#data drift

[mp3monsterme]

Fluent Bit and AI: Unlocking Machine Learning Potential

These days, everywhere you look, there are references to Generative AI, to the point that what have Fluent Bit and GenAI got to do with each other? GenAI has the potential to help with observability, but it also needs observation to measure its performance, whether it is being abused, etc. You may recall a few years back that Microsoft was trailing new AI features for Bing, and after only having…

[jcmarchi]

How Quality Data Fuels Superior Model Performance

New Post has been published on https://thedigitalinsider.com/how-quality-data-fuels-superior-model-performance/

How Quality Data Fuels Superior Model Performance

Here’s the thing no one talks about: the most sophisticated AI model in the world is useless without the right fuel. That fuel is data—and not just any data, but high-quality, purpose-built, and meticulously curated datasets. Data-centric AI flips the traditional script. 

Instead of obsessing over squeezing incremental gains out of model architectures, it’s about making the data do the heavy lifting. This is where performance isn’t just improved; it’s redefined. It’s not a choice between better data or better models. The future of AI demands both, but it starts with the data.

Why Data Quality Matters More Than Ever

According to one survey, 48% of businesses use big data, but a much lower number manage to use it successfully. Why is this the case?

It’s because the foundational principle of data-centric AI is straightforward: a model is only as good as the data it learns from. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential. For example, generative AI systems that produce erroneous outputs often trace their limitations to inadequate training datasets, not the underlying architecture. 

High-quality datasets amplify the signal-to-noise ratio, ensuring models generalize better to real-world scenarios. They mitigate issues like overfitting and enhance the transferability of insights to unseen data, ultimately producing results that align closely with user expectations.

This emphasis on data quality has profound implications. For instance, poorly curated datasets introduce inconsistencies that cascade through every layer of a machine learning pipeline. They distort feature importance, obscure meaningful correlations, and lead to unreliable model predictions. On the other hand, well-structured data allows AI systems to perform reliably even in edge-case scenarios, underscoring its role as the cornerstone of modern AI development.

The Challenges of Data-Centric AI

The thing is, high-quality data is getting harder and harder to come by due to the proliferation of synthetic data and AI developers increasingly relying on it. 

Then again, achieving high-quality data is not without its challenges. One of the most pressing issues is bias mitigation. Datasets often mirror the systemic biases present in their collection process, perpetuating unfair outcomes in AI systems unless addressed proactively. This requires a deliberate effort to identify and rectify imbalances, ensuring inclusivity and fairness in AI-driven decisions.

Another critical challenge is ensuring data diversity. A dataset that captures a wide range of scenarios is essential for robust AI models. However, curating such datasets demands significant domain expertise and resources. For instance, assembling a dataset for prospecting with AI is a process that must account for a myriad of variables. This includes demographic data, activity, response times, social media activity, and company profiles. You must thus 

Label accuracy poses yet another hurdle. Incorrect or inconsistent labeling undermines model performance, particularly in supervised learning contexts. Strategies like active learning—where ambiguous or high-impact samples are prioritized for labeling—can improve dataset quality while reducing manual effort.

Lastly, balancing data volume and quality is an ongoing struggle. While massive, overly influential datasets can enhance model performance, they often include redundant or noisy information that dilutes effectiveness. Smaller, meticulously curated datasets frequently outperform larger, unrefined ones, underscoring the importance of strategic data selection.

Enhancing Dataset Quality: A Multifaceted Approach

Improving dataset quality involves a combination of advanced preprocessing techniques, innovative data generation methods, and iterative refinement processes. One effective strategy is implementing robust preprocessing pipelines. Techniques such as outlier detection, feature normalization, and deduplication ensure data integrity by eliminating anomalies and standardizing inputs. For instance, principal component analysis (PCA) can help reduce dimensionality, enhancing model interpretability without sacrificing performance.

Synthetic data generation has also emerged as a powerful tool in the data-centric AI landscape. When real-world data is scarce or imbalanced, synthetic data can bridge the gap. Technologies like generative adversarial networks (GANs) enable the creation of realistic datasets that supplement existing ones, allowing models to learn from diverse and representative scenarios.

Active learning is another valuable approach. With only the most informative data points for labeling being selected, active learning minimizes resource expenditure while maximizing dataset relevance. This method not only enhances label accuracy but also accelerates the development of high-quality datasets for complex applications.

Data validation frameworks play a crucial role in maintaining dataset integrity over time. Automated tools such as TensorFlow Data Validation (TFDV) and Great Expectations help enforce schema consistency, detect anomalies, and monitor data drift. These frameworks streamline the process of identifying and addressing potential issues, ensuring datasets remain reliable throughout their lifecycle.

Specialized Tools and Technologies

The ecosystem surrounding data-centric AI is expanding rapidly, with specialized tools catering to various aspects of the data lifecycle. Data labeling platforms, for instance, streamline annotation workflows through features like programmatic labeling and integrated quality checks. Tools like Labelbox and Snorkel facilitate efficient data curation, enabling teams to focus on refining datasets rather than managing manual tasks.

Data versioning tools such as DVC ensure reproducibility by tracking changes to datasets alongside model code. This capability is particularly critical for collaborative projects, where transparency and consistency are paramount. In niche industries such as healthcare and legal tech, specialized AI tools optimize data pipelines to address domain-specific challenges. These tailored solutions ensure datasets meet the unique demands of their respective fields, enhancing the overall impact of AI applications.

However, one big issue in executing all of this is the prohibitively expensive nature of AI hardware. Fortunately, the growing availability of rented GPU hosting services further accelerates advancements in data-centric AI. This is an essential part of the global AI ecosystem, as it allows even smaller startups access to quality, refined datasets. 

The Future of Data-Centric AI

As AI models become more sophisticated, the emphasis on data quality will only intensify. One emerging trend is federated data curation, which leverages federated learning frameworks to aggregate insights from distributed datasets while preserving privacy. This collaborative approach allows organizations to share knowledge without compromising sensitive information.

Another promising development is the rise of explainable data pipelines. Just as explainable AI provides transparency into model decision-making, tools for explainable data pipelines will illuminate how data transformations influence outcomes. This transparency fosters trust in AI systems by clarifying their foundations.

AI-assisted dataset optimization represents another frontier. Future advancements in AI will likely automate parts of the data curation process, identifying gaps, correcting biases, and generating high-quality synthetic samples in real time. These innovations will enable organizations to refine datasets more efficiently, accelerating the deployment of high-performing AI systems.

Conclusion

In the race to build smarter AI systems, the focus must shift from merely advancing architectures to refining the data they rely on. Data-centric AI not only improves model performance but also ensures ethical, transparent, and scalable AI solutions. 

As tools and practices evolve, organizations equipped to prioritize data quality will lead the next wave of AI innovation. By embracing a data-first mindset, the industry can unlock unprecedented potential, driving advancements that resonate across every facet of modern life.

[garymdm]

Data Drift: How Does it Affect Your Machine Learning Model?

Across the globe, organizations are tapping into the potential of data to drive informed decision-making, streamline operations, and gain a competitive edge through artificial intelligence and machine-learning. However, amidst this data-driven revolution, a formidable challenge known as “Data Drift” looms, capable of exerting a profound impact on the performance of machine learning models. In…

[unbidden-yidden-deactivated20240]

Okay I'm curious: I've seen a lot of Christians use/refer to the phrase "hosanna in the highest!" which is used in the New Testament and I've frequently heard it pronounced "hoh-ZAHN-ah". However, it's a much older liturgical phrase in Hebrew and definitely not pronounced like that. I want to know: (1) were you taught the actual meaning of this word by your community/do you know what it actually means without googling it, (2) what variety of Christian are you, and (3) if, after googling it, were you correct?

Sorry fellow yidden and other non-Christians; this poll is specific to people who identify as Christian and/or who were raised as such. (Edit: gerim who were raised Christian can vote, but you have to base it off of what you were taught as a Christian, not what you know now.)

Christians who answer: if you googled this after voting yes and were taught wrong about it, please let me know in the notes.

(If you're wondering if you "count" as Christian or having been raised as such, for these purposes I would say interpret it broadly to include anyone who views Jesus as the messiah and grew up reading the New Testament as part of your bible.)

[frameconfessions]

The Pom-2 gifts chemistry levels for each of the hex syndicate members got datamined (it's on the wikia) so the anon talking about the protoframes models getting datamined might get their wish for SFM animation stuff made with them. 👀

.

[deanpinterester]

i was going to make a post telling yall to stop calling godzilla minus one a low-budget film (because it isn't) but then i remembered disney regularly drops 12 million for ONE EPISODE of their shows without nearly the same cultural impact so. yeah godzilla is low-budget as far as i'm concerned idc

[opens-up-4-nobody]

...

[weepingfoxfury]

The man on the radio keeps playing Enya ... not my cup of tea, but then I have coffee. Today's quiz was all about The Boss. The answer was very obvious, even to me, but I never text in. The traffic lady talked about traffic, before going on to say two friends had stayed over and were extreme fans of the man on the radio and would he give a shout out to them. The chef on the radio is in and as it's Friday it's all about fish, prawns and breadcrumbs.

The three minute misery came and went with reports that the data centre people need more of everything to build even more data centres. They need more land ... and electricity ... and funding ... and ... and ... and ... so could the Emeraldians please get off the Emerald Isle. I don't think the chef on the radio has a recipe for that.

The peony has finally flowered. The plant has quite some age on it. I've been here 10 years and it was here before I came. Have been given a Tayberry plant and a blueberry plant. The Tayberry is a cross between a raspberry and a blackberry. I already don't get any blackberries or raspberries from the garden ... so I'm pretty sure if these fruits appear the birds will be the only ones holding up taste and texture score cards.

Pineapple, raspberries and blueberries are on the cake menu today, plus flapjacks, plus the coffee pot. Friday, Friday, Friday and the man on the radio is playing The Drifters 'Up On The Roof' ...

[aquamonstra]

Data could pilot a Jaeger by himself but he and Geordi are drift compatible so he would NEVER.

[antirepurp]

what if i backed up my pc chao garden and started it over and played it like a normal person and not like a dog breeder. as a treat

[lacunasbalustrade]

guess what I’m doing today

[unorthodoxpengu]

A messy animation of the hand break hero

[rastronomicals]

9:50 AM EST December 28, 2024:

The Mercury Program - "Slightly Drifting" From the album A Data Learn the Language (September 10, 2002)

Last song scrobbled from iTunes at Last.fm

[jcmarchi]

DeepMind’s Michelangelo Benchmark: Revealing the Limits of Long-Context LLMs

New Post has been published on https://thedigitalinsider.com/deepminds-michelangelo-benchmark-revealing-the-limits-of-long-context-llms/

DeepMind’s Michelangelo Benchmark: Revealing the Limits of Long-Context LLMs

As Artificial Intelligence (AI) continues to advance, the ability to process and understand long sequences of information is becoming more vital. AI systems are now used for complex tasks like analyzing long documents, keeping up with extended conversations, and processing large amounts of data. However, many current models struggle with long-context reasoning. As inputs get longer, they often lose track of important details, leading to less accurate or coherent results.

This issue is especially problematic in healthcare, legal services, and finance industries, where AI tools must handle detailed documents or lengthy discussions while providing accurate, context-aware responses. A common challenge is context drift, where models lose sight of earlier information as they process new input, resulting in less relevant outcomes.

To address these limitations, DeepMind developed the Michelangelo Benchmark. This tool rigorously tests how well AI models manage long-context reasoning. Inspired by the artist Michelangelo, known for revealing complex sculptures from marble blocks, the benchmark helps discover how well AI models can extract meaningful patterns from large datasets. By identifying where current models fall short, the Michelangelo Benchmark leads to future improvements in AI’s ability to reason over long contexts.

Understanding Long-Context Reasoning in AI

Long-context reasoning is about an AI model’s ability to stay coherent and accurate over long text, code, or conversation sequences. Models like GPT-4 and PaLM-2 perform well with short or moderate-length inputs. However, they need help with longer contexts. As the input length increases, these models often lose track of essential details from earlier parts. This leads to errors in understanding, summarizing, or making decisions. This issue is known as the context window limitation. The model’s ability to retain and process information decreases as the context grows longer.

This problem is significant in real-world applications. For example, in legal services, AI models analyze contracts, case studies, or regulations that can be hundreds of pages long. If these models cannot effectively retain and reason over such long documents, they might miss essential clauses or misinterpret legal terms. This can lead to inaccurate advice or analysis. In healthcare, AI systems need to synthesize patient records, medical histories, and treatment plans that span years or even decades. If a model cannot accurately recall critical information from earlier records, it could recommend inappropriate treatments or misdiagnose patients.

Even though efforts have been made to improve models’ token limits (like GPT-4 handling up to 32,000 tokens, about 50 pages of text), long-context reasoning is still a challenge. The context window problem limits the amount of input a model can handle and affects its ability to maintain accurate comprehension throughout the entire input sequence. This leads to context drift, where the model gradually forgets earlier details as new information is introduced. This reduces its ability to generate coherent and relevant outputs.

The Michelangelo Benchmark: Concept and Approach

The Michelangelo Benchmark tackles the challenges of long-context reasoning by testing LLMs on tasks that require them to retain and process information over extended sequences. Unlike earlier benchmarks, which focus on short-context tasks like sentence completion or basic question answering, the Michelangelo Benchmark emphasizes tasks that challenge models to reason across long data sequences, often including distractions or irrelevant information.

The Michelangelo Benchmark challenges AI models using the Latent Structure Queries (LSQ) framework. This method requires models to find meaningful patterns in large datasets while filtering out irrelevant information, similar to how humans sift through complex data to focus on what’s important. The benchmark focuses on two main areas: natural language and code, introducing tasks that test more than just data retrieval.

One important task is the Latent List Task. In this task, the model is given a sequence of Python list operations, like appending, removing, or sorting elements, and then it needs to produce the correct final list. To make it harder, the task includes irrelevant operations, such as reversing the list or canceling previous steps. This tests the model’s ability to focus on critical operations, simulating how AI systems must handle large data sets with mixed relevance.

Another critical task is Multi-Round Co-reference Resolution (MRCR). This task measures how well the model can track references in long conversations with overlapping or unclear topics. The challenge is for the model to link references made late in the conversation to earlier points, even when those references are hidden under irrelevant details. This task reflects real-world discussions, where topics often shift, and AI must accurately track and resolve references to maintain coherent communication.

Additionally, Michelangelo features the IDK Task, which tests a model’s ability to recognize when it does not have enough information to answer a question. In this task, the model is presented with text that may not contain the relevant information to answer a specific query. The challenge is for the model to identify cases where the correct response is “I don’t know” rather than providing a plausible but incorrect answer. This task reflects a critical aspect of AI reliability—recognizing uncertainty.

Through tasks like these, Michelangelo moves beyond simple retrieval to test a model’s ability to reason, synthesize, and manage long-context inputs. It introduces a scalable, synthetic, and un-leaked benchmark for long-context reasoning, providing a more precise measure of LLMs’ current state and future potential.

Implications for AI Research and Development

The results from the Michelangelo Benchmark have significant implications for how we develop AI. The benchmark shows that current LLMs need better architecture, especially in attention mechanisms and memory systems. Right now, most LLMs rely on self-attention mechanisms. These are effective for short tasks but struggle when the context grows larger. This is where we see the problem of context drift, where models forget or mix up earlier details. To solve this, researchers are exploring memory-augmented models. These models can store important information from earlier parts of a conversation or document, allowing the AI to recall and use it when needed.

Another promising approach is hierarchical processing. This method enables the AI to break down long inputs into smaller, manageable parts, which helps it focus on the most relevant details at each step. This way, the model can handle complex tasks better without being overwhelmed by too much information at once.

Improving long-context reasoning will have a considerable impact. In healthcare, it could mean better analysis of patient records, where AI can track a patient’s history over time and offer more accurate treatment recommendations. In legal services, these advancements could lead to AI systems that can analyze long contracts or case law with greater accuracy, providing more reliable insights for lawyers and legal professionals.

However, with these advancements come critical ethical concerns. As AI gets better at retaining and reasoning over long contexts, there is a risk of exposing sensitive or private information. This is a genuine concern for industries like healthcare and customer service, where confidentiality is critical.

If AI models retain too much information from previous interactions, they might inadvertently reveal personal details in future conversations. Additionally, as AI becomes better at generating convincing long-form content, there is a danger that it could be used to create more advanced misinformation or disinformation, further complicating the challenges around AI regulation.

The Bottom Line

The Michelangelo Benchmark has uncovered insights into how AI models manage complex, long-context tasks, highlighting their strengths and limitations. This benchmark advances innovation as AI develops, encouraging better model architecture and improved memory systems. The potential for transforming industries like healthcare and legal services is exciting but comes with ethical responsibilities.

Privacy, misinformation, and fairness concerns must be addressed as AI becomes more adept at handling vast amounts of information. AI’s growth must remain focused on benefiting society thoughtfully and responsibly.

[romulancider]

oh. i understand the hype about next gen now.

Data,

[driftnoob3]

SBD engine, 2l, built in great britain, around 350hp to the rear wheels with a weight of 480kg, this westfield is the perfect machine for race days, a perfect image of the classic Lotus Eleven