#Big Data Application Development

[beauty4care]

Developing Big Data Applications to Revolutionize Industries

In contemporary times, an increasing number of businesses across various sectors rely heavily on data. The unprecedented ability to collect, analyze, and leverage vast amounts of information has opened up broad opportunities for growth and advancement in data analytics. At the forefront of this transformation lies big data application development, a process that enables companies to harness data for informed decision-making, operational enhancement, and personalized customer experiences.

Big Data Application Development:

Big data application development involves the creation of software systems designed to apply analytics and process large datasets in real-time. Leveraging modern technologies such as machine learning, artificial intelligence, and data analytics, these tools extract meaningful insights from data sources.

Data Application Development Services:

Firms specializing in data application development offer a range of services tailored to maximize the utilization of data capabilities for businesses. These services include custom software development, data integration, and analytics consultations, all aimed at addressing each customer's unique needs and overcoming challenges.

Unlocking Value from Data:

Big data enables organizations to extract greater value from their data resources by providing access to a wealth of information. Through these platforms, companies gain deep insights into customer behavior, market trends, and operational efficiency, facilitating better-informed, data-driven decisions that drive organizational growth.

Enhancing Operational Efficiency:

One of the key benefits of big data application development is its ability to enhance operational efficiency. These applications automate repetitive tasks, streamline processes, and optimize resource allocation, resulting in cost reductions and productivity improvements for organizations.

Driving Innovation:

Big data technology innovations have spurred creativity across industries, enabling advancements that were previously unimaginable. Examples include predictive maintenance in manufacturing and personalized recommendations in retail, showcasing the vast possibilities of data-driven technological innovation.

Improving Customer Experiences:

In today's highly competitive business landscape, delivering exceptional customer experiences is paramount. Big data application development empowers organizations to tap into deeper insights into customer needs, preferences, and behaviors, enabling the delivery of personalized experiences that drive engagement and satisfaction.

Real-World Applications:

Big data technology is making a significant impact across various sectors. In healthcare, it is used to analyze patient data and enhance diagnosis and treatment. In finance, automation is revolutionizing fraud detection, risk management, and investment optimization. Additionally, it is transforming retail marketing through targeted campaigns and improved shopping interfaces.

Conclusion:

Big data application development is revolutionizing industries worldwide, enabling organizations to harness the full potential of their data and drive innovation and productivity. Commerce Pulse offers data application development services to empower businesses with data analytics, informed decision-making, and operational efficiency. By providing the necessary data management tools and expertise, we enable companies to translate raw data into actionable insights and achieve tangible business outcomes. With our comprehensive range of data offerings, businesses can seize the opportunities of the data era and emerge as leaders in the digital landscape, leveraging their data as a strategic asset for sustainable growth and resilience. Our mission is to equip companies with the tools to unlock the potential of their data, enabling them to excel in the digital age.

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

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

[jcmarchi]

Non-fiction books that explore AI's impact on society  - AI News

New Post has been published on https://thedigitalinsider.com/non-fiction-books-that-explore-ais-impact-on-society-ai-news/

Non-fiction books that explore AI's impact on society  - AI News

.pp-multiple-authors-boxes-wrapper display:none; img width:100%;

Artificial Intelligence (AI) is code or technologies that perform complex calculations, an area that encompasses simulations, data processing and analytics.

AI has increasingly grown in importance, becoming a game changer in many industries, including healthcare, education and finance. The use of AI has been proven to double levels of effectiveness, efficiency and accuracy in many processes, and reduced cost in different market sectors. 

AI’s impact is being felt across the globe, so, it is important we understand the effects of AI on society and our daily lives. 

Better understanding of AI and all that it does and can mean can be gained from well-researched AI books.

Books on AI provide insights into the use and applications of AI. They describe the advancement of AI since its inception and how it has shaped society so far. In this article, we will be examining recommended best books on AI that focus on the societal implications.

For those who don’t have time to read entire books, book summary apps like Headway will be of help.

Book 1: “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

Nick Bostrom is a Swedish philosopher with a background in computational neuroscience, logic and AI safety. 

In his book, Superintelligence, he talks about how AI  can surpass our current definitions of intelligence and the possibilities that might ensue.

Bostrom also talks about the possible risks to humanity if superintelligence is not managed properly, stating AI can easily become a threat to the entire human race if we exercise no control over the technology. 

Bostrom offers strategies that might curb existential risks, talks about how Al can be aligned with human values to reduce those risks and suggests teaching AI human values.

Superintelligence is recommended for anyone who is interested in knowing and understanding the implications of AI on humanity’s future.

Book 2: “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

AI expert Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, examines the AI revolution and its impact so far, focusing on China and the USA. 

He concentrates on the competition between these two countries in AI and the various contributions to the advancement of the technology made by each. He highlights China’s advantage, thanks in part to its larger population. 

China’s significant investment so far in AI is discussed, and its chances of becoming a global leader in AI. Lee believes that cooperation between the countries will help shape the future of global power dynamics and therefore the economic development of the world.

In thes book, Lee states AI has the ability to transform economies by creating new job opportunities with massive impact on all sectors. 

If you are interested in knowing the geo-political and economic impacts of AI, this is one of the best books out there. 

Book 3: “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark

Max Tegmark’s Life 3.0 explores the concept of humans living in a world that is heavily influenced by AI. In the book, he talks about the concept of Life 3.0, a future where human existence and society will be shaped by AI. It focuses on many aspects of humanity including identity and creativity. 

Tegmark envisions a time where AI has the ability to reshape human existence. He also emphasises the need to follow ethical principles to ensure the safety and preservation of human life. 

Life 3.0 is a thought-provoking book that challenges readers to think deeply about the choices humanity may face as we progress into the AI era. 

It’s one of the best books to read if you are interested in the ethical and philosophical discussions surrounding AI.

Book 4: “The Fourth Industrial Revolution” by Klaus Schwab

Klaus Martin Schwab is a German economist, mechanical engineer and founder of the World Economic Forum (WEF). He argues that machines are becoming smarter with every advance in technology and supports his arguments with evidence from previous revolutions in thinking and industry.

He explains that the current age – the fourth industrial revolution – is building on the third: with far-reaching consequences.

He states use of AI in technological advancement is crucial and that cybernetics can be used by AIs to change and shape the technological advances coming down the line towards us all.

This book is perfect if you are interested in AI-driven advancements in the fields of digital and technological growth. With this book, the role AI will play in the next phases of technological advancement will be better understood.

Book 5: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil

Cathy O’Neil’s book emphasises the harm that defective mathematical algorithms cause in judging human behaviour and character. The continual use of maths algorithms promotes harmful results and creates inequality.

An example given in  the book is of research that proved bias in voting choices caused by results from different search engines.

Similar examination is given to research that focused Facebook, where, by making newsfeeds appear on users’ timelines, political preferences could be affected.

This book is best suited for readers who want to adventure in the darker sides of AI that wouldn’t regularly be seen in mainstream news outlets.

Book 6: “The Age of Em: Work, Love, and Life when Robots Rule the Earth” by Robin Hanson

An associate professor of economics at George Mason University and a former researcher at the Future of Humanity Institute of Oxford University, Robin Hanson paints an imaginative picture of emulated human brains designed for robots. What if humans copied or “emulated” their brains and emotions and gave them to robots?

He argues that humans who become “Ems” (emulations) will become more dominant in the future workplace because of their higher productivity.

An intriguing book for fans of technology and those who love intelligent predictions of possible futures.

Book 7: “Architects of Intelligence: The truth about AI from the people building it” by Martin Ford

This book was drawn from interviews with AI experts and examines the struggles and possibilities of AI-driven industry.

If you want insights from people actively shaping the world, this book is right for you!

CONCLUSION

These books all have their unique perspectives but all point to one thing – the advantages of AI of today will have significant societal and technological impact. These books will give the reader glimpses into possible futures, with the effects of AI becoming more apparent over time.

For better insight into all aspects of AI, these books are the boosts you need to expand your knowledge. AI is advancing quickly, and these authors are some of the most respected in the field. Learn from the best with these choice reads.

[vasundhara-infotech]

How To Integrate Third-Party Services Into Your Website

[classroomlearning]

BTech CSE: Your Gateway to High-Demand Tech Careers

Apply now for admission and avail the Early Bird Offer

In the digital age, a BTech in Computer Science & Engineering (CSE) is one of the most sought-after degrees, offering unmatched career opportunities across industries. From software development to artificial intelligence, the possibilities are endless for CSE graduates.

Top Job Opportunities for BTech CSE Graduates

Software Developer: Design and develop innovative applications and systems.

Data Scientist: Analyze big data to drive business decisions.

Cybersecurity Analyst: Safeguard organizations from digital threats.

AI/ML Engineer: Lead the way in artificial intelligence and machine learning.

Cloud Architect: Build and maintain cloud-based infrastructure for global organizations.

Why Choose Brainware University for BTech CSE?

Brainware University provides a cutting-edge curriculum, hands-on training, and access to industry-leading tools. Our dedicated placement cell ensures you’re job-ready, connecting you with top recruiters in tech.

👉 Early Bird Offer: Don’t wait! Enroll now and take the first step toward a high-paying, future-ready career in CSE.

Your journey to becoming a tech leader starts here!

[nidhibhati12]

[zoondia-ae]

How to Choose the Right Web Application Firewall for Your Needs

What is a web application firewall?

A web application firewall (WAF) is a security solution that protects web applications from a variety of attacks, including cross-site scripting (XSS), SQL injection, and denial-of-service (DoS) attacks. WAFs work by filtering and monitoring HTTP traffic between a web application and the internet. They can be deployed as hardware, software, or cloud-based solutions.

How does a WAF work?

A WAF works by inspecting HTTP requests and responses for malicious patterns. These patterns are typically defined in a set of rules, which are called policies. When a WAF detects a request that matches a policy, it can take one of several actions, such as blocking the request, logging the request, or rewriting the request.

What are the benefits of using a WAF?

WAFs can provide a number of benefits, including:

Increased security: WAFs can help to protect web applications from a variety of attacks, including XSS, SQL injection, and DoS attacks.

Reduced risk of data breaches: WAFs can help to prevent attackers from stealing sensitive data, such as credit card numbers and passwords.

Improved performance: WAFs can help to improve the performance of web applications by filtering out malicious traffic.

Reduced costs: WAFs can help to reduce the costs of security by preventing attacks and data breaches.

What are the different types of WAFs?

There are three main types of WAFs:

Hardware WAFs: These are WAFs that are deployed as physical appliances. They are typically more expensive than other types of WAFs, but they can provide better performance and security.

Software WAFs: These are WAFs that are deployed as software on a web server or application server. They are typically less expensive than hardware WAFs, but they may not provide the same level of performance and security.

Cloud-based WAFs: These are WAFs that are deployed in the cloud. They are typically the most affordable option, but they may not provide the same level of control as other types of WAFs.

How to choose a WAF

When choosing a WAF, there are a number of factors to consider, including:

The size and complexity of your web applications

The types of attacks you are most concerned about

Your budget

Your technical expertise

It is important to consult with a security expert to help you choose the right WAF for your needs.

Conclusion

WAFs are an important part of a comprehensive web application security strategy. By filtering and monitoring HTTP traffic, WAFs can help to protect web applications from a variety of attacks. When choosing a WAF, it is important to consider the size and complexity of your web applications, the types of attacks you are most concerned about, your budget, and your technical expertise.

ENHANCE YOUR WEB APP’S SECURITY WITH ZOONDIA!

Are you searching for a solution to minimize the risk of a data breach on your web application? Partner with Zoondia, a reputable leader in web application development solutions, and unlock boundless possibilities for advancement in software.

Contact us now to uncover how Zoondia stands ready to be your strategic ally in transforming web app development with state-of-the-art software solutions. Let’s work together to craft a more promising tomorrow for your business.

[sprwork]

Data Science and Big Data Analytics

Data Science and Big Data Analytics drive insights from vast and complex datasets to inform business decisions and innovation. By combining advanced statistical analysis, machine learning, and domain expertise, data scientists uncover valuable patterns, trends, and correlations that organizations use to optimize operations, enhance customer experiences, and develop strategic plans. Big Data Analytics focuses on processing and analyzing massive volumes of data that traditional tools struggle to handle. This multidisciplinary field empowers industries ranging from healthcare to finance, enabling predictive modeling, risk assessment, and personalized recommendations. Ultimately, Data Science and Big Data Analytics fuel data-driven strategies, fostering growth and competitive advantage.

[halcyontechnologies]

Halcyon technologies is one of the largest and most trusted Android application development company in the USA . Since 2003, the company is creating attractive and resilient apps to its clients through the most reliable methods.

With a team of skilled developers, the firm has vast experience in delivering various services including Mobile Web Design, Custom Web Application Development, Android App Development, PHP/.Net Development,iphone app devlopment, Big Data & Development, Cloud Computing iPhone/iPad Applications, etc.

Halcyon technologies services :

Android app Development

iphone app devlopment

Cross Platform Mobile Development

Mobile Application Development

Big Data

Cloud Computing

Digital Marketing

Web Application Development

Artificial Intelligence

QA & Testing

Contact us

9701533238

support@halcyontek.com

www.halcyontek.com

[mostlysignssomeportents]

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

--

tom_bullock (modified) https://www.flickr.com/photos/tombullock/25173469495/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[beauty4care]

Revolutionizing Industries with Big Data

Nowadays, more and more corporations in different sectors are reliant on data. Now that we can collect, analyze, and utilize huge amounts of information like never before, data analytics has broad opportunities for new strengths and development. One of the major driving forces in this transformation is the process of big data application development, which helps companies utilize data to make informed choices, improve operational productivity, and assist customers with personalized experiences.

Big data application development is a set of processes for designing software systems that can apply analytics and process large data sets in real time. Such tools use modern technologies like machine learning, artificial intelligence, and data analytics to derive meaningful information from the data source. Data Application Development Services

A data application development firm provides various services to enable companies to completely use data capabilities. They provide custom software development, data integration, and analytics consultations that are designed in such a way as to meet every customer’s peculiar needs and overcome challenges. Unlocking value from data

With big data, organizations can get access to more information and data, creating more value from their data resources. Through these platforms, companies gain deep insights into customer behaviour, market trends, and overall efficiency, facilitating better-informed, data-driven decisions that benefit the organization by driving growth.

Enhancing operational efficiency

Among the essential benefits of the application of big data is its ability to increase operational efficiency. Applications like these are able to automate repetitive tasks, streamline processes, and optimize resource allocation, thus making things more efficient and effective. The organizations achieve a reduction in costs and an improvement in productivity.

Driving Innovation

The big data technology innovations have also been instrumental in fostering creativity in industries, irrespective of the business model, which might have seemed impossible in the past. Predictive maintenance in manufacturing or personalized recommendation in retail are only a few examples that show us the degree of possibilities when data is being used for technological innovation. Improving customer experiences

Nowadays, in an incredibly competitive business area, offering the best customer experience is of utmost significance for any business. Big data application development empowers organizations to exploit the deeper signs of customers’ needs, preferences, and behaviours, which result in the ability to deliver personalized experiences that lead to more engagement and pleasure for clients. Real-World Applications

The influence of big data technology on the development of an application may be seen from different points of view. For instance, in healthcare, these systems are using patient data to analyse and enhance diagnosis and treatment. In finance, more automation is beginning to take over fraud detection, risk management, and investment optimization. Moreover, they find their place in retail marketing by being able to power targeted marketing campaigns and improve the website shopping interface. Conclusion:

In conclusion, big data application development is transforming sectors all around the world, allowing organizations to reveal the full power of their data sources and to be innovative and productive. Commerce Pulse data application development services make it available to business owners to gain from data analytics, enhanced decision-making, and operational efficiency by offering the required data management tools and skills. Businesses can turn their raw data into actionable insights and achieve tangible business results because of the way we enable them to do so. With our wide range of data offerings, companies now have the ability to react quickly to the data era and become leaders by treating their data as a strategic asset that guides their sustainable growth and resilience. Our goal is to provide companies with the tools to unlock the potential of their data, letting them turn their data into a precious resource to excel in the digital world.

[saphronethaleph]

Fascist, Thus Inefficient

“As you can see, my young apprentice, your friends have failed,” the Emperor said, triumph in his tone. “Now, witness the firepower of this fully armed and operational battle station!”

Luke looked at him in shock.

“Fire at will, Commander!” the Emperor said.

Fourteen months previously…

“Shipment IL-214-73 arriving,” a petty officer reported.

“Thank goodness,” muttered one of the technicians. “After the delays we’ve been having, we need to get those Khyber crystals into the third main focusing array. It’s been on the critical path for a week.”

He brought up the display, frowning. “All right, I think we can make up a bit of time if we just get them straight to cutting and installation.”

“Don’t we need to run them through the testing process first?” a more junior technician asked. “That’s on the list.”

“I know it’s on the list,” the senior tech replied. “But the list was written when they didn’t expect there’d be rebel attacks hitting our supply lines.”

He waved at the screen. “The testing process means heating each individual crystal up to eighteen hundred, even though we know Khyber can all handle temperatures of up to forty-seven-fifty. The cutting process doesn’t rely on heat tolerance either. Any crystalline flaws will come out in cutting, and we can just junk them. It means cutting takes a bit longer, but by going straight to cutting we can save at several hours on the overall process. And you know how much time we’ve lost already.”

The junior tech looked worried, then shook his head.

“All right,” he replied. “I guess so.”

“You need to learn how things are done in practice,” the senior tech said. “No big deal.”

Eleven months previously...

“I’m quite sure Rothana Heavy Engineering’s XJ-15 hypermatter feed systems will meet your needs better than the alternatives,” the Rothana representative said, as Admiral Jerjerrod examined the datasheet.

He wasn’t so sure. The newer units had better specifications, certainly, but they weren’t proven, and they were also somewhat more expensive.

“I don’t think that’s necessarily the case,” he said, out loud. “While I appreciate Rothana’s position, the Sienar alternative has similar flow rates and more proven applications.”

The Rothana representative nodded, sagely.

“I understand entirely,” he said. “However, I must point out that Rothana has some important additional information to present.”

He held out a credit chip, which Jerjerrod took and inspected.

“Owing to the XJ-15’s protracted development, we are willing to provide our test units at cost,” the representative went on. “That is in addition to having a higher production rate than our competitors and a less committed production output.”

Jerjerrod hesitated, then pocketed the credit chip.

“That all seems in order,” he said. “The XJ-15 it is.”

“Marvellous,” the representative declared.

Nine months previously...

“I’ve examined the records that exist from the first Death Star,” a senior technician said. “The amount of strain that was placed on the flash suppression systems was minimal to nonexistent. Even with the full firing that destroyed Alderaan, surviving records indicate that the flash suppressors had no more than a five percent load placed on them – an amount that can be handled by untreated durasteel.”

The other men and women in the meeting looked at the data on the screen behind their colleague.

“You’re suggesting we forego the duratemp treatment on the flash protection systems?” one of the women asked, cautiously. “I can see the advantages, but the downsides seem significant. I’d even say potentially destructive.”

“It is my position that the cost of including the duratemp treatment is unacceptable,” the tech replied. “It takes time and effort, including supervisory attention which cuts into the available man-hours on the project. We only have so much experienced manpower.”

That drew winces, though none of the humans in the room drew attention to the fact that they were spending a lot of that time in interminable meetings.

“In the following presentation, I’ll discuss my proposal and how it could shave as much as one week off the final completion timetable,” the senior tech continued, flicking to the next screen of his presentation. “This model shows how the flash suppression systems are built around the main weapon…”

Six months previously…

“There simply isn’t an option,” the head of personnel replied. “Our existing system is not providing enough technicians and operators.”

“This was quite sufficient for the first Death Star,” Jerjerrod protested.

“The first Death Star was a project that took decades,” the manager replied, shrugging. “It didn’t come up at first, sir – for that I apologize – but if we are going to redress the problem, we need to act now. There is no alternative.”

Jerjerrod rubbed his temples, thinking about the problem.

The fully functional Death Star was going to need hundreds of thousands of qualified technicians and operators, familiar with the systems of the vast battle station, and so many of the men who knew much about the Death Star at the moment were busy building it.

There hadn’t been many left after the destruction of the first battle station, because most of them had been working on it at the time.

“All right,” he said. “So your proposal is…?”

“We keep the same number of trainers for now, but abbreviate the course,” the manager answered. “Two months – at most. Then we have the new graduates train the next batch for two months, and so on. Exponential growth. At twenty students per instructor and a hundred instructors to start with, we’ll end up with eight hundred thousand in six months.”

That was extremely tempting… they wouldn’t be anything like the equal of what they should be, but they could learn on the job.

“All right,” Jerjerrod said. “Approved – see to it.”

One month previously…

“Next item on the checklist?” Commander Jaskier asked.

“Step one hundred and seven,” Technician Mils replied. “Self test.”

She pressed the self-test button, and the computer system clicked and flickered as it ran through the diagnostics.

Data results and readouts went up on the screen, and Jaskier and all the others in the control station watched the results.

None of them had any comment to make about the numbers. The checklist said to run the self test, so that was what they were doing.

“Step one hundred and eight,” Mils went on. “Sign off on results.”

She did that, as well, and Jaskier nodded.

“Good,” he said. “And I believe we’ve finished that half an hour ahead of schedule! Good work, everyone.”

Now.

The firing commands flashed out through the Death Star’s systems, triggering a cascade of further commands, and the whole massive battle station’s main superlaser woke for the first time.

Fifty XJ-15 hypermatter flow regulators controlled the flow of energy from the power core into the power collectors, and the energy being channelled into the system surged rapidly – rising to one hundred and eighteen percent of nominal, above what would have been anticipated, and greater than the one hundred and two percent that the older, more proven Sienar systems would have generated.

Thousands of high powered beams were generated, controlled and focused through an enormous array of Khyber crystals… a small but measurable fraction of which were cheap industrially grown diamonds instead, added to the shipments by subcontractors eager to stretch out their production from the strip-mined planet of Ilum without running so late on their deliveries that financial penalties were imposed.

None of the technicians who were in a position to spot the problem at this stage were actually capable of doing so. Their necessarily abbreviated training had mostly been on what buttons to push, and nobody had the deeper knowledge of the systems to recognize that the system was in an anomalous state.

Then some of the diamonds shattered under the load, allowing the beams free to damage adjacent systems, and in moments the whole of the energy drawn from the hypermatter core was unleashed.

The flash suppression systems were wholly, and fatally, inadequate.

“Watch yourself, Wedge!” Lando called, his head on a swivel, and banked the Falcon around so his ventral turret gunner could clear off one of the TIEs attacking Red Leader. “We’ve got to-”

Then there was a sudden blinding flash, and Lando did a double-take.

The Death Star’s protective shield was instantly, and dramatically, visible – because the entire inside of it was full of plasma and flame, lighting it up as clearly as Ackbar’s briefing had done back before the operation was launched in the first place. Then something blew up on the surface of the forest moon as the plasma followed the funnel of the shield, and the explosive force was no longer contained but began to drift out into space.

“...the kriff?” Lando asked, eventually. “What just happened?”

“Ow,” Darth Vader said, indistinctly, reaching up to feel his helmet, which had been crushed in by an impact with the ceiling.

The Emperor’s throne room seemed to mostly be intact, though there was an Emperor-shaped hole in the window nearest his throne, and Luke had his hands out to either side as he stood on the wall.

“Father, are you all right?” the younger Skywalker asked.

“What happened?” Vader replied. “I remember the Emperor ordering that the Death Star should fire…”

“I don’t know, it exploded just after he said that,” Luke answered. “It turns out that overconfidence was his weakness… do you have any idea where the nearest spaceship is? Keeping the atmosphere in is tiring me out a bit.”

[jcmarchi]

RAGEN: AI framework tackles LLM agent instability

New Post has been published on https://thedigitalinsider.com/ragen-ai-framework-tackles-llm-agent-instability/

RAGEN: AI framework tackles LLM agent instability

Researchers have introduced RAGEN, an AI framework designed to counter LLM agent instability when handling complex situations.

Training these AI agents presents significant hurdles, particularly when decisions span multiple steps and involve unpredictable feedback from the environment. While reinforcement learning (RL) has shown promise in static tasks like solving maths problems or generating code, its application to dynamic, multi-turn agent training has been less explored.   

Addressing this gap, a collaborative team from institutions including Northwestern University, Stanford University, Microsoft, and New York University has proposed StarPO (State-Thinking-Actions-Reward Policy Optimisation).

StarPO offers a generalised approach for training agents at the trajectory level (i.e. it optimises the entire sequence of interactions, not just individual actions.)

Accompanying this is RAGEN, a modular system built to implement StarPO. This enables the training and evaluation of LLM agents, particularly focusing on their reasoning capabilities under RL. RAGEN provides the necessary infrastructure for rollouts, reward assignment, and optimisation within multi-turn, stochastic (randomly determined) environments.

Minimalist environments, maximum insight

To isolate the core learning challenges from confounding factors like extensive pre-existing knowledge or task-specific engineering, the researchers tested LLMs using RAGEN in three deliberately minimalistic, controllable symbolic gaming environments:   

Bandit: A single-turn, stochastic task testing risk-sensitive symbolic reasoning. The agent chooses between options (like ‘Phoenix’ or ‘Dragon’ arms) with different, initially unknown, reward profiles.

Sokoban: A multi-turn, deterministic puzzle requiring foresight and planning, as actions (pushing boxes) are irreversible.

Frozen Lake: A multi-turn, stochastic grid navigation task where movement attempts can randomly fail, demanding planning under uncertainty.

These environments allow for clear analysis of how agents learn decision-making policies purely through interaction.   

Key findings: Stability, rollouts, and reasoning

The study yielded three significant findings concerning the training of self-evolving LLM agents:

The ‘Echo Trap’ and the need for stability

A recurring problem observed during multi-turn RL training was dubbed the “Echo Trap”. Agents would initially improve but then suffer performance collapse, overfitting to locally rewarded reasoning patterns. 

This was marked by collapsing reward variance, falling entropy (a measure of randomness/exploration), and sudden spikes in gradients (indicating training instability). Early signs included drops in reward standard deviation and output entropy.   

To combat this, the team developed StarPO-S, a stabilised version of the framework. StarPO-S incorporates:   

Variance-based trajectory filtering: Focusing training on task instances where the agent’s behaviour shows higher uncertainty (higher reward variance), discarding low-variance, less informative rollouts. This improved stability and efficiency.   

Critic incorporation: Using methods like PPO (Proximal Policy Optimisation), which employ a ‘critic’ to estimate value, generally showed better stability than critic-free methods like GRPO (Group Relative Policy Optimisation) in most tests.   

Decoupled clipping and KL removal: Techniques adapted from other research (DAPO) involving asymmetric clipping (allowing more aggressive learning from positive rewards) and removing KL divergence penalties (encouraging exploration) further boosted stability and performance.   

StarPO-S consistently delayed collapse and improved final task performance compared to vanilla StarPO.   

Rollout quality is crucial

The characteristics of the ‘rollouts’ (simulated interaction trajectories used for training) significantly impact learning. Key factors identified include:   

Task diversity: Training with a diverse set of initial states (prompts), but with multiple responses generated per prompt, aids generalisation. The sweet spot seemed to be moderate diversity enabling contrast between different outcomes in similar scenarios.   

Interaction granularity: Allowing multiple actions per turn (around 5-6 proved optimal) enables better planning within a fixed turn limit, without introducing the noise associated with excessively long action sequences.   

Rollout frequency: Using fresh, up-to-date rollouts that reflect the agent’s current policy is vital. More frequent sampling (approaching an ‘online’ setting) leads to faster convergence and better generalisation by reducing policy-data mismatch.

Maintaining freshness, alongside appropriate action budgets and task diversity, is key for stable training.   

Reasoning requires careful reward design

Simply prompting models to ‘think’ doesn’t guarantee meaningful reasoning emerges, especially in multi-turn tasks. The study found:

Reasoning traces helped generalisation in the simpler, single-turn Bandit task, even when symbolic cues conflicted with rewards.   

In multi-turn tasks like Sokoban, reasoning benefits were limited, and the length of ‘thinking’ segments consistently declined during training. Agents often regressed to direct action selection or produced “hallucinated reasoning” if rewards only tracked task success, revealing a “mismatch between thoughts and environment states.”

This suggests that standard trajectory-level rewards (often sparse and outcome-based) are insufficient. 

“Without fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge[s] through multi-turn RL.”

The researchers propose that future work should explore rewards that explicitly evaluate the quality of intermediate reasoning steps, perhaps using format-based penalties or rewarding explanation quality, rather than just final outcomes.   

RAGEN and StarPO: A step towards self-evolving AI

The RAGEN system and StarPO framework represent a step towards training LLM agents that can reason and adapt through interaction in complex, unpredictable environments.

This research highlights the unique stability challenges posed by multi-turn RL and offers concrete strategies – like StarPO-S’s filtering and stabilisation techniques – to mitigate them. It also underscores the critical role of rollout generation strategies and the need for more sophisticated reward mechanisms to cultivate genuine reasoning, rather than superficial strategies or hallucinations.

While acknowledging limitations – including the need to test on larger models and optimise for domains without easily verifiable rewards – the work opens “a scalable and principled path for building AI systems” in areas demanding complex interaction and verifiable outcomes, such as theorem proving, software engineering, and scientific discovery.

(Image by Gerd Altmann)

See also: How does AI judge? Anthropic studies the values of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[indigofyrebird]

The Library, the Big Sister and the Passcode

1,003 words

Warnings: sweetness!

Omega had begged and begged, pestering Nala Se until she caved. She had seen them in their tubes, learned their numbers, watched them grow. Watched from afar as they formed their close knit band of brothers. 

Physically, they were 3 years old, and she, Omega was 6. Her body was developing at a normal humanoid rate while theirs was accelerated, their purpose in life to grow quickly, to train, to fight. Already, they were starting to change from children to adolescents, personalities separating, becoming different from each other.

Maybe it was the fact that they were kept separate from the regular clones that drew her to them. From the beginning, they were labeled as different, like her. 

Omega waited for them as they finished an intensely physical training session, watching them rough house with each other after. She stood as they exited the training room, a confident smile on her face. 

"Hello!" she said, meeting the eyes of CT-9901 who stood at the front of the group of boys. His eyes were dark, intense, but not unfriendly. 

"Who're you?" CT-9904 said, leaning around his brother, his arms crossed over his chest. His child's voice was raspy, his tone apprehensive. CT-9901 nudged him back protectively. 

"I'm Omega," she said, kneeling down to be their height. She smiled at them. "Nala Se wants me to instruct you in the course of field medicine this afternoon. Come on, I'll show you the library." She stood, watching as they decided amongst themselves and followed her.

The brothers exchanged curious glances, following the older girl through unfamiliar hallways. 

Omega used her passcode to open the large doors, the room behind them enormous. CT-9902 stepped forward, adjusting the small goggles he wore as he gaped open mouthed at the information surrounding them. Row upon row, aisle upon aisle of stored data, lit dimly from behind, each section labeled according to its contents. 

"This...is...this is...." his small voice trailed off, unable to find the words for the sheer amount of knowledge before him. 

The data was passcode protected, he was disappointed to find, the older girl suddenly taking on a specific appeal. He watched with interest as she selected a few training manuals. 

Omega led them to a seating area and sat down in the center of a comfortable sofa. She tucked her legs up under her, kicking off her shoes first. CT-9901 sat to her right. He removed his boots, tucking his legs under himself at her side. His hair was longer than his brother's and it fell over his face. He moved it behind his ear absently.

CT-9904 sat to her left, his feet remaining booted, touching the floor. He watched her shuffling through the datapads and pretended he wasn't interested. 

CT-9903 sat on the floor at her feet, his back to the sofa. Omega looked down at him, smiling. She could tell by the wide eyes he had trained on her that she had his full attention. 

CT-9902 couldn't sit down. He stayed close but scanned through the titles in the large room, filing away his questions. Omega watched him pacing and let him be, turning to her lesson.

She read to them the details of injuries, of wound care, bandaging, bacta application. Of creature bites, of stings, of poisons, allergic reactions and their treatments. When she was finished, she adjusted her legs under her, sitting cross legged. CT-9901 adjusted his legs to sit cross legged beside her. 

CT-9904 had leaned closer to her and was resting his head against her shoulder. When she moved he sat up straight.

CT-9902 had listened intently while continuing his cataloging of the room. 

Omega looked around at the guarded entrance and took a new, larger datapad from her stack. The pad had pictures, drawings of animals on it. She read from the page, her voice softer than it had been. She had found this datapad accidentally on her own one day and had come back several times to re read it. 

It was a story of mythical creatures, some half human, some entirely animal. A story about a door to another world. A story of good, of evil, of witches and magic. She spoke and they listened, her voice and her words enthralling. 

CT-9904 forgot to sit straight, leaning against her again. He chewed his lip as she read about the evil, the darkness. When she read of the boy who was turned from the light he clutched her sleeve.

CT-9902 forgot about the information at his back and leaned behind her to listen closely.

CT-9903 never took his eyes off her, gazing at her with adoration. When she paused, he spoke. "And then?" Omega ruffled his hair and he laughed, beaming at her.

When she moved her legs again, feet going to the other side, CT-9901 did the same. He had leaned against her other shoulder and Omega had placed an arm around him. 

They watched the pictures as she scrolled, listening to the story. The afternoon moved to evening and their stomachs began to growl. Omega laughed. 

"It IS mess time." She turned the datapad off, the light on their faces dimming. She moved to stand and CT-9904 clutched at her briefly before letting go. 

"But...what happens next?" CT-9902 asked. 

"We can finish it tomorrow." Omega said, looking down at their small, hopeful faces. CT-9901, who hadn't said a word yet, opened his mouth and closed it again shyly, his eyes watching her solemnly. She brushed his hair behind an ear, out of his eyes and he blushed.

Omega hoped she was telling them the truth, hoped Nala Se would allow her to continue their lessons with her. 

Before she led them out, she knelt, pressing a spare passcode into the hands of CT-9902. 

"This is excellent," he said, putting it away safely. 

Four small boys followed the older girl through the halls until Nala Se intercepted them, stealing Omega away. 

Four pairs of eyes watched her go and hoped they would see her again

[nitor-infotech]

Embark on a journey into Prompt Engineering, a nuanced art form poised to redefine the future of AI interactions. This transformative force empowers businesses to craft precise queries, unlocking applications in content creation and code generation through the prowess of GenAI. Delve into the intricacies of vulnerabilities and discover best practices, ensuring ethical use and optimal results.

Explore how this dynamic interplay between human intent and machine understanding shapes a landscape where AI seamlessly enriches experiences and capabilities in this blog. 

[venelona-turtle-den]

Ghost Future Leonardo FAQ

(...I'll get a better header picture later. Maybe.)

(Get your own Desktop Old Turtle Man here)

Since I keep getting repeated questions, I've decided to make a FAQ where I can put all explanations to and link to it 👉👉

❓How do I keep Leo on top of my screen applications?

If you want him to stay on top of your windows, right click him, then go to Options - Preferences, then the Ghost(2) tab in the big menu that pops up. Check "Always Show Foreground" and that should do it.

❓Is there any screenshots on how I can install Leo?

Check out this post, it describes how to install Leo, screenshots included

❓Does Leo work on Chromebook, IOS, newer Android, etc? Will he ever?

No. I can't personally make him work on other platforms - unless the developers of the software (called SSP) will make SSP work on those platforms, Leo won't be able to work there.

❓How do I talk to Leo? How do I ask him a question? How do I pet him?

After installing Leo, if you want to interact with him just double click him - anywhere but his face. Right click on him calls more generic options menu.

To pet him, stroke the top of his head with your mouse, no clicking. If you want to know more general area, look at this gif here.

❓Download link for Leonardo doesn't seem to be working, what should I do?

Since the download link in my post leads to the website I made on neocities, sometimes website doesn't work, but you should be able to install Leo straight from the Google Drive here. You can see instructions linked in the second question of this post.

❓My data doesn't save - every time I open Leo, the introduction plays again, what should I do?

Most of the time it means that the files have been installed incorrectly, and folders are not where they should be.

Here's the files you should have in your 'ghost' folder (the location of which will most probably be your 'downloads' folder), and files you should have there

For the first 'ghost' folder, unless you installed desktop ghosts before you will only have 'Future Leonardo' and 'emily4' folders

Inside the 'shell' folder you should have 3 folders - 'Taileo', 'Neon Leon' and 'Chin Stripes', and they all contain lots of pngs and a couple of txt files

Don't worry about 'profile' folder, I believe it just saves the data of ghosts's coordinates on your screen in case you move them around

It might be a problem with a save file - just make sure you don't move 'aya5_variable.cfg' from the 'master' folder, because that's an actual file that contains all the save data.

❓Will you ever make more TMNT ghosts? Other brothers?

No. I have enough stuff I still wish to implement to Leo, and even if I made any other turtle, the overall experience wouldn't be much different. But don't let your dreams be dreams, you can always try and make your beloved turtle live on your desktop yourself! English Desktop Ghost devs community is so lovely, they're always happy to help both new and seasoned devs 💖✨

❓I still have a question!

You're always welcome to send me a question. I might take time responding (perhaps a lot of time...), but if you send it straight to DMs I usually reply within a day. So, DM this blog or @venelona for urgent questions 💌