#data quality in AI

[nnctales]

What AI Cannot Do: AI Limitation

Artificial Intelligence (AI) has made remarkable strides in recent years, revolutionizing industries from healthcare to finance. However, despite its impressive capabilities, there are inherent AI limitation to what it can achieve. Understanding these limitations is crucial for effectively integrating AI into our lives and recognizing its role as a tool rather than a replacement for human…

[jcmarchi]

Allen AI’s Tülu 3 Just Became DeepSeek’s Unexpected Rival

New Post has been published on https://thedigitalinsider.com/allen-ais-tulu-3-just-became-deepseeks-unexpected-rival/

Allen AI’s Tülu 3 Just Became DeepSeek’s Unexpected Rival

The headlines keep coming. DeepSeek’s models have been challenging benchmarks, setting new standards, and making a lot of noise. But something interesting just happened in the AI research scene that is also worth your attention.

Allen AI quietly released their new Tülu 3 family of models, and their 405B parameter version is not just competing with DeepSeek – it is matching or beating it on key benchmarks.

Let us put this in perspective.

The 405B Tülu 3 model is going up against top performers like DeepSeek V3 across a range of tasks. We are seeing comparable or superior performance in areas like math problems, coding challenges, and precise instruction following. And they are also doing it with a completely open approach.

They have released the complete training pipeline, the code, and even their novel reinforcement learning method called Reinforcement Learning with Verifiable Rewards (RLVR) that made this possible.

Developments like these over the past few weeks are really changing how top-tier AI development happens. When a fully open source model can match the best closed models out there, it opens up possibilities that were previously locked behind private corporate walls.

The Technical Battle

What made Tülu 3 stand out? It comes down to a unique four-stage training process that goes beyond traditional approaches.

Let us look at how Allen AI built this model:

Stage 1: Strategic Data Selection

The team knew that model quality starts with data quality. They combined established datasets like WildChat and Open Assistant with custom-generated content. But here is the key insight: they did not just aggregate data – they created targeted datasets for specific skills like mathematical reasoning and coding proficiency.

Stage 2: Building Better Responses

In the second stage, Allen AI focused on teaching their model specific skills. They created different sets of training data – some for math, others for coding, and more for general tasks. By testing these combinations repeatedly, they could see exactly where the model excelled and where it needed work. This iterative process revealed the true potential of what Tülu 3 could achieve in each area.

Stage 3: Learning from Comparisons

This is where Allen AI got creative. They built a system that could instantly compare Tülu 3’s responses against other top models. But they also solved a persistent problem in AI – the tendency for models to write long responses just for the sake of length. Their approach, using length-normalized Direct Preference Optimization (DPO), meant the model learned to value quality over quantity. The result? Responses that are both precise and purposeful.

When AI models learn from preferences (which response is better, A or B?), they tend to develop a frustrating bias: they start thinking longer responses are always better. It is like they are trying to win by saying more rather than saying things well.

Length-normalized DPO fixes this by adjusting how the model learns from preferences. Instead of just looking at which response was preferred, it takes into account the length of each response. Think of it as judging responses by their quality per word, not just their total impact.

Why does this matter? Because it helps Tülu 3 learn to be precise and efficient. Rather than padding responses with extra words to seem more comprehensive, it learns to deliver value in whatever length is actually needed.

This might seem like a small detail, but it is crucial for building AI that communicates naturally. The best human experts know when to be concise and when to elaborate – and that is exactly what length-normalized DPO helps teach the model.

Stage 4: The RLVR Innovation

This is the technical breakthrough that deserves attention. RLVR replaces subjective reward models with concrete verification.

Most AI models learn through a complex system of reward models – essentially educated guesses about what makes a good response. But Allen AI took a different path with RLVR.

Think about how we currently train AI models. We usually need other AI models (called reward models) to judge if a response is good or not. It is subjective, complex, and often inconsistent. Some responses might seem good but contain subtle errors that slip through.

RLVR flips this approach on its head. Instead of relying on subjective judgments, it uses concrete, verifiable outcomes. When the model attempts a math problem, there is no gray area – the answer is either right or wrong. When it writes code, that code either runs correctly or it does not.

Here is where it gets interesting:

The model gets immediate, binary feedback: 10 points for correct answers, 0 for incorrect ones

There is no room for partial credit or fuzzy evaluation

The learning becomes focused and precise

The model learns to prioritize accuracy over plausible-sounding but incorrect responses

RLVR Training (Allen AI)

The results? Tülu 3 showed significant improvements in tasks where correctness matters most. Its performance on mathematical reasoning (GSM8K benchmark) and coding challenges jumped notably. Even its instruction-following became more precise because the model learned to value concrete accuracy over approximate responses.

What makes this particularly exciting is how it changes the game for open-source AI. Previous approaches often struggled to match the precision of closed models on technical tasks. RLVR shows that with the right training approach, open-source models can achieve that same level of reliability.

A Look at the Numbers

The 405B parameter version of Tülu 3 competes directly with top models in the field. Let us examine where it excels and what this means for open source AI.

Math

Tülu 3 excels at complex mathematical reasoning. On benchmarks like GSM8K and MATH, it matches DeepSeek’s performance. The model handles multi-step problems and shows strong mathematical reasoning capabilities.

Code

The coding results prove equally impressive. Thanks to RLVR training, Tülu 3 writes code that solves problems effectively. Its strength lies in understanding coding instructions and producing functional solutions.

Precise Instruction Following

The model’s ability to follow instructions stands out as a core strength. While many models approximate or generalize instructions, Tülu 3 demonstrates remarkable precision in executing exactly what is asked.

Opening the Black Box of AI Development

Allen AI released both a powerful model and their complete development process.

Every aspect of the training process stands documented and accessible. From the four-stage approach to data preparation methods and RLVR implementation – the entire process lies open for study and replication. This transparency sets a new standard in high-performance AI development.

Developers receive comprehensive resources:

Complete training pipelines

Data processing tools

Evaluation frameworks

Implementation specifications

This enables teams to:

Modify training processes

Adapt methods for specific needs

Build on proven approaches

Create specialized implementations

This open approach accelerates innovation across the field. Researchers can build on verified methods, while developers can focus on improvements rather than starting from zero.

The Rise of Open Source Excellence

The success of Tülu 3 is a big moment for open AI development. When open source models match or exceed private alternatives, it fundamentally changes the industry. Research teams worldwide gain access to proven methods, accelerating their work and spawning new innovations. Private AI labs will need to adapt – either by increasing transparency or pushing technical boundaries even further.

Looking ahead, Tülu 3’s breakthroughs in verifiable rewards and multi-stage training hint at what is coming. Teams can build on these foundations, potentially pushing performance even higher. The code exists, the methods are documented, and a new wave of AI development has begun. For developers and researchers, the opportunity to experiment with and improve upon these methods marks the start of an exciting chapter in AI development.

Frequently Asked Questions (FAQ) about Tülu 3

What is Tülu 3 and what are its key features?

Tülu 3 is a family of open-source LLMs developed by Allen AI, built upon the Llama 3.1 architecture. It comes in various sizes (8B, 70B, and 405B parameters). Tülu 3 is designed for improved performance across diverse tasks including knowledge, reasoning, math, coding, instruction following, and safety.

What is the training process for Tülu 3 and what data is used?

The training of Tülu 3 involves several key stages. First, the team curates a diverse set of prompts from both public datasets and synthetic data targeted at specific skills, ensuring the data is decontaminated against benchmarks. Second, supervised finetuning (SFT) is performed on a mix of instruction-following, math, and coding data. Next, direct preference optimization (DPO) is used with preference data generated through human and LLM feedback. Finally, Reinforcement Learning with Verifiable Rewards (RLVR) is used for tasks with measurable correctness. Tülu 3 uses curated datasets for each stage, including persona-driven instructions, math, and code data.

How does Tülu 3 approach safety and what metrics are used to evaluate it?

Safety is a core component of Tülu 3’s development, addressed throughout the training process. A safety-specific dataset is used during SFT, which is found to be largely orthogonal to other task-oriented data.

What is RLVR?

RLVR is a technique where the model is trained to optimize against a verifiable reward, like the correctness of an answer. This differs from traditional RLHF which uses a reward model.

[bmpmp3]

i am pretty excited for the miku nt update early access tomorrow. the demonstrations have sounded pretty solid so far and tbh i am super intrigued by the idea of hybrid concatenative+ai vocal synthesis, i wanna see what people doooo with it. show me it nowwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww

[bigleapblog]

Your Guide to B.Tech in Computer Science & Engineering Colleges

In today's technology-driven world, pursuing a B.Tech in Computer Science and Engineering (CSE) has become a popular choice among students aspiring for a bright future. The demand for skilled professionals in areas like Artificial Intelligence, Machine Learning, Data Science, and Cloud Computing has made computer science engineering colleges crucial in shaping tomorrow's innovators. Saraswati College of Engineering (SCOE), a leader in engineering education, provides students with a perfect platform to build a successful career in this evolving field.

Whether you're passionate about coding, software development, or the latest advancements in AI, pursuing a B.Tech in Computer Science and Engineering at SCOE can open doors to endless opportunities.

Why Choose B.Tech in Computer Science and Engineering?

Choosing a B.Tech in Computer Science and Engineering isn't just about learning to code; it's about mastering problem-solving, logical thinking, and the ability to work with cutting-edge technologies. The course offers a robust foundation that combines theoretical knowledge with practical skills, enabling students to excel in the tech industry.

At SCOE, the computer science engineering courses are designed to meet industry standards and keep up with the rapidly evolving tech landscape. With its AICTE Approved, NAAC Accredited With Grade-"A+" credentials, the college provides quality education in a nurturing environment. SCOE's curriculum goes beyond textbooks, focusing on hands-on learning through projects, labs, workshops, and internships. This approach ensures that students graduate not only with a degree but with the skills needed to thrive in their careers.

The Role of Computer Science Engineering Colleges in Career Development

The role of computer science engineering colleges like SCOE is not limited to classroom teaching. These institutions play a crucial role in shaping students' futures by providing the necessary infrastructure, faculty expertise, and placement opportunities. SCOE, established in 2004, is recognized as one of the top engineering colleges in Navi Mumbai. It boasts a strong placement record, with companies like Goldman Sachs, Cisco, and Microsoft offering lucrative job opportunities to its graduates.

The computer science engineering courses at SCOE are structured to provide a blend of technical and soft skills. From the basics of computer programming to advanced topics like Artificial Intelligence and Data Science, students at SCOE are trained to be industry-ready. The faculty at SCOE comprises experienced professionals who not only impart theoretical knowledge but also mentor students for real-world challenges.

Highlights of the B.Tech in Computer Science and Engineering Program at SCOE

Comprehensive Curriculum: The B.Tech in Computer Science and Engineering program at SCOE covers all major areas, including programming languages, algorithms, data structures, computer networks, operating systems, AI, and Machine Learning. This ensures that students receive a well-rounded education, preparing them for various roles in the tech industry.

Industry-Relevant Learning: SCOE’s focus is on creating professionals who can immediately contribute to the tech industry. The college regularly collaborates with industry leaders to update its curriculum, ensuring students learn the latest technologies and trends in computer science engineering.

State-of-the-Art Infrastructure: SCOE is equipped with modern laboratories, computer centers, and research facilities, providing students with the tools they need to gain practical experience. The institution’s infrastructure fosters innovation, helping students work on cutting-edge projects and ideas during their B.Tech in Computer Science and Engineering.

Practical Exposure: One of the key benefits of studying at SCOE is the emphasis on practical learning. Students participate in hands-on projects, internships, and industry visits, giving them real-world exposure to how technology is applied in various sectors.

Placement Support: SCOE has a dedicated placement cell that works tirelessly to ensure students secure internships and job offers from top companies. The B.Tech in Computer Science and Engineering program boasts a strong placement record, with top tech companies visiting the campus every year. The highest on-campus placement offer for the academic year 2022-23 was an impressive 22 LPA from Goldman Sachs, reflecting the college’s commitment to student success.

Personal Growth: Beyond academics, SCOE encourages students to participate in extracurricular activities, coding competitions, and tech fests. These activities enhance their learning experience, promote teamwork, and help students build a well-rounded personality that is essential in today’s competitive job market.

What Makes SCOE Stand Out?

With so many computer science engineering colleges to choose from, why should you consider SCOE for your B.Tech in Computer Science and Engineering? Here are a few factors that make SCOE a top choice for students:

Experienced Faculty: SCOE prides itself on having a team of highly qualified and experienced faculty members. The faculty’s approach to teaching is both theoretical and practical, ensuring students are equipped to tackle real-world challenges.

Strong Industry Connections: The college maintains strong relationships with leading tech companies, ensuring that students have access to internship opportunities and campus recruitment drives. This gives SCOE graduates a competitive edge in the job market.

Holistic Development: SCOE believes in the holistic development of students. In addition to academic learning, the college offers opportunities for personal growth through various student clubs, sports activities, and cultural events.

Supportive Learning Environment: SCOE provides a nurturing environment where students can focus on their academic and personal growth. The campus is equipped with modern facilities, including spacious classrooms, labs, a library, and a recreation center.

Career Opportunities After B.Tech in Computer Science and Engineering from SCOE

Graduates with a B.Tech in Computer Science and Engineering from SCOE are well-prepared to take on various roles in the tech industry. Some of the most common career paths for CSE graduates include:

Software Engineer: Developing software applications, web development, and mobile app development are some of the key responsibilities of software engineers. This role requires strong programming skills and a deep understanding of software design.

Data Scientist: With the rise of big data, data scientists are in high demand. CSE graduates with knowledge of data science can work on data analysis, machine learning models, and predictive analytics.

AI Engineer: Artificial Intelligence is revolutionizing various industries, and AI engineers are at the forefront of this change. SCOE’s curriculum includes AI and Machine Learning, preparing students for roles in this cutting-edge field.

System Administrator: Maintaining and managing computer systems and networks is a crucial role in any organization. CSE graduates can work as system administrators, ensuring the smooth functioning of IT infrastructure.

Cybersecurity Specialist: With the growing threat of cyberattacks, cybersecurity specialists are essential in protecting an organization’s digital assets. CSE graduates can pursue careers in cybersecurity, safeguarding sensitive information from hackers.

Conclusion: Why B.Tech in Computer Science and Engineering at SCOE is the Right Choice

Choosing the right college is crucial for a successful career in B.Tech in Computer Science and Engineering. Saraswati College of Engineering (SCOE) stands out as one of the best computer science engineering colleges in Navi Mumbai. With its industry-aligned curriculum, state-of-the-art infrastructure, and excellent placement record, SCOE offers students the perfect environment to build a successful career in computer science.

Whether you're interested in AI, data science, software development, or any other field in computer science, SCOE provides the knowledge, skills, and opportunities you need to succeed. With a strong focus on hands-on learning and personal growth, SCOE ensures that students graduate not only as engineers but as professionals ready to take on the challenges of the tech world.

If you're ready to embark on an exciting journey in the world of technology, consider pursuing your B.Tech in Computer Science and Engineering at SCOE—a college where your future takes shape.

[wickedhawtwexler]

i am so sick of people using chatgpt to generate descriptions for ebay items ughhhh

[trekwiz]

I watched a training on career development; the premise was that project managers should treat their career like a project. And one really stupid comment stuck with me: "salary should not be in your goals. That's like choosing your software before knowing the project requirements."

It was ironic, because one of his goals was "work-life balance at a remote workplace." 🙄

It was a lot of fluff about making lists of what you like to do at work and what you don't, and that somehow translates to finding your dream job. He discouraged using luck-based strategies, in favor of...a luck based strategy of mentoring people who will hopefully inspire you. 🙃

And I'm just like. "Ok, project manager. You haven't accounted for your assumptions."

But also. Knowing your budget is important to being a project manager. There's a minimum budget needed to succeed. If you're not planning that out early, you didn't really plan your project.

And I'm sitting there thinking that next, for me, isn't a reassessment of the tasks I perform. I like the tasks well enough. Next is getting a $50k-70k wage increase, to be in line with the industry average for people with my skills, performing my tasks, at my level of experience in this region. It's a 32 hour work week. And more paid time off.

I don't care if I get a fancy new title. I don't care if it's a more prestigious company. I don't care if there are more interesting challenges. I've grown my skills. It's past time to grow my lifestyle. And that's not going to happen from a like and dislike list, and mentoring people.

[snickerdoodlles]

*pinches nose bridge* even if there wasn’t 6 degrees of separation between AO3 and generative AI, has anyone in this tag even considered that if it was possible for individuals to fuck up generative AI or their training datasets just by writing a/b/o fic, then fascists, bigots, or even just internet trolls could and would fuck it up worse with hate speech

[soloh]

I hate when I say things like "oh I want an ipod classic but with bluetooth so I can use wireless headphones" and some peanut comes in and replies with "so a smartphone with spotify?" No. I want a 160GB+ rectangular monstrosity where I can download every version of every song I want to it and it does nothing except play music and I don't need a data connection and don't have to pay a subscription to not have ads and don't have popups suggesting terrible AI playlists all over the menus.

Gimme the clicky wheel and song titles like "My Chemical Romance- The Black Parade- Blood (Bonus Track)- secret track- album rip- high quality"

[ambrosiaventures]

How Pharmaceutical Consulting Can Help Launch Your New Product Successfully

Ambrosia Ventures, we ensure your product launch achieves maximum impact by utilizing our expertise in biopharma consulting, which makes us a trusted pharmaceutical consulting service provider in the US. Here's the way to transform your product launch strategy into a blueprint for success through pharmaceutical consulting services

[jcmarchi]

David Driggers, CTO of Cirrascale – Interview Series

New Post has been published on https://thedigitalinsider.com/david-driggers-cto-of-cirrascale-interview-series/

David Driggers, CTO of Cirrascale – Interview Series

David Driggers is the Chief Technology Officer at Cirrascale Cloud Services, a leading provider of deep learning infrastructure solutions. Guided by values of integrity, agility, and customer focus, Cirrascale delivers innovative, cloud-based Infrastructure-as-a-Service (IaaS) solutions. Partnering with AI ecosystem leaders like Red Hat and WekaIO, Cirrascale ensures seamless access to advanced tools, empowering customers to drive progress in deep learning while maintaining predictable costs.

Cirrascale is the only GPUaaS provider partnering with major semiconductor companies like NVIDIA, AMD, Cerebras, and Qualcomm. How does this unique positioning benefit your customers in terms of performance and scalability? As the industry evolves from Training Models to the deployment of these models called Inferencing, there is no one size fits all.  Depending upon the size and latency requirements of the model, different accelerators offer different values that could be important. Time to answer, cost per token advantages, or performance per watt can all affect the cost and user experience.  Since Inferencing is for production these features/capabilities matter.

What sets Cirrascale’s AI Innovation Cloud apart from other GPUaaS providers in supporting AI and deep learning workflows? Cirrascale’s AI Innovation Cloud allows users to try in a secure, assisted, and fully supported manner new technologies that are not available in any other cloud.  This can aid not only in cloud technology decisions but also in potential on-site purchases.

How does Cirrascale’s platform ensure seamless integration for startups and enterprises with diverse AI acceleration needs? Cirrascale takes a solution approach for our cloud.  This means that for both startups and enterprises, we offer a turnkey solution that includes both the Dev-Ops and Infra-Ops.  While we call it bare-metal to distinguish our offerings as not being shared or virtualized, Cirrascale fully configures all aspects of the offering including fully configuring the servers, networking, Storage, Security and User Access requirements prior to turning the service over to our clients. Our clients can immediately start using the service rather than having to configure everything themselves.

Enterprise-wide AI adoption faces barriers like data quality, infrastructure constraints, and high costs. How does Cirrascale address these challenges for businesses scaling AI initiatives? While Cirrascale does not offer Data Quality type services, we do partner with companies that can assist with Data issues.  As far as Infrastructure and costs, Cirrascale can tailor a solution specific to a client’s specific needs which results in better overall performance and related costs specific to the customer’s requirements.

With Google’s advancements in quantum computing (Willow) and AI models (Gemini 2.0), how do you see the landscape of enterprise AI shifting in the near future? Quantum Computing is still quite a way off from prime time for most folks due to the lack of programmers and off-the-shelf programs that can take advantage of the features.  Gemini 2.0 and other large-scale offerings like GPT4 and Claude are certainly going to get some uptake from Enterprise customers, but a large part of the Enterprise market is not prepared at this time to trust their data with 3rd parties, and especially ones that may use said data to train their models.

Finding the right balance of power, price, and performance is critical for scaling AI solutions. What are your top recommendations for companies navigating this balance? Test, test, test. It is critical for a company to test their model on different platforms. Production is different than development—cost matters in production. Training may be one and done, but inferencing is forever.  If performance requirements can be met at a lower cost, those savings fall to the bottom line and might even make the solution viable.  Quite often deployment of a large model is too expensive to make it practical for use. End users should also seek companies that can help with this testing as often an ML Engineer can help with deployment vs. the Data Scientist that created the model.

How is Cirrascale adapting its solutions to meet the growing demand for generative AI applications, like LLMs and image generation models? Cirrascale offers the widest array of AI accelerators, and with the proliferation of LLMs and GenAI models ranging both in size and scope (like multi-modal scenarios), and batch vs. real-time, it truly is a horse for a course scenario.

Can you provide examples of how Cirrascale helps businesses overcome latency and data transfer bottlenecks in AI workflows? Cirrascale has numerous data centers in multiple regions and does not look at network connectivity as a profit center.  This allows our users to “right-size” the connections needed to move data, as well as utilize more that one location if latency is a critical feature.  Also, by profiling the actual workloads, Cirrascale can assist with balancing latency, performance and cost to deliver the best value after meeting performance requirements.

What emerging trends in AI hardware or infrastructure are you most excited about, and how is Cirrascale preparing for them? We are most excited about new processors that are purpose built for inferencing vs. generic GPU-based processors that luckily fit quite nicely for training, but are not optimized for inference use cases which have inherently different compute requirements than training.

Thank you for the great interview, readers who wish to learn more should visit Cirrascale Cloud Services.

[globosetechnologysolutions1]

Unlock the potential of your AI models with accurate video transcription services. From precise annotations to seamless data preparation, transcription is essential for scalable AI training.

[rajeshwaria]

How AI and Machine Learning Are Revolutionizing Data Quality Assurance

In the fast-paced world of business, data quality is critical to operational success. Without accurate and consistent data, organizations risk making poor decisions that can lead to lost opportunities and financial setbacks. Fortunately, advancements in Artificial Intelligence (AI) and Machine Learning (ML) are transforming Data Quality Management (DQM), offering businesses innovative solutions to enhance data accuracy, streamline processes, and ensure that their data is fit for strategic use.

The Role of Data Quality in Business Success

Data is the driving force behind most modern business processes. From customer insights to financial forecasts, data informs virtually every decision. However, poor-quality data can have a devastating impact, leading to inaccuracies, delayed decisions, and inefficient resource allocation. Reports show that poor data quality costs businesses billions annually, underscoring the need for effective DQM strategies.

In this environment, AI and ML technologies offer immense value by providing the tools needed to detect and address data quality issues quickly and efficiently. By automating key aspects of DQM, these technologies help businesses minimize human error, reduce operational inefficiencies, and ensure their data supports better decision-making.

How AI and Machine Learning Enhance Data Quality Management

AI and ML are at the forefront of transforming DQM practices. With their ability to process large volumes of data and learn from patterns, these technologies allow businesses to address traditional data management challenges such as redundancy, inaccuracies, and slow data integration.

Automated Data Cleansing

Data cleansing, the process of detecting and correcting inaccuracies or inconsistencies, is one of the primary areas where AI and ML shine. These technologies can scan vast datasets to identify errors, duplicates, and inconsistencies, automatically correcting them without manual intervention. By leveraging AI’s ability to recognize data patterns and ML's predictive capabilities, organizations can ensure that their data is always clean and consistent.

Efficient Data Integration

One of the major hurdles businesses face is integrating data from various sources. AI and ML technologies facilitate seamless integration by mapping relationships between datasets and ensuring data from multiple sources is aligned. These systems ensure that data flows smoothly between departments, platforms, and systems, eliminating silos that can hinder decision-making and creating a more cohesive data environment.

Real-Time Data Monitoring and Alerts

AI-driven monitoring systems track data quality metrics in real-time. Whenever data quality falls below acceptable thresholds, these systems send instant alerts, allowing businesses to respond quickly to any issues. Machine learning algorithms continuously analyze trends and anomalies, providing valuable insights that help refine DQM processes and avoid potential pitfalls before they impact the business.

Predictive Insights for Proactive Data Governance

AI and ML are revolutionizing predictive analytics in DQM. By analyzing historical data, these technologies can predict potential data quality issues, allowing businesses to take preventive measures before problems occur. This foresight leads to better governance and more efficient data management practices, ensuring data remains accurate and compliant with regulations.

Practical Applications of AI and ML in Data Quality Management

Numerous industries are already benefiting from AI and ML technologies in DQM. A global tech company used machine learning to clean customer data, improving data accuracy by over 30%. In another example, a healthcare provider leveraged AI-powered systems to monitor clinical data, reducing errors and improving patient outcomes. These real-world applications show the immense value AI and ML bring to data quality management.

Conclusion

Incorporating AI and Machine Learning into Data Quality Management is essential for businesses aiming to stay competitive in a data-driven world. By automating error detection, improving integration, and offering predictive insights, these technologies enable organizations to maintain the highest standards of data quality. As companies continue to navigate the complexities of data, leveraging AI and ML will be crucial for maintaining a competitive edge. At Infiniti Research, we specialize in helping organizations implement AI-powered DQM strategies to drive better business outcomes. Contact us today to learn how we can assist you in enhancing your data quality management practices.

For more information please contact

[codedusoftware]

How Custom Software Development Transforms Modern Businesses: Insights from CodEduIn an era dominated by rapid technological advancements, businesses are under immense pressure to stay competitive, efficient, and customer-focused. Off-the-shelf software, while useful, often falls short in addressing the unique challenges and dynamic needs of individual businesses. This is where custom software development steps in—a solution tailored specifically to meet the requirements of a business.

CodEdu Software Technologies, based in Cochin, Kerala, specializes in creating innovative, customer-centric software solutions that empower businesses to streamline operations, improve productivity, and enhance customer experiences. In this blog, we’ll explore how custom software development is transforming modern businesses and why partnering with CodEdu can be a game-changer.

What Is Custom Software Development? Custom software development involves designing, developing, and deploying software solutions tailored to meet a business's specific requirements. Unlike generic, off-the-shelf software, custom solutions are built from the ground up to align with a company’s processes, goals, and challenges.

This personalized approach allows businesses to create tools that integrate seamlessly with their existing operations, enhancing efficiency and providing a competitive edge.

The Key Benefits of Custom Software Development

Tailored to Specific Business Needs Custom software is designed to address a company’s unique requirements. Whether it’s automating a workflow, integrating with other tools, or solving specific challenges, the solution is built to fit seamlessly into the business ecosystem.

For example, an e-commerce business may require a software system that combines inventory management, personalized customer recommendations, and a secure payment gateway. Off-the-shelf software may provide one or two of these features but rarely all in an integrated manner.

Enhanced Efficiency and Productivity Custom software eliminates redundancies and streamlines operations. By automating repetitive tasks and integrating seamlessly with existing tools, businesses can significantly reduce manual effort and focus on core activities.

CodEdu has worked with several businesses to create custom solutions that enhance efficiency. One notable example is a manufacturing client who needed real-time tracking of production cycles. The tailored solution reduced delays and optimized resource allocation, saving the client both time and money.

Scalability for Future Growth One of the major limitations of off-the-shelf software is its inability to scale. As businesses grow and evolve, their software needs change. Custom software, on the other hand, is designed with scalability in mind.

CodEdu’s solutions are built to grow alongside businesses, allowing for easy updates and additional features as new challenges and opportunities arise.

Improved Security Data security is a top concern for businesses today. Custom software allows for the integration of advanced security features tailored to the specific vulnerabilities of the organization.

Unlike generic solutions that use standard security protocols, custom software incorporates unique safeguards, making it harder for malicious actors to breach the system.

Cost-Effectiveness in the Long Run While the initial investment for custom software may be higher than purchasing off-the-shelf solutions, it offers significant savings in the long run. Businesses avoid recurring licensing fees, third-party tool integration costs, and inefficiencies caused by mismatched software capabilities.

Real-World Applications of Custom Software Development Custom software development is revolutionizing industries by offering solutions that address specific operational challenges. Here are some examples of how businesses are leveraging tailored solutions:

E-Commerce Industry E-commerce companies face unique challenges, such as managing large inventories, providing personalized customer experiences, and ensuring secure transactions. Custom software can integrate inventory management systems, CRM tools, and AI-driven recommendation engines into a single platform, streamlining operations and boosting sales.

Healthcare Sector The healthcare industry requires solutions that ensure patient confidentiality, streamline appointment scheduling, and manage medical records efficiently. Custom software allows healthcare providers to deliver telemedicine services, maintain compliance with industry regulations, and improve patient outcomes.

Education and Training Educational institutions and training academies are leveraging custom Learning Management Systems (LMS) to provide personalized learning experiences. CodEdu has developed platforms that enable online assessments, real-time feedback, and interactive learning tools for students.

Logistics and Supply Chain Logistics companies require software that provides real-time tracking, route optimization, and automated billing. CodEdu has partnered with logistics providers to build solutions that reduce operational costs and enhance customer satisfaction.

How CodEdu Approaches Custom Software Development At CodEdu Software Technologies, we believe in a collaborative, customer-centric approach to software development. Here’s how we ensure the delivery of high-quality solutions:

Understanding Business Needs Our process begins with a detailed consultation to understand the client’s goals, pain points, and operational workflows. This ensures that the solution aligns perfectly with the business’s requirements.

Agile Development Methodology We adopt an agile approach to development, breaking the project into smaller, manageable phases. This allows for flexibility, regular feedback, and timely delivery of the final product.

Cutting-Edge Technology Our team leverages the latest technologies, including AI, machine learning, cloud computing, and blockchain, to deliver innovative and robust solutions.

Ongoing Support and Maintenance Software development doesn’t end with deployment. We provide ongoing support and updates to ensure the solution remains effective as the business evolves.

Future Trends in Custom Software Development The world of custom software development is continuously evolving. Here are some trends that are shaping the future:

AI and Machine Learning Integration Artificial Intelligence (AI) and machine learning are enabling businesses to automate processes, predict trends, and provide personalized customer experiences. From chatbots to predictive analytics, these technologies are transforming industries.

Cloud-Based Solutions Cloud computing is revolutionizing software development by offering scalability, accessibility, and cost efficiency. Businesses are increasingly adopting cloud-based custom software to enable remote access and collaboration.

IoT-Driven Solutions The Internet of Things (IoT) is creating opportunities for custom software that connects devices and collects data in real-time. This is particularly beneficial in industries such as healthcare, logistics, and manufacturing.

Low-Code and No-Code Platforms Low-code and no-code platforms are simplifying the development process, allowing businesses to create custom software with minimal technical expertise. While not a replacement for traditional development, these platforms are enabling faster prototyping and iteration.

Why Choose CodEdu for Custom Software Development? CodEdu Software Technologies stands out as a trusted partner for custom software development. Here’s why:

Experienced Team: Our developers bring years of experience in crafting innovative solutions for diverse industries. Customer-Centric Approach: We prioritize your business goals, ensuring the software delivers real value. Proven Track Record: With a portfolio of successful projects, CodEdu has earned a reputation for delivering quality and reliability. End-to-End Services: From consultation to development and post-deployment support, we handle every aspect of the project. Conclusion Custom software development is no longer an option but a necessity for businesses aiming to stay competitive in today’s digital landscape. It empowers organizations to streamline operations, enhance security, and deliver exceptional customer experiences.

CodEdu Software Technologies, with its expertise in innovation and customer-centric solutions, is the ideal partner to help businesses harness the power of custom software. Whether you’re a startup looking to establish a strong foundation or an established enterprise aiming to optimize operations, our tailored solutions can drive your success.

Ready to transform your business? Contact CodEdu Software Technologies today and let’s build the future together.

[techahead-software-blog]

What is AI-Ready Data? How to Get Your There?

Data powers AI systems, enabling them to generate insights, predict outcomes, and transform decision-making. However, AI’s impact hinges on the quality and readiness of the data it consumes. A recent Harvard Business Review report reveals a troubling trend: approximately 80% of AI projects fail, largely due to poor data quality, irrelevant data, and a lack of understanding of AI-specific data requirements.

As AI technologies are projected to contribute up to $15.7 trillion to the global economy by 2030, the emphasis on AI-ready data is more urgent than ever. Investing in data readiness is not merely technical; it’s a strategic priority that shapes AI’s effectiveness and a company’s competitive edge in today’s data-driven landscape.

(Source: Statista)

Achieving AI-ready data requires addressing identified gaps by building strong data management practices, prioritizing data quality enhancements, and using technology to streamline integration and processing. By proactively tackling these issues, organizations can significantly improve data readiness, minimize AI project risks, and unlock AI’s full potential to fuel innovation and growth.

In this article, we’ll explore what constitutes AI-ready data and why it is vital for effective AI deployment. We will also examine the primary obstacles to data readiness, the characteristics that define AI-ready data, and the practices for data preparation. Furthermore, well discuss how to align data with specific use-case requirements. By understanding these elements, businesses can ensure their data is not only AI-ready but optimized to deliver substantial value.

Key Takeaways:

AI-ready data is essential for maximizing the efficiency and impact of AI applications, as it ensures data quality, structure, and contextual relevance.

Achieving AI-ready data requires addressing data quality, completeness, and consistency, which ultimately enhances model accuracy and decision-making.

AI-ready data enables faster, more reliable AI deployment, reducing time-to-market and increasing operational agility across industries.

Building AI-ready data involves steps like cataloging relevant datasets, assessing data quality, consolidating data sources, and implementing governance frameworks.

AI-ready data aligns with future technologies like generative AI, positioning businesses to adapt to advancements and leverage scalable, next-generation solutions.

What is AI-Ready Data?

AI-ready data refers to data that is meticulously prepared, organized, and structured for optimized use in artificial intelligence applications. This concept goes beyond simply accumulating large data volumes; it demands data that is accurate, relevant, and formatted specifically for AI processes. With AI-ready data, every element is curated for compatibility with AI algorithms, ensuring data can be swiftly analyzed and interpreted.

High quality: AI-ready data is accurate, complete, and free from inconsistencies. These factors ensure that AI algorithms function without bias or error.

Relevant Structure: It is organized according to the AI model’s needs, ensuring seamless integration and enhancing processing efficiency.

Contextual Value: Data must provide contextual depth, allowing AI systems to extract and interpret meaningful insights tailored to specific use cases.

In essence, AI-ready data isn’t abundant, it’s purposefully refined to empower AI-driven solutions and insights.

Key Characteristics of AI-Ready Data

High Quality

For data to be truly AI-ready, it must demonstrate high quality across all metrics—accuracy, consistency, and reliability. High-quality data minimizes risks, such as incorrect insights or inaccurate predictions, by removing errors and redundancies. When data is meticulously validated and free from inconsistencies, AI models can perform without the setbacks caused by “noisy” or flawed data. This ensures AI algorithms work with precise inputs, producing trustworthy results that bolster strategic decision-making.

Structure Format

While AI systems can process unstructured data (e.g., text, images, videos), structured data vastly improves processing speed and accuracy. Organized in databases or tables, structured data is easier to search, query, and analyze, significantly reducing the computational burden on AI systems. With AI-ready data in structured form, models can perform complex operations and deliver insights faster, supporting agile and efficient AI applications. For instance, structured financial or operational data enables rapid trend analysis, fueling responsive decision-making processes.

Comprehensive Coverage

AI-ready data must cover a complete and diverse spectrum of relevant variables. This diversity helps AI algorithms account for different scenarios and real-world complexities, enhancing the model’s ability to make accurate predictions. 

For example, an AI model predicting weather patterns would benefit from comprehensive data, including temperature, humidity, wind speed, and historical patterns. With such diversity, the AI model can better understand patterns, make reliable predictions, and adapt to new situations, boosting overall decision quality.

Timeline and Relevance

For data to maintain its AI readiness, it must be current and pertinent to the task. Outdated information can lead AI models to make erroneous predictions or irrelevant decisions, especially in dynamic fields like finance or public health. AI-ready data integrates recent updates and aligns closely with the model’s goals, ensuring that insights are grounded in present-day realities. For instance, AI systems for fraud detection rely on the latest data patterns to identify suspicious activities effectively, leveraging timely insights to stay a step ahead of evolving threats.

Data Integrity and Security

Security and integrity are foundational to trustworthy AI-ready data. Data must remain intact and safe from breaches to preserve its authenticity and reliability. With robust data integrity measures—like encryption, access controls, and validation protocols—AI-ready data can be protected from unauthorized alterations or leaks. This security not only preserves the quality of the AI model but also safeguards sensitive information, ensuring compliance with privacy standards. In healthcare, for instance, AI models analyzing patient data require stringent security to protect patient privacy and trust.

Key Drivers of AI-Ready Data

Understanding the drivers behind the demand for AI-ready data is essential. Organizations can harness the power of AI technologies better by focusing on these factors.

Vendor-Provided Models

Many AI models, especially in generative AI, come from external vendors. To fully unlock their potential, businesses must optimize their data. Pre-trained models thrive on high-quality, structured data. By aligning their data with these models’ requirements, organizations can maximize results and streamline AI integration. This compatibility ensures that AI-ready data empowers enterprises to achieve impactful outcomes, leveraging vendor expertise effectively.

Data Availability and Quality

Quality data is indispensable for effective AI performance. Many companies overlook data challenges unique to AI, such as bias and inconsistency. To succeed, organizations must ensure that AI-ready data is accurate, representative, and free of bias. Addressing these factors establishes a strong foundation, enabling reliable, trustworthy AI models that perform predictably across use cases.

Disruption of Traditional Data Management

AI’s rapid evolution disrupts conventional data management practices, pushing for dynamic, innovative solutions. Advanced strategies like data fabrics and augmented data management are becoming critical for optimizing AI-ready data. Techniques like knowledge graphs enhance data context, integration, and retrieval, making AI models smarter. This shift reflects a growing need for data management innovations that fuel efficient, AI-driven insights.

Bias and Hallucination Mitigation

New solutions tackle AI-specific challenges, such as bias and hallucination. Effective data management structures and prepares AI-ready data to minimize these issues. By implementing strong data governance and quality control, companies can reduce model inaccuracies and biases. This proactive approach fosters more reliable AI models, ensuring that decisions remain unbiased and data-driven.

Integration of Structured and Unstructured Data

Generative AI blurs the line between structured and unstructured data. Managing diverse data formats is crucial for leveraging generative AI’s potential. Organizations need strategies to handle and merge various data types, from text to video. Effective integration enables AI-ready data to support complex AI functionalities, unlocking powerful insights across multiple formats.

5 Steps to AI-Ready Data

The ideal starting point for public-sector agencies to advance in AI is to establish a mission-focused data strategy. By directing resources to feasible, high-impact use cases, agencies can streamline their focus to fewer datasets. This targeted approach allows them to prioritize impact over perfection, accelerating AI efforts.

While identifying these use cases, agencies should verify the availability of essential data sources. Building familiarity with these sources over time fosters expertise. Proper planning can also support bundling related use cases, maximizing resource efficiency by reducing the time needed to implement use cases. Concentrating efforts on mission-driven, high-impact use cases strengthens AI initiatives, with early wins promoting agency-wide support for further AI advancements.

Following these steps can ensure agencies select the right datasets that meet AI-ready data standards.

Step 1: Build a Use Case Specific Data  Catalog

The chief data officer, chief information officer, or data domain owner should identify relevant datasets for prioritized use cases. Collaborating with business leaders, they can pinpoint dataset locations, owners, and access protocols. Tailoring data discovery to agency-specific systems and architectures is essential. Successful data catalog projects often include collaboration with system users and technical experts and leverage automated tools for efficient data discovery.

For instance, one federal agency conducted a digital assessment to identify datasets that drive operational efficiency and cost savings. This process enabled them to build a catalog accessible to data practitioners across the agency.

Step 2: Assess Data Quality and Completeness

AI success depends on high-quality, complete data for prioritized use cases. Agencies should thoroughly audit these sources to confirm their AI-ready data status. One national customs agency did this by selecting priority use cases and auditing related datasets. In the initial phases, they required less than 10% of their available data.

Agencies can adapt AI projects to maximize impact with existing data, refining approaches over time. For instance, a state-level agency improved performance by 1.5 to 1.8 times using available data and predictive analytics. These initial successes paved the way for data-sharing agreements, focusing investment on high-impact data sources.

Step 3: Aggregate Prioritized Data Sources

Selected datasets should be consolidated within a data lake, either existing or purpose-built on a new cloud-based platform. This lake serves analytics staff, business teams, clients, and contractors. For example, one civil engineering organization centralized procurement data from 23 resource planning systems onto a single cloud instance, granting relevant stakeholders streamlined access.

Step 4: Evaluate Data Fit

Agencies must evaluate AI-ready data for each use case based on data quantity, quality, and applicability. Fit-for-purpose data varies depending on specific use case requirements. Highly aggregated data, for example, may lack the granularity needed for individual-level insights but may still support community-level predictions. 

Analytics teams can enhance fit by:

Selecting data relevant to use cases.

Developing a reusable data model with the necessary fields and tables.

Systematically assessing data quality to identify gaps.

Enriching the data model iteratively, adding parameters or incorporating third-party data.

A state agency, aiming to support care decisions for vulnerable populations, found their initial datasets incomplete and in poor formats. They improved quality through targeted investments, transforming the data for better model outputs.

Step 5: Governance and Execution

Establishing a governance framework is essential to secure AI-ready data and ensure quality, security, and metadata compliance. This framework doesn’t require exhaustive rules but should include data stewardship, quality standards, and access protocols across environments.

In many cases, existing data storage systems can meet basic security requirements. Agencies should assess additional security needs, adopting control standards such as those from the National Institute of Standards and Technology. For instance, one government agency facing complex security needs for over 150 datasets implemented a strategic data security framework. They simplified the architecture with a use case–level security roadmap and are now executing a long-term plan.

For public-sector success, governance and agile methods like DevOps should be core to AI initiatives. Moving away from traditional development models is crucial, as risk-averse cultures and longstanding policies can slow progress. However, this transition is vital to AI-ready data initiatives, enabling real-time improvements and driving impactful outcomes.

Challenges to AI-Ready Data

While AI-ready data promises transformative potential, achieving it poses significant challenges. Organizations must recognize and tackle these obstacles to build a strong, reliable data foundation.

Data Silos

Data silos arise when departments store data separately, creating isolated information pockets. This fragmentation hinders the accessibility, analysis, and usability essential for AI-ready data.

Impact: AI models thrive on a comprehensive data view to identify patterns and make predictions. Silos restrict data scope, resulting in biased models and unreliable outputs.

Solution: Build a centralized data repository, such as a data lake, to aggregate data from diverse sources. Implement cross-functional data integration to dismantle silos, ensuring AI-ready data flows seamlessly across the organization.

Data Inconsistency

Variations in data formats, terms, and values across sources disrupt AI processing, creating confusion and inefficiencies.

Impact: Inconsistent data introduces errors and biases, compromising AI reliability. For example, a model with inconsistent gender markers like “M” and “Male” may yield flawed insights.

Solution: Establish standardized data formats and definitions. Employ data quality checks and validation protocols to catch inconsistencies. Utilize governance frameworks to uphold consistency across the AI-ready data ecosystem.

Data Quality

Poor data quality—like missing values or errors—undermines the accuracy and reliability of AI models.

Impact: Unreliable data leads to skewed predictions and biased models. For instance, missing income data weakens a model predicting purchasing patterns, impacting its effectiveness.

Solution: Use data cleaning and preprocessing to resolve quality issues. Apply imputation techniques for missing values and data enrichment to fill gaps, reinforcing AI-ready data integrity.

Data Privacy and Security

Ensuring data privacy and security is crucial, especially when managing sensitive information under strict regulations.

Impact: Breaches and privacy lapses damage reputations and erodes trust, while legal penalties strain resources. AI-ready data demands rigorous security to safeguard sensitive information.

Solution: Implement encryption, access controls, and data masking to secure AI-ready data. Adopt privacy-enhancing practices, such as differential privacy and federated learning, for safer model training.

You can read about the pillars of AI security.

Skill Shortages

Developing and maintaining an AI-ready data infrastructure requires specialized skills in data science, engineering, and AI.

Impact: Without skilled professionals, organizations struggle to govern data, manage quality, and design robust AI solutions, stalling progress toward AI readiness.

Solution: Invest in hiring and training for data science and engineering roles. Collaborate with external consultants or partner with AI and data management experts to bridge skill gaps.

Why is AI-Ready Data Important?

Accelerated AI Development

AI-ready data minimizes the time data scientists spend on data cleaning and preparation, shifting their focus to building and optimizing models. Traditional data preparation can be tedious and time-consuming, especially when data is unstructured or lacks consistency. With AI-ready data, data is pre-cleaned, labeled, and structured, allowing data scientists to jump straight into analysis. This efficiency translates into a quicker time-to-market, helping organizations keep pace in a rapidly evolving AI landscape where every minute counts.

Improved Model Accuracy

The accuracy of AI models hinges on the quality of the data they consume. AI-ready data is not just clean; it’s relevant, complete, and up-to-date. This enhances model precision, as high-quality data reduces biases and errors. For instance, if a retail company has AI-ready data on customer preferences, its models will generate more accurate recommendations, leading to higher customer satisfaction and loyalty. In essence, AI-ready data helps unlock better predictive accuracy, ensuring that organizations make smarter, data-driven decisions.

Streamlined MLOps for Consistent Performance

Machine Learning Operations (MLOps) ensure that AI models perform consistently from development to deployment. AI-ready data plays a vital role here by ensuring that both historical data (used for training) and real-time data (used in production) are aligned and in sync. This consistency supports smoother transitions between training and deployment phases, reducing model degradation over time. Streamlined MLOps mean fewer interruptions in production environments, helping organizations implement AI faster, and ensuring that models remain robust and reliable in the long term.

Cost Reduction Through Optimized Data Protection

AI projects can be costly, especially when data preparation takes a significant portion of a project’s budget. AI-ready data cuts down the need for extensive manual preparation, enabling engineers to invest time in high-value tasks. This shift not only reduces labor costs but also shortens project timelines, which is particularly advantageous in competitive industries where time-to-market can impact profitability. In essence, the more AI-ready a dataset, the less costly the AI project becomes, allowing for more scalable AI implementations.

Improved Data Governance and Compliance

In a regulatory environment, data governance is paramount, especially as AI decisions become more scrutinized. AI-ready data comes embedded with metadata and lineage information, ensuring that data’s origin, transformations, and usage are documented. This audit trail is crucial when explaining AI-driven decisions to stakeholders, including customers and regulators. Proper governance and transparency are not just compliance necessities—they build trust and enhance accountability, positioning the organization as a responsible AI user.

Future-Proofing for GenAI

With the rapid advancement in generative AI (GenAI), organizations need to prepare now to capitalize on future AI applications. Forward-thinking companies are already developing GenAI-ready data capabilities, setting the groundwork for rapid adoption of new AI technologies.

AI-ready data ensures that, as the AI landscape evolves, the organization’s data is compatible with new AI models, reducing rework and accelerating adoption timelines. This preparation creates a foundation for scalability and adaptability, enabling companies to lead rather than follow in ‌AI evolution.

Reducing Data Preparation Time for Data Scientists

It’s estimated that data scientists spend around 39% of their time preparing data, a staggering amount given their specialized skill sets. By investing in AI-ready data, companies can drastically reduce this figure, allowing data scientists to dedicate more energy to model building and optimization. When data is already clean, organized, and ready to use, data scientists can direct their expertise toward advancing AI’s strategic goals, accelerating innovation, and increasing overall productivity.

Conclusion

In the data-driven landscape, preparing AI-ready data is essential for any organization aiming to leverage artificial intelligence effectively. AI-ready data is not just about volume; it’s about curating data that is accurate, well-structured, secure, and highly relevant to specific business objectives. High-quality data enhances the predictive accuracy of AI models, ensuring reliable insights that inform strategic decisions. By investing in robust data preparation processes, organizations can overcome common AI challenges like biases, errors, and data silos, which often lead to failed AI projects.

Moreover, AI-ready data minimizes the time data scientists spend on tedious data preparation, enabling them to focus on building and refining models that drive innovation. For businesses, this means faster time-to-market, reduced operational costs, and improved adaptability to market changes. Effective data governance and security measures embedded within AI-ready data also foster trust, allowing organizations to meet regulatory standards and protect sensitive information. 

As AI technology continues to advance, having a foundation of AI-ready data is crucial for scalability and flexibility. This preparation not only ensures that current AI applications perform optimally but also positions the organization to quickly adopt emerging AI innovations, such as generative AI, without extensive rework. In short, prioritizing AI-ready data today builds resilience and agility, paving the way for sustained growth and a competitive edge in the future.

Source URL: https://www.techaheadcorp.com/blog/what-is-ai-ready-data-how-to-get-your-there/

[compunnelinc]

Explore the dynamic intersection of AI and data security compliance as we head into 2025. Our in-depth blog examines how artificial intelligence is reshaping data protection strategies, uncovering emerging trends, and presenting new challenges. Learn how organizations can navigate these changes to stay compliant with evolving regulations and safeguard their sensitive information effectively. Gain valuable insights and practical tips on integrating AI technologies into your data security practices. Read now to stay ahead of the curve and discover actionable strategies for enhancing your data security in the AI era!

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