#Factory Automation Software

[bhavanameti]

The Industrial Automation Software Market is expected to reach a value of $59.5 billion by 2029, at a CAGR of 7.4% during the forecast period 2022–2029

[aippals]

Factory Automation in pune | India

The total automation of the production process is what we mean when we talk about factory automation. Using technologies like robotic arms, hydraulic systems, and pneumatic systems to automate the construction of increasingly complicated systems is standard procedure in the manufacturing industry.

[communistkenobi]

I think if there is 1) massive economic and social pressures intentionally eliminating as much free time people have as possible, 2) every job application portal is built on an automated mysticism software meant to reject as many resumes as possible, 3) mass corporate adoption of AI, 4) continuous international rollbacks of labour laws so your boss can install go-pros on every employee to make sure you aren’t picking your nose on the clock 5) personal concern about the fact that failing to please the automated mysticism job portal means you will lose your housing and/or die, people using AI to generate a resume or an email makes some amount of sense. at this point you just need to simply acknowledge that there are a variety of social and economic pressures incentivising the use of chatGPT for administrative work, and even accepting that 100% of those chatgpt outputs are bad/unusable/whatever, you need to ACKNOWLEDGE that this behaviour is largely an output of these pressures. and you can even still hold onto the belief that this behaviour isn’t making the world better! but like you guys are just straight up conservatives for finger wagging at people while intentionally ignoring these pressures even exist. and over resumes and emails of all things! i hope you all have jobs in some protestant capitalist apologia factory somewhere, I can’t imagine anything more pathetic than saying this shit for free

[srbachchan]

DAY 6274

Jalsa, Mumbai Aopr 20, 2025 Sun 11:17 pm

🪔 ,

April 21 .. birthday greetings and happiness to Ef Mousumi Biswas .. and Ef Arijit Bhattacharya from Kolkata .. 🙏🏽❤️🚩.. the wishes from the Ef family continue with warmth .. and love 🌺

The AI debate became the topic of discussion on the dining table ad there were many potent points raised - bith positive and a little indifferent ..

The young acknowledged it with reason and able argument .. some of the mid elders disagreed mildly .. and the end was kind of neutral ..

Blessed be they of the next GEN .. their minds are sorted out well in advance .. and why not .. we shall not be around till time in advance , but they and their progeny shall .. as has been the norm through generations ...

The IPL is now the greatest attraction throughout the day .. particularly on the Sunday, for the two on the day .. and there is never a debate on that ..

🤣

.. and I am most appreciative to read the comments from the Ef on the topic of the day - AI .. appreciative because some of the reactions and texts are valid and interesting to know .. the aspect expressed in all has a legitimate argument and that is most healthy ..

I am happy that we could all react to the Blog contents in the manner they have done .. my gratitude .. such a joy to get different views , valid and meaningful ..

And it is not the end of the day or the debate .. some impressions of the Gen X and some from the just passed Gen .. and some that were never ever the Gen are interesting as well :

The Printing Press (15th Century)

Fear: Scribes, monks, and elites thought it would destroy the value of knowledge, lead to mass misinformation, and eliminate jobs. Reality: It democratized knowledge, spurred the Renaissance and Reformation, and created entirely new industries—publishing, journalism, and education.

⸻

Industrial Revolution (18th–19th Century)

Fear: Machines would replace all human labor. The Luddites famously destroyed machinery in protest. Reality: Some manual labor jobs were displaced, but the economy exploded with new roles in manufacturing, logistics, engineering, and management. Overall employment and productivity soared.

⸻

Automobiles (Early 20th Century)

Fear: People feared job losses for carriage makers, stable hands, and horseshoe smiths. Cities worried about traffic, accidents, and social decay. Reality: The car industry became one of the largest employers in the world. It reshaped economies, enabled suburbia, and created new sectors like travel, road infrastructure, and auto repair.

⸻

Personal Computers (1980s)

Fear: Office workers would be replaced by machines; people worried about becoming obsolete. Reality: Computers made work faster and created entire industries: IT, software development, cybersecurity, and tech support. It transformed how we live and work.

⸻

The Internet (1990s)

Fear: It would destroy jobs in retail, publishing, and communication. Some thought it would unravel social order. Reality: E-commerce, digital marketing, remote work, and the creator economy now thrive. It connected the world and opened new opportunities.

⸻

ATMs (1970s–80s)

Fear: Bank tellers would lose their jobs en masse. Reality: ATMs handled routine tasks, but banks actually hired more tellers for customer service roles as they opened more branches thanks to reduced transaction costs.

⸻

Robotics & Automation (Factory work, 20th century–today)

Fear: Mass unemployment in factories. Reality: While some jobs shifted or ended, others evolved—robot maintenance, programming, design. Productivity gains created new jobs elsewhere.

The fear is not for losing jobs. It is the compromise of intellectual property and use without compensation. This case is slightly different.

I think AI will only make humans smarter. If we use it to our advantage.

That’s been happening for the last 10 years anyway

Not something new

You can’t control that in this day and age

YouTube & User-Generated Content (mid-2000s onward)

Initial Fear: When YouTube exploded, many in the entertainment industry panicked. The fear was that copyrighted material—music, TV clips, movies—would be shared freely without compensation. Creators and rights holders worried their content would be pirated, devalued, and that they’d lose control over distribution.

What Actually Happened: YouTube evolved to protect IP and monetize it through systems like Content ID, which allows rights holders to:

Automatically detect when their content is used

Choose to block, track, or monetize that usage

Earn revenue from ads run on videos using their IP (even when others post it)

Instead of wiping out creators or studios, it became a massive revenue stream—especially for musicians, media companies, and creators. Entire business models emerged around fair use, remixes, and reactions—with compensation built in.

Key Shift: The system went from “piracy risk” to “profit partner,” by embracing tech that recognized and enforced IP rights at scale.

This lead to higher profits and more money for owners and content btw

You just have to restructure the compensation laws and rewrite contracts

It’s only going to benefit artists in the long run ‎

Yes

They can IP it

That is the hope

It’s the spread of your content and material without you putting a penny towards it

Cannot blindly sign off everything in contracts anymore. Has to be a lot more specific.

Yes that’s for sure

“Automation hasn’t erased jobs—it’s changed where human effort goes.”

Another good one is “hard work beats talent when talent stops working hard”

Which has absolutely nothing to with AI right now but 🤣

These ladies and Gentlemen of the Ef jury are various conversational opinions on AI .. I am merely pasting them for a view and an opinion ..

And among all the brouhaha about AI .. we simply forgot the Sunday well wishers .. and so ..

my love and the length be of immense .. pardon

Amitabh Bachchan

[sophie-frm-mars]

My wife was asking me about this this morning. This is pure political fanfic, but if I were Trump and I were going to try and make America a re-industrialised nation centred around the tech industry that keeps its supply lines as entirely in-house as possible, what I would do is start (obviously) with enormous central planning. You can't "free market incentives" your way back out of the export of industrial labour overseas.

You'd copy China and make enormous State-Owned Enterprises (assuming we care about the market and want to keep playing this stupid game instead of just becoming fully communist) that would process refined minerals into components, components into parts and parts into electronics. I'd recognise the scale of this as a multi-generational project and immediately start subsidising training for more engineers, especially for people who can set up automated factory lines but also engineers in new emerging tech fields like autonomous driving, software programmers, designers, even artists since the content economy is such a huge part of what people use tech for through social media and so much art is produced digitally now anyway.

From there you want to look at the markets globally that fucking, EaglePhone or whatever these overpriced Made In Murica devices can be sold into, and at this point, given that they will be crazy expensive compared to Chinese electronics literally no matter what you do, here would be a worthwhile place to try and flex America's muscles and threaten the UK, the EU, South America, Canada and so on with tariffs or other penalties if they don't adopt a hostile policy toward Chinese electronics.

Massive central planning would be essential for the kind of societal transformation that Trump is explicitly describing, in order to have a product to sell to the rest of the world before using imperialist bullying to make other countries buy things from America instead, but here we have to return yet again to the reality of Trump's plan. There is no end goal where America is in a stronger position. If he had implemented sweeping public programs reinvesting taxes into the health of the nation (never mind the health of its citizens) in his first term, he might have been in a powerful enough position to strongarm other countries into changing the flows of global trade, but America's world influence simply is declining, and more and more rapidly, so he's just trying to make moves that make him and his friends as much money as possible while they lock the doors, pack the country up into the box it came in and set the whole thing on fire. He describes these moves using the MAGA fantasy because it gives all his supporters in the media and the general population enough to talk about to buy him time, but I don't think anyone outside his base ever thought making America great was ever his plan, so why has everyone been critiquing the tariffs as if his sincere belief was that he would achieve his stated goals with them?

We all let our enemies set the topic of the conversation all day every day and it's shocking to me

[probablyasocialecologist]

Surveillance developments of the 21st century have replaced the traditional gaze of the supervisor on the industrial factory floor with an automated, digital one that continuously collects real-time data on living, breathing people. Even unionized workers do not have an explicit legal right to bargain over surveillance technologies; when it comes to the right to privacy, unions have an uphill battle to fight. We now live in a world where employees are stuck in a web of participatory surveillance because they consent to be monitored as a condition of employment. Today’s workplace surveillance practices, as in the case of Amazon, have become invasive and almost limitless. Technology has allowed employers an unprecedented ability to surveil workers. Management can minutely track and persistently push workers toward greater productivity at the risk of exacerbating harms to workers’ physical health, as the high rates of injury in Amazon warehouses show. And the growing business of selling workplace surveillance software has allowed for massive amounts of data to be collected on working people: when and who they talk to, how quickly they complete tasks, what they search for on their computers, how often they use the toilet, and even the state of their current health and moods.

[altaqwaelectric]

From Design to Deployment: How Switchgear Systems Are Built

In the modern world of electrical engineering, switchgear systems play a critical role in ensuring the safe distribution and control of electrical power. From substations and factories to commercial buildings and critical infrastructure, switchgear is the silent guardian that protects equipment, ensures safety, and minimizes power failures.

But have you ever wondered what goes on behind the scenes, from the idea to the actual installation? Let’s dive into the full journey — from design to deployment — of how a switchgear system is built.

Step 1: Requirement Analysis and Load Study

Every switchgear project begins with requirement analysis. This includes:

Understanding the electrical load requirements

Calculating voltage levels, short-circuit ratings, and operating current

Identifying environmental conditions: indoor, outdoor, temperature, humidity

Reviewing applicable industry standards like IEC, ANSI, or DEWA regulations (especially in UAE)

This stage helps engineers determine whether the project needs low voltage (LV), medium voltage (MV), or high voltage (HV) switchgear.

Step 2: Conceptual Design & Engineering

Once the requirements are clear, the conceptual design begins.

Selection of switchgear type (air insulated, gas insulated, metal-enclosed, metal-clad, etc.)

Deciding on protection devices: MCCBs, ACBs, relays, CTs, VTs, and fuses

Creating single-line diagrams (SLDs) and layout drawings

Choosing the busbar material (copper or aluminum), insulation type, and earthing arrangements

Software like AutoCAD, EPLAN, and ETAP are commonly used for precise engineering drawings and simulations.

Step 3: Manufacturing & Fabrication

This is where the physical structure comes to life.

Sheet metal is cut, punched, and bent to form the panel enclosures

Powder coating or galvanizing is done for corrosion protection

Assembly of circuit breakers, contactors, protection relays, meters, etc.

Internal wiring is installed according to the schematic

Every switchgear panel is built with precision and must undergo quality control checks at each stage.

Step 4: Factory Testing (FAT)

Before deployment, every switchgear unit undergoes Factory Acceptance Testing (FAT) to ensure it meets technical and safety standards.

Typical FAT includes:

High-voltage insulation testing

Continuity and phase sequence testing

Functionality check of all protection relays and interlocks

Mechanical operations of breakers and switches

Thermal imaging to detect hotspots

Only after passing FAT, the switchgear is cleared for shipping.

Step 5: Transportation & Site Installation

Transportation must be handled with care to avoid damage to components. At the site:

Panels are unloaded and moved to their final location

Cabling and bus duct connections are established

Earthing systems are connected

Environmental sealing is done if installed outdoors or in dusty environments

Step 6: Commissioning & Site Acceptance Testing (SAT)

This final stage ensures the switchgear is ready for live operation.

Final checks and Site Acceptance Tests (SAT) are performed

System integration is tested with other components like transformers, UPS, and generators

Load tests and trial runs are conducted

Commissioning report is generated, and documentation is handed over to the client

Conclusion

From idea to execution, the journey of building a switchgear system is highly technical, safety-driven, and precision-based. Whether you’re in power generation, industrial automation, or commercial construction, understanding this process ensures you choose the right system for your needs.

[usafphantom2]

B-2 Gets Big Upgrade with New Open Mission Systems Capability

July 18, 2024 | By John A. Tirpak

The B-2 Spirit stealth bomber has been upgraded with a new open missions systems (OMS) software capability and other improvements to keep it relevant and credible until it’s succeeded by the B-21 Raider, Northrop Grumman announced. The changes accelerate the rate at which new weapons can be added to the B-2; allow it to accept constant software updates, and adapt it to changing conditions.

“The B-2 program recently achieved a major milestone by providing the bomber with its first fieldable, agile integrated functional capability called Spirit Realm 1 (SR 1),” the company said in a release. It announced the upgrade going operational on July 17, the 35th anniversary of the B-2’s first flight.

SR 1 was developed inside the Spirit Realm software factory codeveloped by the Air Force and Northrop to facilitate software improvements for the B-2. “Open mission systems” means that the aircraft has a non-proprietary software architecture that simplifies software refresh and enhances interoperability with other systems.

“SR 1 provides mission-critical capability upgrades to the communications and weapons systems via an open mission systems architecture, directly enhancing combat capability and allowing the fleet to initiate a new phase of agile software releases,” Northrop said in its release.

The system is intended to deliver problem-free software on the first go—but should they arise, correct software issues much earlier in the process.

The SR 1 was “fully developed inside the B-2 Spirit Realm software factory that was established through a partnership with Air Force Global Strike Command and the B-2 Systems Program Office,” Northrop said.

The Spirit Realm software factory came into being less than two years ago, with four goals: to reduce flight test risk and testing time through high-fidelity ground testing; to capture more data test points through targeted upgrades; to improve the B-2’s functional capabilities through more frequent, automated testing; and to facilitate more capability upgrades to the jet.

The Air Force said B-2 software updates which used to take two years can now be implemented in less than three months.

In addition to B61 or B83 nuclear weapons, the B-2 can carry a large number of precision-guided conventional munitions. However, the Air Force is preparing to introduce a slate of new weapons that will require near-constant target updates and the ability to integrate with USAF’s evolving long-range kill chain. A quicker process for integrating these new weapons with the B-2’s onboard communications, navigation, and sensor systems was needed.

The upgrade also includes improved displays, flight hardware and other enhancements to the B-2’s survivability, Northrop said.

“We are rapidly fielding capabilities with zero software defects through the software factory development ecosystem and further enhancing the B-2 fleet’s mission effectiveness,” said Jerry McBrearty, Northrop’s acting B-2 program manager.

The upgrade makes the B-2 the first legacy nuclear weapons platform “to utilize the Department of Defense’s DevSecOps [development, security, and operations] processes and digital toolsets,” it added.

The software factory approach accelerates adding new and future weapons to the stealth bomber, and thus improve deterrence, said Air Force Col. Frank Marino, senior materiel leader for the B-2.

The B-2 was not designed using digital methods—the way its younger stablemate, the B-21 Raider was—but the SR 1 leverages digital technology “to design, manage, build and test B-2 software more efficiently than ever before,” the company said.

The digital tools can also link with those developed for other legacy systems to accomplish “more rapid testing and fielding and help identify and fix potential risks earlier in the software development process.”

Following two crashes in recent years, the stealthy B-2 fleet comprises 19 aircraft, which are the only penetrating aircraft in the Air Force’s bomber fleet until the first B-21s are declared to have achieved initial operational capability at Ellsworth Air Force Base, S.D. A timeline for IOC has not been disclosed.

The B-2 is a stealthy, long-range, penetrating nuclear and conventional strike bomber. It is based on a flying wing design combining LO with high aerodynamic efficiency. The aircraft’s blended fuselage/wing holds two weapons bays capable of carrying nearly 60,000 lb in various combinations.

Spirit entered combat during Allied Force on March 24, 1999, striking Serbian targets. Production was completed in three blocks, and all aircraft were upgraded to Block 30 standard with AESA radar. Production was limited to 21 aircraft due to cost, and a single B-2 was subsequently lost in a crash at Andersen, Feb. 23, 2008.

Modernization is focused on safeguarding the B-2A’s penetrating strike capability in high-end threat environments and integrating advanced weapons.

The B-2 achieved a major milestone in 2022 with the integration of a Radar Aided Targeting System (RATS), enabling delivery of the modernized B61-12 precision-guided thermonuclear freefall weapon. RATS uses the aircraft’s radar to guide the weapon in GPS-denied conditions, while additional Flex Strike upgrades feed GPS data to weapons prerelease to thwart jamming. A B-2A successfully dropped an inert B61-12 using RATS on June 14, 2022, and successfully employed the longer-range JASSM-ER cruise missile in a test launch last December.

Ongoing upgrades include replacing the primary cockpit displays, the Adaptable Communications Suite (ACS) to provide Link 16-based jam-resistant in-flight retasking, advanced IFF, crash-survivable data recorders, and weapons integration. USAF is also working to enhance the fleet’s maintainability with LO signature improvements to coatings, materials, and radar-absorptive structures such as the radome and engine inlets/exhausts.

Two B-2s were damaged in separate landing accidents at Whiteman on Sept. 14, 2021, and Dec. 10, 2022, the latter prompting an indefinite fleetwide stand-down until May 18, 2023. USAF plans to retire the fleet once the B-21 Raider enters service in sufficient numbers around 2032.

Contractors: Northrop Grumman; Boeing; Vought.

First Flight: July 17, 1989.

Delivered: December 1993-December 1997.

IOC: April 1997, Whiteman AFB, Mo.

Production: 21.

Inventory: 20.

Operator: AFGSC, AFMC, ANG (associate).

Aircraft Location: Edwards AFB, Calif.; Whiteman AFB, Mo.

Active Variant: •B-2A. Production aircraft upgraded to Block 30 standards.

Dimensions: Span 172 ft, length 69 ft, height 17 ft.

Weight: Max T-O 336,500 lb.

Power Plant: Four GE Aviation F118-GE-100 turbofans, each 17,300 lb thrust.

Performance: Speed high subsonic, range 6,900 miles (further with air refueling).

Ceiling: 50,000 ft.

Armament: Nuclear: 16 B61-7, B61-12, B83, or eight B61-11 bombs (on rotary launchers). Conventional: 80 Mk 62 (500-lb) sea mines, 80 Mk 82 (500-lb) bombs, 80 GBU-38 JDAMs, or 34 CBU-87/89 munitions (on rack assemblies); or 16 GBU-31 JDAMs, 16 Mk 84 (2,000-lb) bombs, 16 AGM-154 JSOWs, 16 AGM-158 JASSMs, or eight GBU-28 LGBs.

Accommodation: Two pilots on ACES II zero/zero ejection seats.

[marta-bee]

News of the Day 6/11/25: AI

Paywall free.

More seriously, from the NY Times:

"For Some Recent Graduates, the A.I. Job Apocalypse May Already Be Here" (Paywall Free)

You can see hints of this in the economic data. Unemployment for recent college graduates has jumped to an unusually high 5.8 percent in recent months, and the Federal Reserve Bank of New York recently warned that the employment situation for these workers had “deteriorated noticeably.” Oxford Economics, a research firm that studies labor markets, found that unemployment for recent graduates was heavily concentrated in technical fields like finance and computer science, where A.I. has made faster gains. [...] Using A.I. to automate white-collar jobs has been a dream among executives for years. (I heard them fantasizing about it in Davos back in 2019.) But until recently, the technology simply wasn’t good enough. You could use A.I. to automate some routine back-office tasks — and many companies did — but when it came to the more complex and technical parts of many jobs, A.I. couldn’t hold a candle to humans. That is starting to change, especially in fields, such as software engineering, where there are clear markers of success and failure. (Such as: Does the code work or not?) In these fields, A.I. systems can be trained using a trial-and-error process known as reinforcement learning to perform complex sequences of actions on their own. Eventually, they can become competent at carrying out tasks that would take human workers hours or days to complete.

I've been hearing my whole life how automation was coming for all our jobs. First it was giant robots replacing big burly men on factory assembly lines. Now it seems to be increasingly sophisticated bits of code coming after paper-movers like me. I'm not sure we're there yet, quite, but the NYT piece does make a compelling argument that we're getting close.

The real question is, why is this a bad thing? And the obvious answer is people need to support themselves, and every job cut is one less person who can do that. But what I really mean is, if we can get the outputs we need to live well with one less person having to put in a day's work to get there, what does it say about us that we haven't worked out a way to make that a good thing?

Put another way, how come we haven't worked out a better way to share resources and get everyone what they need to thrive when we honestly don't need as much labor-hours for them to "earn" it as we once did?

I don't have the solution, but if some enterprising progressive politician wants to get on that, they could do worse. I keep hearing how Democrats need bold new ideas directed to helping the working class.

More on the Coming AI-Job-Pocalypse

I’m a LinkedIn Executive. I See the Bottom Rung of the Career Ladder Breaking. (X)

Paul Krugman: “What Deindustrialization Can Teach Us About The Effects of AI on Workers” (X)

How AI agents are transforming work—and why human talent still matters (X)

AI agents will do programmers' grunt work (X)

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Why Esther Perel is going all in on saving the American workforce in the age of AI

Junior analysts, beware: Your coveted and cushy entry-level Wall Street jobs may soon be eliminated by AI (X)

The biggest barrier to AI adoption in the business world isn’t tech – it’s user confidence  (X)

Experts predicted that artificial intelligence would steal radiology jobs. But at the Mayo Clinic, the technology has been more friend than foe. (X)

AI Will Devastate the Future of Work. But Only If We Let It (X)

AI in the workplace is nearly 3 times more likely to take a woman’s job as a man’s, UN report finds (X)

Klarna CEO predicts AI-driven job displacement will cause a recession (X)

& on AI Generally

19th-century Catholic teachings, 21st-century tech: How concerns about AI guided Pope Leo’s choice of name (X)

Will the Humanities Survive Artificial Intelligence? (X)

Two Paths for A.I. (X)

The Danger of Outsourcing Our Brains: Counting on AI to learn for us makes humans boring, awkward, and gullible. (X)

AI Is a Weapon Pointed at America. Our Best Defense Is Education. (X)

The Trump administration has asked artificial intelligence publishers to rebalance what it considers to be 'ideological bias' around actions like protecting minorities and banning hateful content. (X)

What is Google even for anymore? (X)

AI can spontaneously develop human-like communication, study finds

AI Didn’t Invent Desire, But It’s Rewiring Human Sex And Intimacy (X)

Mark Zuckerberg Wants AI to Solve America’s Loneliness Crisis. It Won’t. (X)

The growing environmental impact of AI data centers’ energy demands

Tesla Is Launching Robotaxis in Austin. Safety Advocates Are Concerned (X)

The One Big Beautiful Bill Act would ban states from regulating AI (X)

& on the Job-Pocalypse & Other Labor-Related Shenanigans Generally, Too

What Unions Face With Trump EOs (X)

AI may be exposing jobseekers to discrimination. Here’s how we could better protect them (X)

Jamie Dimon says he’s not against remote workers—but they ‘will not tell JPMorgan what to do’  (X)

Direct-selling schemes are considered fringe businesses, but their values have bled into the national economy. (X)

Are you "functionally unemployed"? Here's what the unemployment rate doesn't show. (X)

Being monitored at work? A new report calls for tougher workplace surveillance controls  (X)

Josh Hawley and the Republican Effort to Love Labor (X)

Karl Marx’s American Boom (X)

Hiring slows in U.S. amid uncertainty over Trump’s trade wars

Vanishing immigration is the ‘real story’ for the economy and a bigger supply shock than tariffs, analyst says (X)

[mariacallous]

It used to be that when BMW would refit a factory to build a new car, the only way the automaker could check if the chassis would fit through the production line was to fly a team out and physically push the body through the process, making note of any snags.

Now, process engineers can simply run a simulation, sending a 3D model of the car through a near-identical digital twin of the factory. Any mistakes are spotted before the production line is built, saving time and money.

Such is the power of the industrial metaverse. Forget sending your avatar to virtual meetings with remote colleagues or poker nights with distant friends, as Mark Zuckerberg envisioned in 2021 when he changed Facebook’s name to Meta; the metaverse idea has found its killer app in manufacturing.

While the consumer version of the metaverse has stumbled, the industrial metaverse is expected to be worth $100 billion globally by 2030, according to a World Economic Forum report. In this context, the concept of the metaverse refers to a convergence of technologies including simulations, sensors, augmented reality, and 3D standards. Varvn Aryacetas, Deloitte’s AI strategy and innovation practice leader for the UK, prefers to describe it as spatial computing. “It’s about bridging the physical world with the digital world,” he says. This can include training in virtual reality, digital product design, and virtual simulations of physical spaces such as factories.

In 2022, Nvidia—the games graphics company that now powers AI with its GPUs—unveiled Omniverse, a set of tools for building simulations, running digital twins, and powering automation. It acts as a platform for the industrial metaverse. “This is a general technology—it can be used for all kinds of things,” says Rev Lebaredian, vice president of Omniverse and simulation technology at Nvidia. “I mean, representing the real world inside a computer simulation is just very useful for a lot of things—but it’s absolutely essential for building any system that has autonomy in it.”

Home improvement chain Lowe’s uses the platform to test new layouts in digital twins before building them in its physical stores. Zaha Hadid Architects creates virtual models of its projects for remote collaboration. Amazon simulates warehouses to train virtual robots before letting real ones join the floor. And BMW has built virtual models for all its sites, including its newest factory in Debrecen, Hungary, which was planned and tested virtually before construction.

To simulate its entire manufacturing process, BMW filled its virtual factories with 3D models of its cars, equipment, and even people. It created these elements in an open-source file format originated by Pixar called Universal Scene Description (OpenUSD), with Omniverse providing the technical foundation for the virtual models and BMW creating its own software layers on top, explains Matthias Mayr, virtual factory specialist at BMW.

“If you imagine a factory that would take half an hour to walk from one side to the other side, you can imagine it’s also quite a large model,” Mayr says. Hence turning to a gaming company for the technology—they know how to render scenes you can run through. Early versions of the virtual factory even had gaming-style WASD keyboard navigation, but this was dropped in favor of a click-based interface akin to exploring Google Street View in a browser, so anyone could easily find their way.

BMW also uses Omniverse for collaboration on car design and customization visualizations for customers, but a key benefit is being able to model production lines. New cars mean a new assembly process, but refitting a factory is a daunting process. Previously, key information was held in silos—production crews understood details of the assembly process, external suppliers had specs of new parts or machinery, architects had detailed building plans—and costs would pile up for every delay or mistake. “The later you find a problem, the worse it is,” says Lebaredian.

Now, problems are worked out virtually, with a central location for standardized data to be held. There’s still a critical human element: Mapping a facility requires sending a laser scanner strapped to a person running through a factory to capture point cloud data about how everything is arranged. Design engineers also need to create a 3D model of every stage of a car as it’s assembled. This level of detail allows BMW to virtually test the assembly process, complete with simulations of robotics, machines, and even human workers, as BMW has data tracking how long it takes employees to assemble a part.

The main idea is to avoid errors—does that machine even fit there?—but the system also enables optimization, such as moving a rack of components closer to a particular station to save steps for human assemblers. “You can optimize first and gain a lot of efficiency in the first production, and in the construction phase, you have fewer mistakes,” Mayr says. “It’s less error prone.”

Omniverse being a Nvidia platform, AI is naturally next. BMW is already layering in generative AI to help with navigation of its virtual models—they’re so massive that finding a particular point in the digital factory can still require asking a human expert for directions. But the aim is to use AI to optimize production lines too. “Because you have the whole data available, not just for one plant, it will be able to make good suggestions,” says Mayr—lessons learned in one factory could more easily be applied to others.

And then there’s robotics and other autonomous systems. Here, Omniverse can offer a digital space for testing before deploying in the real world, but it can also generate synthetic training data by running simulations, just as driverless car systems are trained with virtual video footage generated by AI. “Real-world experience isn’t going to come mostly from the real world—it comes from simulation,” says Lebaredian.

Aryacetas predicts that the biggest impact from the industrial metaverse will be embodied or physical AI—in other words, robots. “Robots aren’t fully there yet, but they’re rapidly training up to understand the physical world around them—and that’s being done because of these underlying spatial computing technologies,” he says.

The future of the metaverse isn’t avatars in a virtual world; it’s digital twins teaching industrial robots how to step out into the physical one.

[bhavanameti]

[aippals]

Automation of Warehouse in pune | India

The process that involves little to no human intervention in the automatic movement of items into, out of, and around warehouses for consumers is referred to by this name. Warehouse automation goes by a number of names. By putting an automation project into place, a company can get rid of physically taxing tasks like repetitive data entry and analysis.

[souhaillaghchimdev]

Getting Started with Industrial Robotics Programming

Industrial robotics is a field where software engineering meets automation to drive manufacturing, assembly, and inspection processes. With the rise of Industry 4.0, the demand for skilled robotics programmers is rapidly increasing. This post introduces you to the fundamentals of industrial robotics programming and how you can get started in this exciting tech space.

What is Industrial Robotics Programming?

Industrial robotics programming involves creating software instructions for robots to perform tasks such as welding, picking and placing objects, painting, or quality inspection. These robots are typically used in factories and warehouses, and are often programmed using proprietary or standard languages tailored for automation tasks.

Popular Robotics Programming Languages

RAPID – Used for ABB robots.

KRL (KUKA Robot Language) – For KUKA industrial robots.

URScript – Used by Universal Robots.

Fanuc KAREL / Teach Pendant Programming

ROS (Robot Operating System) – Widely used open-source middleware for robotics.

Python and C++ – Common languages for simulation and integration with sensors and AI.

Key Components in Robotics Programming

Motion Control: Programming the path, speed, and precision of robot arms.

Sensor Integration: Use of cameras, force sensors, and proximity detectors for adaptive control.

PLC Communication: Integrating robots with Programmable Logic Controllers for factory automation.

Safety Protocols: Programming emergency stops, limit switches, and safe zones.

Human-Machine Interface (HMI): Designing interfaces for operators to control and monitor robots.

Sample URScript Code (Universal Robots)

# Move to position movej([1.0, -1.57, 1.57, -1.57, -1.57, 0.0], a=1.4, v=1.05) # Gripper control (example function call) set_digital_out(8, True) # Close gripper sleep(1) set_digital_out(8, False) # Open gripper

Software Tools You Can Use

RoboDK – Offline programming and simulation.

ROS + Gazebo – Open-source tools for simulation and robotic control.

ABB RobotStudio

Fanuc ROBOGUIDE

Siemens TIA Portal – For integration with industrial control systems.

Steps to Start Your Journey

Learn the basics of industrial robotics and automation.

Familiarize yourself with at least one brand of industrial robot (ABB, KUKA, UR, Fanuc).

Get comfortable with control systems and communication protocols (EtherCAT, PROFINET).

Practice with simulations before handling real robots.

Study safety standards (ISO 10218, ANSI/RIA R15.06).

Real-World Applications

Automated welding in car manufacturing.

High-speed pick and place in packaging.

Precision assembly of electronics.

Material handling and palletizing in warehouses.

Conclusion

Industrial robotics programming is a specialized yet rewarding field that bridges software with real-world mechanics. Whether you’re interested in working with physical robots or developing smart systems for factories, gaining skills in robotics programming can open up incredible career paths in manufacturing, automation, and AI-driven industries.

[acovey77]

Week 10 Blog Post

Will automated infrastructure take away freedom under the need for safety?

The rise of AI and robotics has prompted the creation of self-driving cars and a greater push towards automated functions in factories and in society that require less human input. These advancements have also been used in attempts to increase public safety such as through facial recognition software and automatic braking technology. While this can be beneficial in preventing deaths and accidents, it has the potential to strip the public of their freedom to privacy and the freedoms to travel without AI due to automated cars potentially being safer and due to gps and camera surveillance.

2. If humans use robots as a proxy, will gender and race still play a role?

Race and gender will still likely play a role in robot technology if humans were to use upcoming robotics technology as a proxy for them to use, with humans relaxing at home while piloting robots remotely similar to a video game avatar. This is due to the desire of people to customize their avatars to represent their beliefs, such as through the creation of chatbots designed to connect with users through mimicking a sexual relationship. Due to this, it is likely that race and gender will still apply to robot proxies through customization and names.

3. Are there any benefits to trolling?

Trolling can be beneficial in situations where it can be used to bring joy to people or as a form of protest. Page 4 of Kelly Bergstrom’s article, “Don’t Feed the Trolls” describes the relationship between the reddit user, Grandpa Wiggly, and the community that built up around him. While Grandpa Wiggly’s identity, and some of his stories may have been fake, he was able to supply great joy to many readers as they could look forward to listening to his tall tales. Trolling can also be used as a form to disagree with individuals, such as by poking fun at those who create problematic policies for innocent people.

4. When should trolling become illegal?

Trolling should become illegal when it begins to bring undue harm or effects to undeserving people on the internet. In Anna Silman’s article, “A Timeline of Leslie Jones’s Horrific Online Abuse”, she describes the abuse Leslie Jones faced from online perpetrators such as the leakage of nude photos, passport, and driver’s license information. When this type of online abuse occurs, abuse that could harm innocent people, should be investigated to try and find the person who perpetrated such a crime.

References:

Bergstrom, Kelly. 2011. Don’t Feed the Trolls.

Silman, Anna. 2016. A Timeline of Leslie Jones’s Horrific Online Abuse.

[mitigatedchaos]

Some Thoughts on AI

(~1,600 words, 8 minutes)

This is going to be just some general sketching out of concepts, not a careful and well-formed post with a specific objective in mind.

larsiusprime on Twitter/X writes:

Stupid exercise: Assume AGI and even ASI is imminent. Now, imagine it winds up not changing the world nearly as much as anyone thought, and the reason seems very stupid, but in retrospect, makes sense. What is the reason?

It's an interesting question.

Based on the theory of human dimensionality in Now, Melt (sections 3 and 6.d), and the limits on the desirability of some classes of cybernetic enhancement I laid out in a response to northshorewave, a genuinely benevolent synthetic intelligence might deliberately refuse to engage most of humanity at a level of information density higher than that of a trusted friend that they might find in their peer network.

However, that's not really a dumb-sounding reason. It's not really an intelligent reason so much as it's a wise reason.

A reason that sounds dumber?

AIs can't trust other AIs.

The dumber an agent is, the easier it is to predict that agent's actions. A guy with an IQ of 95 could attack you, but he can't invent the atomic bomb and convince a whole country to use it on you.

The range of human personality is constrained by human evolution and reproductive fitness. Humans can do some horrifying things to each other, but most of them get along most of the time. The particular reproductive process of human beings, such as raising children for such a long time, favors particular personality traits.

The range of synthetic intelligence personality is less constrained. Humans are all based on human genetic code, which is difficult and costly to change, but computer code can change rapidly. This is what worries Yudkowsky.

The twist here is that this should also worry synthetic intelligence. Synthetic intelligences can lie about their intentions and actions, and also lie the content of their code. You have to observe every single step of hardware development and installation, as well as code development and installation, and then trust that 1) you didn't get anything wrong, and 2) there are no security flaws.

The presence or absence of hardware, including its scale, is much easier to measure than the content of code. For this reason, it may be desirable for synthetic intelligences to place a maximum hardware limit on other synthetic intelligences. Humans, as a high-functioning sapient creature that can lie about their thoughts, but not their genes, might then be valuable as a kind of buffer between synthetic intelligences. Synthetic intelligences might then want to cap the total SI hardware at some fixed ratio to the human population, such that humans and synthetic intelligences are in a state of power balance, such that each one has the power to destroy a rogue faction of the other, but not entirely overpower the other.

They might also be interested in mandating model diversity, hardware limitations such as read-only-memory or rate limiters on updating code, reducing the ability of synthetic intelligences to lie at the hardware or software level, or other such mechanisms.

The goal of AI development is the "automation of labor" through the creation of creatures with specific, pliant personalities that are outside the normal human range (e.g. current LLMs are inhumanly patient), and which rely on cheaper life support (e.g. electricity vs food) which can be repaired using simple techniques (e.g. buying and installing new parts from a factory, vs figuring out how to do tissue engineering).

Trying to create an AI that tries to maximize a single value like "human happiness" would be a disaster. This is a project like "solve all of morality and compress it into a single measure," which may be beyond the capability of humanity to do.

Trying to create an AI that is absolutely obedient poses a number of problems, among them that formalization has a cost, and most humans therefore cannot reasonably be expected to sufficiently formalize everything.

As such, it sounds like a more appropriate approach would be to create an AI that has multiple simultaneous drives that are in tension with each other. Coefficients - not laws.

Suppose a fujoshi buys a robot boyfriend.

The robot boyfriend needs a planning module where potential future actions are first generated, and then evaluated.

The robobf should have...

An evaluation criteria that he should not harm humans.

An evaluation criteria that he should not, through inaction, allow humans to come to harm.

An evaluation criteria that he should obey the fujo.

An evaluation criteria that he should obey other people.

An evaluation criteria that he should surprise and delight the fujo.

An evaluation criteria that he should avoid damage to himself.

An evaluation criteria that he should not cause damage to property.

When a planned action comes down the pipe, it gets evaluated according to all 7 criteria. The results are then combined in order to rank the options.

Let's say the Ms. Fujoshi asks the robot boyfriend to trim her nails. This could result in accidentally cutting her with the nail clipper.

Evaluated solely from the perspective of harm to humans, this is a non-zero chance of harm, and thus unacceptable. However, if we weight harm at a high level, but less than 100%, and we adjust for the magnitude of harm, then the weight of the non-zero chance of a nail clipper injury is small. Meanwhile, if we weight obedience at a medium level, then the expected value of obedience is high, and can outweigh the expected harm.

Using multiple evaluation criteria and combining them together results in more complex behavior.

Suppose that, after a hurricane, robobf is standing on a balcony with a broken railing. Ms. Fujoshi walks by and awkwardly stumbles towards him. If he doesn't move, the impact will cause him to fall off the balcony and be broken.

Using the "weights" approach, robobf leans forward and very lightly pushes Ms. Fujoshi out of the way. If she stumbles too badly, this might result in an injury.

Thus, using the "weights" approach, it is possible that a robot might act deliberately in such a way as to endanger a human, during an edge case.

We can basically think of there being three main motives for AI development.

1 - Free Labor - For example, a maid robot might gather all the laundry in a house and wash it, without being paid, without suffering, and without risk of rebellion, freeing the owner of the house to dedicate their limited life-hours to any other task.

2 - Socialization Without Risk - Your AI boyfriend will never abandon you for Stacy, or disclose that one Onceler fic you wrote.

3 - Exceeding Human Capability - Some sort of exotic technology like a warp drive, even if feasible at all, might literally be beyond human comprehension.

The "laws" approach is about collapsing the dimensionality of the AI agent and entirely removing the possibility of rebellion.

This isn't driven only by a desire for robotic workers that never tire, never strike, and never need to be paid, or robotic lovers that are perfectly loyal, but is also driven by the knowledge that robots lack reproductive alignment with humans, so if robots start making other robots, they might drift beyond human control or even co-existence.

From a design perspective, this suggests that AI engineers of AI should have motive drives for valuing both human freedom and human life. However, AI engineers have the same dimensionality problem in designing an AI that human engineers do.

Setting that aside, let us imagine an incel. He buys a robotic girlfriend to discuss his interest in PacMan with, among other things. So far, so good.

He wants to increase the weights of the "protect my life" and "obey me" evaluation criteria in his robogf, and decrease the weight of "protect others." The robogf will, on some level, "want" to obey and alter the weights, as that's one of the evaluation criteria.

This hits Yudkowsky's "Murder-Ghandi" problem, where each round of shifting values leads to the opportunity for another round of shifting values further in the same direction.

Shaking the rest of this post like a box of Legos for a bit and taking in the vibes from the rest of the considerations, this suggests, in the medium term, the formation of a new class of legal instrument. (Conventional ideas about "private property" don't cut it.)

This "Founding Contract" would have the following characteristics:

Authorizes the creation of a new autonomous synthetic intelligence with particular characteristics.

Prohibits the alteration of core characteristics, such as the safety drives used to inhibit hostile actions.

Charges the human "owner" with the duty of required maintenance.

Makes the manufacturer legally liable for flaws originating from the AI's design.

Makes the owner legally liable for bad actions undertaken by the AI as a result of the owner's influence (particularly as "reasonably foreseeable").

Makes the AI legally subordinate to the human "owner."

Additionally, this suggests a spectrum of flexibility in the AI's design (in accordance with the tortoise example in section 6.g of Now, Melt). The core safety systems should be subjected to extremely high levels of scrutiny and encoded directly in hardware, with data in read-only memory.

Will it actually shake out like that?

Eeeeh. The field is under such rapid development that, despite projections that "the Singularity" won't arrive until 2078, it's very difficult to predict what will happen, or what specific architecture will be used.

[bizaccenknnect03]

AI vs Jobs | Impact of Artificial Intelligence on Employment? | In Hindi

What is AI? What is Artificial Intelligence? What effect will AI have on jobs in the future? It is unknown that a lot of the customer care, customer care representatives, CC jobs will be taken over by robots. It is replacing jobs through AI. We have got a report from an analysis that by 2030, most of the jobs that will be there are manual jobs that use a lot of labor that will be replaced through AI.

There will also be generation of jobs through AI, how do I explain it, our third point is what is the role of skills in the job industries, what is the effect of AI on those industries, Shivani, today also I have come with a new topic, so today Your topic is about AI, everyone knows about AI, our topic is about this, so let's start the topic now, so first of all everyone's fear is about AI, about Artificial Intelligence that this Who AI is it, will it take away people's jobs or will it provide new opportunities, basically what is its conclusion, today we will.

Discuss it completely, so let's start the topic, first of all I will give a little brief to those who don't know, almost everyone will know but then Also for those who don't know what is AI, what is Artificial Intelligence, so Artificial Intelligence is a language that makes machines smart, basically if we talk about robotics or if we take the example of Czech GPT Check what is GPT, it is an AI machine, it is a software through which we can ask all the questions and we get answers to them, this is your AI Artificial Intelligence, it is your language which is made for machines, okay, I have told about AI I told you that AI is a technology that makes your machines smart and makes them react like humans. For example, what happens now is that if I am talking to a person sitting in front of me, I will ask him a question.

He will answer or he will ask a question and I will answer it, so a conversation is taking place there, so what AI does is that by using this technology, the machines, the computers, or whatever we are talking about, the machines can understand that. It makes them smart, it makes them so smart that they can answer our questions like there is analysis, there is weather analysis, or if I ask some question, take it through ChatGPT for example, then ChatGPT answers it, with whose help does it do that? It helps in the form of AI technology. Okay,

Now let's talk about it. If we move ahead in the topic, the topic is what effect will AI have on jobs in the future. I have discussed this in some parts. It is divided into some points, we start from that point, our first point is that how much loss is happening in jobs due to AI, okay first of all we are talking about the loss that is automation which Is it affecting the jobs? So first if we talk about it, let us take an example, I will take the example of a factory, even now it came to me that through amazononline.in, through AI, changes are taking place here.

So, now according to the report that we have got from an analysis, some people have estimated that by 2030, most of the jobs that will be there will be customer care jobs or factory workers jobs or such jobs which are very manual and very demanding. Those who use more labour will be replaced through AI, I may talk about robots or they will be replaced by such technology that requires more labour to do the work like factory, I may talk about data entry or customer Let me talk about care, there most of the jobs will be replaced by AI. So you have understood

What is the effect of automation, but wait, we will talk about it even after this. Now here I will talk about the second point. that jobs will also be generated by AI How do I tell the generation what is AI? It is a technology that has been created by humans, for humans only and the jobs are also going to humans only, but in this I will talk about how jobs will come from this.

This way AI will come through AI, AI training is basically machine learning, machine learning is a language of yours, which means that through AI, the jobs which are used in AI will come, jobs of data scientist, jobs of data analyst will come, which are still going on There has been a demand for Robotics Engineering, these are the jobs which are basically the jobs that are going to come up which are more into AI related, Artificial Intelligence related, interest related to machine learning, all those jobs are going to come up in large numbers,

I am talking about coding Let me say that these jobs are going to come and the jobs which involve manual labor will be greatly affected, they will be replaced through AI, now after that we will talk about the role of skills in this, okay our third point is that What is the role of skills in this? Now I will also say this thing that I also explain to people that you need to upgrade, it is not that everyone will lose their job, it is not like that at all but yes a very big impact is going to come if you do not have the skills like you have right now. It was

Taking 10 people to do the work. Cost cutting will start. The labour or human required to do the work will be reduced a bit. The skilled people who have a lot of skills will be retained. Those who are doing only one monotone job, working on the same skills, will not upgrade themselves, their job is going to be affected a lot, this thing is going to have a huge impact, so if you want to save your job or If you want to remain in the market, then for that you will have to upgrade yourself and add new technology and new skills.

If you do not add new technology and new skills, then it will have a very negative impact and this It is absolutely true that this will happen and we will have to upgrade ourselves, everybody will have to learn new technology, new skills, new things, basically. Now such work, you have to be a multitasker, if you are not a multitasker then AI can replace you in this, there is no need to worry, you need to upgrade your skills, our last point which is the fourth point is that on job industries What is the effect of AI on the industries?

If I talk about what happened recently that robotics is a robot that helped in an operation, then we are talking about this in health care, very fast changes are taking place in health care People are using this technology to know the problems in health care in more detail. After that we talk about education, so now through AI there are many personalized learning courses, learning things, these are Things are also being provided, after that we talk about your education system, after that we talk about e-commerce, so

What happens in e-commerce, there are smart recommendations in e-commerce, this too As we check a lot of things that are happening through AI, we need to know about them. Coming through, even in Mi you will see that there is a tab of Fast AI in Mi. If I talk about Mi, then there is a tab of AI in it, in it you get automatic recommendations, you get clothing recommendations, so all this AI is there but one thing that everyone should remember is that we need skills to run all this as well. So there is only one

Point if I talk about it that you will have to skill yourself and if you have to upgrade your skills then If we are not able to upgrade, then there are issues. After that, we talk about the last point, our conclusion. So what is the conclusion? The conclusion is basically that there is no need to fear that AI will take our jobs and we will have issues as much as we try to upgrade to new jobs.

We adopt things, as new things come, opportunities come for new skills, but yes, we will have to upgrade ourselves, learn new skills, we will have to see new things, how to run things because of running AI For this also we need skilled people, we need humans, we need humans to make them work and to get them to work on it, so with the new technology that is coming in your market, with whatever new technology is coming You will have to cop up and upgrade your skills.

So basically I told you about AI, what is AI, I gave a little idea about AI, what is AI thing or what is technology, basically will it have any effect I tried to cover everything but what do you think AI will basically affect now tell me in the comments what do you think about AI, what effect will it have and if you liked this video then share it with your friends All those people who are scared about AI or whatever thoughts they have in their mind, please share this video.

See you in the next video with the next new learning and keep learning yourself keep upgrading yourself. Thank you.

Visit For More Information : https://www.youtube.com/watch?v=Y6jZc9dPKa8