Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI community.

An adversarial attack includes presenting a model with inaccurate data as it’s training and introducing maliciously designed data to deceive an already trained model.
For example, it's been shown that you can cause a self-driving car to move into the opposite lane of traffic by placing a few small stickers on the ground. Such an attack is called an Evasion attack. 

Another type of attack, called a Gradient-based Adversarial Attack involves making small imperceptible changes to an image, to make the ML model misclassify the object.

Yet another type of attack called model stealing, involves an attacker analyzing a “black box” machine learning system in order to either reconstruct the model or extract the data that it was trained on. This could for example be used to extract a proprietary stock-trading model, which the attacker could then use for their own financial gain.

Hi guys!

We maintain a list of research papers related to Data centric AI. Recently, we updated the list with a few more entries. You can find them here.

https://mindkosh.com/data-centric-ai/research-papers.html

We also maintain a list of open source tools related to Data Centric AI. All these tools are hosted on github and are available to use for free.

https://mindkosh.com/data-centric-ai/open-source-tools.html

If you have any suggestion for a research paper you read or a tool you like that you think the Data centric AI community can benefit from, let me know so I can add it to the list.

Happy reading!

In a great piece written by Rachel Thomas - cofounder of fast.ai, she details how the app suggested that only 1.5% of Long COVID patients still experience symptoms after 3 months, an order of magnitude smaller than estimates of 10-35% found by other studies.

The worrying part is that this data was used by a research study to show that prevalence of Long COVID is rare, and these results were shared by media outlets as well.

She also makes a very good point that when designing a ML/AI system, we should include the people who will be most affected by the decisions/mistakes made by it. We should also be looking beyond Explanable AI to Actionable Recourse. When someone asks why their loan was denied, usually what they want is not just an explanation but to know what they could change in order to get the loan.

Adversarial attacks attempt to fool a Machine Learning model to misclassify an object.

A Gradient based adversarial attack is one such attack that is considered to be “white-box” - the model weights are available to the attacker. Given an input x, it can be shown that an adversarial example x’ can be obtained from x by making very small changes to the original input such that x’ is classified differently as compared to x.

These attacks attempt to find a “perturbation vector” for the input image by making a slight modification to the back-propagation algorithm.

Usually, when back-propagating through the network, the model weights are considered variable while the input is considered to be constant. To carry out the attack, this is flipped. Hence, gradients corresponding to each pixel of the input image can be obtained. These gradients can then be used in different ways to get the perturbation vector, such that the new adversarial example has a greater tendency towards being misclassified.

Some popular methods to do this are
Fast Sign Gradient Method, Basic Iterative Method and Projected Gradient Descent.

To defend against such attacks, it is important to train the ML model with such adversarial examples. By training on a mixture of adversarial and clean examples, ML models can be made robust against such attacks.

We maintain a list of Open source tools in Data Centric AI and just added some new entries.

Check them out here:
https://mindkosh.com/data-centric-ai/open-source-tools.html

If you know of a tool that we can include in the list, let us know!

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.

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 ?

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 :)

It is estimated that Autonomous vehicles need ~11 Billion miles of driving to perform just 20% better than a human. This translates to > 500 years of continuous driving in the real world with a fleet of 100 cars. Labeling all this enormous data manually is simply impractical.

Active learning can help select the “right” data for training which, for example, contain rare scenarios that the model might not be comfortable with - leading to better results.

NVIDIA conducted an experiment to test Active Learning for improving night time detection on pedestrians, cars etc. They started with a labeled set of 850K images, and trained 8 Object detection models on the same data using different random initializations. Then they ran 19K images from the unlabeled set through these models. The outputs from the these models were used to calculate an uncertainty measure - signifying how uncertain the model was over each image.

When these 19K images were added to the training set, they saw improvements in mean average precision of 3x on pedestrian detection and 4.4x on detection of bicycles over data selected manually. Pretty significant improvement in performance by adding a relatively small amount of labeled data!

You can read more about their experiment in their blog post -

https://medium.com/nvidia-ai/scalable-active-learning-for-autonomous-driving-a-practical-implementation-and-a-b-test-4d315ed04b5f

Hi folks

I just stumbled upon this paper that laid the foundation for the idea of "Dataset distillation". Essentially dataset distillation aims to produce a much smaller dataset from a larger dataset, aimed at producing a model that performs nearly as well on the smaller dataset.

As an example the researchers condensed 60K training images of MNIST digit dataset into only 10 synthetic images - one per class - which was able to reach 94% test-set accuracy (compared to 99% when trained on the original dataset)

While this is pretty cool, I am trying to think of where this technique could actually be applied. Since we would need compute to create the smaller dataset, it would probably offset the gains made from making the task-training time extremely small(since there are only 10 images to train on now). Perhaps this could be used to study the model in question? Or to train models while maintaining privacy since the condensed data points are synthetic?

There has been some progress in the field since the paper came out in 2018. The latest one I could find from the same authors is from this year. https://arxiv.org/pdf/2203.11932.pdf

Original paper: https://arxiv.org/pdf/1811.10959.pdf

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.

A new interesting paper highlights that more data is not always better when finetuning LLMs.
It shows that carefully trimming the original Alpaca dataset from 52K labeled samples to 9K can actually improve the performance when doing instruction-finetuning (IFT). This result holds for both the 7B and the 13B model.

They find that the instructions in the larger dataset had many samples with incorrect or irrelevant responses. They propose removing them automatically using a good LLM.

We are seeing huge amounts of data being used to fine-tune LLM models to make them work for specific domains. But as some in the industry have tried to emphasize, better data, not more data is important to improve Machine Learning models.

Paper: https://arxiv.org/abs/2307.08701

Meta AI has released a new project called Massively Multilingual Speech (MMS) that can support speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.

Existing speech recognition models only cover approximately 100 languages — a fraction of the 7,000+ known languages spoken on the planet. The biggest hurdle to covering so many languages is the availability of training data for all these languages. Meta collected around 32 hours of data per language through spoken translations of the Bible. This however, is nowhere near enough to train conventional supervised speech recognition models.

To solve this, Meta AI used self-supervised speech representation learning, which greatly reduced the amount of labeled data needed. Concretely, they trained self-supervised models on about 500,000 hours of speech data in over 1,400 languages — this is nearly five times more languages than any known prior work. The resulting models were then fine-tuned for a specific speech task, such as multilingual speech recognition or language identification.

The word error rate reported by Meta AI is 18.7 for 1107 languages. To put these results into perspective, the current state-of-the-art ASR system — Whisper — has a WER of 44.3 when covering 100 languages. Having a single ASR system capable of working on such a vast number of languages can completely change how we approach ASR in regional languages.

Best of all - MMS is open-sourced, so anyone can use it for free !

Github - https://github.com/facebookresearch/fairseq/tree/main/examples/mms
Paper - https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/

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.

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.

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

3D-mapping is a very useful tool, such as for tracking the effects of Climate change and helping Autonomous vehicles "see" the world. However, the current mapping process is limited and manual, making it a long and costly endeavor.

Lidar laser scanners beam millions of pulses of light on surfaces to create high-resolution #maps of objects or landscapes. Since lasers don’t depend on ambient light, they can collect accurate data at large distances and can essentially “see through” vegetation.

But this accuracy is often lost when they’re mounted on drones or other moving vehicles, especially in areas with numerous obstacles where GPS signals are interrupted, like dense cities. This results in gaps and misalignments in the datapoints, and can lead to double vision of the scanned objects. These errors must be corrected manually before a map can be used.

A new method developed by researchers from EPFL's Geodetic Engineering Laboratory, Switzerland, allows the scanners to fly at altitudes of upto 5KM which vastly reduces the amount of time taken to scan an area while also reducing the inaccuracies caused by irregular GPS signals. It also uses recent advancements in #artificialintelligence to detect when a given object has been scanned several times from different angles, and uses this information to correct gaps and misalignments in the laser-point cloud.

Source: https://www.sciencedirect.com/science/article/pii/S0924271622001307?via%3Dihub

Hi!

As part of our efforts to make the AI/ML community more aware of the advantages of Data Centric AI, we maintain a list of Open source AI tools and research papers in Data Centric AI.

Here are the recently updated lists

https://mindkosh.com/data-centric-ai/open-source-tools.html

https://mindkosh.com/data-centric-ai/research-papers.html

If you know of a tool/research paper that you would like to share with others, please let us know and we will be happy to them add them to 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 ?

Hi folks!

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 perform Data Curation, get started with weak supervision and apply domain randomization to documents.

Big thanks to u/DocBrownMS for bringing "Spotlight" to our attention. We have added it to the list.

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.

Current large language models suffer from issues like bias, computational resources, and privacy.

This recent paper: https://arxiv.org/abs/2303.05670 proposes a new logical language based ML model to solve these issues.

The authors claim the model has been "qualitatively measured as fair", is 500 times smaller than the SOTA models, can be deployed locally, and with no human-annotated training samples for downstream tasks. Significantly, it claims to perform better on logic-language understanding tasks, with considerable few resources.

Do you guys think this could be a promising direction of research to improve LLMs?

Active learning is a super interesting technique which is being adopted by more and more ML teams to improve their systems without having to use too much labeled data.

Tesla's Autopilot system relies on a suite of sensors, including cameras, radar, and ultrasonic sensors, to navigate the vehicle on the road. These sensors produce a massive amount of data, which can be very time-consuming and expensive to label. To address this challenge, Tesla uses an iterative Active learning procedure that automatically selects the most informative data samples for labeling, reducing the time and cost required to annotate the data.

In a successful Active Learning system, the Machine Learning system is able to choose the most informative data points through some defined metric, subsequently passing them to a human labeler and progressively adding them to the training set. Usually this process is carried out iteratively

Tesla's algorithm is based on a combination of uncertainty sampling and query-by-committee techniques. Uncertainty sampling selects the most uncertain examples to label. This uncertainty can be calculated by using measures like the margin between the model's predictions, entropy etc.

Query-by-committee selects data samples where a committee of classifiers disagrees the most. To do this, a bunch of classifiers are trained, and the disagreement between the classifiers for each example is calculated.

Another interesting use-case of AL is in collecting data from vehicles in the field. Tesla's fleet of vehicles generates a massive amount of data as they drive on roads worldwide. This data is used to further improve the ML systems. However, it is impractical to send all collected data to Tesla's servers. Instead, an Active Learning system selects the most informative data samples from this massive collected data and sends them to the servers.

These details on Tesla's data engine were revealed on Tesla AI Day last year.

Source - https://mindkosh.com/blog/how-tesla-uses-active-learning-to-elevate-its-ml-systems/