#Generative AI Data Labeling Services

[apexcovantage]

Generative AI | High-Quality Human Expert Labeling | Apex Data Sciences

Apex Data Sciences combines cutting-edge generative AI with RLHF for superior data labeling solutions. Get high-quality labeled data for your AI projects.

[hashtagloveloses]

should you delete twitter and get bluesky? (or just get a bluesky in general)? here's what i've found:

yes. my answer was no before bc the former CEO of twitter who also sucked, jack dorsey, was on the board, but he left as of may 2024, and things have gotten a lot better. also a lot of japanese and korean artists have joined

don't delete your twitter. lock your account, use a service to delete all your tweets, delete the app off of your phone, and keep your account/handle so you can't be impersonated.

get a bluesky with the same handle, even if you won't use it, also so you won't be impersonated.

get the sky follower bridge extension for chrome or firefox. you can find everyone you follow on twitter AND everyone you blocked so you don't have to start fresh: https://skyfollowerbridge.com/

learn how to use its moderation tools (labelers, block lists, NSFW settings) so you can immediately cut out the grifters, fascists, t*rfs, AI freaks, have the NSFW content you want to see if you so choose, and moderate for triggers. here's a helpful thread with a lot of tools.

the bluesky phone app is pretty good, but there is also tweetdeck for bluesky, called https://deck.blue/ on desktop, if you miss tweetdeck.

bluesky has explicitly stated they do not use your data to train generative AI, which is nice to hear from an up and coming startup. obviously we can’t trust these companies and please use nightshade and glaze, but it’s good to hear.

[mostlysignssomeportents]

What kind of bubble is AI?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[mariacallous]

The Trump administration’s Federal Trade Commission has removed four years’ worth of business guidance blogs as of Tuesday morning, including important consumer protection information related to artificial intelligence and the agency’s landmark privacy lawsuits under former chair Lina Khan against companies like Amazon and Microsoft. More than 300 blogs were removed.

On the FTC’s website, the page hosting all of the agency’s business-related blogs and guidance no longer includes any information published during former president Joe Biden’s administration, current and former FTC employees, who spoke under anonymity for fear of retaliation, tell WIRED. These blogs contained advice from the FTC on how big tech companies could avoid violating consumer protection laws.

One now deleted blog, titled “Hey, Alexa! What are you doing with my data?” explains how, according to two FTC complaints, Amazon and its Ring security camera products allegedly leveraged sensitive consumer data to train the ecommerce giant’s algorithms. (Amazon disagreed with the FTC’s claims.) It also provided guidance for companies operating similar products and services. Another post titled “$20 million FTC settlement addresses Microsoft Xbox illegal collection of kids’ data: A game changer for COPPA compliance” instructs tech companies on how to abide by the Children’s Online Privacy Protection Act by using the 2023 Microsoft settlement as an example. The settlement followed allegations by the FTC that Microsoft obtained data from children using Xbox systems without the consent of their parents or guardians.

“In terms of the message to industry on what our compliance expectations were, which is in some ways the most important part of enforcement action, they are trying to just erase those from history,” a source familiar tells WIRED.

Another removed FTC blog titled “The Luring Test: AI and the engineering of consumer trust” outlines how businesses could avoid creating chatbots that violate the FTC Act’s rules against unfair or deceptive products. This blog won an award in 2023 for “excellent descriptions of artificial intelligence.”

The Trump administration has received broad support from the tech industry. Big tech companies like Amazon and Meta, as well as tech entrepreneurs like OpenAI CEO Sam Altman, all donated to Trump’s inauguration fund. Other Silicon Valley leaders, like Elon Musk and David Sacks, are officially advising the administration. Musk’s so-called Department of Government Efficiency (DOGE) employs technologists sourced from Musk’s tech companies. And already, federal agencies like the General Services Administration have started to roll out AI products like GSAi, a general-purpose government chatbot.

The FTC did not immediately respond to a request for comment from WIRED.

Removing blogs raises serious compliance concerns under the Federal Records Act and the Open Government Data Act, one former FTC official tells WIRED. During the Biden administration, FTC leadership would place “warning” labels above previous administrations’ public decisions it no longer agreed with, the source said, fearing that removal would violate the law.

Since President Donald Trump designated Andrew Ferguson to replace Khan as FTC chair in January, the Republican regulator has vowed to leverage his authority to go after big tech companies. Unlike Khan, however, Ferguson’s criticisms center around the Republican party’s long-standing allegations that social media platforms, like Facebook and Instagram, censor conservative speech online. Before being selected as chair, Ferguson told Trump that his vision for the agency also included rolling back Biden-era regulations on artificial intelligence and tougher merger standards, The New York Times reported in December.

In an interview with CNBC last week, Ferguson argued that content moderation could equate to an antitrust violation. “If companies are degrading their product quality by kicking people off because they hold particular views, that could be an indication that there's a competition problem,” he said.

Sources speaking with WIRED on Tuesday claimed that tech companies are the only groups who benefit from the removal of these blogs.

“They are talking a big game on censorship. But at the end of the day, the thing that really hits these companies’ bottom line is what data they can collect, how they can use that data, whether they can train their AI models on that data, and if this administration is planning to take the foot off the gas there while stepping up its work on censorship,” the source familiar alleges. “I think that's a change big tech would be very happy with.”

[tangentiallly]

One way to spot patterns is to show AI models millions of labelled examples. This method requires humans to painstakingly label all this data so they can be analysed by computers. Without them, the algorithms that underpin self-driving cars or facial recognition remain blind. They cannot learn patterns.

The algorithms built in this way now augment or stand in for human judgement in areas as varied as medicine, criminal justice, social welfare and mortgage and loan decisions. Generative AI, the latest iteration of AI software, can create words, code and images. This has transformed them into creative assistants, helping teachers, financial advisers, lawyers, artists and programmers to co-create original works.

To build AI, Silicon Valley’s most illustrious companies are fighting over the limited talent of computer scientists in their backyard, paying hundreds of thousands of dollars to a newly minted Ph.D. But to train and deploy them using real-world data, these same companies have turned to the likes of Sama, and their veritable armies of low-wage workers with basic digital literacy, but no stable employment.

Sama isn’t the only service of its kind globally. Start-ups such as Scale AI, Appen, Hive Micro, iMerit and Mighty AI (now owned by Uber), and more traditional IT companies such as Accenture and Wipro are all part of this growing industry estimated to be worth $17bn by 2030.

Because of the sheer volume of data that AI companies need to be labelled, most start-ups outsource their services to lower-income countries where hundreds of workers like Ian and Benja are paid to sift and interpret data that trains AI systems.

Displaced Syrian doctors train medical software that helps diagnose prostate cancer in Britain. Out-of-work college graduates in recession-hit Venezuela categorize fashion products for e-commerce sites. Impoverished women in Kolkata’s Metiabruz, a poor Muslim neighbourhood, have labelled voice clips for Amazon’s Echo speaker. Their work couches a badly kept secret about so-called artificial intelligence systems – that the technology does not ‘learn’ independently, and it needs humans, millions of them, to power it. Data workers are the invaluable human links in the global AI supply chain.

This workforce is largely fragmented, and made up of the most precarious workers in society: disadvantaged youth, women with dependents, minorities, migrants and refugees. The stated goal of AI companies and the outsourcers they work with is to include these communities in the digital revolution, giving them stable and ethical employment despite their precarity. Yet, as I came to discover, data workers are as precarious as factory workers, their labour is largely ghost work and they remain an undervalued bedrock of the AI industry.

As this community emerges from the shadows, journalists and academics are beginning to understand how these globally dispersed workers impact our daily lives: the wildly popular content generated by AI chatbots like ChatGPT, the content we scroll through on TikTok, Instagram and YouTube, the items we browse when shopping online, the vehicles we drive, even the food we eat, it’s all sorted, labelled and categorized with the help of data workers.

Milagros Miceli, an Argentinian researcher based in Berlin, studies the ethnography of data work in the developing world. When she started out, she couldn’t find anything about the lived experience of AI labourers, nothing about who these people actually were and what their work was like. ‘As a sociologist, I felt it was a big gap,’ she says. ‘There are few who are putting a face to those people: who are they and how do they do their jobs, what do their work practices involve? And what are the labour conditions that they are subject to?’

Miceli was right – it was hard to find a company that would allow me access to its data labourers with minimal interference. Secrecy is often written into their contracts in the form of non-disclosure agreements that forbid direct contact with clients and public disclosure of clients’ names. This is usually imposed by clients rather than the outsourcing companies. For instance, Facebook-owner Meta, who is a client of Sama, asks workers to sign a non-disclosure agreement. Often, workers may not even know who their client is, what type of algorithmic system they are working on, or what their counterparts in other parts of the world are paid for the same job.

The arrangements of a company like Sama – low wages, secrecy, extraction of labour from vulnerable communities – is veered towards inequality. After all, this is ultimately affordable labour. Providing employment to minorities and slum youth may be empowering and uplifting to a point, but these workers are also comparatively inexpensive, with almost no relative bargaining power, leverage or resources to rebel.

Even the objective of data-labelling work felt extractive: it trains AI systems, which will eventually replace the very humans doing the training. But of the dozens of workers I spoke to over the course of two years, not one was aware of the implications of training their replacements, that they were being paid to hasten their own obsolescence.

— Madhumita Murgia, Code Dependent: Living in the Shadow of AI

[charlignon]

PSA for artists: beware of Bluesky

TL;DR: Bluesky sends all content to a 3rd party that use it for generative AI content

I am reposting a thread from @/Oric_y on twitter, you can read it here !

So there's a lot of artists wanting to hop to BlueSky as an alternative to Twitter. You may want to be made aware that any and all posts to it are fed through 3rd party AI and will be used as training data for image/text generation.

Bluesky uses a 3rd party service to label posts contents. For this, they use "http://thehive.ai". Bluesky is open source, so this can be confirmed here.By itself, this would not be an issue. AI for labeling posts isn't problematic. However, hive also provides services for generative AI (images, text, video). Which, again, can be easily confirmed on their own website here.

Reading their privacy policy, they collect anything submitted and will use it as training data for ALL of their services. In full, here

Which brings back to the initial statement. Every post submitted to BlueSky is also submitted to Hive, where it will be used as training data for generative AI.

So yeah, proceed with caution !

[sarkariresultdude]

Japan Government Job Results: An Overview of the Examination System and Selection Process

 Japan’s government jobs, frequently regarded as prestigious and stable profession choices, attract heaps of candidates every year. The hiring manner for those jobs is aggressive and requires candidates to go through rigorous examinations and reviews. The results of these authorities activity examinations determine the choice of candidates for diverse administrative, technical, and law enforcement positions. This article affords an in-intensity take a look at Japan’s authorities process effects, the exam gadget, selection method, and latest traits in public sector employment.

Japan Government Recruitment Result For Indians 

1. Japan’s Government Employment System

The Japanese authorities offers employment opportunities at national, prefectural, and municipal tiers. Positions in the national authorities are labeled into:

General Service (Ippan-shoku): Administrative and clerical roles.

Specialized Service (Tokutei-shoku): Roles requiring unique technical understanding.

Public Security (Keisatsu and Jieitai): Law enforcement and defense positions.

Government corporations, which include the National Personnel Authority (NPA), oversee the hiring technique for civil carrier roles, ensuring fairness and transparency inside the choice of applicants.

2. Examination System for Government Jobs

Japan’s government task examinations are established into three primary ranges:

Class I (Sogo-shoku): High-degree managerial and coverage-making positions, specifically for university graduates.

Class II (Ippan-shoku): Mid-level administrative roles requiring a college degree.

Class III (Shokuin-shoku): Entry-level clerical and assist body of workers roles for high faculty graduates.

A. Structure of the Examinations

The examination method includes a couple of levels:

Written Examination: Tests applicants on popular know-how, reasoning, mathematics, and subject-unique understanding.

Aptitude and Psychological Assessments: Evaluates persona trends, decision-making capabilities, and ethical requirements.

Interviews: Conducted by way of panels to assess candidates’ suitability for the role.

Physical Fitness Test (for Security Jobs): Essential for police, protection, and firefighting roles.

Three. Announcement of Job Results

Government activity effects are introduced on authentic websites, via nearby authorities workplaces, and in newspapers. The consequences commonly consist of:

List of shortlisted candidates.

Individual score reports.

Instructions for the next segment, inclusive of medical examinations or extra interviews.

The National Personnel Authority and different authorities our bodies offer transparency in end result booklet, allowing candidates to get right of entry to their scores and ratings.

4. Recent Trends in Government Job Recruitment

A. Digitalization of Examination and Result Announcement

With advancements in technology, many authorities groups have shifted to online examinations and result announcements. This guarantees efficiency and reduces paperwork.

B. Increasing Demand for Specialized Skills

Japan’s government is emphasizing the recruitment of applicants with know-how in:

Information Technology (Cybersecurity, AI, Data Science)

Environmental Sciences (Climate Change, Sustainable Development)

International Relations (Diplomatic and Trade Policies)

C. Efforts to Promote Gender Equality

The government has applied measures to boom the participation of women in public service. Policies such as bendy work arrangements and same pay projects were delivered.

Five. Challenges inside the Government Job Selection Process

Despite the structured hiring system, a few challenges persist:

High Competition: Thousands of candidates follow for restrained positions, making choice rather competitive.

Lengthy Process: The exam and result statement system can take months, main to uncertainty amongst applicants.

Aging Workforce: The government faces problems in attracting younger skills because of perceived tension in paintings lifestyle.

[rachellaurengray]

AI & Tech-Related Jobs Anyone Could Do

Here’s a list of 40 jobs or tasks related to AI and technology that almost anyone could potentially do, especially with basic training or the right resources:

Data Labeling/Annotation

AI Model Training Assistant

Chatbot Content Writer

AI Testing Assistant

Basic Data Entry for AI Models

AI Customer Service Representative

Social Media Content Curation (using AI tools)

Voice Assistant Testing

AI-Generated Content Editor

Image Captioning for AI Models

Transcription Services for AI Audio

Survey Creation for AI Training

Review and Reporting of AI Output

Content Moderator for AI Systems

Training Data Curator

Video and Image Data Tagging

Personal Assistant for AI Research Teams

AI Platform Support (user-facing)

Keyword Research for AI Algorithms

Marketing Campaign Optimization (AI tools)

AI Chatbot Script Tester

Simple Data Cleansing Tasks

Assisting with AI User Experience Research

Uploading Training Data to Cloud Platforms

Data Backup and Organization for AI Projects

Online Survey Administration for AI Data

Virtual Assistant (AI-powered tools)

Basic App Testing for AI Features

Content Creation for AI-based Tools

AI-Generated Design Testing (web design, logos)

Product Review and Feedback for AI Products

Organizing AI Training Sessions for Users

Data Privacy and Compliance Assistant

AI-Powered E-commerce Support (product recommendations)

AI Algorithm Performance Monitoring (basic tasks)

AI Project Documentation Assistant

Simple Customer Feedback Analysis (AI tools)

Video Subtitling for AI Translation Systems

AI-Enhanced SEO Optimization

Basic Tech Support for AI Tools

These roles or tasks could be done with minimal technical expertise, though many would benefit from basic training in AI tools or specific software used in these jobs. Some tasks might also involve working with AI platforms that automate parts of the process, making it easier for non-experts to participate.

[ishmam11]

Earn money online in micro job

A micro job is a small, short-term task or project that can be completed quickly, often within minutes or hours. These tasks usually require minimal skill, and workers are paid a small amount of money for each task. Micro jobs are typically posted on online platforms, connecting freelancers or gig workers with companies or individuals who need small tasks

Examples of Micro Jobs:

Data Entry: Entering data into a spreadsheet or system.

Survey Participation: Answering online surveys or providing feedback on products or services.

Content Moderation: Reviewing and filtering content (e.g., flagging inappropriate comments or images).

App Testing: Testing apps or websites and providing feedback.

Social Media Tasks: Liking, sharing, or following pages on social media.

Image Tagging: Labeling images with appropriate tags (useful in AI training).

Transcription: Converting short audio clips into text.

Small Writing Tasks: Writing short product descriptions or reviews.

Pros and Cons:

• Pros: Flexibility, can work from anywhere, doesn’t usually require extensive experience, and allows people to earn money in spare time.

• Cons: Generally low pay per task, no job security or benefits, and payment can vary greatly between platforms.

Micro jobs can be a quick way to earn extra cash, but they are typically not suited for stable, long-term income.

[greenoperator]

Microsoft Azure Fundamentals AI-900 (Part 6)

Microsoft Azure AI Fundamentals: Explore computer vision

An area of AI where software systems perceive the world visually, through cameras, images, and videos.

Computer vision is one of the core areas of AI

It focuses on what the computer can “see” and make sense of it

Azure resources for Computer vision

Computer Vision - use this if you’re not going to use any other cognitive services or if you want to track costs separately

Cognitive Services - general cognitive services resources include Computer vision along with other services.

Analyzing images with the computer vision service

Analyze an image evaluate objects that are detect

Generate human readable phrase or sentence that can describe what image is detected

If multiple phrases are created for an image, each will have an associated confidence score

Image descriptions are based on sets of thousands of recognizable objects used to suggest tags for an image

Tags are associated with the image as metadata and summarizes attributes of the image.

Similar to tagging, but it can identify common objects in the picture.

It draws a bounding box around the object with coordinates on the image.

It can identify commercial brands.

The service has an existing database of thousands of recognized logos

If a brand name is in the image, it returns a score of 0 to 1

Detects where faces are in an image

Draws a bounding box

Facial analysis capabilities exist because of the Face Service

It can detect age, mood, attributes, etc.

Currently limited set of categories.

Objects detected are compared to existing categories and it uses the best fit category

86 categories exist in the list

Celebrities

Landmarks

It can read printed and hand written content.

Detect image types - line drawing vs photo

Detect image color schemes - identify the dominant foreground color vs overall colors in an image

Genrate thumbnails

Moderate content - detect images with adult content, violent or gory scenes

Classify images with the Custom Vision Service

Image classification is a technique where the object in an image is being classified

You need data that consists of features and labels

Digital images are made up of an array of pixel values. These are used as features to train the model based on known image classes

Most modern image classification solutions are based on deep learning techniques.

They use Convolutional neural Networks (CNNS) to uncover patterns in the pixels to a particular class.

Model Training

To train a model you must upload images to a training resource and label them with class labels

Custom Vision Portal is the application where the training occurs in

Additionally it can use Custom Vision service programming languages-specific SDKs

Model Evaluation

Precision - percentage of the class predictions made by the model that are correct

Recall - percentage of the class predictions the model identified correctly

Average Precision - Overall metric using precision and recall

Detect objects in images with the Custom Vision service

The class of each object identified

The probability score of the object classification

The coordinates of a bounding box of each object.

Requires training the object detection model, you must tag the classes and bounding box coordinates in a training set of images

This can be time consuming, but the Custom Vision portal makes this straightforward

The portal will suggest areas of the image where discrete objects are detected and you can add a class label

It also has Smart Tagging, where it suggests classes and bounding boxes to use for training

Precision - percentage of the class predictions made by the model that are correct

Recall - percentage of the class predictions the model identified correctly

Mean Average Precision (mAP) - Overall metric using precision and recall across all classes

Detect and analyze faces with the Face Service

Involves identifying regions of an image that contain a human face

It returns a bounding box that form a rectangle around the face

Moving beyond face detection, some algorithms return other information like facial landmarks (nose, eyes, eyebrows, lips, etc)

Facial landmarks can be used as features to train a model.

Another application of facial analysis. Used to train ML models to identify known individuals from their facial features.

More generally known as facial recognition

Requires multiple images of the person you want to recognize

Security - to build security applications and is used more and more no mobile devices

Social Media - use to automatically tag people and friends in photos.

Intelligent Monitoring - to monitor a persons face, for example when they are driving to determine where they are looking

Advertising - analyze faces in an image to direct advertisements to an appropriate demographic audience

Missing persons - use public camera systems with facial recognition to identify if a person is a missing person

Identity validation - use at port of entry kiosks to allow access/special entry permit

Blur - how blurry the face is

Exposure - aspects such as underexposed or over exposed and applies to the face in the image not overall image exposure

Glasses - if the person has glasses on

Head pose - face orientation in 3d space

Noise - visual noise in the image.

Occlusion - determines if any objects cover the face

Read text with the Computer Vision service

Submit an image to the API and get an operation ID

Use the operation ID to check status

When it’s completed get the result.

Pages - one for each page of text and orientation and page size

Lines - the lines of text on a page

Words - the words in a line of text including a bounding box and the text itself

Analyze receipts with the Form recognizer service

Matching field names to values

Processing tables of data

Identifying specific types of field, such as date, telephone number, addresses, totals, and other

Images must be JPEG, PNG, BMP, PDF, TIFF

File size < 50 MB

Image size between 50x50 pixels and 10000x10000 pixels

PDF documents no larger than 17 inches x 17 inches

You can train it with your own data

It just requires 5 samples to train it

Microsoft Azure AI Fundamentals: Explore decision support

Monitoring blood pressure

Evaluating mean tie between failures for hardware products

Part of the decision services category

Can be used with REST API

Sensitivity parameter is from 1 to 99

Anomalies are values outside expected values or ranges of values

The sensitivity boundary can be configured when making the API call

It uses a boundary, set as a sensitivity value, to create the upper and lower boundaries for anomaly detection

Calculated using concepts known as expectedValue, upperMargin, lowerMargin

If a value exceeds either boundary, then it is an anomaly

upperBoundary = expectedValue + (100-marginScale) * upperMargin

The service accepts data in JSON format.

It supports a maximum of 8640 data points. Break this down into smaller requests to improve the performance.

When to use Anomaly Detector

Process the algorithm against an entire set of data at one time

It creates a model based on your complete data set and the finds anomalies

Uses streaming data by comparing previously seen dat points to the last datapoint to determine if your latest one is an anomaly.

Model is created using the data points you send and determines if the current point is an anomaly.

Microsoft Azure AI Fundamentals: Explore natural language processing

Analyze Text with the Language Service

Used to describe solutions that involve extracting information from large volumes of unstructured data.

Analyzing text is a process to evaluate different aspects of a document or phrase, to gain insights about that text.

Text Analytics Techniques

Interpret words like “power”, “powered”, and “powerful” as the same word.

Convert to tree like structures (Noun phrases)

Often used for sentiment analysis

Determine the language of a document or text

Perform sentiment analysis (positive or negative)

Extract key phrases from text to indicate key talking points

Identify and categorize entities (places, people, organizations, etc)

Get started with Text analysis

Language name

ISO 6391 language code

Score as a level of confidence n the language returned.

Evaluates text to return a sentiment score and labels for each sentence

Useful for detecting positive or negative sentiment

Classification is between 0 to 1 with 1 being most positive

A score of 0.5 is indeterminate sentiment.

The phrase doesn’t have sufficient information to determine the sentiment.

Mixing language content with the language you tell it will return 0.5 also

Key Phrase extraction

Used to determine the main talking points of a text or a document

Depending on the volume this can take longer, so you can use the key phrase extraction capabilities of the Language Service to summarize main points.

Key phrase extraction can provide context about the document or text

Entity Recognition

Person

Location

OrganizationQuantity

DateTime

URL

Email

US-based phone number

IP address

Recognize and Synthesize Speech

Acoustic model - converts audio signal to phonemes (representation of specific sounds)

Language model - maps the phonemes to words using a statistical algorithm to predict the most probably sequence of words based on the phonemes

ability to generate spoken output

Usually converting text to speech

This process tokenizes the set to break it down into individual words, assign phonetic sounds to each word

It then breaks the phonetic transcription to prosodic units to create phonemes for the audio

Get started with speech on Azure

Use this for demos, presentations, or scenarios where a person is speaking

In real time it can translate to many lunges as it processes

Audio files with Shared access signature (SAS) URI can be used and results are received asynchronously.

Jobs will start executing within minutes, but no estimate is provided for when the job changes to running state

Used to convert text to speech

Voices can be selected that will vocalize the text

Custom voices can be developed

Voices are trained using neural networks to overcome limitations in speech synthesis with regards to intonation.

Translate Text and Speech

Where each word is translated to the corresponding word in the target language

This approach has issues. For example, a direct word to word translation may not exist or the literal translation may not be the correct meaning of the phrase

Machine learning has to also understand the semantic context of the translation.

This provides more accurate translation of the input phrase or phrases

Grammar, formal versus informal, colloquialism all need to be considered

Text and speech translation

Profanity filtering - remove or do not translate profamity

Selective translation - tag content that isn’t to be translated (brand names, code names, etc)

Speech to text - transcribe speech from an audio source to text format.

Text to speech - used to generate spoken audio from a text source

Speech translation - translate speech in one language to text or speech in another

Create a language model with Conversational language Understanding

A None intent exists.

This should be used when no intent has been identified and should provide a message to a user.

Getting started with Conversational Language Understanding

Authoring the model - Defining entities, intents, and utterances to use to train the model

Entity Prediction - using the model after it is published.

Define intents based on actions a user would want to perform

Each intent should include a variety of utterances as examples of how a user may express the intent

If the intent can be applied to multiple entities, include sample utterances for each potential entity.

Machine-Learned - learned by the model during training from context in the sample utterances you provide

List - Defined as a hierarchy of lists and sublists

RegEx - regular expression patterns

Pattern.any - entities used with patterns to define complex entities that may be hard to extract from sample utterances

After intents and entities are created you train the model.

Training is the process of using your sample utterances to teach the model to match natural language expressions that a user may say to probable intents and entities.

Training and testing are iterative processes

If the model does not match correctly, you create more utterances, retrain, and test.

When results are satisfactory, you can publish the model.

Client applications can use the model by using and endpoint for the prediction resource

Build a bot with the Language Service and Azure Bot Service

Knowledge base of question and answer pairs. Usually some built-in natural language processing model to enable questions and can understand the semantic meaning

Bot service - to provide an interface to the knowledge base through one or more channels

Microsoft Azure AI Fundamentals: Explore knowledge mining

Used to describe solutions that involve extracting information from large volumes of unstructured data.

It has a services in Cognitive services to create a user-managed index.

The index can b meant for internal use only or shared with the public.

It can use other Cognitive Services capabilities to extract the information

What is Azure Cognitive Search?

Provides a programmable search engine build on Apache Lucene

Highly available platform with 99.9% uptime SLA for cloud and on-premise assets

Data from any source - accepts data form any source provided in JSON format with auto crawling support for selected data sources in Azure

Full text search and analysis - Offers full text search capabilities supporting both simple query and full Lucene query syntax

AI Powered search - has Cognitive AI capabilities built in for image and text analysis from raw content

Multi-lingual - offers linguistic analysis for 56 langues

Geo-enabled - supports geo-search filtered based on proximity to a physical location

Configurable user experience - it includes capabilities to improve the user experience (autocomplete, autosuggest, pagination, hit highlighting, etc)

Identify elements of a search solution

Folders with files,

Text in a database

Etc

Use a skillset to Define an enrichment pipeline

Key Phrase Extraction - uses a pre-trained model to detect important phrases based on term placement, linguistic rules, proximity to terms

Text Translation - pre-trained model to translate the input text into various languages for normalization or localization use cases

Image Analysis Skills - uses an image detection algorithm to identify the content of an image an generate a text description

Optical Character Recognition Skills - extract printed or handwritten text from images, photos, videos

Understand indexes

Index schema - index includes a definition of the structure of the data in the documents to read.

Index attributes - Each field in a document the index stores its name, the data type, supported behaviors (searchable, sortable, etc)

Best indexes use only the features that are required/needed

Use an indexer to build an index

Push method - JSON data is pushed into a search index via a REST API or a .NET SDK. Most flexible and with least restrictions

Pull method - Search service indexer pulls from popular Azure data sources and if necessary exports the Tinto JSON if its not already in that format

Use the pull method to load data with an indexer

Azure Cognitive search’s indexer is a crawler that extracts searchable text and metadata form an external Azure data source an populates a search index using field-to-field mapping between the data and the index.

Data import monitoring and verification

Indexers only import new or updated documents. It is normal to see zero documents indexed

Health information is displayed in a dashboard.

You can monitor the progress of the indexing

Making changes to an index

You need to drop and recreate indexes if you need to make changes to the field definitions

An approach to update your index without impacting your users is to create a new index with a new name

After importing data, switch to the new index.

Persist enriched data in a knowledge store

A knowledge store is persistent storage of enriched content.

The knowledge store is to store the data generated from Ai enrichment in a container.

[gts6465]

Top Video Data Collection Services for AI and Machine Learning

Introduction

In the contemporary landscape dominated by artificial intelligence, video data is essential for the training and enhancement of machine learning models, particularly in fields such as computer vision, autonomous systems, surveillance, and retail analytics. However, obtaining high-quality video data is not a spontaneous occurrence; it necessitates meticulous planning, collection, and annotation. This is where specialized Video Data Collection Services become crucial. In this article, we will examine the characteristics that define an effective video data collection service and showcase how companies like GTS.AI are establishing new benchmarks in this industry.

Why Video Data Is Crucial for AI Models

Video data provides comprehensive and dynamic insights that surpass the capabilities of static images or text. It aids machine learning models in recognizing movement and patterns in real time, understanding object behavior and interactions, and enhancing temporal decision-making in complex environments. Video datasets are essential for various real-world AI applications, including the training of self-driving vehicles, the advancement of smart surveillance systems, and the improvement of gesture recognition in augmented and virtual reality.

What to Look for in a Video Data Collection Service

When assessing a service provider for the collection of video datasets, it is essential to take into account the following critical factors:

Varied Environmental Capture

Your models must be able to generalize across different lighting conditions, geographical locations, weather variations, and more. The most reputable providers offer global crowd-sourced collection or customized video capture designed for specific environments.

2. High-Quality, Real-Time Capture

Quality is paramount. Seek services that provide 4K or HD capture, high frame rates, and various camera angles to replicate real-world situations.

3. Privacy Compliance

In light of the growing number of regulations such as GDPR and HIPAA, it is imperative to implement measures for face and license plate blurring, consent tracking, and secure data management.

Annotation and Metadata

Raw footage alone is insufficient. The most reputable providers offer annotated datasets that include bounding boxes, object tracking, activity tagging, and additional features necessary for training supervised learning models.

Scalability

Regardless of whether your requirement is for 100 or 100,000 videos, the provider must possess the capability to scale efficiently without sacrificing quality.

GTS.AI: A Leader in Video Data Collection Services

At GTS.AI, we focus on delivering tailored, scalable, and premium video dataset solutions for AI and ML teams across various sectors.

What Sets GTS.AI Apart?

Our unique advantages include a global reach through a crowdsource network in over 100 countries, enabling diverse data collection.

We offer flexible video types, accommodating indoor retail and outdoor traffic scenarios with scripted, semi-scripted, and natural video capture tailored to client needs.

Our compliance-first approach ensures data privacy through anonymization techniques and adherence to regulations.

Additionally, we provide an end-to-end workflow that includes comprehensive video annotation services such as frame-by-frame labeling, object tracking, and scene segmentation.

For those requiring quick access to data, our systems are designed for rapid deployment and delivery while maintaining high quality.

Use Cases We Support

Autonomous Driving and Advanced Driver Assistance Systems,

Smart Surveillance and Security Analytics,

Retail Behavior Analysis,

Healthcare Monitoring such as Patient Movement Tracking,

Robotics and Human Interaction,

Gesture and Action Recognition.

Ready to Power Your AI Model with High-Quality Video Data?

Regardless of whether you are developing next-generation autonomous systems or creating advanced security solutions, Globose Technology Solution .AI's video data collection services can provide precisely what you require—efficiently, rapidly, and with accuracy.

[mariacallous]

AI projects like OpenAI’s ChatGPT get part of their savvy from some of the lowest-paid workers in the tech industry—contractors often in poor countries paid small sums to correct chatbots and label images. On Wednesday, 97 African workers who do AI training work or online content moderation for companies like Meta and OpenAI published an open letter to President Biden, demanding that US tech companies stop “systemically abusing and exploiting African workers.”

Most of the letter’s signatories are from Kenya, a hub for tech outsourcing, whose president, William Ruto, is visiting the US this week. The workers allege that the practices of companies like Meta, OpenAI, and data provider Scale AI “amount to modern day slavery.” The companies did not immediately respond to a request for comment.

A typical workday for African tech contractors, the letter says, involves “watching murder and beheadings, child abuse and rape, pornography and bestiality, often for more than 8 hours a day.” Pay is often less than $2 per hour, it says, and workers frequently end up with post-traumatic stress disorder, a well-documented issue among content moderators around the world.

The letter’s signatories say their work includes reviewing content on platforms like Facebook, TikTok, and Instagram, as well as labeling images and training chatbot responses for companies like OpenAI that are developing generative-AI technology. The workers are affiliated with the African Content Moderators Union, the first content moderators union on the continent, and a group founded by laid-off workers who previously trained AI technology for companies such as Scale AI, which sells datasets and data-labeling services to clients including OpenAI, Meta, and the US military. The letter was published on the site of the UK-based activist group Foxglove, which promotes tech-worker unions and equitable tech.

In March, the letter and news reports say, Scale AI abruptly banned people based in Kenya, Nigeria, and Pakistan from working on Remotasks, Scale AI’s platform for contract work. The letter says that these workers were cut off without notice and are “owed significant sums of unpaid wages.”

“When Remotasks shut down, it took our livelihoods out of our hands, the food out of our kitchens,” says Joan Kinyua, a member of the group of former Remotasks workers, in a statement to WIRED. “But Scale AI, the big company that ran the platform, gets away with it, because it’s based in San Francisco.”

Though the Biden administration has frequently described its approach to labor policy as “worker-centered.” The African workers’ letter argues that this has not extended to them, saying “we are treated as disposable.”

“You have the power to stop our exploitation by US companies, clean up this work and give us dignity and fair working conditions,” the letter says. “You can make sure there are good jobs for Kenyans too, not just Americans."

Tech contractors in Kenya have filed lawsuits in recent years alleging that tech-outsourcing companies and their US clients such as Meta have treated workers illegally. Wednesday’s letter demands that Biden make sure that US tech companies engage with overseas tech workers, comply with local laws, and stop union-busting practices. It also suggests that tech companies “be held accountable in the US courts for their unlawful operations aboard, in particular for their human rights and labor violations.”

The letter comes just over a year after 150 workers formed the African Content Moderators Union. Meta promptly laid off all of its nearly 300 Kenya-based content moderators, workers say, effectively busting the fledgling union. The company is currently facing three lawsuits from more than 180 Kenyan workers, demanding more humane working conditions, freedom to organize, and payment of unpaid wages.

“Everyone wants to see more jobs in Kenya,” Kauna Malgwi, a member of the African Content Moderators Union steering committee, says. “But not at any cost. All we are asking for is dignified, fairly paid work that is safe and secure.”

[hiteshrivani]

Green AI: Sustainable Practices in Machine Learning Development

As artificial intelligence (AI) and machine learning (ML) advance at breakneck speed, there is growing awareness of their environmental footprint. Training large models can consume enormous amounts of energy and generate significant carbon emissions. This reality has given rise to a new movement— Green AI —which focuses on reducing the ecological impact of AI while maintaining performance and innovation. Sustainable AI isn't just an option anymore; it's a responsibility.

Traditional AI development often prioritizes accuracy, speed, and scale, but this can come at a cost. Large-scale models like GPT and BERT require massive datasets and high-powered GPUs running for days or weeks. The carbon emissions from a single model training run can rival that of a cross-country flight. With growing concern over climate change, developers and researchers are rethinking how we build smarter systems more sustainably.

Green AI advocates for energy-efficient model training, optimization techniques that reduce computational demand, and the use of renewable energy sources for data centers. Techniques like model pruning, quantization, and knowledge distillation help shrink models without significantly impacting performance. These strategies not only lower power usage—they make AI more accessible to organizations with limited resources.

Another important consideration is data efficiency. Instead of relying on massive amounts of labeled data, sustainable ML encourages techniques like transfer learning and semi-supervised learning. These approaches reuse existing models or require fewer data points, which reduces both environmental and financial costs. Smart data strategies are not just green—they're practical.

Moreover, organizations are starting to measure and disclose the environmental cost of their AI projects. Metrics like "energy usage per prediction" or "CO2 emissions per training hour" are emerging as standard benchmarks. This level of transparency helps organizations make informed choices and gives consumers insight into how their digital tools are impacting the planet.

For businesses seeking to build sustainable AI systems, professional AI and ML development services can provide guidance. These experts help optimize models, design efficient architectures, and deploy eco-friendly AI at scale. Whether it's transitioning to cloud platforms with renewable energy sources or minimizing runtime on edge devices, smart development choices can significantly reduce environmental impact.

Sustainable AI also aligns with consumer values. More users and investors are prioritizing companies that demonstrate environmental responsibility. Green AI isn't just good for the planet—it's a competitive differentiator. Brands that adopt sustainable practices are viewed as more trustworthy, innovative, and future-ready.

In a world where both innovation and sustainability are critical, Green AI stands at the intersection. By combining cutting-edge technology with eco-conscious practices, we can shape a future where progress doesn't cost the planet. Building smarter should also mean building greener.

#GreenAI #SustainableTech #EcoFriendlyAI #MachineLearning #AIInnovation #Eth

[freedom-of-fanfic]

Thank you for preserving these!

Coming back to say: here’s some reasons to hold out against using generative AI as much as you can*.

On the ethics side:

The ‘free’ AI programs available to the general public are unethically trained on stolen data

(Said stolen data has been found to include CSAM/CSEM)

AI generation requires lots of electricity & is bad for the environment

AI is heavily supplemented by underpaid human labor that’s hidden on purpose

On the labor side:

AI has only one real value for companies who look to incorporate it: reducing its reliance on human labor. If it’s not doing that, then why spend money on it? It needs to be a cheaper replacement for something else, and that something is human labor. That’s its selling point.

And thus: generative AI is being sold to your boss/potential commissioner as your cheaper competitor.

Although the actual potential for generative AI’s output is doubtful, companies are eager to use AI to cut creative labor out of the production process and thus the profit structure. artists are noticing.

For example: Companies refusing to include anti-AI language in contracts, prompting strikes

AI is replacing people … but mostly making jobs for those who remain even harder than before

That last point is important to me bc if you won’t try to avoid using generative AI for the sake of the people whose work was stolen to train it, or for the environment, or for creatives getting financially squeezed by it … you should avoid it because it’s not going to be around forever.

On the economic side:

Generative AI as it stands … really can’t replace humans no matter how hard AI companies try to sell it as a replacement. If it turns out to be a useless expense, then why buy it?

If it turns out nobody will buy it … why keep selling it? & in fact that’s the problem: not nearly enough people are buying use of generative AI services/models to make it profitable.

If it’s not profitable (bc ppl actively don’t like it & it doesn’t work well), the companies selling generative AI will stop selling it, will close their doors, will stop offering generative AI for free …

And all we’ll have is a bunch of collapsed AI startups & lost creative jobs for no reason.

The AI bubble will crash, & when it does, all that will happen is a lot of wealth will have transferred to already-wealthy people who were willing to throw massive amounts of money down the drain just to make everyone else a little poorer

Outside of fandom, AI is getting rammed down our throats bc it’s all about profit. Generative AI is meant to steal what little profit artists still make commercially. Let’s not let it take up space in fandom, too!

I can’t force anyone to not use AI, of course, & I don’t expect ppl who already use it to respect any of my reasons to not use it. But i hope this post gives you some reasons to not use it.

(You know who’s actually profiting heavily from AI? Scammers.)

*a lot of things are labeled ‘AI’ but aren’t really generative AI, & sometimes you can’t avoid using AI bc of work or something. But do your best, even if only for yourself.

like i'm sorry but we as a fandom have to stay firm on our anti-AI values. we cannot suddenly start giving AI a pass when it's something we "want to see" like destiel kisses. it's not suddenly fine. we're not going to start using AI to make fanfic scenes come to life or audio AI to make characters "say" stuff we want to hear. you have GOT to be firm on your anti-AI stance. if you start making exceptions then suddenly anything will fly. fandom is for real art and creations made by real people. no AI fanfics. no AI art. no AI rendered "bonus" scenes. no AI audio. none of it has a place here.

[whitelabelseocompaniesusa]

Top White Label SEO Companies in the USA Revolutionizing Digital Marketing in 2025

New York, USA – The world of digital marketing is undergoing a dramatic transformation in 2025, and leading the charge are the top white label SEO companies in the USA. These companies are not only adapting to the rapid evolution of the digital space, but they are also setting new standards in how businesses approach online visibility, search engine performance, and scalable client growth. Among these frontrunners, White Label SEO Companies USA is emerging as a definitive name that is helping agencies and businesses across the globe redefine their marketing strategies.

The Evolution of White Label SEO in 2025

As digital competition intensifies, traditional SEO methods are no longer enough. Businesses now require agile, data-driven, and results-oriented solutions. White label SEO services have become essential for marketing agencies that want to scale without hiring in-house experts or managing complex SEO campaigns directly. In 2025, white label partnerships have become more sophisticated, integrating AI tools, automation, and deep analytics   all without sacrificing human strategy and creativity.

The demand for white label SEO has surged, particularly in the USA, where small-to-midsize agencies are looking to deliver expert-level SEO to clients without the overhead. White label companies take care of all backend operations   keyword research, content creation, technical audits, backlink building, local SEO, and monthly reporting   allowing agencies to resell these services under their own brand. This is where companies like White Label SEO Companies USA are making a big impact.

How White Label SEO Companies USA is Changing the Game

White Label SEO Companies USA has positioned itself as a major player in this growing industry by providing tailored SEO solutions that align with the latest Google algorithm updates and digital trends. Their approach is far from cookie-cutter. Every campaign is custom-designed to meet the client’s niche, goals, and audience behavior. From on-page optimization to advanced link strategies, the team works silently behind the scenes to ensure client agencies get the credit  and the results.

What sets White Label SEO Companies USA apart is its commitment to transparency and partnership. Rather than just being a backend provider, the company works closely with agencies to understand their client base, craft winning strategies, and maintain long-term relationships. Their white label reports are clean, professional, and designed to blend seamlessly into any agency’s branding. The clients of these agencies never even know there's a third party involved  all they see are excellent SEO outcomes.

The Digital Marketing Landscape Has Shifted

Digital marketing in 2025 is more complex than ever. Google’s emphasis on user experience, site speed, mobile-first indexing, and authoritative content has forced SEO providers to raise the bar. Spammy link building and keyword stuffing are not only outdated   they are dangerous. The top white label SEO companies in the USA have adapted to these changes by investing in quality over quantity and by focusing on genuine content creation, ethical link outreach, and comprehensive technical optimization.

The rise of generative AI tools, like ChatGPT, has added new layers to content marketing. While AI can help with content drafting, it takes a strategic SEO mind to ensure that content is optimized, targeted, and authoritative. This is where professional SEO companies come into play. White Label SEO Companies USA blends the efficiency of AI tools with the experience of seasoned SEO specialists to create campaigns that are both scalable and effective.

Client Success Is the New Currency

In 2025, results speak louder than promises. Agencies are now judged by how well they can deliver real growth for their clients. Rankings, traffic, conversions   that’s the new currency. The best white label SEO providers understand this. They focus on KPIs that matter and deliver reports that show clear improvements month after month.

Clients who have partnered with agencies working with White Label SEO Companies USA have seen consistent improvements in their online visibility, search engine rankings, and overall business growth. Whether it’s helping a local plumber get to the top of Google Maps or enabling a national eCommerce site to outrank competitors, these companies are driving measurable results.

Partnerships Built on Trust and Results

The relationship between a digital agency and its white label SEO provider must be built on trust. In many cases, the agency is putting its own brand on the line. If the SEO doesn’t work, it’s the agency that faces the fallout. That’s why the top white label SEO companies in the USA, especially White Label SEO Companies USA, place so much emphasis on consistent communication, performance tracking, and white-glove service.

By staying ahead of trends, adapting to the ever-changing landscape of Google algorithms, and offering flexible solutions, these providers ensure that their agency partners can confidently scale their offerings and grow their revenue streams   without worrying about fulfillment quality or missed deadlines.

Looking Ahead  What’s Next for White Label SEO

The future of white label SEO in the United States looks bright. As more agencies understand the power of outsourcing to specialists, demand will continue to rise. With AI-assisted tools making SEO faster and data more accessible, what will matter most is how companies use that data to craft effective, user-centric strategies.

White Label SEO Companies USA is leading the charge into the next chapter of digital marketing. They are investing in cutting-edge tools, expanding their team of SEO experts, and continuing to partner with agencies that want to grow fast, smart, and sustainably.

Conclusion

The digital marketing world in 2025 belongs to those who can adapt quickly and execute effectively. For marketing agencies, partnering with a reliable white label SEO company is no longer optional   it’s essential. With companies like White Label SEO Companies USA setting new standards in service quality, performance, and scalability, the future of SEO in the United States is in good hands.

Whether you’re a new agency looking to expand your service offerings or an established marketing firm in need of SEO firepower, now is the time to explore the power of white label SEO. Trust the experts, deliver better results, and grow your business with the help of leaders like White Label SEO Companies USA.

Contact Information

Name: Pearl Lemon Address: Pearl Lemon Ltd. Kemp House, 152 – 160 City Road London, EC1V 2NX United Kingdom Phone Number: +442071833436 Website: https://pearllemon.com/

[precallai]

How AI Is Revolutionizing Contact Centers in 2025

As contact centers evolve from reactive customer service hubs to proactive experience engines, artificial intelligence (AI) has emerged as the cornerstone of this transformation. In 2025, modern contact center architectures are being redefined through AI-based technologies that streamline operations, enhance customer satisfaction, and drive measurable business outcomes.

This article takes a technical deep dive into the AI-powered components transforming contact centers—from natural language models and intelligent routing to real-time analytics and automation frameworks.

1. AI Architecture in Modern Contact Centers

At the core of today’s AI-based contact centers is a modular, cloud-native architecture. This typically consists of:

NLP and ASR engines (e.g., Google Dialogflow, AWS Lex, OpenAI Whisper)

Real-time data pipelines for event streaming (e.g., Apache Kafka, Amazon Kinesis)

Machine Learning Models for intent classification, sentiment analysis, and next-best-action

RPA (Robotic Process Automation) for back-office task automation

CDP/CRM Integration to access customer profiles and journey data

Omnichannel orchestration layer that ensures consistent CX across chat, voice, email, and social

These components are containerized (via Kubernetes) and deployed via CI/CD pipelines, enabling rapid iteration and scalability.

2. Conversational AI and Natural Language Understanding

The most visible face of AI in contact centers is the conversational interface—delivered via AI-powered voice bots and chatbots.

Key Technologies:

Automatic Speech Recognition (ASR): Converts spoken input to text in real time. Example: OpenAI Whisper, Deepgram, Google Cloud Speech-to-Text.

Natural Language Understanding (NLU): Determines intent and entities from user input. Typically fine-tuned BERT or LLaMA models power these layers.

Dialog Management: Manages context-aware conversations using finite state machines or transformer-based dialog engines.

Natural Language Generation (NLG): Generates dynamic responses based on context. GPT-based models (e.g., GPT-4) are increasingly embedded for open-ended interactions.

Architecture Snapshot:

plaintext

CopyEdit

Customer Input (Voice/Text)

       ↓

ASR Engine (if voice)

       ↓

NLU Engine → Intent Classification + Entity Recognition

       ↓

Dialog Manager → Context State

       ↓

NLG Engine → Response Generation

       ↓

Omnichannel Delivery Layer

These AI systems are often deployed on low-latency, edge-compute infrastructure to minimize delay and improve UX.

3. AI-Augmented Agent Assist

AI doesn’t only serve customers—it empowers human agents as well.

Features:

Real-Time Transcription: Streaming STT pipelines provide transcripts as the customer speaks.

Sentiment Analysis: Transformers and CNNs trained on customer service data flag negative sentiment or stress cues.

Contextual Suggestions: Based on historical data, ML models suggest actions or FAQ snippets.

Auto-Summarization: Post-call summaries are generated using abstractive summarization models (e.g., PEGASUS, BART).

Technical Workflow:

Voice input transcribed → parsed by NLP engine

Real-time context is compared with knowledge base (vector similarity via FAISS or Pinecone)

Agent UI receives predictive suggestions via API push

4. Intelligent Call Routing and Queuing

AI-based routing uses predictive analytics and reinforcement learning (RL) to dynamically assign incoming interactions.

Routing Criteria:

Customer intent + sentiment

Agent skill level and availability

Predicted handle time (via regression models)

Customer lifetime value (CLV)

Model Stack:

Intent Detection: Multi-label classifiers (e.g., fine-tuned RoBERTa)

Queue Prediction: Time-series forecasting (e.g., Prophet, LSTM)

RL-based Routing: Models trained via Q-learning or Proximal Policy Optimization (PPO) to optimize wait time vs. resolution rate

5. Knowledge Mining and Retrieval-Augmented Generation (RAG)

Large contact centers manage thousands of documents, SOPs, and product manuals. AI facilitates rapid knowledge access through:

Vector Embedding of documents (e.g., using OpenAI, Cohere, or Hugging Face models)

Retrieval-Augmented Generation (RAG): Combines dense retrieval with LLMs for grounded responses

Semantic Search: Replaces keyword-based search with intent-aware queries

This enables agents and bots to answer complex questions with dynamic, accurate information.

6. Customer Journey Analytics and Predictive Modeling

AI enables real-time customer journey mapping and predictive support.

Key ML Models:

Churn Prediction: Gradient Boosted Trees (XGBoost, LightGBM)

Propensity Modeling: Logistic regression and deep neural networks to predict upsell potential

Anomaly Detection: Autoencoders flag unusual user behavior or possible fraud

Streaming Frameworks:

Apache Kafka / Flink / Spark Streaming for ingesting and processing customer signals (page views, clicks, call events) in real time

These insights are visualized through BI dashboards or fed back into orchestration engines to trigger proactive interventions.

7. Automation & RPA Integration

Routine post-call processes like updating CRMs, issuing refunds, or sending emails are handled via AI + RPA integration.

Tools:

UiPath, Automation Anywhere, Microsoft Power Automate

Workflows triggered via APIs or event listeners (e.g., on call disposition)

AI models can determine intent, then trigger the appropriate bot to complete the action in backend systems (ERP, CRM, databases)

8. Security, Compliance, and Ethical AI

As AI handles more sensitive data, contact centers embed security at multiple levels:

Voice biometrics for authentication (e.g., Nuance, Pindrop)

PII Redaction via entity recognition models

Audit Trails of AI decisions for compliance (especially in finance/healthcare)

Bias Monitoring Pipelines to detect model drift or demographic skew

Data governance frameworks like ISO 27001, GDPR, and SOC 2 compliance are standard in enterprise AI deployments.

Final Thoughts

AI in 2025 has moved far beyond simple automation. It now orchestrates entire contact center ecosystems—powering conversational agents, augmenting human reps, automating back-office workflows, and delivering predictive intelligence in real time.

The technical stack is increasingly cloud-native, model-driven, and infused with real-time analytics. For engineering teams, the focus is now on building scalable, secure, and ethical AI infrastructures that deliver measurable impact across customer satisfaction, cost savings, and employee productivity.

As AI models continue to advance, contact centers will evolve into fully adaptive systems, capable of learning, optimizing, and personalizing in real time. The revolution is already here—and it's deeply technical.