#Quality System Development

[methodsense]

AI Consulting Service for Medical Devices

Artificial intelligence software algorithms to learn from the real-world use of the medical device to improve its performance. We offer specialized healthcare AI consulting services that help companies achieve their goals. To more details, reach our website.

[negativespace06]

as metal breaks and bends

[velaraffricate]

so I've been working on my latest conlang, irkan osla (or just osla for short), for a bit now and would like to showcase its writing system in this post! osla has a syllabic alphabet, not too dissimilar to korean hangul, where letters are stacked according to certain rules to make syllable blocks.

osla's syllable structure is (C)(C)V(V)(C), here's how the stacks work for each type of syllable:

all letters have small, wide, and tall forms depending on their position in the syllable. here are all the letters with their IPA value and romanization:

and here's an example text! i translated parts of the minecraft end poem into osla. maybe i'll make another post just focusing on the grammar when it's more developed. the poem says in english:

What did this player dream? This player dreamed of sunlight and trees. Of fire and water. It dreamed it created. And it dreamed it destroyed. It dreamed it hunted, and was hunted. It dreamed of shelter.

Does it know that we love it? That the universe is kind? Sometimes, through the noise of its thoughts, it hears the universe, yes.

this post is getting long, so under the cut you'll find a "sans-serif" version as well as the poem in osla and its gloss if you're also a linguistics nerd and wanna know what's going on under the hood (the roman numerals stand for the 3 noun classes)! thanks for reading!

The way regular people would write something quickly on a piece of paper with a regular pen is an aspect of creating neographies that I feel is often overlooked, so I developed this sans-serif version that people would probably be more likely to use when writing their shopping lists or diary entries:

And, finally, here's the poem translation:

pak oṇḍul phan wimbakis?

DET.I.SG.PROX play-AGN what dream-PST.3SG.I?

pak oṇḍul lümaṇiuṣerothi han buloni an wimbakis. kaṣkhaothi han nilothi an. wimbakis, run sëmamkis. wimbakis, run xokthakis, han bumxokthakis. zöga an wimbakis.

DET.I.SG.PROX play-AGN sunlight-II.SG.DAT and tree-II.PL.DAT of dream-PST.3SG.I. Fire-II.SG.DAT and water-II.SG.DAT of. dream-PST.3SG.I, that create-PST.3SG.I. dream-PST.3SG.I, that hunt-PST.3SG.I, and PASS-hunt-PST.3SG.I. shelter of dream-PST.3SG.I.

ṭauraka, run kaak samare? run glutsüna flia?

know-NP.3SG.I, that 3SG.I.ABS love-NP.1PL? that universe kind?

imba ethamo, khaṣiŋli an ka’am hu’aŋni pitë, glutsüna ṣaraka, ti.

some time-NOM.III.PL, noise-ACC.II.SG of 3SG.I.GEN thought-NOM.III.PL through, universe hear-NP.3SG.I, yes.

[4th-make-quail]

remember that interview i had that i really wanted to get the job for? WELL i'm going in for a THIRD INTERVIEW tomorrow with the fucking MD ahahaha!!! they have 2 roles now, the new one for quality systems engineer which needs lead auditor which i don't have (i have internal auditor), and my agency are repping another candidate for it, so they're trying to push it as being the pair of us working together well cos i have the textiles background and he doesn't

SO LET'S SEE HOW IT GOES I GUESS!!!! annoyingly it's at 1pm which means i'll be travelling during lunchtime which i hate but at least this time i won't have to do a 2hr factory tour sdgkhlf'g

[mimiyanna]

Stealing this image from twitter and bringing it over here, because holy shit, some of the clowns I have been seeing talk about the game lately.

[prussianmemes]

what's incredible about these maps is how hard western euros will cope at the idea that their enlightened and superior society could ever be worse than *flips through notebook* those uncivilized lesser slavic poles and ukrainians. russians and croats also are lying too.

what's funny is that this yearly EU study is conducted in such a way that literally accounts for bias in reporting and cultural stigma, yet still they will cope and are unable to consider that based social democrat scandinavian finns beat their wives more than poles

(don't look at spousal murder rates in scandinavia ha ha)

[cheapcheapfaker]

On one hand i would like to have a large baby. my family and gilgamesh’s come from a long line of beefy, nine pound plus chunkers. I was 9 and something lbs with a full head of hair and almost a week late. I want that baby fully cooked and maybe a little overdone. tons of studies not just anecdotal show that they just seem sort of… nicer and easier to deal with, like the biggest of the litter. they sleep more. they dont struggle in general as much and they gain weight easily and they also move a little slower. not saying they dont hit milestones but a fat ass baby will stay in its potted plant lump stage a little while longer before jumping into the running around sticking fingers in outlets stage.

on the other hand, i am so so concerned for the sanctity of my gooch. a nine pound baby will tear my grundle asunder. my taint to shreds.

[bitnestloop]

BitNest

BitNest: The Leader of the Digital Finance Revolution

BitNest is a leading platform dedicated to driving digital financial innovation and ecological development. We provide comprehensive cryptocurrency services, including saving, lending, payment, investment and many other functions, creating a rich financial experience for users.

Our story began in 2022 with the birth of the BitNest team, which has since opened a whole new chapter in digital finance. Through relentless effort and innovation, the BitNest ecosystem has grown rapidly to become one of the leaders in digital finance.

The core functions of BitNest ecosystem include:

Savings Service: Users can deposit funds into BitNest's savings system through smart contracts to obtain stable returns. We are committed to providing users with a safe and efficient savings solution to help you achieve your financial goals. Lending Platform: BitNest lending platform provides users with convenient borrowing services, users can use cryptocurrencies as collateral to obtain loans for stablecoins or other digital assets. Our lending system is safe and reliable, providing users with flexible financial support. Payment Solution: BitNest payment platform supports users to make secure and fast payment transactions worldwide. We are committed to creating a borderless payment network that allows users to make cross-border payments and remittances anytime, anywhere. Investment Opportunities: BitNest provides diversified investment opportunities that allow users to participate in trading and investing in various digital assets and gain lucrative returns. Our investment platform is safe and transparent, providing users with high-quality investment channels. Through continuous innovation and efforts, BitNest has become a leader in digital finance and is widely recognised and trusted globally. We will continue to be committed to promoting the development of digital finance, providing users with more secure and efficient financial services, and jointly creating a better future for digital finance.

[lancecharleson]

Playing Bioshock 1 again for the first time in a long while, it made me realise just how much of 7th generation gaming we take for granted.

Among all the piss-filtered dark-age talk of how so many AAA games around this time eventually became homogenously Gears of War/COD-like in order to capitalise on their success, we tend to memory hole how this generation started off with a bang with titles like this, Prey (2006), Kameo: Elements of Power, and Little Big Planet.

It was also the last time we would ever see this many original single player games with AAA backing debut in a generation, until the 2010s where said industry would start going all in on the live-service multiplayer model.

While the indie/mid-budget gaming scene has picked up the torch of continuing to bring us fantastic SP games that have themselves become legends in their own right, there's something I find truly magical about playing a SP game that, not only has a solid premise and design philosophy going on, but also has the kind of high production values that AAA can afford them.

[emorphistechno]

Healthcare Analytics Software Development enables accurate and timely data analysis for better clinical decision-making, saving lives & costs.

According to a recent survey, the healthcare sector produces immense quantities of data via electronic medical records (EMR), electronic health records (EHR), and health information exchange (HIE). Nonetheless, the difficulty arises in competently examining and leveraging this data to enhance decision-making and proficiently manage it. Healthcare analytics software development services provide an answer to these predicaments.

Healthcare analytics can also be integrated with telemedicine app development and can help various features in this type of heathcare app development 

[luxrayz64]

I saw a post a while back responding to criticism of botw as being "a good game but not a good zelda game", and they responded with how the Zelda Formula was getting tired and stale and botw was a response to that, that it was meant to harken back to the original zelda game where it just drops you in and it makes you find everything on yr own. which like is fine and good and all but. you do know that the original zelda had 8 full unique (as unique as they could be on the nes) dungeons right. the original zelda game had dungeon items. they didn't need to take out one of zeldas defining gameplay aspects and replace it with the fundamentally inferior shrines and divine beasts. you can make a game non linear and refresh its gameplay without taking out one of the series' strongest aspects.

[turtlesandfrogs]

What I was taught growing up: Wild edible plants and animals were just so naturally abundant that the indigenous people of my area, namely western Washington state, didn't have to develop agriculture and could just easily forage/hunt for all their needs.

The first pebble in what would become a landslide: Native peoples practiced intentional fire, which kept the trees from growing over the camas praire.

The next: PNW native peoples intentionally planted and cultivated forest gardens, and we can still see the increase in biodiversity where these gardens were today.

The next: We have an oak prairie savanna ecosystem that was intentionally maintained via intentional fire (which they were banned from doing for like, 100 years and we're just now starting to do again), and this ecosystem is disappearing as Douglas firs spread, invasive species take over, and land is turned into European-style agricultural systems.

The Land Slide: Actually, the native peoples had a complex agricultural and food processing system that allowed them to meet all their needs throughout the year, including storing food for the long, wet, dark winter. They collected a wide variety of plant foods (along with the salmon, deer, and other animals they hunted), from seaweeds to roots to berries, and they also managed these food systems via not only burning, but pruning, weeding, planting, digging/tilling, selectively harvesting root crops so that smaller ones were left behind to grow and the biggest were left to reseed, and careful harvesting at particular times for each species that both ensured their perennial (!) crops would continue thriving and that harvest occurred at the best time for the best quality food. American settlers were willfully ignorant of the complex agricultural system, because being thus allowed them to claim the land wasn't being used. Native peoples were actively managing the ecosystem to produce their food, in a sustainable manner that increased biodiversity, thus benefiting not only themselves but other species as well.

So that's cool. If you want to read more, I suggest "Ancient Pathways, Ancestral Knowledge: Ethnobotany and Ecological Wisdom of Indigenous Peoples of Northwestern North America" by Nancy J. Turner

[cool-wilson-david]

Prompt Engine Commercial by Karthik Ramani Review

Prompt Engine Commercial by Karthik Ramani – Discover Why Prompt Engine Pro is the Ultimate Tool for Entrepreneurs and Creatives Prompt Engine Commercial by Karthik Ramani. When it comes to tools that simplify workflows, Prompt Engine Pro emerges as a top choice due to its seamless functionality and innovative features. Unlike conventional extensions or collections of prompts, this app works as…

[hritika1]

Explore the Indonesia Air Quality Monitoring System Market growth, challenges, and top players with forecasts up to 2028. Gain insights into revenue trends, market outlook, and key developments shaping the industry's future.

[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.

[jivuinfosolutions]

Innovative Digital Solutions by Jivu Infosolutions

Our expertise spans Web Development Services, Mobile Application Development, E-commerce Website Design, and specialized ERP solutions tailored for schools, construction firms, and hotels.

Visit us at our blog:- https://jivuinfosolutions.blogspot.com/2024/12/innovative-digital-solutions-by-jivu.html