#Machine Learning in Manufacturing Market

[sameertope]

Evolving Manufacturing through Machine Learning: Analyzing Market Trends, Applications, and Competitive Scene till 2030

As per the latest research report by Kings Research, the global machine learning in manufacturing market was valued at USD 921.3 Million in 2022 and is estimated to grow to USD 8,776.7 Million by 2030, recording a robust 33.35% CAGR from 2023-2030. The study covers segmentation analysis, restraints, drivers, lucrative growth opportunities, challenges, regional analysis, and competitive landscape, along with an in-depth study of the key players leading the market.

An in-depth analysis of the global machine learning in manufacturing market has been conducted on the basis of various research methodologies, such as primary and secondary research, qualitative and quantitative research, and Porter's five forces model, among others. The goal of the research study is to provide industry leaders with lucrative growth opportunities and insights into the latest trends, as well as continuously evolving market dynamics on a global level.

Request Sample PDF of the Report: https://www.kingsresearch.com/request-sample/machne-learning-in-manufacturing-market-22

Segmentation Analysis

The global machine learning in manufacturing market study will provide readers with detailed information about various segments such as application, type, and so forth. The primary goal of segmentation analysis is to understand the diverse needs, preferences, and behaviors of different customer segments, enabling businesses to tailor their marketing strategies and offerings more effectively.

Competitive Landscape

The competitive landscape covered in the global machine learning in manufacturing market report offers insights into the overall market environment specifically focusing on the companies that are operating in the sector to gain a superior industry footing, attract customers, and accomplish corporate objectives. The study gives a detailed analysis of key players, highlighting their strengths, weaknesses, strategies, and market positioning.

Key players

Rockwell Automation

Robert Bosch GmbH

Intel Corporation

Siemens

General Electric Company

Microsoft

Sight Machine

SAP SE

IBM Corporation

The global Machine Learning in Manufacturing Market is segmented as:

By Production Stage

Pre-Production

Post-Production

By Job Function

R&D

Manufacturing

Finance

Sales

Marketing

Others

By Application

Semiconductors and Electronics

Heavy Metals & Machine Manufacturing

Pharmaceuticals

Automobile

Energy & Power

Food & Beverages

Others

Market Dynamics

The research report on the global machine learning in manufacturing market includes factors that are expected to influence consumer behavior, trends, and changes within a market over the forecast period. These dynamics are driven by the interactions between supply and demand, consumer behavior, competition, technological advancements, economic conditions, government policies, and other external influences.

Regional Analysis

While studying specific markets, it is necessary to understand and analyze market trends, customer behavior, and business performance at a regional or geographical level. Regional analysis involves dividing a larger market or territory into smaller geographic areas to gain insights into specific regional patterns and variations.

The global machine learning in manufacturing market is meticulously segmented into various regions, namely North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. This approach enables the provision of region-specific information.

Why Buy This Report?

Gain comprehensive insights into market trends and growth drivers.

Make informed business decisions with accurate market forecasts.

Stay ahead in the industry with a thorough competitive analysis.

Identify lucrative regional and segment opportunities.

Strategically plan investments and expansions in the global machine-learning in manufacturing market.

About Us:

Kings Research stands as a renowned global market research firm. With a collaborative approach, we work closely with industry leaders, conducting thorough assessments of trends and developments. Our primary objective is to provide decision-makers with tailored research reports that align with their unique business objectives. Through our comprehensive research studies, we strive to empower leaders to make informed decisions.

Our team comprises individuals with diverse backgrounds and a wealth of knowledge in various industries. At Kings Research, we offer a comprehensive range of services aimed at assisting you in formulating efficient strategies to achieve your desired outcomes. Our objective is to significantly enhance your long-term progress through these tailored solutions.

[labzenix]

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[eibaqin]

umumnya fastener dapat diklasifikasikan menjadi dua tipe yang berdasarkan pada cara pengencangan dan bentuk kepalanya yaitu baut ( bolt ) dan sekrup ( screw ).

Di era ini kebutuhan konsumen akan banyaknya produktivitas fastener semakin melonjak, bahkan hampir seluruh bidang industri manufaktur,

[despazito]

is it like…unethical to have anime figurines? Because of overproduction? Are you a bad person if you have those or video games or something? I’m not trying to be rude I’m just genuinely asking if those are things that you should abstain from if you want to be a good person

i think it can be easy for guilt-prone people like myself to get carried away and argue our way into a corner where everything we enjoy and consume is in some way unethical or wasteful, but imo the key is always moderation and learning where the stuff we like comes from.

i'll admit i'm not really into fig collecting, i like soft toys so i know more about the production of plush compared to the life cycle of a figurine. if you care about workers rights and being less wasteful, you can research different manufacturers and buy from those whose production methods match your values. yes its all plastic but i don't think abstaining from all plastic consumption is sadly realistic.

the easiest way to be an eco-conscious fig collector is to buy used figs from the resell market, if you're into older series this is often the only way to get merch anyway. on average japanese used merch is in better shape than north american merch and resellers have higher quality control standards.

you can also be vocal and give feedback to merch companies and support products that are more eco-friendly. Goodsmile recently tried to launch a greener cardboard box for their nendoroids, but unfortunately backtracked due to consumer backlash. i think we should also have a discussion about blind boxes, i love the knickknacks but imo the concept encourages excess consumption. being vocal and organizing like-minded people about steering your hobby in a more responsible direction i think is more productive than just abandoning something you enjoy.

I think it's just important to be mindful of overconsumption. are you buying this collectors item just to tick off a box or is it something you will genuinely cherish? sometimes it can be both, but if you find yourself collecting more out of obligation to your collection than out of joy i would reassess my relationship with said collection.

i didn't touch on video games but considering many games now are digital releases i don't think its really harmful to buy games or ever was. i think we should ask ourselves how much graphics power is enough and to keep games accessible to lower performing older machines instead of an arms race demanding you keep up with the latest specs to run a game that inexplicably requires 100 GB of disc space. i think the biggest battle is to vouch for the longevity of gaming PCs to reduce e-waste and dialing back required specs in the game dev industry, some installs nowadays are just needlessly bloated.

i would heavily caution against falling into a good person/bad person dichotomy with stuff like this, it can really mess with your head to have this mindset and at its most severe can enter moral OCD territory. unless you specifically enjoy collecting orphan skulls as a hobby i think there's always levels of nuance.

[askagamedev]

Summer 2025 Game Development Student Internship Roundup, Part 2

Internship recruiting season has begun for some large game publishers and developers. This means that a number of internship opportunities for summer 2025 have been posted and will be collecting applicants. Internships are a great way to earn some experience in a professional environment and to get mentorship from those of us in the trenches. If you’re a student and you have an interest in game development as a career, you should absolutely look into these.

This is part 2 of this year's internship roundup. [Click here for part 1].

Associate Development Manager Co-op/Internship - Summer 2025 (Sports FC QV)

Game Product Manager Intern (Summer 2025)

Music Intern

EA Sports FC Franchise Activation Intern

Associate Character Artist Intern

Client Engineer Intern

Visual Effects Co-Op

Associate Environment Artist Co-Op (Summer 2025)

Game Design Intern (Summer 2025)

Game Design Co-Op (Summer 2025)

Concept Art Intern - Summer 2025

UI Artist Intern - Summer 2025 (Apex Legends)

Assistant Development Manager Intern

Global Audit Intern

Creator Partnerships Intern - Summer 2025

Technical Environment Art Intern - Summer 2025 (Apex Legends)

Intern, FC Franchise Activation, UKI

Tech Art Intern - Summer 2025 (Apex Legends)

Software Engineer Intern

UI Artist Intern

Game Designer Intern

FC Franchise Activation Intern

Software Engineer Intern

Product UX/UI Designer

Software Engineer Intern

Enterprise, Experiences FP&A Intern

Game Designer Intern

Software Engineer Intern

Development Manager Co-Op (Summer 2025)

Software Engineer Intern

PhD Software Engineer Intern

Character Artist Intern

2D Artist Intern - Summer 2025

Software Engineer Intern (UI)

Entertainment FP&A Intern

Game Design Co-Op (Summer 2025)

Data Science Intern

Production Manager Intern

Software Engineer Intern

Channel Delivery Intern

FC Pro League Operations Intern

World Artist Intern

Experience Design Co-Op

Media and Lifecycle Planning Intern

Software Engineer Intern - Summer 2025

Software Engineer Intern - Summer 2025

Intern, FC Franchise Activation, North America

Creative Copywriter Intern

Game Design Intern

Social Community Manager Co-Op

Business Intelligence Intern

Software Engineer Intern (F1)

Total Rewards Intern - MBA level

Intern - Office Administration

Digital Communication Assistant – Internship (6 months) february/march 2025 (W/M/NB)

International Events Assistant - Stage (6 mois) Janvier 2025 (H/F/NB)

Intern Cinematic Animator

Research Internship (F/M/NB) - Neural Textures for Complex Materials - La Forge

Research Internship (F/M/NB) - Efficient Neural Representation of Large-Scale Environments - La Forge

Research Internship (F/M/NB) – High-Dimensional Inputs for RL agents in Dynamic Video Games Environments - La Forge

Research Internship (F/M/NB) – Crafting NPCs & Bots behaviors with LLM/VLM - La Forge

3D Art Intern

Gameplay Programmer Intern

Intern Game Tester

Etudes Stratégiques Marketing – Stage (6 mois) Janvier 2025 (F/H/NB)

Localization Assistant– Stage (6 mois) Avril 2025 (F/H/NB)

Fraud & Analyst Assistant - Stage (6 mois) Janvier 2025 (F/H/NB)

Payment & Analyst Assistant - Stage (6 mois) Janvier 2025 (F/H/NB)

Media Assistant – Stage (6 mois) Janvier 2025 (F/H/NB)

IT Buyer Assistant - Alternance (12 mois) Mars 2025 (H/F/NB)

Event Coordinator Assistant - Stage (6 mois) Janvier 2025 (H/F/NB)

Communication & PR Assistant - Stage (6 mois) Janvier 2025 (F/H/NB)

Brand Manager Assistant - MARKETING DAY - Stage (6 mois) Janvier 2025 (F/N/NB)

Manufacturing Planning & Products Development Assistant - Stage (6 mois) Janvier 2025 (H/F/NB)

Retail Analyst & Sales Administration Assistant - Stage (6 mois) Janvier 2025 (H/F/NB)

UI Designer Assistant - Stage (6 mois) Janvier 2025 (F/M/NB)

Esports Communication Assistant

Machine Learning Engineer Assistant – Stage (6 mois) Janvier/Mars 2025 (F/H/NB)

Social Media Assistant – Stage (6 mois) Janvier 2025 (F/H/NB)

[realcleverscience]

AI

As someone who follows AI news a lot, I really wish the general public was aware of what's going on with it.

AIs are improving at a dramatic rate, I'd say basically doubling in ability every 6 months (possibly even exponential growth by now). If you base your idea of what AI can do based on a tool you used last year, then you are wildly out of date.

Hell, if you base your idea of AI on the tools out right now, you're still out of date - because in 6 months the field will have advanced dramatically.

Again, this sounds crazy but that's because we're not used to this kind of growth. But consider this: Some people have estimated that the latest AI tools could potentially do 1-9% of economically useful tasks. (e.g. it can write emails for you, saving you time. Or it can summarize spreadsheets. whatever.) Let's assume AI can currently do just 1% of tasks. Clearly no-one is in danger of losing their job from just 1%. But if AI is doubling its abilities every 6 months, that means in a year it'll do (1x2x2 =) 4%. In two years from now (4x2x2=) 16%. In three years (16*2*2=) 64%. In four years, 100%. Now, I think there are still challenges to reaching 100%. (E.g. AIs still have master robotics; better 'real world' modeling, and more) But I think we could definitely hit at least 50% with our current trajectory. This could mean that everyone doubles their output, or it could mean that companies maintain output but fire half the workers. Or some mix. But if even 10% of people lose their jobs, our economy is gonna be in crisis. If 50% lose their jobs, it will be a nightmare. (The US great depression of the 1920s saw around 24% unemployment; the US already has 4% unemployment). In addition, even minor growth in unemployment can seriously stress our social safety nets (which are already being damaged by the current administration in the us). So let alone 5, 10, 20 percentage point gains.

And if AI keeps its pace of improvement, that basically means that within a year, the situation should get twice as severe. Forever. AIs will never get worse at performing. They will continue to learn and improve at ever faster rates while human roles will shrink by ever faster rates.

in short: AIs are getting very good, very quickly. The job market is going to get turned on its head. There are a lot of major changes just around the corner. AI is here. AGI is nearly here. ASI is on the horizon.

Buckle Up.

p.s. Even if you hate AI, you cannot stop it. Also, there are lots of reasons to love AI. It could help us achieve a world of plenty for all, excellent health, etc. But, that depends on society. If we keep our current system, it will be a nightmare. But if we reconfigure, this could be the greatest gift to humanity at large. Pps. I know a lot of people are concerned about ai companies controlling ai. i want you all to know that there is a ton of open-source programming for AI happening. The capitalist angle here isn't really the ai as much as the data and data centers. and even then, the data is becoming less important (e.g. we can now create artificial data), so it's really about the people who own the server farms and chip manufacturing (as well as the supply chains). Anyways, my point here is that we shouldn't oppose AI. We should recruit AI to our cause. i.e. we need new economics for an age where machines do most of the work.

[thursdayisbetterthanfriday]

Cybernetics with Chinese Characteristics & why we suck at the real Grand Strategy Game

Part 2 - The Quickening

Back in 2023, I wrote this more blog-like post about the mid 20th century McCarthyite purges of the Jet Propulsion Laboratory and the knock on effects that had - Namely the inception of the Chinese nuclear program, one-child policy and Chinese computing scene.

Since nothing is new under the sun, we have recently witnessed yet another example of America shooting itself in the foot, yet again, due to it's McCarthyite style purge of Chinese technology.

The release of the Chinese created AI system DeepSeek R1 last week has lead to the largest US stock market loss in history with NVIDIA stock decimated.

A record $465 Billion was wiped off its valuation in a single day. In 2024, the government of Turkey spent this much in a year on it's responsibilities?

Why did this happen?

As always, a lot can be put down to US foreign policy, and the in-intended implications of seemingly positive actions.

Do you want to start a trade war?

Back in the relatively uncontroversial days of the first Trump Presidency (Yes it does feel odd saying that) there were scandals with hardware provided by Chinese company Huawei. This led to the  National Defense Authorization Act for Fiscal Year 2019 which explicitly banned Huawei and ZTE's hardware from use in US Government institutions. It also meant the US had to authorise US component manufacturer purchases by these companies.

Crucially this had a 27 month window. This allowed both companies to switch suppliers, and production to domestic suppliers. This actually led to Chinese chip advances. Following on from this came the 2022 move by the US Department of Commerce: "Commerce Implements New Export Controls on Advanced Computing and Semiconductor Manufacturing Items to the People’s Republic of China (PRC) ". This further limited the supply of semiconductor, supercomputer, and similar hardware to the PRC and associated countries.

Ok, well so far this is fairly dry stuff. You might think it would hamper Chinese development and, to some extent, it did.

It also proved to be the main catalyst for one financial quant.

Meet the Quant

Meet Liang Wenfeng (梁文锋). Educated to masters level, Liang was keen to apply machine learning methods to various field, but couldn't get a break. Finally, in the mid 2000's, he settled on a career investigating quantitative trading using machine learning techniques.

He became successful, founding several trading firms based around using machine learning methods, but his interest in base AI never seemed to cease. It was in 2021 that he started purchasing multiple NVIDIA GPUs to create a side project, leading to the creation of DeepSeek in 2023.

Now, due to import limitations, there were limitations on computation. This, however, did not stop DeepSeek's programming team.

Instead they used it as their strength.

Constrains Breed Innovation

For many years, the Western model of AI releases have focussed on making ever larger and larger models.

Why?

Let's break this down from an evolutionary point of view. Modern Western technology companies are largely monopolistic and monolithic. Many of these companies have previously hired staff at higher salaries not to fill roles, but to deny their competitors, and middle market firms, high-flying staff.

They also closely guard trade secrets. What's the training data? What algorithms were used in construction? Guess you'd better chat up some Silicon Valley bros at parties to find out.

For these kinds of firms, having control over large models, housed in data centres makes perfect sense. Controlling model deployment on their own computing systems, and not using local machines, means that they can not only control their systems more carefully, it also means that they can gatekeep access.

If your business model is to allow people to access your models on your servers, and your employees are focussed on making the biggest, best, models, there is no impetus to innovate more efficient, smaller models.

Companies such as OpenAI therefore have the following traits:

Research/Model focus on size over efficiency

Profit driven culture, with emphasis on closed source code

OpenAI's initial focus was as a non-for-profit developing Artificial General Intelligence. This became a for-profit driven company over time. - “I personally chose the price and thought we would make some money.” - Sam Altman

Staff working within paradigm they set in the early 2020's with established code libraries and direct contact with hardware companies creating chips

Significant capital investment - Upwards of several $ billions

DeepSeek, in comparison, is slightly different

For DeepSeek, necessity made innovation necessary. In order to create similar, or better models, than their counterparts, they needed to significantly optimise their code. This requires significantly more work to create, and write, libraries compared to OpenAI.

DeepSeek was started by financial quants, with backgrounds in mainly mathematics and AI. With a focus on mathematics and research, the main drive of many in the company has been exploration of the research space over concerns about profitability.

DeepSeek has also done what OpenAI stopped years ago: actually releasing the code and data for their models. Not only can these models therefore be run via their own gated servers, anyone can replicate their work and make their own system.

For DeepSeek, their traits were:

Research/Model focus on both efficiency and accuracy

Research driven culture, with open nature - “Basic science research rarely offers high returns on investment” - Liang Wenfeng

Strong mathematical background of staff, with ability to work around software, and hardware, constraints

Low capital investment of around $5.5 million

From an evolutionary point of view, DeepSeek's traits have outcompeted those of OpenAI.

More efficient models cost less to run. They also more portable to local machines.

The strong ability of DeepSeek's research focussed staff allowed them to innovate around hardware constraints

Opening up the code to everyone allows anyone (still with the right hardware) to make their own version.

To top it off, the cost to make, and run, DeepSeek R1 is a fraction of the cost of OpenAI's model

House of Cards

Now we can return to today. NVIDIA has lost significant market value. It's not just limited to NVIDIA, but to the entire US technology sector with the most AI adjacent companies losing from 10% to 30% of their valuation in a single day.

The culture, and business model, of OpenAI isn't just limited to OpenAI, but to the entire US technology ecosystem. The US model has been to create rentier-style financial instruments at sky-high valuations.

US tech stocks have been one of the only success stories for America over the past few decades, ever since the offshoring of many manufacturing industries. Like a lost long-unemployed Detroit auto-worker the US has been mainlining technology like Fentanyl, ignoring the anti-trust doctors advice, injecting pure deregulated substances into its veins.

The new AI boom? A new stronger hit, ready for Wall Street, and Private Equity to tie the tourniquet around its arm and pump it right into the arteries.

Like Prometheus, DeepSeek has delved deep and retrieved fire from the algorithmic gods, and shown it's creation to the world. The stock market is on fire, as the traders are coming off of their high, realising they still live in the ruin of barren, decrepit, warehouses and manufactories. The corporate heads, and company leaders reigning over the wreckage like feudal lords, collecting tithes from the serfs working their domain.

A Tale of Two Cities

The rise of DeepSeek isn't just a one-off story of derring-do in the AI world: It's a symbolic representation of the changing world order. DeepSeek is but one company among many who are outcompeting the US, and the world, in innovation.

Where once US free-markets led the world in manufacturing, technology and military capability, now the US is a country devoid of coherent state regulated free-market principles - its place as the singular world power decimated by destroying the very systems which made it great.

"Our merchants and master-manufacturers complain much of the bad effects of high wages in raising the price, and thereby lessening the sale of their goods both at home and abroad. They say nothing concerning the bad effects of high profits. They are silent with regard to the pernicious effects of their own gains. They complain only of those of other people." - Adam Smith, The Wealth of Nations

By selling the jobs of working class communities to overseas businesses, destroying unions and creating rentier based business models without significant anti-trust measures, US business and political elites have sealed the present fate of the country.

The CCP led, but strongly anti-trust enforcing, China has been able to innovate, ironically, using the free-market principles of Adam Smith to rise up and create some of the world's best innovations. The factories, opened by Western business leaders to avoid union/worker labour costs in their own countries, have led Shenzhen, and similar cities, to become hubs of technological innovation - compounding their ability to determine the future of technologies across the world.

Will America be able to regain its position on top? It's too early to say, but the innovative, talented, people who made America in the 20th century can certainly do it again.

As Franklin D. Roosevelt once said: “The liberty of a democracy is not safe if the people tolerated the growth of private power to a point where it becomes stronger than the democratic state itself...

We know now that Government by organized money is just as dangerous as Government by organized mob.

Never before in all our history have these forces been so united against one candidate as they stand today. They are unanimous in their hate for me—and I welcome their hatred.”

Until then, here's a farewell to the American Century 在那之前, 再见美国世纪

[bliow]

AGARTHA Aİ - DEVASA+ (4)

In an era where technology and creativity intertwine, AI design is revolutionizing the way we conceptualize and create across various industries. From the runway to retail, 3D fashion design is pushing boundaries, enabling designers to craft intricate garments with unparalleled precision. Likewise, 3D product design is transforming everything from gadgets to furniture, allowing for rapid prototyping and innovation. As we explore these exciting advancements, platforms like Agartha.ai are leading the charge in harnessing artificial intelligence to streamline the design process and inspire new ideas. 

AI design

Artificial intelligence (AI) has revolutionized numerous industries, and the realm of design is no exception. By leveraging the power of machine learning and advanced algorithms, AI is transforming the way designers create, innovate, and deliver their products. AI-driven tools enable designers to harness vast amounts of data, allowing for more informed decision-making and streamlined workflows.

In the context of graphic design, AI can assist artists in generating ideas, creating unique visuals, and even automating repetitive tasks. For instance, programs powered by AI design can analyze trends and consumer preferences, producing designs that resonate with target audiences more effectively than traditional methods. This shift not only enhances creativity but also enables designers to focus on strategic thinking and ideation.

Moreover, AI is facilitating personalized design experiences. With the help of algorithms that analyze user behavior, products can be tailored to meet the specific needs and tastes of individuals. This level of customization fosters deeper connections between brands and consumers, ultimately driving customer satisfaction and loyalty in an increasingly competitive market.

3D fashion design

In recent years, 3D fashion design has revolutionized the way we create and visualize clothing. Using advanced software and tools, designers can create lifelike virtual garments that allow for innovative experimentation without the need for physical fabric. This trend has not only streamlined the design process but has also significantly reduced waste in the fashion industry.

Moreover, 3D fashion design enables designers to showcase their creations in a more interactive manner. By utilizing 3D modeling and rendering technologies, designers can present their collections in virtual environments, making it easier for clients and consumers to appreciate the nuances of each piece. This immersive experience also helps in gathering valuable feedback before producing the final product.

Furthermore, the integration of 3D fashion design with augmented reality (AR) and virtual reality (VR) technologies is bringing a fresh perspective to the industry. Consumers can virtually try on clothes from the comfort of their homes, thereby enhancing the shopping experience. As this field continues to evolve, it promises to bridge the gap between creativity and technology, paving the way for a sustainable and forward-thinking fashion future.

3D product design

3D product design has revolutionized the way we conceptualize and create products. With advanced software tools and technologies, designers can now create highly detailed and realistic prototypes that are not only visually appealing but also functional. This process allows for a quicker iteration of ideas, enabling designers to experiment with various styles and functionalities before arriving at the final design.

One of the significant advantages of 3D product design is the ability to visualize products in a virtual environment. Designers can see how their creations would look in real life, which is essential for understanding aesthetics and usability. Additionally, this technology enables manufacturers to identify potential issues in the design phase, reducing costs associated with prototype development and rework.

Moreover, the rise of 3D printing has further enhanced the significance of 3D product design. Designers can swiftly turn their digital models into tangible products, allowing for rapid prototyping and small-batch manufacturing. This agility not only speeds up the time-to-market for new products but also paves the way for more innovative designs that were previously impossible to execute.

Agartha.ai

Agartha.ai is a revolutionary platform that merges artificial intelligence with innovative design, creating a new avenue for designers and creators alike. With the rapid advancements in technology, Agartha.ai leverages AI to streamline various design processes, enabling users to produce unique and captivating designs with ease.

The platform provides tools that empower both emerging and established designers to explore the possibilities of AI design. By utilizing intelligent algorithms, Agartha.ai can assist in generating design options, ensuring that creativity is not hindered but enhanced. This results in a more efficient workflow and allows designers to focus on the conceptual aspects of their projects.

One of the standout features of Agartha.ai is its ability to adapt to different design disciplines, such as 3D fashion design and 3D product design. By supporting a broad spectrum of design fields, it positions itself as a versatile tool that meets the evolving needs of today's creative professionals. Whether it's crafting intricate fashion pieces or developing innovative product designs, Agartha.ai is at the forefront of the design revolution.

[mostlysignssomeportents]

This day in history

THIS WEDNESDAY (October 23) at 7PM, I'll be in DECATUR, GEORGIA, presenting my novel THE BEZZLE at EAGLE EYE BOOKS.

#10yrsago Mercilessly pricking the bubbles of AI, Big Data, machine learning https://spectrum.ieee.org/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts

#10yrsago American businesses devour themselves to enrich the 1% https://www.nakedcapitalism.com/2014/10/goldman-makes-it-official-that-the-stock-market-is-manipulated-buybacks-drive-valuations.html

#10yrsago WATCH: top Scientologists heaping abuse on apostate https://www.youtube.com/watch?v=EG70fhg0wL4

#5yrsago Griefer terrorizes baby by taking over their Nest babycam…again https://www.siliconvalley.com/2019/10/18/the-voice-from-our-nest-camera-threatened-to-steal-our-baby/

#5yrsago It’s dismayingly easy to make an app that turns a smart-speaker into a password-stealing listening device and sneak it past the manufacturer’s security checks https://arstechnica.com/information-technology/2019/10/alexa-and-google-home-abused-to-eavesdrop-and-phish-passwords/

#5yrsago A shrewd guess about the Haunted Mansion’s mysterious Squeaky Door Ghost https://longforgottenhauntedmansion.blogspot.com/2019/10/the-squeaky-door-ghost.html

#5yrsago Rep Katie Porter: an Elizabeth Warren protege and single mom who destroys bumbling, mediocre rich guys in Congressional hearings https://newrepublic.com/article/155268/house-representative-katie-porter-schools-ben-carson-orea-jamie-dimon

#5yrsago Haunted Mansion/Ikea mashup tee https://www.teepublic.com/t-shirt/4196890-haunted-mansion-ikea-instructions

#1yrago The internet's original sin https://pluralistic.net/2023/10/21/the-internets-original-sin/

Tor Books as just published two new, free LITTLE BROTHER stories: VIGILANT, about creepy surveillance in distance education; and SPILL, about oil pipelines and indigenous landback.

[mariacallous]

In 2023, the fast-fashion giant Shein was everywhere. Crisscrossing the globe, airplanes ferried small packages of its ultra-cheap clothing from thousands of suppliers to tens of millions of customer mailboxes in 150 countries. Influencers’ “#sheinhaul” videos advertised the company’s trendy styles on social media, garnering billions of views.

At every step, data was created, collected, and analyzed. To manage all this information, the fast fashion industry has begun embracing emerging AI technologies. Shein uses proprietary machine-learning applications — essentially, pattern-identification algorithms — to measure customer preferences in real time and predict demand, which it then services with an ultra-fast supply chain.

As AI makes the business of churning out affordable, on-trend clothing faster than ever, Shein is among the brands under increasing pressure to become more sustainable, too. The company has pledged to reduce its carbon dioxide emissions by 25 percent by 2030 and achieve net-zero emissions no later than 2050.

But climate advocates and researchers say the company’s lightning-fast manufacturing practices and online-only business model are inherently emissions-heavy — and that the use of AI software to catalyze these operations could be cranking up its emissions. Those concerns were amplified by Shein’s third annual sustainability report, released late last month, which showed the company nearly doubled its carbon dioxide emissions between 2022 and 2023.

“AI enables fast fashion to become the ultra-fast fashion industry, Shein and Temu being the fore-leaders of this,” said Sage Lenier, the executive director of Sustainable and Just Future, a climate nonprofit. “They quite literally could not exist without AI.” (Temu is a rapidly rising ecommerce titan, with a marketplace of goods that rival Shein’s in variety, price, and sales.)

In the 12 years since Shein was founded, it has become known for its uniquely prolific manufacturing, which reportedly generated over $30 billion of revenue for the company in 2023. Although estimates vary, a new Shein design may take as little as 10 days to become a garment, and up to 10,000 items are added to the site each day. The company reportedly offers as many as 600,000 items for sale at any given time with an average price tag of roughly $10. (Shein declined to confirm or deny these reported numbers.) One market analysis found that 44 percent of Gen Zers in the United States buy at least one item from Shein every month.

That scale translates into massive environmental impacts. According to the company’s sustainability report, Shein emitted 16.7 million total metric tons of carbon dioxide in 2023 — more than what four coal power plants spew out in a year. The company has also come under fire for textile waste, high levels of microplastic pollution, and exploitative labor practices. According to the report, polyester — a synthetic textile known for shedding microplastics into the environment — makes up 76 percent of its total fabrics, and only 6 percent of that polyester is recycled.

And a recent investigation found that factory workers at Shein suppliers regularly work 75-hour weeks, over a year after the company pledged to improve working conditions within its supply chain. Although Shein’s sustainability report indicates that labor conditions are improving, it also shows that in third-party audits of over 3,000 suppliers and subcontractors, 71 percent received a score of C or lower on the company’s grade scale of A to E — mediocre at best.

Machine learning plays an important role in Shein’s business model. Although Peter Pernot-Day, Shein’s head of global strategy and corporate affairs, told Business Insider last August that AI was not central to its operations, he indicated otherwise during a presentation at a retail conference at the beginning of this year.

“We are using machine-learning technologies to accurately predict demand in a way that we think is cutting edge,” he said. Pernot-Day told the audience that all of Shein’s 5,400 suppliers have access to an AI software platform that gives them updates on customer preferences, and they change what they’re producing to match it in real time.

“This means we can produce very few copies of each garment,” he said. “It means we waste very little and have very little inventory waste.” On average, the company says it stocks between 100 to 200 copies of each item — a stark contrast with more conventional fast-fashion brands, which typically produce thousands of each item per season, and try to anticipate trends months in advance. Shein calls its model “on-demand,” while a technology analyst who spoke to Vox in 2021 called it “real-time” retail.

At the conference, Pernot-Day also indicated that the technology helps the company pick up on “micro trends” that customers want to wear. “We can detect that, and we can act on that in a way that I think we’ve really pioneered,” he said. A designer who filed a recent class action lawsuit in a New York District Court alleges that the company’s AI market analysis tools are used in an “industrial-scale scheme of systematic, digital copyright infringement of the work of small designers and artists,” that scrapes designs off the internet and sends them directly to factories for production.

In an emailed statement to Grist, a Shein spokesperson reiterated Peter Pernot-Day’s assertion that technology allows the company to reduce waste and increase efficiency and suggested that the company’s increased emissions in 2023 were attributable to booming business. “We do not see growth as antithetical to sustainability,” the spokesperson said.

An analysis of Shein’s sustainability report by the Business of Fashion, a trade publication, found that last year, the company’s emissions rose at almost double the rate of its revenue — making Shein the highest-emitting company in the fashion industry. By comparison, Zara’s emissions rose half as much as its revenue. For other industry titans, such as H&M and Nike, sales grew while emissions fell from the year before.

Shein’s emissions are especially high because of its reliance on air shipping, said Sheng Lu, a professor of fashion and apparel studies at the University of Delaware. “AI has wide applications in the fashion industry. It’s not necessarily that AI is bad,” Lu said. “The problem is the essence of Shein’s particular business model.”

Other major brands ship items overseas in bulk, prefer ocean shipping for its lower cost, and have suppliers and warehouses in a large number of countries, which cuts down on the distances that items need to travel to consumers.

According to the company’s sustainability report, 38 percent of Shein’s climate footprint comes from transportation between its facilities and to customers, and another 61 percent come from other parts of its supply chain. Although the company is based in Singapore and has suppliers in a handful of countries, the majority of its garments are produced in China and are mailed out by air in individually addressed packages to customers. In July, the company sent about 900,000 of these to the US every day.

Shein’s spokesperson told Grist that the company is developing a decarbonization road map to address the footprint of its supply chain. Recently, the company has increased the amount of inventory it stores in US warehouses, allowing it to offer American customers quicker delivery times, and increased its use of cargo ships, which are more carbon-efficient than cargo planes.

“Controlling the carbon emissions in the fashion industry is a really complex process,” Lu said, adding that many brands use AI to make their operations more efficient. “It really depends on how you use AI.”

There is research that indicates using certain AI technologies could help companies become more sustainable. “It’s the missing piece,” said Shahriar Akter, an associate dean of business and law at the University of Wollongong in Australia. In May, Akter and his colleagues published a study finding that when fast-fashion suppliers used AI data management software to comply with big brands’ sustainability goals, those companies were more profitable and emitted less. A key use of this technology, Atker says, is to closely monitor environmental impacts, such as pollution and emissions. “This kind of tracking was not available before AI-based tools,” he said.

Shein told Grist it does not use machine-learning data management software to track emissions, which is one of the uses of AI included in Akter’s study. But the company’s much-touted usage of machine-learning software to predict demand and reduce waste is another of the uses of AI included in the research.

Regardless, the company has a long way to go before meeting its goals. Grist calculated that the emissions Shein reportedly saved in 2023 — with measures such as providing its suppliers with solar panels and opting for ocean shipping — amounted to about 3 percent of the company’s total carbon emissions for the year.

Lenier, from Sustainable and Just Future, believes there is no ethical use of AI in the fast-fashion industry. She said that the largely unregulated technology allows brands to intensify their harmful impacts on workers and the environment. “The folks who work in fast-fashion factories are now under an incredible amount of pressure to turn out even more, even faster,” she said.

Lenier and Lu both believe that the key to a more sustainable fashion industry is convincing customers to buy less. Lu said if companies use AI to boost their sales without changing their unsustainable practices, their climate footprints will also grow accordingly. “It’s the overall effect of being able to offer more market-popular items and encourage consumers to purchase more than in the past,” he said. “Of course, the overall carbon impact will be higher.”

[elbiotipo]

Notes on technology in Campoestela:

Most spaceships are single-stage-to-orbit. They have rather standard jet engines to lift off from the ground like a standard plane.

To get into orbit, they use a rocket engine that uses a solid fuel made of a HIGHLY combustible (yet stable) carbon-nitrogen compound which allows a better fuel than anything previous. This was first discovered by Iranian scientists who named it "Nafta".

(sí, Beto tiene que estacionar su camión espacial para cargar nafta)

Nafta was a big discovery on its time, allowing cheap SSTO rockets. Nowadays it's produced in many worlds and widely available. It also has uses as weaponry, but it's not that efficient.

Nafta is used for lift-off and orbital burns. For manuevering in space, there are small jets on the nose and tail of spaceships, similar to the Space Shuttle.

Spaceship piloting is still not an easy task, but it's comparable to being a jet pilot, about 4 or 5 years to master. Hard, but something on the reach of many people. People from the generation ship clans are a bit more used to it and often represent an outsized part of space pilots, but there's always many wellers (from down the gravity well) who get their licenses too.

The hardest thing is always landing. Especially given all the different gravities, atmospheres, orbits and such you have to learn in each different case, even with all the automation in the world. Many spacers feel confident sticking to one or at most two or three planets they know.

Pilots that only do shuttle or cargo runs in the same star system or planet are called "Starters", because they go around the same star. It's rude, but many spacers do it.

FTL travel is another thing. FTL travel is done using a ring-like structure that projects a bubble around the ship and takes it to a (completely made-up for the setting) dimension called the Aether. The Aether is one of the meta-dimensions (there might be more) that uphold reality. Conveniently, you can use it as a shortcut to travel between stars, which project "shadows" on the Aether.

The Aether has its own navigation, with currents and whirpools and areas of thick dark matter (which, for cinematic purposes, actually look like bright nebulae) There are routes that are easier to travel and navigate, and these are where the most visited worlds are. Even stars that are close in real space might be very hard to get in Aetheric space, so there's routes that can take you all over the galaxy in a week, while many other places are out of reach.

Navigating the Aether is very similar to flying a plane through a cloudy sky. Some spacer says it's even easier than flying in real space.

Staying on the aether depends on how much you can keep the fields upholding your "bubble". This depends on the energy of your ship. Big ships can travel all over the galaxy but they have enormous energy consumption requirements.

Smaller ships (such as Beto's Mastropiero) dock with a ring-like structure that allows them to make short jumps. The average jump in an explored route is about 12-48 hours, so it's much like aircraft flights.

Exploring new aetheric routes is something that is very romanticized but in reality is a tedious process of jumping, cataloguing new systems (many of them empty and useful only as refuelling stations), seeing where the streams go and end, how they change, and more.

There is no FTL radio or live communication. There is a kind of aetheric radar that allows you to see incoming ships and do some morse-like communication, but it's not very efficient, there is no such thing as a galactic internet (though it's said ancient civilizations had one)

Aether travel engines require very sophisticated manufacturing and materials, which were hard for humans to develop. This was long only in the hands of governments and corporations, but after the Machine War, accessible aether starships hit the civilian market.

Smaller ships are still used by governments (more like loose "leagues") to do what big ships can't: supply satellites and equipment to remote bases, small-scale transport of engineers, researchers, aether "meteorology" and exploration, etc. This is very much like bush planes in remote regions or the role of Aeroflot in developing the USSR.

While humans in the setting, like most species, are composed of many different leagues, cultures and organizations, their technology is remarkably consistent. This is because cheap and reliable spaceflight depends on very reliable standarization. Some of the spaceship parts used six centuries after Gagarin are still the same used in the Soyuz. The ISO is perhaps one of the most enduring legacies of human civilization, along with FIFA.

[eibaqin]

Jika anda memutuskan untuk berusaha sendiri, dibawah ini beberapa tips sukses untuk menjadi pengusaha workshop bubut sendiri ala tukang bubut!

[avesouls]

the makings of a soulless weapon.

how did aveira come to be? a brief insider on how aveira became known as who she is.

warnings: hint towards parental death. i think that's all.

word count: 652. not proofread. merely a lore piece.

work written by avesouls / satorangel. do not steal.

Growing up as a child, Aveira was described as a bright, young girl. A compassionate, delicate flower whose smile rivalled the summer sun. Once an innocent soul who found many joys in the simplest things humankind offered; the sight of her and her mother in the mirror as they brushed their teeth together; the feeling of being on top of the world whenever her father placed her on his shoulders; giggling as she chased her young brother in the local park, whilst her parents watched on fondly on the gingham blanket. 

Many believe that even when you grow into an adult, the child within you lives on. The innocence tucked away comfortably in one’s soul, only to reveal itself once it finds safety within another individual or comfortable environment. At least, this is something that Aveira’s mother had once consoled her with, back when she was afraid of growing up. 

If Aveira had the chance to, she’d scold her mother for lying. 

Since her parents death, growing up had become quite the blur. Whether it was due to all of the changes happening in her life at once,  or because she was forced to grow up sooner than intended, she could never quite explain. Perhaps it was deeper than that, her brain forcefully making her forget what she had to endure in order to become the woman she was now. 

Not by her own design, but by those who had intended to use her for their dirty work. 

An innocent being manufactured into a killing machine. A shady organisation deeply rooted within the black market who were able to provide professional killers to those who had plenty of money to spare or even government officials who wanted to keep order in the most extreme of ways. Forced to train at the same level as what you’d expect within a military practice, Aveira’s mind would eventually be molded into that of a hollow shell. Gone with the innocence that her family had much pride in; gone with the tender, bright joy of a gentle soul; gone with the softness her heart had once embraced. 

Aveira, at the age of 17, would successfully complete her first mission. Blood on her hands that could never be traced back to her. Just like they had planned with the rewiring of her brain. 

At the age of 18, she would eventually be hired by the very woman she soon would kill almost a decade later. A woman with the nickname Keira, who moved to different countries frequently to hide her tracks. A manipulative yet intelligent woman who’s skill with computing could never be rivalled, meaning her only weakness was her physical strength. She initially used Aveira as a bodyguard. 

But after seeing just what this young woman was truly capable of, she used her for her own villainous tasks. At the age of 21, Aveira became her most trusted lackey. Her right-hand man. Truly, her everything. 

Aveira’s skills with many weapons were incredibly useful. Whilst she preferred the close combat that her blade provided, it didn’t take away the skills she had developed with other weapons. Poisons, rifles, even a bow and arrow in some severe cases. Her agility was off the charts in comparison to the others working under her, and with the trained stealth Aveira came with, it wouldn’t take long for her to be a very dangerous being on the premises. 

Today, at the age of 26, Aveira now remains a rogue. With the only thing she truly knows is how to kill, along with the addictive rush adrenaline provided her, she doesn’t see herself stopping. Even when she stares at herself in the mirror, she cannot see that childlike spirit inside her. 

All she can see is the blood that paints her pale skin crimson, a myriad of stories splashed across her flesh that she had no interest in learning or caring for. 

[acuvate-updates]

How Agentic AI & RAG Revolutionize Autonomous Decision-Making

In the swiftly advancing realm of artificial intelligence, the integration of Agentic AI and Retrieval-Augmented Generation (RAG) is revolutionizing autonomous decision-making across various sectors. Agentic AI endows systems with the ability to operate independently, while RAG enhances these systems by incorporating real-time data retrieval, leading to more informed and adaptable decisions. This article delves into the synergistic relationship between Agentic AI and RAG, exploring their combined impact on autonomous decision-making.

Overview

Agentic AI refers to AI systems capable of autonomous operation, making decisions based on environmental inputs and predefined goals without continuous human oversight. These systems utilize advanced machine learning and natural language processing techniques to emulate human-like decision-making processes. Retrieval-Augmented Generation (RAG), on the other hand, merges generative AI models with information retrieval capabilities, enabling access to and incorporation of external data in real-time. This integration allows AI systems to leverage both internal knowledge and external data sources, resulting in more accurate and contextually relevant decisions.

Read more about Agentic AI in Manufacturing: Use Cases & Key Benefits

What is Agentic AI and RAG?

Agentic AI: This form of artificial intelligence empowers systems to achieve specific objectives with minimal supervision. It comprises AI agents—machine learning models that replicate human decision-making to address problems in real-time. Agentic AI exhibits autonomy, goal-oriented behavior, and adaptability, enabling independent and purposeful actions.

Retrieval-Augmented Generation (RAG): RAG is an AI methodology that integrates a generative AI model with an external knowledge base. It dynamically retrieves current information from sources like APIs or databases, allowing AI models to generate contextually accurate and pertinent responses without necessitating extensive fine-tuning.

Know more on Why Businesses Are Embracing RAG for Smarter AI

Capabilities

When combined, Agentic AI and RAG offer several key capabilities:

Autonomous Decision-Making: Agentic AI can independently analyze complex scenarios and select effective actions based on real-time data and predefined objectives.

Contextual Understanding: It interprets situations dynamically, adapting actions based on evolving goals and real-time inputs.

Integration with External Data: RAG enables Agentic AI to access external databases, ensuring decisions are based on the most current and relevant information available.

Enhanced Accuracy: By incorporating external data, RAG helps Agentic AI systems avoid relying solely on internal models, which may be outdated or incomplete.

How Agentic AI and RAG Work Together

The integration of Agentic AI and RAG creates a robust system capable of autonomous decision-making with real-time adaptability:

Dynamic Perception: Agentic AI utilizes RAG to retrieve up-to-date information from external sources, enhancing its perception capabilities. For instance, an Agentic AI tasked with financial analysis can use RAG to access real-time stock market data.

Enhanced Reasoning: RAG augments the reasoning process by providing external context that complements the AI's internal knowledge. This enables Agentic AI to make better-informed decisions, such as recommending personalized solutions in customer service scenarios.

Autonomous Execution: The combined system can autonomously execute tasks based on retrieved data. For example, an Agentic AI chatbot enhanced with RAG can not only answer questions but also initiate actions like placing orders or scheduling appointments.

Continuous Learning: Feedback from executed tasks helps refine both the agent's decision-making process and RAG's retrieval mechanisms, ensuring the system becomes more accurate and efficient over time.

Read more about Multi-Meta-RAG: Enhancing RAG for Complex Multi-Hop Queries

Example Use Case: Customer Service

Customer Support Automation Scenario: A user inquiries about their account balance via a chatbot.

How It Works: The Agentic AI interprets the query, determines that external data is required, and employs RAG to retrieve real-time account information from a database. The enriched prompt allows the chatbot to provide an accurate response while suggesting payment options. If prompted, it can autonomously complete the transaction.

Benefits: Faster query resolution, personalized responses, and reduced need for human intervention.

Example: Acuvate's implementation of Agentic AI demonstrates how autonomous decision-making and real-time data integration can enhance customer service experiences.

2. Sales Assistance

Scenario: A sales representative needs to create a custom quote for a client.

How It Works: Agentic RAG retrieves pricing data, templates, and CRM details. It autonomously drafts a quote, applies discounts as instructed, and adjusts fields like baseline costs using the latest price book.

Benefits: Automates multi-step processes, reduces errors, and accelerates deal closures.

3. Healthcare Diagnostics

Scenario: A doctor seeks assistance in diagnosing a rare medical condition.

How It Works: Agentic AI uses RAG to retrieve relevant medical literature, clinical trial data, and patient history. It synthesizes this information to suggest potential diagnoses and treatment options.

Benefits: Enhances diagnostic accuracy, saves time, and provides evidence-based recommendations.

Example: Xenonstack highlights healthcare as a major application area for agentic AI systems in diagnosis and treatment planning.

4. Market Research and Consumer Insights

Scenario: A business wants to identify emerging market trends.

How It Works: Agentic RAG analyzes consumer data from multiple sources, retrieves relevant insights, and generates predictive analytics reports. It also gathers customer feedback from surveys or social media.

Benefits: Improves strategic decision-making with real-time intelligence.

Example: Companies use Agentic RAG for trend analysis and predictive analytics to optimize marketing strategies.

5. Supply Chain Optimization

Scenario: A logistics manager needs to predict demand fluctuations during peak seasons.

How It Works: The system retrieves historical sales data, current market trends, and weather forecasts using RAG. Agentic AI then predicts demand patterns and suggests inventory adjustments in real-time.

Benefits: Prevents stockouts or overstocking, reduces costs, and improves efficiency.

Example: Acuvate’s supply chain solutions leverage predictive analytics powered by Agentic AI to enhance logistics operations

How Acuvate Can Help

Acuvate specializes in implementing Agentic AI and RAG technologies to transform business operations. By integrating these advanced AI solutions, Acuvate enables organizations to enhance autonomous decision-making, improve customer experiences, and optimize operational efficiency. Their expertise in deploying AI-driven systems ensures that businesses can effectively leverage real-time data and intelligent automation to stay competitive in a rapidly evolving market.

Future Scope

The future of Agentic AI and RAG involves the development of multi-agent systems where multiple AI agents collaborate to tackle complex tasks. Continuous improvement and governance will be crucial, with ongoing updates and audits necessary to maintain safety and accountability. As technology advances, these systems are expected to become more pervasive across industries, transforming business processes and customer interactions.

In conclusion, the convergence of Agentic AI and RAG represents a significant advancement in autonomous decision-making. By combining autonomous agents with real-time data retrieval, organizations can achieve greater efficiency, accuracy, and adaptability in their operations. As these technologies continue to evolve, their impact across various sectors is poised to expand, ushering in a new era of intelligent automation.