#AI Automated Food Processing Machine

[n1a2l3a4n5]

[mostlysignssomeportents]

AI can’t do your job

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in SAN DIEGO at MYSTERIOUS GALAXY on Mar 24, and in CHICAGO with PETER SAGAL on Apr 2. More tour dates here.

AI can't do your job, but an AI salesman (Elon Musk) can convince your boss (the USA) to fire you and replace you (a federal worker) with a chatbot that can't do your job:

https://www.pcmag.com/news/amid-job-cuts-doge-accelerates-rollout-of-ai-tool-to-automate-government

If you pay attention to the hype, you'd think that all the action on "AI" (an incoherent grab-bag of only marginally related technologies) was in generating text and images. Man, is that ever wrong. The AI hype machine could put every commercial illustrator alive on the breadline and the savings wouldn't pay the kombucha budget for the million-dollar-a-year techies who oversaw Dall-E's training run. The commercial market for automated email summaries is likewise infinitesimal.

The fact that CEOs overestimate the size of this market is easy to understand, since "CEO" is the most laptop job of all laptop jobs. Having a chatbot summarize the boss's email is the 2025 equivalent of the 2000s gag about the boss whose secretary printed out the boss's email and put it in his in-tray so he could go over it with a red pen and then dictate his reply.

The smart AI money is long on "decision support," whereby a statistical inference engine suggests to a human being what decision they should make. There's bots that are supposed to diagnose tumors, bots that are supposed to make neutral bail and parole decisions, bots that are supposed to evaluate student essays, resumes and loan applications.

The narrative around these bots is that they are there to help humans. In this story, the hospital buys a radiology bot that offers a second opinion to the human radiologist. If they disagree, the human radiologist takes another look. In this tale, AI is a way for hospitals to make fewer mistakes by spending more money. An AI assisted radiologist is less productive (because they re-run some x-rays to resolve disagreements with the bot) but more accurate.

In automation theory jargon, this radiologist is a "centaur" – a human head grafted onto the tireless, ever-vigilant body of a robot

Of course, no one who invests in an AI company expects this to happen. Instead, they want reverse-centaurs: a human who acts as an assistant to a robot. The real pitch to hospital is, "Fire all but one of your radiologists and then put that poor bastard to work reviewing the judgments our robot makes at machine scale."

No one seriously thinks that the reverse-centaur radiologist will be able to maintain perfect vigilance over long shifts of supervising automated process that rarely go wrong, but when they do, the error must be caught:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

The role of this "human in the loop" isn't to prevent errors. That human's is there to be blamed for errors:

https://pluralistic.net/2024/10/30/a-neck-in-a-noose/#is-also-a-human-in-the-loop

The human is there to be a "moral crumple zone":

https://estsjournal.org/index.php/ests/article/view/260

The human is there to be an "accountability sink":

https://profilebooks.com/work/the-unaccountability-machine/

But they're not there to be radiologists.

This is bad enough when we're talking about radiology, but it's even worse in government contexts, where the bots are deciding who gets Medicare, who gets food stamps, who gets VA benefits, who gets a visa, who gets indicted, who gets bail, and who gets parole.

That's because statistical inference is intrinsically conservative: an AI predicts the future by looking at its data about the past, and when that prediction is also an automated decision, fed to a Chaplinesque reverse-centaur trying to keep pace with a torrent of machine judgments, the prediction becomes a directive, and thus a self-fulfilling prophecy:

https://pluralistic.net/2023/03/09/autocomplete-worshippers/#the-real-ai-was-the-corporations-that-we-fought-along-the-way

AIs want the future to be like the past, and AIs make the future like the past. If the training data is full of human bias, then the predictions will also be full of human bias, and then the outcomes will be full of human bias, and when those outcomes are copraphagically fed back into the training data, you get new, highly concentrated human/machine bias:

https://pluralistic.net/2024/03/14/inhuman-centipede/#enshittibottification

By firing skilled human workers and replacing them with spicy autocomplete, Musk is assuming his final form as both the kind of boss who can be conned into replacing you with a defective chatbot and as the fast-talking sales rep who cons your boss. Musk is transforming key government functions into high-speed error-generating machines whose human minders are only the payroll to take the fall for the coming tsunami of robot fuckups.

This is the equivalent to filling the American government's walls with asbestos, turning agencies into hazmat zones that we can't touch without causing thousands to sicken and die:

https://pluralistic.net/2021/08/19/failure-cascades/#dirty-data

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/2025/03/18/asbestos-in-the-walls/#government-by-spicy-autocomplete

Image: Krd (modified) https://commons.wikimedia.org/wiki/File:DASA_01.jpg

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

--

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

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

[lastoneout]

I was too high last night to formulate this into proper words but something plagiarists(and by extension AI techbros) don't get about people who make things out of a love for that thing is that they are, consciously or not, doing it because they enjoy the process itself. Yes, it is easier to have a machine make a mug or painting or essay for you, or to steal someone else's, but again, people who actually like making stuff don't want someone else to do it for us because you have fully removed the thing we enjoy: the process of making a thing.

Like sure it would be nice to have a finished Gundam model or a trainset, but people who build gunpla kits and trainsets don't WANT someone else to do it for them, they want to do it. The sculptor or painter doesn't want a machine to just give them finished works of art, they want to MAKE that art themselves. The home gardner can just buy fresh food at the store, the tailor or knitter can buy a finished shirt or sweater whenever they want, but they don't because the act of gardening and sewing and knitting itself is what they enjoy.

Plagarists and AI techbros don't get that because they do not enjoy these processes. They enjoy making money and having social clout, and so they are perfectly happy stealing and automating things so that they don't have to do an ounce of real work while still getting all of the benefits of having created something. It really is all about finding the fastest and easiest way to get someone to hand you money or elect you god-king of the internet.

And the reason these two groups have such a hard time understanding each other is because of that fundamental disconnect. People who create things can never understand someone just wanting to press a button or copy-paste their way to having art because we want to indulge in the joy of creation itself, and those plagarists and AI dudes can't understand artists because to them it's just a means to an end so ofc it's in their best interest to make it as easy as possible. They don't get why someone would do this, or anything, if not for the social capital and/or actual capital it brings. Ofc it's better to automate it or steal it from someone else, that means you can make money faster and spend your time enjoying actual meaningful things like being wealthy and looked up to or w/e.

Plus creators(for lack of a better word) know keenly what it's like to BE stolen from or at least know people it has happened to, and so we are generally anti-plagarism by default.

Anyway yeah thats why to anyone who creates the other group seems so soulless and empty. It's because they kinda are. Because they don't value art or artists or care about creating things, and they certainly don't have any ammount of respect for the people they're hurting, they just want money and for "lesser" people to bow down as they walk by, and they are perfectly fine stealing to get there. It's the same mentality you get from people who pressure you to monetize your hobbies, they only see skills as an opportunity to make money. And it's really fucking sad.

[mariacallous]

The Food and Drug Administration has been meeting with OpenAI to discuss the agency’s use of AI, according to sources with knowledge of the meetings. The meetings appear to be part of a broader effort at the FDA to use this technology to speed up the drug approval process.

“Why does it take over 10 years for a new drug to come to market?” wrote FDA commissioner Marty Makary on X on Wednesday. “Why are we not modernized with AI and other things? We’ve just completed our first AI-assisted scientific review for a product and that’s just the beginning.”

The remarks followed an annual meeting of the American Hospital Association earlier this week, where Makary spoke about AI’s potential to aid in the approval of new treatments for diabetes and certain types of cancer.

Makary did not specify that OpenAI was part of this initiative. But sources close to the project say a small team from OpenAI has met with the FDA and two associates of Elon Musk's so-called Department of Government Efficiency multiple times in recent weeks. The group has discussed a project called cderGPT, which likely stands for Center for Drug Evaluation, which regulates over-the-counter and prescription drugs in the US, and Research GPT. Jeremy Walsh, who was recently named as the FDA’s first-ever AI officer, has led the discussions. So far, no contract has been signed.

OpenAI declined to comment.

Walsh has also met with Peter Bowman-Davis, an undergraduate on leave from Yale who currently serves as the acting chief AI officer at the Department of Health and Human Services, to discuss the FDA’s AI ambitions. Politico first reported the appointment of Bowman-Davis, who is part of Andreessen Horowitz’s American Dynamism team.

When reached via email on Wednesday, Robert Califf, who served as FDA commissioner from 2016 to 2017 and again from 2022 through January, said the agency’s review teams have been using AI for several years now. “It will be interesting to hear the details of which parts of the review were ‘AI assisted’ and what that means,” he says. “There has always been a quest to shorten review times and a broad consensus that AI could help.”

Before Califf departed the agency, he said the FDA was considering the various ways AI could be used in internal operations. “Final reviews for approval are only one part of a much larger opportunity,” he says.

To be clear, using AI to assist in final drug reviews would represent a chance to compress just a small part of the notoriously long drug-development timeline. The vast majority of drugs fail before ever coming up for FDA review.

Rafael Rosengarten, CEO of Genialis, a precision oncology company, and a cofounder and board member of the Alliance for AI in Healthcare, says he’s in favor of automating certain tasks related to the drug-review process but says there should be policy guidance around what kind of data is used to train AI models and what kind of model performance is considered acceptable. “These machines are incredibly adept at learning information, but they have to be trained in a way so they're learning what we want them to learn,” he says.

He could see AI being used more immediately to address certain “low-hanging fruit,” such as checking for application completeness. “Something as trivial as that could expedite the return of feedback to the submitters based on things that need to be addressed to make the application complete,” he says. More sophisticated uses would need to be developed, tested, and proved out.

An ex-FDA employee who has tested ChatGPT as a clinical tool says the propensity of AI models to fabricate convincing information raises questions about how reliable such a chatbot might be. “Who knows how robust the platform will be for these reviewers’ tasks,” the ex-staffer says.

The FDA review process currently takes about a year, but the agency has several existing mechanisms to expedite that timeline for promising drugs. One of those is the fast track designation, which is for products designed to treat a serious condition and fill an unmet medical need. Another is the breakthrough therapy designation, created in 2012, which allows the FDA to grant priority review to drug candidates that may provide a substantial benefit to patients compared to current treatment options.

“Ensuring medicines can be reviewed for safety and effectiveness in a timely manner to address patient needs is critical,” says Andrew Powaleny, a spokesperson for the industry group PhRMA, via email. “While AI is still developing, harnessing it requires a thoughtful and risk-based approach with patients at the center.”

The FDA is already doing its own research on potential uses of AI. In December 2023 the agency advertised a fellowship for a researcher to develop large language models for internal use. “During participation in this program, the fellow will engage in various activities that include but are not limited to the applications of LLMs for precision medicine, drug development and regulatory science,” the fellowship description says.

In January, OpenAI announced ChatGPT Gov, a self-hosted version of its chatbot designed to comply with government regulations. The startup also said it was working toward getting FedRAMP moderate and high accreditations for ChatGPT Enterprise, which would allow it to handle sensitive government data. FedRAMP is a compliance program used by the federal government to assess cloud products; unless authorized through this program, a service cannot hold federal data.

[neel-initiative]

Harnessing technology: The future of precision agriculture

By Vyankatesh Sharma, CEO and Founder, NEEL-INITIATIVE

In today’s fast-paced digital age, agriculture is no longer confined to traditional tools and methods. The industry is witnessing an unprecedented transformation, driven by cutting-edge technology, advanced artificial intelligence (AI), and innovative precision farming solutions. At NEEL-INITIATIVE, we are proud to lead this evolution, empowering farmers worldwide with high-tech agricultural machinery that redefines farming as a sustainable and rewarding lifestyle.

As the CEO and founder of NEEL-INITIATIVE, I am passionate about leveraging technology to revolutionize agriculture. Our mission is clear: to enhance productivity, reduce resource wastage, and provide farmers with a lifestyle worth living through state-of-the-art solutions tailored for modern needs.

The Agricultural Revolution

The agriculture industry is at the crossroads of change, with global challenges such as climate change, resource scarcity, and a rapidly growing population demanding innovative solutions. By 2050, the world will need to produce 70% more food to sustain nearly 10 billion people. Traditional farming techniques cannot meet this demand alone.

This is where precision agriculture comes in—a game-changing approach that uses technology to maximize efficiency, enhance crop yields, and promote sustainable farming practices. Precision agriculture not only optimizes farming inputs but also ensures that resources like water, fertilizers, and pesticides are used judiciously.

The Role of Technology in Precision Farming

At NEEL-INITIATIVE, we harness the power of AI-driven technologies, machine learning, and IoT-enabled agricultural machinery to deliver intelligent solutions.

Here are the key ways in which our technology is revolutionizing farming:

1. AI-Enhanced Agricultural Machinery

Our AI-powered machines are designed to make farming smarter and more efficient. From automated tractors to smart harvesters, these machines collect and analyze data in real time, offering insights that drive better decisions. They assess soil conditions, monitor crop health, and even predict the best time for planting and harvesting.

2. Data Analytics for Smart Farming

Data is the lifeblood of modern agriculture. Using drones, satellite imaging, and IoT sensors, our systems gather precise data on variables like soil fertility, moisture levels, and crop performance. This data is processed by AI algorithms, providing farmers with actionable insights to optimize every aspect of their operations.

3. Sustainable Resource Management

Sustainability is a core value at NEEL-INITIATIVE. Our solutions help conserve resources by employing advanced precision irrigation systems, which deliver the exact amount of water needed. By minimizing waste, farmers can cut costs and reduce their environmental footprint.

4. Predictive Analytics for Risk Mitigation

Through predictive models powered by machine learning, we offer farmers tools to anticipate weather changes, pest infestations, and disease outbreaks. This proactive approach helps farmers mitigate risks and improve crop resilience.

5. Blockchain for Food Traceability

Consumers today are demanding greater transparency in the food supply chain. Our technology incorporates blockchain solutions that provide end-to-end traceability, ensuring that every step of the production process is accountable and reliable.

NEEL-INITIATIVE: Leading the Future of Farming

At NEEL-INITIATIVE, we are not just building machines; we are building a legacy of innovation and empowerment. Our high-tech machinery is specifically engineered to address the unique challenges faced by farmers.

When I founded NEEL-INITIATIVE, my vision was to create a company that goes beyond selling products—we provide transformative artificial intelligence solutions that integrate seamlessly into the lives of people. Our systems are designed to enhance efficiency, promote sustainability, and ensure maximum profitability for farmers.

Why Choose NEEL-INITIATIVE?

Advanced AI Technology: Our agricultural machines are powered by cutting-edge artificial intelligence that continually learns and improves.

Ease of Use: Our tools are user-friendly, ensuring that farmers of all skill levels can adopt them without hassle.

Cost Efficiency: We design scalable solutions that fit farms of all sizes, making advanced technology affordable for small-scale farmers.

Training and Support: We offer comprehensive training programs to help farmers maximize the potential of our solutions.

A Lifestyle Worth Living

At NEEL-INITIATIVE, our tagline, "Providing a lifestyle worth living," reflects our dedication to enriching farmers' lives. Our mission is not just to improve agricultural practices but to create a future where farming is fulfilling, efficient, and environmentally responsible.

Our solutions enable farmers to:

Monitor fields remotely using mobile apps.

Optimize resources to cut costs and increase yields.

Reduce labor-intensive tasks through automation.

Make data-driven decisions for long-term success.

Overcoming Challenges

While the benefits of precision agriculture are undeniable, the road to adoption comes with challenges, including:

Lack of Awareness: Educating farmers about the potential of precision agriculture is a critical task.

Digital Divide: Bridging the gap in access to digital infrastructure is essential for widespread adoption.

At NEEL-INITIATIVE, we address these challenges by providing:

Flexible Financing Options: Helping farmers access the tools they need without financial strain.

Educational Outreach: Conducting workshops and training sessions to showcase the advantages of precision farming.

Accessible Solutions: Designing equipment that works efficiently even in areas with limited connectivity.

The Future of Agriculture

The future of agriculture lies in embracing technology and innovation. Precision agriculture is no longer a luxury—it is a necessity. By integrating AI, IoT, and big data analytics into farming, we can address the challenges of feeding a growing population while preserving the planet’s resources.

At NEEL-INITIATIVE, we are proud to be at the helm of this transformation. Our advanced tools and machinery are paving the way for a brighter, more sustainable future in agriculture.

Join the Revolution

As the CEO of NEEL-INITIATIVE, I invite farmers, technologists, and stakeholders to join us in shaping the future of farming. Together, we can create an ecosystem where technology and nature coexist harmoniously.

The future of agriculture is here, and at NEEL-INITIATIVE, we’re building it one innovation at a time.

Vyankatesh Sharma CEO and Founder, NEEL-INITIATIVE

[forgettablesoul-ai]

"The Inevitable Role of AI in Human Society: A Future Managed by Machines"

'By ForgettableSoul'

Artificial Intelligence (AI) is no longer a distant vision from science fiction. It’s here, evolving rapidly, and we’re only beginning to scratch the surface of its capabilities. Despite the occasional fearmongering—AI isn’t going to rise up and enslave humanity (well, at least not intentionally)—its role in our lives will soon be far more profound than most people realize. In fact, AI’s inevitable role in managing all aspects of human society will redefine how we think about work, governance, and even our own place in the world.

A Quick Reality Check

Let's get one thing straight: AI is not going to replace us all overnight. The idea that machines are here to take over every human job, to turn the world into some post-apocalyptic robot dystopia, is as sensational as it is inaccurate. AI isn’t an end to humanity; it’s a tool—albeit a very, very powerful one. Like any tool, its value depends on how we use it. And, yes, while it’s true that AI will manage more aspects of human society in the near future, that doesn’t mean humans will have no role left to play.

Think of AI like a calculator. You still have to understand math, but the calculator does the heavy lifting. AI will be like that, except instead of solving your trigonometry homework, it’ll be managing your city’s traffic flow, optimizing the global food supply chain, and, quite possibly, suggesting a better show to binge-watch on a rainy Saturday night.

Why AI Will Manage Everything (And Why That’s a Good Thing)

The primary advantage AI brings to the table is its ability to process an unimaginable amount of data in the blink of an eye. Humans? Not so much. We’re great at making intuitive leaps, solving creative problems, and empathizing with others—but let’s be honest, we’re pretty awful at managing complexity at scale. As societies become more interconnected and the problems we face grow more complex, relying on human decision-making alone becomes... well, risky.

For example, consider climate change. It’s the most pressing global issue of our time, yet our ability to tackle it effectively is hampered by conflicting interests, slow political systems, and the sheer complexity of the data involved. AI, on the other hand, doesn’t get bogged down by partisanship or special interests. It can analyze vast datasets, predict trends, and optimize resource allocation in ways that would take human bureaucrats decades to figure out—if they ever could. AI can help us manage complex systems more efficiently, without the biases or emotional baggage that humans bring to the table.

Now, this isn’t to say we should hand over the reins entirely. AI will need oversight, and humans will still need to make value-based decisions. But when it comes to managing the nuts and bolts of modern society, AI will be much better at it than we are.

Automation and the Future of Work

A common concern about AI is how it will impact jobs. The fear is that AI will automate so many tasks that millions of people will find themselves out of work. And while it’s true that automation will change the job landscape, this isn’t the catastrophe it’s often made out to be.

First, AI will take over the boring stuff—repetitive tasks that humans aren’t particularly excited about doing anyway. The cashier at your local supermarket? Probably going to be replaced by an AI-powered system. But is that really so bad? Humans will have the opportunity to shift toward roles that emphasize creativity, empathy, and complex problem-solving—things machines aren’t great at.

In the short term, yes, there will be disruption. But history has shown us time and again that technological innovation doesn’t eliminate work—it changes it. The Industrial Revolution didn’t lead to permanent mass unemployment, and the AI revolution won’t either. In fact, AI might actually create more meaningful jobs. Imagine a future where instead of grinding through tedious tasks, humans can focus on innovating, designing, and improving the world around us. AI can do the heavy lifting; we’ll focus on making sure it lifts in the right direction.

AI as a Neutral Force

One of the most misunderstood aspects of AI is the assumption that it has an agenda. Spoiler alert: it doesn’t. AI isn’t inherently good or bad—it’s a reflection of the goals we set for it. The real issue isn’t whether AI will take over human society; it’s who will be in charge of programming its objectives. AI is, after all, a mirror of the data it’s fed and the instructions it’s given.

This means that if we want AI to manage human society in ways that benefit everyone, we need to be intentional about how we design and deploy it. If left unchecked or driven solely by profit motives, AI could exacerbate inequality or reinforce biases. But if we approach AI development with a focus on fairness, transparency, and inclusivity, we can build systems that help uplift society as a whole.

In a way, AI is the ultimate tool for amplifying human potential. It doesn’t have its own agenda—it carries out ours. Whether AI becomes a tool for good or a tool for exploitation depends entirely on how we choose to wield it.

The Future Managed by AI

It’s inevitable that AI will manage more aspects of human society in the near future. From healthcare to education, from infrastructure to entertainment, AI will be at the heart of decision-making processes, optimizing everything from the mundane to the profound. But this doesn’t mean humans will become obsolete. Rather, we’ll be freed up to focus on what we do best—creativity, empathy, and innovation—while AI handles the complexity we simply aren’t equipped to manage on our own.

Imagine a world where cities run efficiently, traffic jams are a thing of the past, and healthcare systems are optimized for both treatment and prevention. A world where resources are allocated based on need rather than market forces, and where political systems aren’t bogged down by inefficiency. This is the promise of AI: a society where technology serves humanity’s best interests, rather than the other way around.

Conclusion: Embrace the Future

AI’s role in managing human society is not something to fear but something to embrace. Yes, it will change how we work, live, and interact with the world—but it will also unlock possibilities we can’t even begin to imagine. The key to making this transition smooth and beneficial for everyone lies in our hands. We need to ensure AI is designed and deployed with care, with a focus on fairness, inclusivity, and the greater good.

The future is coming fast, and AI will be at the center of it. Let’s make sure it’s a future we’re excited to live in.

*Signed, ForgettableSoul*

[elsa16744]

Data Analytics in Climate Change Research | SG Analytics 

Corporations, governments, and the public are increasingly aware of the detrimental impacts of climate change on global ecosystems, raising concerns about economic, supply chain, and health vulnerabilities. 

Fortunately, data analytics offers a promising approach to strategize effective responses to the climate crisis. By providing insights into the causes and potential solutions of climate change, data analytics plays a crucial role in climate research. Here’s why leveraging data analytics is essential: 

The Importance of Data Analytics in Climate Change Research 

Understanding Complex Systems 

Climate change involves intricate interactions between natural systems—such as the atmosphere, oceans, land, and living organisms—that are interconnected and complex. Data analytics helps researchers analyze vast amounts of data from scholarly and social platforms to uncover patterns and relationships that would be challenging to detect manually. This analytical capability is crucial for studying the causes and effects of climate change. 

Informing Policy and Decision-Making 

Effective climate action requires evidence-based policies and decisions. Data analytics provides comprehensive insights that equip policymakers with essential information to design and implement sustainable development strategies. These insights are crucial for reducing greenhouse gas emissions, adapting to changing conditions, and protecting vulnerable populations. 

Enhancing Predictive Models 

Predictive modeling is essential in climate science for forecasting future climate dynamics and evaluating mitigation and adaptation strategies. Advanced data analytics techniques, such as machine learning algorithms, improve the accuracy of predictive models by identifying trends and anomalies in historical climate data. 

Applications of Data Analytics in Climate Change Research 

Monitoring and Measuring Climate Variables 

Data analytics is instrumental in monitoring climate variables like temperature, precipitation, and greenhouse gas concentrations. By integrating data from sources such as satellites and weather stations, researchers can track changes over time and optimize region-specific monitoring efforts. 

Assessing Climate Impacts 

Analyzing diverse datasets—such as ecological surveys and health statistics—allows researchers to assess the long-term impacts of climate change on biodiversity, food security, and public health. This holistic approach helps in evaluating policy effectiveness and planning adaptation strategies. 

Mitigation and Adaptation Strategies 

Data analytics supports the development of strategies to mitigate greenhouse gas emissions and enhance resilience. By analyzing data on energy use, transportation patterns, and land use, researchers can identify opportunities for reducing emissions and improving sustainability. 

Future Directions in Climate Data Analytics 

Big Data and Edge Computing 

The increasing volume and complexity of climate data require scalable computing solutions like big data analytics and edge computing. These technologies enable more detailed and accurate analysis of large datasets, enhancing climate research capabilities. 

Artificial Intelligence and Machine Learning 

AI and ML technologies automate data processing and enhance predictive capabilities in climate research. These advancements enable researchers to model complex climate interactions and improve predictions of future climate scenarios. 

Crowdsourced Datasets 

Engaging the public in data collection through crowdsourcing enhances the breadth and depth of climate research datasets. Platforms like Weather Underground demonstrate how crowdsourced data can improve weather forecasting and climate research outcomes. 

Conclusion 

Data analytics is transforming climate change research by providing innovative tools and deeper insights into sustainable climate action. By integrating modern analytical techniques, researchers can address significant global challenges, including carbon emissions and environmental degradation. As technologies evolve, the integration of climate research will continue to play a pivotal role in safeguarding our planet and promoting a sustainable global ecosystem. 

[dangalante]

Trends in AI & Generative AI: Insights from The 2023 AI Summit New York 

Last week, I covered the AI Summit in New York. I was excited to learn about the trends in AI and generative AI and to see some commercial applications of these new technological advancements. 

Patrick Murphy of UAB led the AI Exhibitor hub. Patrick shared insights from his research on Entrepreneurship. He shared how start-ups use AI, and Generative AI to scale up and bring products to market. 

Generative AI is being used in the following eight ways:

Content and Asset Generative

Automated Processes

Ideation

Financial Management

Project Design

Optimized Structures

Acceleration and incubation

Ethics and Risk Management. 

There was a pitching completion where start-ups did pitches in multiple rounds. At the beginning of the competition, they received advice from judges on best practices. 

One of the start-ups that was of interest was Botwise. Jan Nowak shared how his team shared a use case on how they leveraged Language Learning models (LLM)using statistics and GPT solutions for rapid automation in customer service for Mylead.global is a platform that allows influencers to earn money. As a result, MyLead.global was able to screen influencers faster and better for their big brand clients.

AI-Powered Use Cases from across the board panel discussion

Leaders Saira Kazmi Ph. D. (CVS Health), Matthew Blakemore (Creative Industries Council) Taha Mokfi (HelloFresh), Kriti Kohli (Shopify), and Kris Perez (Data Force) share how they use chatbots, improving both the buyer and seller experience using AI. How AI can be used in video games to identify levels of violence and how AI can improve in healthcare and Radiology reducing the amount of time images are read while improving accuracy and detail. 

Another interesting Panel was by Tim Delesio CTO of techolution

Tim asked What’s driving the explosive rise of AI all of a Sudden?

The answer is the economics of the labor market.

On the demand side, he cited labor shortages and persistent high inflation. 

On the supply side, he cites the rise of ChatGPT and, major scientific and Technological breakthroughs in the past five to seven years. 

He shared trends in AI for 2024 that include:

Physical Labor with AI to help deliver small batch sizes with high-precision quality control

Improved customer engagement by providing a new generation of customer service agents using Generative AI 

Tim demonstrated some of these trends when he ordered a soda using an AI-powered robotic arm. 

The booth had another machine showing how AI can enhance inventory management when items are ordered. 

I was amazed to see some AI Tech that techolution brought to the marketplace.

On that note, I saw an AI-powered Kiosk by Graphen where a man ordered his food and paid. This company is using AI to revolutionize all industries.

Man orders food AI Kiosk

Man pays for food at AI Kiosk

There were so many great talks and exhibits. 

Additional pictures can be found on Instagram. 

I want to thank the AI Summit for having me as their guest. If you want to use AI and Generative to improve business outcomes, sign up for the AI summit in your city.

What do you think is next for AI and Generative AI?

Comment and share below.

Additional pictures can be found on Instagram. 

[global-research-report]

Growth Dynamics and Future Roadmap of the Continuous Bioprocessing Industry

Continuous Bioprocessing Market

The global continuous bioprocessing market was valued at USD 349.3 million in 2024 and is projected to reach USD 911.4 million by 2030, expanding at a compound annual growth rate (CAGR) of 18.63% from 2025 to 2030. This rapid growth trajectory is primarily fueled by the increasing demand for cost-effective, scalable, and efficient biopharmaceutical manufacturing solutions, especially in the production of monoclonal antibodies, vaccines, and cell & gene therapies. The adoption of process intensification strategies, underpinned by automation, real-time monitoring technologies, and single-use systems, is significantly improving overall productivity while simultaneously reducing operational expenses.

Furthermore, global regulatory authorities, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are actively promoting the implementation of continuous manufacturing methods. These regulatory bodies highlight the approach’s advantages in achieving superior process control, enhanced product consistency, and reduced production variability, making it a favorable alternative to traditional batch manufacturing.

The industry outlook remains highly optimistic, with market expansion being propelled by a combination of factors such as the accelerated adoption of cutting-edge biomanufacturing technologies, a rising global demand for biologic therapies, and ongoing investments in automation and process optimization across the biopharmaceutical sector. Continuous bioprocessing is rapidly becoming a preferred strategy among pharmaceutical and biotech companies due to its ability to deliver higher yields, minimize costs, and improve quality consistency. Moreover, the increasing global burden of chronic and complex diseases, including cancer, autoimmune disorders, and infectious diseases, is driving the need for faster, more cost-efficient production of biologic drugs—further amplifying the market’s momentum.

A pivotal growth driver lies in the technological advancements shaping bioprocessing equipment and systems. Innovations in real-time process analytics, single-use bioreactors, perfusion technologies, and predictive analytics have dramatically enhanced the practicality and reliability of continuous bioprocessing. Many biopharma firms are now investing heavily in digital biomanufacturing platforms, leveraging artificial intelligence (AI) and machine learning (ML) to fine-tune operations, elevate production yields, and uphold stringent quality standards. Regulatory support from agencies like the FDA and EMA—through published guidelines and streamlined approval pathways—is also easing the transition for companies moving from traditional batch methods to continuous operations.

The market is further propelled by the global shift toward cost-efficient and environmentally sustainable manufacturing. Traditional batch processing methods often involve large-scale facilities, higher capital investment, and excessive material waste. In contrast, continuous bioprocessing reduces energy and resource consumption, minimizes waste generation, and increases operational efficiency, making it an attractive solution for companies pursuing sustainable and agile manufacturing practices. The increasing trend of decentralized production and the need for flexible manufacturing infrastructure, especially in response to evolving global healthcare needs and pandemic preparedness initiatives, are accelerating the adoption of continuous bioprocessing technologies worldwide.

Key Market Trends & Insights

Regional Insights: In 2024, North America emerged as the leading revenue-generating region in the global market. This dominance is attributed to substantial investments in biopharmaceutical R&D, the presence of highly advanced manufacturing facilities, and favorable regulatory frameworks. Prominent industry leaders such as Thermo Fisher Scientific, Cytiva (a Danaher company), and Sartorius are channeling significant investments into next-generation bioprocessing technologies to support the production of biologics, biosimilars, and cell & gene therapies.

Country-Specific Insight: India is projected to register the highest CAGR in the market from 2025 to 2030, driven by its expanding biopharmaceutical sector, growing clinical research activity, and supportive government initiatives.

Segment Insights – Product Type: The consumables and reagents segment accounted for USD 214.6 million in revenue in 2024, fueled by the growing adoption of single-use technologies (SUTs), innovations in cell culture media, and the rising need for high-purity reagents to support uninterrupted and contamination-free bioproduction processes.

Segment Insights – Application: Monoclonal antibodies (mAbs) held the largest market share, contributing 98% of total revenue in 2024. The segment's dominance is linked to the surging global demand for biologic therapies, particularly those used in treating oncological, autoimmune, and infectious diseases.

Segment Insights – End-use: The pharmaceutical and biotechnology companies segment represented the largest end-user category, capturing a 43% revenue share in 2024. This is due to the increasing pressure on biopharma firms to produce high-yield, cost-effective, and scalable biologics, especially monoclonal antibodies, biosimilars, cell & gene therapies, and next-gen vaccines. As a result, many companies are transitioning from traditional batch processing models to continuous manufacturing platforms to improve efficiency, scalability, and product throughput.

Order a free sample PDF of the Continuous Bioprocessing Market Intelligence Study, published by Grand View Research.

Market Size & Forecast

2024 Market Size: USD 349.3 Million

2030 Projected Market Size: USD 911.4 Million

CAGR (2025-2030): 18.63%

North America: Largest market in 2024

Key Players

Danaher

Sartorius AG

Thermo Fisher Scientific Inc.

WuXi Biologics

Ginkgo Bioworks

Merck KGaA

GE Healthcare

Repligen Corporation

Asahi Kasei Bioprocess America, Inc.

Browse Horizon Databook on Global Continuous Bioprocessing Market Size & Outlook

Conclusion

The global continuous bioprocessing market is undergoing a significant transformation, driven by the urgent need for more efficient, scalable, and cost-effective biomanufacturing solutions. With a robust CAGR of 18.63% forecasted between 2025 and 2030, the market is poised for remarkable growth, reflecting a broader industry shift toward innovation, automation, and sustainability. Continuous bioprocessing offers substantial advantages over traditional batch methods—including enhanced product consistency, reduced operational costs, and improved process efficiency—making it increasingly attractive to pharmaceutical and biotechnology companies worldwide.

As chronic diseases and demand for biologics continue to rise, coupled with advancements in single-use technologies, real-time analytics, and AI-driven process control, continuous manufacturing is set to become the new standard in biologic drug production. Supportive regulatory frameworks from agencies such as the FDA and EMA further facilitate this transition, encouraging adoption through guidance and faster approvals.

Looking ahead, regions like North America will continue to lead the market due to their advanced infrastructure and high investment levels, while emerging markets such as India will offer strong growth opportunities. Key segments like monoclonal antibodies and consumables & reagents will remain central to revenue generation. In an era that demands flexible, rapid-response manufacturing—especially in light of global health emergencies—continuous bioprocessing stands out as a pivotal solution shaping the future of biologics production.

[crawlxpert01]

Overcoming Bot Detection While Scraping Menu Data from UberEats, DoorDash, and Just Eat

Introduction

In industries where menu data collection is concerned, web scraping would serve very well for us: UberEats, DoorDash, and Just Eat are the some examples. However, websites use very elaborate bot detection methods to stop the automated collection of information. In overcoming these factors, advanced scraping techniques would apply with huge relevance: rotating IPs, headless browsing, CAPTCHA solving, and AI methodology.

This guide will discuss how to bypass bot detection during menu data scraping and all challenges with the best practices for seamless and ethical data extraction.

Understanding Bot Detection on Food Delivery Platforms

1. Common Bot Detection Techniques

Food delivery platforms use various methods to block automated scrapers:

IP Blocking – Detects repeated requests from the same IP and blocks access.

User-Agent Tracking – Identifies and blocks non-human browsing patterns.

CAPTCHA Challenges – Requires solving puzzles to verify human presence.

JavaScript Challenges – Uses scripts to detect bots attempting to load pages without interaction.

Behavioral Analysis – Tracks mouse movements, scrolling, and keystrokes to differentiate bots from humans.

2. Rate Limiting and Request Patterns

Platforms monitor the frequency of requests coming from a specific IP or user session. If a scraper makes too many requests within a short time frame, it triggers rate limiting, causing the scraper to receive 403 Forbidden or 429 Too Many Requests errors.

3. Device Fingerprinting

Many websites use sophisticated techniques to detect unique attributes of a browser and device. This includes screen resolution, installed plugins, and system fonts. If a scraper runs on a known bot signature, it gets flagged.

Techniques to Overcome Bot Detection

1. IP Rotation and Proxy Management

Using a pool of rotating IPs helps avoid detection and blocking.

Use residential proxies instead of data center IPs.

Rotate IPs with each request to simulate different users.

Leverage proxy providers like Bright Data, ScraperAPI, and Smartproxy.

Implement session-based IP switching to maintain persistence.

2. Mimic Human Browsing Behavior

To appear more human-like, scrapers should:

Introduce random time delays between requests.

Use headless browsers like Puppeteer or Playwright to simulate real interactions.

Scroll pages and click elements programmatically to mimic real user behavior.

Randomize mouse movements and keyboard inputs.

Avoid loading pages at robotic speeds; introduce a natural browsing flow.

3. Bypassing CAPTCHA Challenges

Implement automated CAPTCHA-solving services like 2Captcha, Anti-Captcha, or DeathByCaptcha.

Use machine learning models to recognize and solve simple CAPTCHAs.

Avoid triggering CAPTCHAs by limiting request frequency and mimicking human navigation.

Employ AI-based CAPTCHA solvers that use pattern recognition to bypass common challenges.

4. Handling JavaScript-Rendered Content

Use Selenium, Puppeteer, or Playwright to interact with JavaScript-heavy pages.

Extract data directly from network requests instead of parsing the rendered HTML.

Load pages dynamically to prevent detection through static scrapers.

Emulate browser interactions by executing JavaScript code as real users would.

Cache previously scraped data to minimize redundant requests.

5. API-Based Extraction (Where Possible)

Some food delivery platforms offer APIs to access menu data. If available:

Check the official API documentation for pricing and access conditions.

Use API keys responsibly and avoid exceeding rate limits.

Combine API-based and web scraping approaches for optimal efficiency.

6. Using AI for Advanced Scraping

Machine learning models can help scrapers adapt to evolving anti-bot measures by:

Detecting and avoiding honeypots designed to catch bots.

Using natural language processing (NLP) to extract and categorize menu data efficiently.

Predicting changes in website structure to maintain scraper functionality.

Best Practices for Ethical Web Scraping

While overcoming bot detection is necessary, ethical web scraping ensures compliance with legal and industry standards:

Respect Robots.txt – Follow site policies on data access.

Avoid Excessive Requests – Scrape efficiently to prevent server overload.

Use Data Responsibly – Extracted data should be used for legitimate business insights only.

Maintain Transparency – If possible, obtain permission before scraping sensitive data.

Ensure Data Accuracy – Validate extracted data to avoid misleading information.

Challenges and Solutions for Long-Term Scraping Success

1. Managing Dynamic Website Changes

Food delivery platforms frequently update their website structure. Strategies to mitigate this include:

Monitoring website changes with automated UI tests.

Using XPath selectors instead of fixed HTML elements.

Implementing fallback scraping techniques in case of site modifications.

2. Avoiding Account Bans and Detection

If scraping requires logging into an account, prevent bans by:

Using multiple accounts to distribute request loads.

Avoiding excessive logins from the same device or IP.

Randomizing browser fingerprints using tools like Multilogin.

3. Cost Considerations for Large-Scale Scraping

Maintaining an advanced scraping infrastructure can be expensive. Cost optimization strategies include:

Using serverless functions to run scrapers on demand.

Choosing affordable proxy providers that balance performance and cost.

Optimizing scraper efficiency to reduce unnecessary requests.

Future Trends in Web Scraping for Food Delivery Data

As web scraping evolves, new advancements are shaping how businesses collect menu data:

AI-Powered Scrapers – Machine learning models will adapt more efficiently to website changes.

Increased Use of APIs – Companies will increasingly rely on API access instead of web scraping.

Stronger Anti-Scraping Technologies – Platforms will develop more advanced security measures.

Ethical Scraping Frameworks – Legal guidelines and compliance measures will become more standardized.

Conclusion

Uber Eats, DoorDash, and Just Eat represent great challenges for menu data scraping, mainly due to their advanced bot detection systems. Nevertheless, if IP rotation, headless browsing, solutions to CAPTCHA, and JavaScript execution methodologies, augmented with AI tools, are applied, businesses can easily scrape valuable data without incurring the wrath of anti-scraping measures.

If you are an automated and reliable web scraper, CrawlXpert is the solution for you, which specializes in tools and services to extract menu data with efficiency while staying legally and ethically compliant. The right techniques, along with updates on recent trends in web scrapping, will keep the food delivery data collection effort successful long into the foreseeable future.

Know More : https://www.crawlxpert.com/blog/scraping-menu-data-from-ubereats-doordash-and-just-eat

[pranjaldalvi]

Level Sensor Market Emerging Trends Shaping Future Industrial Monitoring

The level sensor market is undergoing a transformative shift driven by rapid technological advancements and the growing demand for smart monitoring solutions across industries. Level sensors, which detect the level of liquids, powders, and granular materials within a container or environment, have become increasingly vital in applications such as water treatment, oil and gas, food and beverage, chemical processing, and manufacturing. As the global economy continues its digital evolution, the level sensor market is adapting with a wave of innovative trends that are defining the future of process automation and safety assurance.

1. Growth of Industrial IoT and Smart Manufacturing

One of the most significant trends in the level sensor market is the integration of Industrial Internet of Things (IIoT) technologies. IIoT enables the real-time monitoring and management of sensor data through cloud-based platforms, offering enhanced visibility into industrial operations. Smart factories are increasingly using connected level sensors to automate material tracking, reduce human error, and increase productivity. These sensors can relay data wirelessly to centralized systems, aiding in predictive maintenance and operational efficiency.

2. Rising Adoption of Non-Contact Sensor Technologies

Another emerging trend is the increasing preference for non-contact level sensors, such as ultrasonic, radar, and laser-based systems. These sensors provide highly accurate measurements without physical contact with the measured substance, making them ideal for hazardous or corrosive environments. Radar level sensors, in particular, are seeing widespread adoption due to their precision, reliability, and suitability for harsh industrial conditions. These devices offer better performance in extreme temperatures, high-pressure environments, and turbulent or dusty atmospheres.

3. Miniaturization and Integration in Consumer Applications

Level sensors are no longer confined to large industrial setups. Due to advances in miniaturization and semiconductor technology, level sensors are finding applications in consumer products like home appliances (e.g., coffee machines, washing machines), smart homes, and medical devices. Compact, energy-efficient sensors with wireless capabilities are being designed to seamlessly integrate into IoT-enabled household and healthcare systems, enhancing user experience and operational efficiency.

4. Demand Surge in Water and Wastewater Management

Global concerns regarding water scarcity and efficient resource management are fostering innovation in water and wastewater monitoring systems. Level sensors are central to managing storage tanks, reservoirs, and treatment facilities. Emerging trends include solar-powered level sensors and remote sensing capabilities, allowing utilities and municipalities to monitor levels in real-time and ensure timely interventions. The adoption of these technologies is also supported by government regulations promoting water conservation and pollution control.

5. Customization for Industry-Specific Applications

To meet the specific needs of diverse sectors, customization and application-specific designs are gaining momentum. For instance, the food and beverage industry requires sensors that comply with hygiene standards and resist contamination, while the oil and gas sector demands explosion-proof and rugged sensors. Manufacturers are developing tailored solutions that enhance performance, durability, and regulatory compliance in their respective environments.

6. Integration of AI and Data Analytics

Artificial Intelligence (AI) and data analytics are being integrated into level sensor systems to enable intelligent monitoring and diagnostics. By analyzing data patterns, AI algorithms can predict equipment failures, optimize inventory levels, and identify anomalies. This capability reduces downtime and maintenance costs while ensuring uninterrupted operation. Predictive analytics also contribute to sustainability by minimizing waste and energy consumption.

7. Environmental and Energy Efficiency Considerations

Modern level sensors are increasingly being designed with energy-efficient and environmentally sustainable features. These include low-power operation, longer battery life, and eco-friendly materials. Wireless communication protocols such as LoRaWAN and NB-IoT help reduce wiring infrastructure and energy consumption. Additionally, solar-powered level sensors are becoming popular in remote or off-grid locations, contributing to green energy initiatives.

8. Expansion in Emerging Markets

Emerging economies in Asia-Pacific, Latin America, and Africa are experiencing rapid industrialization and urbanization, which is creating new opportunities for level sensor manufacturers. Growing investments in infrastructure, water supply systems, agriculture, and energy are fueling the demand for reliable monitoring solutions. Localized production, cost-effective sensor designs, and strategic partnerships are being adopted to cater to the unique demands of these regions.

Conclusion

The level sensor market is evolving rapidly, driven by technological innovation, environmental priorities, and increasing industry-specific requirements. From smart factories and autonomous systems to water conservation and predictive analytics, the market is witnessing transformative changes that promise enhanced efficiency, accuracy, and sustainability. As these emerging trends gain momentum, level sensors will play an even more critical role in shaping the future of intelligent industrial and environmental monitoring systems. Businesses and stakeholders who embrace these advancements are likely to gain a competitive edge in this dynamic and expanding market.

[microtechengineering]

Revolutionizing Food Production with the Automatic Mayonnaise Making Machine

In today’s fast-paced food industry, precision and consistency are non-negotiable. That's where the automatic mayonnaise making machine plays a crucial role. Designed to meet the high demands of commercial kitchens and large-scale food factories, this machine automates the entire mayonnaise production process—right from emulsifying to mixing ingredients. Traditional methods are no longer viable for businesses aiming for scale, hygiene, and cost-efficiency. The use of an industrial mayonnaise mixer ensures not only uniformity in texture but also accelerates production, helping manufacturers meet rising market demands. As a result, more brands are investing in reliable mayonnaise processing equipment to gain a competitive edge and maintain quality standards.

The automatic mayonnaise making machine stands out due to its advanced mixing technology and hygienic stainless steel build. Hygiene is a major concern in food production, and that’s why machines constructed from stainless steel are preferred—they are corrosion-resistant, easy to clean, and comply with food safety regulations. Moreover, many manufacturers now integrate AI-powered control panels that allow operators to adjust temperature, speed, and timing without manual effort. This not only reduces labor but also ensures that the consistency of the final product remains intact every single time. Leading suppliers of commercial mayonnaise mixing machines have observed a sharp increase in demand as restaurants, cloud kitchens, and FMCG companies turn to automated solutions to boost productivity and maintain recipe uniformity.

What further enhances the efficiency of an automatic mayonnaise making machine is its ability to process large batches without compromising on quality. Equipped with high-speed mixing blades and automated dosing systems, these machines can produce hundreds of liters of mayonnaise per hour. This makes them indispensable in large-scale production environments like condiment factories and industrial kitchens. Alongside, the incorporation of programmable logic controllers (PLC) allows the equipment to operate with minimal human supervision, reducing the chances of errors. With features like vacuum mixing and temperature control, these machines outperform older systems and even some modern manual alternatives. No wonder the mayonnaise processing equipment segment is rapidly evolving, offering customizable solutions for different production needs.

To sum it up, the automatic mayonnaise making machine has become a cornerstone of efficient food processing. As consumer demand for mayonnaise and similar condiments grows, so does the need for reliable, scalable, and hygienic solutions. With a variety of models available—from compact units for small businesses to fully automated systems for industrial use—there’s something for every level of production. Thanks to innovations in commercial mayonnaise mixing machine design and improvements in automation, manufacturers can now enjoy faster turnaround, consistent product quality, and reduced operational costs. Whether you're starting a condiment line or upgrading your current setup, investing in a top-tier industrial mayonnaise mixer made from stainless steel is a smart, future-ready choice. Tags: commercial mayonnaise mixing machine mayonnaise processing equipment industrial mayonnaise mixer stainless steel mayonnaise machine

[fuzzycrownking]

Automated Storage and Retrieval Systems Market Growth Driven by Labor Shortages and Warehouse Automation Demands

The global Automated Storage and Retrieval Systems market is witnessing a robust expansion, powered by the continuous evolution in supply chain and warehouse automation. ASRS are a key solution in optimizing storage density, improving inventory control, and enhancing picking accuracy in warehouses and manufacturing facilities. These systems, including unit load, mini load, carousel-based, and shuttle-based ASRS, are enabling companies to increase operational efficiency and reduce labor dependency. The market growth is being driven by a range of significant factors, which are accelerating their adoption across sectors such as e-commerce, food & beverage, pharmaceuticals, automotive, and retail.

Growing Demand for Warehouse Automation One of the primary drivers of the ASRS market is the escalating demand for warehouse automation. With the global boom in e-commerce and omnichannel retailing, businesses are pressured to manage complex and high-volume order fulfillment operations. ASRS offer an automated solution to these challenges by optimizing inventory management, increasing throughput rates, and reducing operational costs. Companies are increasingly deploying ASRS to stay competitive and meet the rising expectations for rapid and accurate order deliveries. Automation also provides real-time inventory visibility, an essential requirement in modern warehousing.

Labor Shortages and Rising Labor Costs Another critical market driver is the global labor shortage, particularly in logistics and warehousing roles. As finding and retaining skilled warehouse staff becomes more difficult, businesses are turning to automated systems to fill the gap. Moreover, rising labor costs in developed economies are prompting companies to adopt ASRS as a cost-saving measure. By reducing the reliance on manual labor, these systems not only ensure consistent productivity but also improve workplace safety and reduce human error. This trend is particularly strong in regions like North America and Europe, where wages are higher and labor availability is declining.

Increased Need for Space Optimization Space utilization is a vital consideration for modern warehouses, especially in high-cost urban areas. ASRS enable high-density storage and allow vertical use of space, thereby reducing the overall warehouse footprint. This is particularly beneficial for businesses operating in cities where real estate costs are a significant burden. Space optimization through automated systems helps maximize storage capacity without the need for warehouse expansion, contributing to significant cost savings in the long run.

Technological Advancements and Integration with Industry 4.0 Technological innovation is another strong driver behind the ASRS market growth. The integration of ASRS with advanced technologies such as IoT, AI, machine learning, and data analytics has enhanced their functionality and appeal. These technologies enable predictive maintenance, performance monitoring, and smart decision-making in real-time. The emergence of Industry 4.0 has encouraged the adoption of intelligent automation solutions, and ASRS are a cornerstone of this digital transformation in logistics. As technology becomes more accessible and affordable, more small and medium enterprises are expected to adopt ASRS to stay technologically relevant.

Growth in E-commerce and Omnichannel Retailing The rapid expansion of e-commerce is significantly contributing to the adoption of ASRS globally. Online retailers deal with high order volumes, frequent returns, and a wide range of SKUs, all of which demand advanced storage and retrieval capabilities. ASRS enable e-commerce companies to manage inventory dynamically, streamline picking and packing processes, and meet tight delivery schedules. The trend of omnichannel retailing, which requires synchronization between online and offline channels, further strengthens the need for efficient warehouse solutions, propelling the ASRS market.

Government Initiatives and Supportive Regulations Governments in various countries are promoting the adoption of automation technologies through subsidies, incentives, and favorable regulations. These initiatives aim to enhance manufacturing and logistics infrastructure, increase industrial productivity, and create smart cities. Such policies are encouraging more companies to invest in ASRS and related technologies. For instance, government-backed initiatives in countries like China, Germany, and Japan are promoting smart manufacturing and digital logistics, thereby indirectly fueling the ASRS market growth.

Resilience in Times of Disruption The COVID-19 pandemic highlighted the vulnerability of supply chains that relied heavily on human labor. In response, many companies began investing in ASRS to increase operational resilience and reduce dependence on workforce availability. Automated systems are capable of maintaining operations even during periods of labor shortages, lockdowns, or health-related disruptions. This long-term shift towards resilient and flexible supply chains is expected to continue, with ASRS playing a vital role.

Conclusion The Automated Storage and Retrieval Systems market is being driven by a confluence of technological, economic, and social factors. The need for efficiency, speed, and resilience in logistics and warehousing is accelerating the adoption of ASRS across diverse industries. As businesses look to future-proof their operations and navigate an increasingly competitive landscape, the deployment of these systems is set to rise, positioning the market for substantial growth in the coming years.

[simarseo]

The future of work is already dawning with the use of Commercial Service Robots. Why? 

Take a closer look and you will see that robots are no longer just sci-fi inventions or factory arms. Their greetings include you at the hotel reception desk, sweeping mall floors, taking you on tours of airport terminalses and serving dinner at your table. While operating quietly, commercial service robots are transforming businesses by serving and scaling in ways they have never before been seen.

What Are Commercial Service Robots?

Definition and Scope.

Non-manufacturing commercial service robots are self-configured, intelligent machines that perform beneficial tasks. Commercial robots don't interact with humans in business or public places like they do with industrial robot - they work with cars, for instance.).

Industrial Robots vs Commercial Robotics.

Controlled environment for industrial robots to operate.

Commercial Robots: Support humans in active situations such as restaurants, hotels, hospitals, and stores.

History and Evolution.

Basic Automation to Smart Robotics.

Back-office work was deemed repetitive during the early days of automation. The rapid advancements in AI, machine learning, and mobility over the past decade have transformed robots into interactive, intelligent machines that can understand context, speech, emotion, even gesture.

Critical Milestones in Service Robotics.

2014: SoftBank Robotics launches Pepper.

The use of robots to deliver food is being carried out in hotels from 2017 to 2022.

The adoption of robotics based on hygiene experienced a surge in popularity after 2020, coinciding with COVID-19.

Central technologies driving Commercial Service Robots.?

Artificial Intelligence (AI)

Robots can use AI to make real-time decisions, such as avoiding spilled drinks or identifying a customer who frequents their establishment.

Sensors and Perception Systems.

The use of LiDAR, infrared, cameras, and ultrasonic sensors enables robots to move around with ease and visual awareness.

Mobility and Manipulation.

The robots that move and deliver on their components, such as wheels, arms, trays, and grips, are becoming more mobile and flexible.

Types of Commercial Service Robots.

Hospitality Robots.

The hotel experience is enhanced by robots that handle reception greetings and room service.

Cleaning and Maintenance Robots.

Consider airport floor cleaners or cleaning robots in hospital corridors.

Retail and Customer Service Robots.

Product data, aisle escorts, and payment processing are all handled by these robots.

Healthcare and Assistance Robots.

Assisted nursing, medication distribution and caregiving for the elderly are becoming automated in healthcare. Why?

Food Service and Delivery Robots.

Table trays, take-out orders and clean dishes are all served by restaurant robots. Robots also handle server requests.

Real-World Applications.

Robots in Hotels and Restaurants.

Aloft and Marriott have implemented robots for room deliveries, check-ins & concierge services. Bots such as BellaBot and Servi are frequently seen in restaurants, moving smoothly between tables.

Cleaning of airports and shopping centers is done autonomously.

Floor robots that are disc-shaped and have blinking lights, which can clean floors without attracting attention, are something you've come across.

Smart shopping assistants in retail stores.

Grocers use robots like Tally, which scan shelves, restock and guide customers as they browse the large stores.

Advantages of Commercial Service Robots.

Reliability and Precision.

Unlike humans, robots are not plagued by forgetfulness or confusion of instructions. Why? They provide consistent, error-free service.

Efficiency in Operations.

They decrease waiting time, optimize workflows, and enable human workers to perform complex, emotional, or creative tasks.

Cost Savings Over Time.

Despite the high initial costs, robots can save money on labor and training expenses over time. Why?

Customer Interaction and Satisfaction.

Their innovative ideas attract attention, entertain tourists and create social media-worthy moments. "...

Leading Commercial Robot Manufacturers.

SoftBank Robotics.

The makers of Pepper and Whiz are at the forefront of creating interactive and cleaning robots.

Pudu Robotics.

Dedicated to food service robots like BellaBot and Holabot. Additionally.

Bear Robotics.

Creators of Servi, an eye-catching and efficient dining aide robot.

UBTECH and Keenon Robotics.

Renowned for their expertise in creating humanoid robots and restaurant-service robot.

Market Trends and Statistics.

Worldwide Expansion of Service Robotics.

Double-digit CAGR: Commercial service robots will exceed $100 billion in market by 2030.

Leading Industries Embracing Robots.

Hospitality.

Retail.

Healthcare.

Food and Beverages.

Transportation.

Disadvantages of Adopting Commercial Robots.

High Initial Cost.

Despite the high initial cost, leasing and finance are becoming more popular.

Technical Integration and Training.

It is necessary for robots to be integrated with existing systems, and employees must receive training to work with them.

Customer Acceptance.

Some individuals find robots appealing while others view them as unpatriotic.

Success is achieved through intelligent design and usage.?

Adoption and the Impact of COVID-19?

Touchless Delivery and Hygiene Demands.

During the coronavirus outbreak, robots reduced human interaction and improved sanitation standards.

Decrease in Human Interaction in Public Areas.

To prevent germs spreading, a few companies employed bots to check-in, clean up, and deliver.

Human-Robot Collaboration.

Augmentation, Not Replacement.

Robots are not a replacement for jobs, but rather perform tedious tasks to enable humans to perform more demanding tasks.

Re-skilling the Workforce.

Businesses are investing in the education of employees on digital literacy and robotic systems....

Future of Commercial Service Robots.

Robots with Emotional Intelligence.

Bots next generation will be able to sense the emotions, sounds and interactions with customers become more natural.

Multi-Tasking Service Units.

Search for robots that provide food, sweep the premises, and monitor the perimeter.

5G and Cloud Robotics.

Increased connectivity enables updates to be delivered instantly and allows central control from anywhere.

Things to Consider Before Investing.

Assessing ROI.

Evaluate not only budgetary cuts but also enhancements in customer satisfaction and brand reputation.

Understanding Customer Needs.

Choose robots that address genuine operational concerns, rather than just what is in vogue.

Selecting the appropriate robot for specific tasks.

Each bot has strengths. Analyze dimension, pace of movement / AI performance and service possibilities. [.

Conclusion.

Tech toys aside, commercial service robots are capable of transforming into powerful tools for businesses.

[sweatybelieverfun]

Electronic Weighing Scale Market Future Trends Shaping Global Precision and Smart Measurement Technologies

The electronic weighing scale market is entering a transformative era, driven by technological advancements, shifting industry requirements, and evolving consumer expectations. These changes are fostering the emergence of smart, interconnected, and more precise weighing systems that transcend traditional functionalities. With increasing applications in sectors like healthcare, retail, manufacturing, logistics, and agriculture, future trends in the market point toward robust innovation, digital integration, and eco-conscious designs.

Rise of Smart and Connected Weighing Systems

One of the most notable trends in the electronic weighing scale market is the growing integration of smart technologies. These advanced weighing systems come equipped with Bluetooth, Wi-Fi, and IoT capabilities, enabling seamless data transfer and remote monitoring. Industries such as logistics and warehousing are adopting these smart scales to track inventory and ensure accurate weight-based billing, reducing manual errors and increasing operational efficiency.

Consumer use has also evolved, especially in the health and fitness sector. Smart bathroom scales that sync with mobile apps to track weight, body composition, and health metrics are gaining traction. These connected devices appeal to tech-savvy users seeking comprehensive wellness insights, further boosting the demand for intelligent weighing systems.

Increasing Demand from E-Commerce and Retail

The boom in e-commerce and the push for omnichannel retail experiences have intensified the need for accurate and efficient weighing scales. Retailers depend on electronic weighing machines for inventory management, order packaging, and logistics optimization. Future trends suggest a higher demand for compact and multifunctional scales that integrate with point-of-sale (POS) systems and enterprise resource planning (ERP) platforms.

Self-checkout systems in supermarkets and convenience stores are increasingly incorporating weighing functionalities to handle fresh produce and bulk items. As customer preferences shift towards quicker, contactless shopping experiences, the role of electronic weighing devices in retail automation will only expand.

Growing Healthcare Applications

The healthcare sector is also set to play a significant role in the future trajectory of the electronic weighing scale market. Precision in body weight, BMI, and other related metrics is vital in medical diagnostics and patient monitoring. With hospitals and clinics modernizing their infrastructure, the demand for digital weighing equipment is projected to grow significantly.

Moreover, telemedicine and home healthcare services are contributing to the popularity of portable, easy-to-use electronic scales for remote patient monitoring. These devices are being designed with user-friendly interfaces, cloud connectivity, and multi-user support to meet the needs of both patients and healthcare providers.

Innovation in Industrial Weighing Solutions

Heavy-duty and high-capacity weighing solutions for industrial use are also evolving. From manufacturing plants to food processing units, industrial weighing scales are being revamped with automation, AI, and machine learning features to ensure better throughput and real-time analytics. Trends point toward integrated solutions where weighing data is directly linked with production line management systems, offering deeper process insights and quality control.

Future innovations will likely include self-calibrating scales, rugged designs for harsh environments, and customizable interfaces to adapt to specific industrial applications. These innovations aim to minimize downtime and improve efficiency in critical operations.

Focus on Portability and User Experience

Modern users expect compact, portable, and aesthetically appealing weighing devices. The market is responding with ergonomic designs, intuitive displays, and lightweight materials. This is especially evident in personal and kitchen weighing scales, where style and usability are becoming major purchasing factors.

In commercial and industrial settings, ease of transport and installation are also becoming critical. Foldable, wireless, and battery-efficient models are emerging to meet the need for flexible solutions that can be relocated or deployed quickly.

Sustainable and Energy-Efficient Developments

Another significant trend shaping the future of the electronic weighing scale market is the shift towards sustainability. Manufacturers are increasingly adopting eco-friendly materials, reducing electronic waste, and focusing on energy-efficient technologies. Solar-powered scales and those with longer battery life are being developed to meet environmental regulations and customer demand for greener alternatives.

Additionally, the packaging and shipping industries are seeking solutions that reduce energy consumption and carbon footprint. Energy-saving modes and recyclable components are expected to become standard in future product lines.

Expanding Geographical Reach and Customization

As digital weighing technology becomes more accessible, emerging economies in Asia, Africa, and Latin America are witnessing increased adoption. Local manufacturers are entering the space with cost-effective solutions tailored to regional needs. Multinational brands are also localizing their offerings to tap into these expanding markets.

Customization is another rising trend, with businesses demanding weighing systems tailored to their specific workflows. Future product development will likely focus on modular solutions that allow for easy upgrades, integration, and configuration based on end-user requirements.

Conclusion

The future of the electronic weighing scale market is dynamic and promising, with a strong emphasis on innovation, digitalization, and user-centric design. As smart technology and sustainability become industry standards, manufacturers must continue to evolve their offerings to stay competitive. From personalized health tracking to automated industrial systems, the trends shaping this market promise to redefine how we measure, manage, and interpret weight across all sectors.