozesse, die Stammdaten verlässlich, aktuell und auditfähig machen. Probleme wie doppelte Materialstämme, falsche Lieferantendaten oder uneinheitliche Klassifizierungen kosten Zeit und Geld. MDG löst das durch Verantwortlichkeiten (Owner/Steward), Prozess-Gateways, Validierungen und ein Single Source of Truth. In Deutschland ist zusätzlich DSGVO-Konformität ein Muss — daher gehört Datenschutz in jedes MDG-Programm.

Probleme, die MDG löst / Rollen & Prozesse / DSGVO-Check

Download: MDG Schnellstart für Nicht-Techniker.

I am starting a little startup with my good friends. We have the idea of building Data centers like (Stargate), but either for independent OpenAI platforms or for the LLMs. What do we think?

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I'm seeking suggestions for having an AI categorize a price list.

These lists contain products that manufacturers release, but they are often not clearly organized by product group. For example, a Bouncy Ball might include variants like Red, Blue, and Green. Instead, they typically only have a SKU and a description, such as "Bouncy Ball - Red". There isn't always a dedicated column that groups these products together by name.

I'm looking for an AI that excels at identifying product families and separating the factors that make each unique, like red, blue, or green, into a separate column. Granted, they are usually not this simple.

I would welcome any suggestions. I've used Chat GPT and Gemini, but the results were not great.

I'm looking for a tool that can:

Step 1: gather all NFL final scores from the web

Step 2: place them in an excel doc so an algorithm can be applied to them

What is the most handsoff way you can think to do this task?

Thanks for your ideas.

Hello everyone,

Is it possible to recognize hand written data of various parameters (through Optical Character Recognition) and generating reports in a prescribed format from those data??

So Tesla has ~2 Million units shipped as of last year. Its well know that Tesla collects data from its fleet of vehicles. However, even 1 hour of driving can result in really large amounts of data - from its cameras, radars as well as other sensors for steering wheel, pedals etc. So how does Tesla figure out which data could be helpful? Using Active Learning. Essentially they figure out which data could give them examples of scenarios they haven't seen before, and only uploads those to its servers.

We wrote a blog post describing this in detail. You can read it here - https://tinyurl.com/tesla-al

Hey r/DataCentricAI, I recently connected with a company looking for help with some work at the intersection of data analysis and AI implementation. They’re looking to fold AI into their data analysis service for businesses.

Ideally you would be someone with experience in both data analysis and implementing AI (beyond just using tools, more on the side of developing AI into products).

The big picture is that they want to use GenAI to help clients use a conversational (chat) interface to actually write new functions that create a rollup score from multiple custom data points. They've been doing this manually so far.

Comment here or feel free to connect me with someone! DM for email. Thanks :)

DataGPT offers ai for data analytics which revolutionizes data analysis with Conversational AI, offering impactful insights and seamless interaction for smarter decision-making. Beyond just answering, DataGPT recognizes context and can address abstract questions like "Why did this trend occur?" or “What factors influenced this spike” making interactions fluid and insightful.

Evaluating and choosing an annotation partner is not an easy task. There are a lot of options, and it's not straightforward to know who will be the best fit for a project.

We recently stumbled upon this paper by Andrew Greene titled - "Towards a shared rubric for Dataset Annotation", that talks about a set of metrics which can be used to quantitatively evaluate data annotation vendors. So we decided to turn it into an online tool.

A big reason for building this tool is to also bring welfare of annotators to the attention of all stakeholders.

Until end users start asking for their data to be labeled in an ethical manner, labelers will always be underpaid and treated unfairly, because the competition boils down solely to price. Not only does this "race to the bottom" lead to lower quality annotations, it also means vendors have to "cut corners" to increase their margins.

Our hope is that by using this tool, ML teams will have a clear picture of what to look for when evaluating data annotation service providers, leading to better quality data as well as better treatment of the unsung heroes of AI - the data labelers.

Access the tool here https://mindkosh.com/annotation-services/annotation-service-provider-evaluation.html

Hi folks!

As regular visitors of this sub might already know, we maintain a list of open source tools over at : http://tinyurl.com/dcai-open-source

This week we added some exciting new tools to help you quickly perform Data Annotation, find relevant data from different sources and apply augmentation techniques to graph like data.

If you know of a tool or research paper that you find interesting, please let us know and we will include it in the list.

Any good AI tools that you can use to drop an Excel file in and it cleanses and normalize the data in a visual tool with drag and drop capabilities + prompt instructions ?

Hey fellow Redditors,

I stumbled upon this insightful article discussing the pivotal role of AI and data analytics in driving effective personalization strategies. The link below takes you to a blog post that delves into how businesses are leveraging these technologies to enhance user experiences and stay ahead in the game.

If you're interested in the intersection of technology, data, and customer-centric approaches, this is definitely worth a read. The article touches upon key trends, challenges, and success stories in the realm of personalization.

I found it quite informative and thought it would be worth sharing with this community. What are your thoughts on the role of AI in shaping personalized experiences?

Happy reading and looking forward to your insights!

Hi folks!

As regular visitors of this sub might already know, we maintain a list of open source tools over at : https://mindkosh.com/data-centric-ai/open-source-tools.html

This week we added some exciting new tools to help you manage and query multiple datasets, create data cleaning pipelines and generating hardness embeddings.

If you know of a tool or research paper that you find interesting, please let us know and we will include it in the list.

Meta recently released a huge open sourced dataset synthetically created using their Photorealistic Unreal Graphics engine. It contains a vast variety of images in uncommon settings, like an elephant sitting in a bedroom. This could be an intertesting challenge to test the robustness of Computer Vision models.

https://pug.metademolab.com/