#AI Data Processing

[verschlimmbesserung]

New aerial surveillance footage obtained by the Southern Environmental Law Center has found that Musk's artificial intelligence company, xAI, is using 35 methane gas generators to power its "Colossus" supercomputer facility, the backbone of its flagship Grok. That's 20 more generators than the 15 xAI filed permits for, and 35 more than it was approved to use.

Generators like xAI's emit huge amounts of nitrogen dioxide, a highly reactive gas which causes irreversible respiratory damage over time. And that's before you consider its effects on the ozone layer, or its contribution to acid rain, smog, and nutrient pollution in local soil and waterways. With 35 generators now chugging along, that's a whole chorus of turbines spewing the toxic gas into low-income, minority-led communities 24/7.

[hathuablog1]

"Understanding Grok Technology Behind AI Databases: Revolutionizing Data Processing and Intelligence" 2025

Grok Technology: In the ever-evolving world of artificial intelligence (AI), various technologies are being developed to improve the way we process and manage data. Among the many innovations, Grok technology stands out as one of the most transformative tools, especially in the context of AI-driven databases. Grok, an advanced AI model designed to comprehend and process information deeply, has…

[itesservices]

Enhance your business operations with AI-based data processing. This technology optimizes data management, automates repetitive tasks, and enables smarter decision-making. By leveraging AI, organizations can improve accuracy, reduce costs, and boost efficiency. Stay ahead in the competitive landscape by integrating AI-driven solutions for streamlined workflows and actionable insights. Embrace the future of data processing today! 

[anghraine]

Nothing to do with anything else, but one of the most annoying aspects of autism is being hypersensitive to touch. I feel compelled to change into a very loose nightgown every evening because otherwise the sensation of anything against my skin by that time makes me want to claw my flesh from my body.

But, simultaneously, there's part of my mind that responds to ... like, a hug or something very small with "I yearn for human connection and some kind of touch however minimal, yay! a hug!" And meanwhile the other part is screaming "BAD BAD BAD RED ALERT BAD," like having a green and red light simultaneously flashing in my head.

Doesn't feel great, Bob!

[insert-game]

i hate gen AI so much i wish crab raves upon it

[mister-forest]

Chiques fíjense de activar la opción de no compartir datos en el apartado "Visibilidad" en Ajustes ‼️‼️

[marginal-effect]

getting a samsung phone was probably one of the worst mistakes ive made tech wise in years its like having a iphone all over again except worse somehow

[lazer-exe]

"spotify wrapped was clearly AI"

Two questions. What, exactly, do you think AI is? And did you think spotify had people HAND PICKING your top songs before this???? be for real

[professor-rye]

I feel like, with the uproar over Nanowrimo right now, we have an opportunity to really push back at shitty AI, but I feel like we also need to be smart about it.

Just saying "Generative AI is bad! Fuck you!" is not going to make a huge dent in shitty ai practices, because they'll just dismiss us out of hand. But if we ask the really hard hitting questions, then we might be able to start making some level of progress.

Mozilla is actually doing a ton of good work towards this very goal.

They've been working to try to shift industry goals towards more transparent, conscientious, and sustainable practices, and I think their approach has a lot of promise.

AI is not inherently bad or harmful (hells, even generative AI isn't. It's just a tool, thus neutral at its core), but harmful practices and a lack of transparency make it to where we can not fucking trust them, at least in their current iterations.

But the cat is out of the fucking bag, and its not going back in even if we do point out all the harm. Too many people like the idea of making their lives easier, and you can't deny the overwhelming potential that AI offers.

But that doesn't mean we have to tolerate the harm it currently causes.

[frank-olivier]

Deep Learning, Deconstructed: A Physics-Informed Perspective on AI’s Inner Workings

Dr. Yasaman Bahri’s seminar offers a profound glimpse into the complexities of deep learning, merging empirical successes with theoretical foundations. Dr. Bahri’s distinct background, weaving together statistical physics, machine learning, and condensed matter physics, uniquely positions her to dissect the intricacies of deep neural networks. Her journey from a physics-centric PhD at UC Berkeley, influenced by computer science seminars, exemplifies the burgeoning synergy between physics and machine learning, underscoring the value of interdisciplinary approaches in elucidating deep learning’s mysteries.

At the heart of Dr. Bahri’s research lies the intriguing equivalence between neural networks and Gaussian processes in the infinite width limit, facilitated by the Central Limit Theorem. This theorem, by implying that the distribution of outputs from a neural network will approach a Gaussian distribution as the width of the network increases, provides a probabilistic framework for understanding neural network behavior. The derivation of Gaussian processes from various neural network architectures not only yields state-of-the-art kernels but also sheds light on the dynamics of optimization, enabling more precise predictions of model performance.

The discussion on scaling laws is multifaceted, encompassing empirical observations, theoretical underpinnings, and the intricate dance between model size, computational resources, and the volume of training data. While model quality often improves monotonically with these factors, reaching a point of diminishing returns, understanding these dynamics is crucial for efficient model design. Interestingly, the strategic selection of data emerges as a critical factor in surpassing the limitations imposed by power-law scaling, though this approach also presents challenges, including the risk of introducing biases and the need for domain-specific strategies.

As the field of deep learning continues to evolve, Dr. Bahri’s work serves as a beacon, illuminating the path forward. The imperative for interdisciplinary collaboration, combining the rigor of physics with the adaptability of machine learning, cannot be overstated. Moreover, the pursuit of personalized scaling laws, tailored to the unique characteristics of each problem domain, promises to revolutionize model efficiency. As researchers and practitioners navigate this complex landscape, they are left to ponder: What unforeseen synergies await discovery at the intersection of physics and deep learning, and how might these transform the future of artificial intelligence?

Yasaman Bahri: A First-Principle Approach to Understanding Deep Learning (DDPS Webinar, Lawrence Livermore National Laboratory, November 2024)

Sunday, November 24, 2024

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[innovatexblog]

How Large Language Models (LLMs) are Transforming Data Cleaning in 2024

Data is the new oil, and just like crude oil, it needs refining before it can be utilized effectively. Data cleaning, a crucial part of data preprocessing, is one of the most time-consuming and tedious tasks in data analytics. With the advent of Artificial Intelligence, particularly Large Language Models (LLMs), the landscape of data cleaning has started to shift dramatically. This blog delves into how LLMs are revolutionizing data cleaning in 2024 and what this means for businesses and data scientists.

The Growing Importance of Data Cleaning

Data cleaning involves identifying and rectifying errors, missing values, outliers, duplicates, and inconsistencies within datasets to ensure that data is accurate and usable. This step can take up to 80% of a data scientist's time. Inaccurate data can lead to flawed analysis, costing businesses both time and money. Hence, automating the data cleaning process without compromising data quality is essential. This is where LLMs come into play.

What are Large Language Models (LLMs)?

LLMs, like OpenAI's GPT-4 and Google's BERT, are deep learning models that have been trained on vast amounts of text data. These models are capable of understanding and generating human-like text, answering complex queries, and even writing code. With millions (sometimes billions) of parameters, LLMs can capture context, semantics, and nuances from data, making them ideal candidates for tasks beyond text generation—such as data cleaning.

To see how LLMs are also transforming other domains, like Business Intelligence (BI) and Analytics, check out our blog How LLMs are Transforming Business Intelligence (BI) and Analytics.

Traditional Data Cleaning Methods vs. LLM-Driven Approaches

Traditionally, data cleaning has relied heavily on rule-based systems and manual intervention. Common methods include:

Handling missing values: Methods like mean imputation or simply removing rows with missing data are used.

Detecting outliers: Outliers are identified using statistical methods, such as standard deviation or the Interquartile Range (IQR).

Deduplication: Exact or fuzzy matching algorithms identify and remove duplicates in datasets.

However, these traditional approaches come with significant limitations. For instance, rule-based systems often fail when dealing with unstructured data or context-specific errors. They also require constant updates to account for new data patterns.

LLM-driven approaches offer a more dynamic, context-aware solution to these problems.

How LLMs are Transforming Data Cleaning

1. Understanding Contextual Data Anomalies

LLMs excel in natural language understanding, which allows them to detect context-specific anomalies that rule-based systems might overlook. For example, an LLM can be trained to recognize that “N/A” in a field might mean "Not Available" in some contexts and "Not Applicable" in others. This contextual awareness ensures that data anomalies are corrected more accurately.

2. Data Imputation Using Natural Language Understanding

Missing data is one of the most common issues in data cleaning. LLMs, thanks to their vast training on text data, can fill in missing data points intelligently. For example, if a dataset contains customer reviews with missing ratings, an LLM could predict the likely rating based on the review's sentiment and content.

A recent study conducted by researchers at MIT (2023) demonstrated that LLMs could improve imputation accuracy by up to 30% compared to traditional statistical methods. These models were trained to understand patterns in missing data and generate contextually accurate predictions, which proved to be especially useful in cases where human oversight was traditionally required.

3. Automating Deduplication and Data Normalization

LLMs can handle text-based duplication much more effectively than traditional fuzzy matching algorithms. Since these models understand the nuances of language, they can identify duplicate entries even when the text is not an exact match. For example, consider two entries: "Apple Inc." and "Apple Incorporated." Traditional algorithms might not catch this as a duplicate, but an LLM can easily detect that both refer to the same entity.

Similarly, data normalization—ensuring that data is formatted uniformly across a dataset—can be automated with LLMs. These models can normalize everything from addresses to company names based on their understanding of common patterns and formats.

4. Handling Unstructured Data

One of the greatest strengths of LLMs is their ability to work with unstructured data, which is often neglected in traditional data cleaning processes. While rule-based systems struggle to clean unstructured text, such as customer feedback or social media comments, LLMs excel in this domain. For instance, they can classify, summarize, and extract insights from large volumes of unstructured text, converting it into a more analyzable format.

For businesses dealing with social media data, LLMs can be used to clean and organize comments by detecting sentiment, identifying spam or irrelevant information, and removing outliers from the dataset. This is an area where LLMs offer significant advantages over traditional data cleaning methods.

For those interested in leveraging both LLMs and DevOps for data cleaning, see our blog Leveraging LLMs and DevOps for Effective Data Cleaning: A Modern Approach.

Real-World Applications

1. Healthcare Sector

Data quality in healthcare is critical for effective treatment, patient safety, and research. LLMs have proven useful in cleaning messy medical data such as patient records, diagnostic reports, and treatment plans. For example, the use of LLMs has enabled hospitals to automate the cleaning of Electronic Health Records (EHRs) by understanding the medical context of missing or inconsistent information.

2. Financial Services

Financial institutions deal with massive datasets, ranging from customer transactions to market data. In the past, cleaning this data required extensive manual work and rule-based algorithms that often missed nuances. LLMs can assist in identifying fraudulent transactions, cleaning duplicate financial records, and even predicting market movements by analyzing unstructured market reports or news articles.

3. E-commerce

In e-commerce, product listings often contain inconsistent data due to manual entry or differing data formats across platforms. LLMs are helping e-commerce giants like Amazon clean and standardize product data more efficiently by detecting duplicates and filling in missing information based on customer reviews or product descriptions.

Challenges and Limitations

While LLMs have shown significant potential in data cleaning, they are not without challenges.

Training Data Quality: The effectiveness of an LLM depends on the quality of the data it was trained on. Poorly trained models might perpetuate errors in data cleaning.

Resource-Intensive: LLMs require substantial computational resources to function, which can be a limitation for small to medium-sized enterprises.

Data Privacy: Since LLMs are often cloud-based, using them to clean sensitive datasets, such as financial or healthcare data, raises concerns about data privacy and security.

The Future of Data Cleaning with LLMs

The advancements in LLMs represent a paradigm shift in how data cleaning will be conducted moving forward. As these models become more efficient and accessible, businesses will increasingly rely on them to automate data preprocessing tasks. We can expect further improvements in imputation techniques, anomaly detection, and the handling of unstructured data, all driven by the power of LLMs.

By integrating LLMs into data pipelines, organizations can not only save time but also improve the accuracy and reliability of their data, resulting in more informed decision-making and enhanced business outcomes. As we move further into 2024, the role of LLMs in data cleaning is set to expand, making this an exciting space to watch.

Large Language Models are poised to revolutionize the field of data cleaning by automating and enhancing key processes. Their ability to understand context, handle unstructured data, and perform intelligent imputation offers a glimpse into the future of data preprocessing. While challenges remain, the potential benefits of LLMs in transforming data cleaning processes are undeniable, and businesses that harness this technology are likely to gain a competitive edge in the era of big data.

[landunderthewave]

Watched a video about these "AI assistants" that Meta has launched with celebrity faces (Kendall Jenner, Snoop Dogg etc.). Somebody speculated/mentioned in the comments that eventually Meta wants to sell assistant apps to companies, but that makes ... no sense.

If they mean in the sense of a glorified search engine that gives you subtly wrong answers half the time and can't do math, sure - not that that's any different than the stuff that already exists (????)

But if they literally mean assistant, that's complete bogus. The bulk of an assistant's job is organizing things - getting stuff purchased, herding a bunch of hard-to-reach people into the same meeting, booking flights and rides, following up on important conversations. Yes, for some of these there's already an app that has automated the process to a degree. But if these processes were sufficiently automated, companies would already have phased out assistant positions. Sticking a well-read chat bot on top of Siri won't solve this.

If I ask my assistant to get me the best flight to New York, I don't want it to succeed 80 % of time and the rest of the time, book me a flight at 2 a.m. or send me to New York, Florida or put me on a flight that's 8 hours longer than necessary. And yes, you can probably optimize an app + chat bot for this specific task so it only fails 2 % of the time. But you cannot optimize a program to be good at everything–booking flights, booking car rentals, organizing catering, welcoming people at the front desk and basically any other request a human could think off. What you're looking for is a human brain and body. Humans can improvise, prioritize, make decisions, and, very importantly, interact freely with the material world. Developing a sufficiently advanced assistant is a pipe dream.

[mudwerks]

(via It’s a “fake PR stunt”: Artists hate Meta’s AI data deletion process | Ars Technica)

This August, when Meta began allowing people to submit requests to delete personal data from third parties used to train Meta’s generative AI models, many artists and journalists interpreted this new process as Meta’s very limited version of an opt-out program. CNBC explicitly referred to the request form as an “opt-out tool.”

This is a misconception. In reality, there is no functional way to opt out of Meta’s generative AI training.

Artists who have tried to use Meta’s data deletion request form have learned this the hard way and have been deeply frustrated with the process. “It was horrible,” illustrator Mignon Zakuga says. Over a dozen artists shared with WIRED an identical form letter they received from Meta in response to their queries. In it, Meta says it is “unable to process the request” until the requester submits evidence that their personal information appears in responses from Meta’s generative AI.

so - this looks like the best way to go currently...

...The tool, called Nightshade, is intended as a way to fight back against AI companies that use artists’ work to train their models without the creator’s permission.