#supervised ai

[mostlysignssomeportents]

Humans are not perfectly vigilant

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in BOSTON with Randall "XKCD" Munroe (Apr 11), then PROVIDENCE (Apr 12), and beyond!

Here's a fun AI story: a security researcher noticed that large companies' AI-authored source-code repeatedly referenced a nonexistent library (an AI "hallucination"), so he created a (defanged) malicious library with that name and uploaded it, and thousands of developers automatically downloaded and incorporated it as they compiled the code:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

These "hallucinations" are a stubbornly persistent feature of large language models, because these models only give the illusion of understanding; in reality, they are just sophisticated forms of autocomplete, drawing on huge databases to make shrewd (but reliably fallible) guesses about which word comes next:

https://dl.acm.org/doi/10.1145/3442188.3445922

Guessing the next word without understanding the meaning of the resulting sentence makes unsupervised LLMs unsuitable for high-stakes tasks. The whole AI bubble is based on convincing investors that one or more of the following is true:

There are low-stakes, high-value tasks that will recoup the massive costs of AI training and operation;

There are high-stakes, high-value tasks that can be made cheaper by adding an AI to a human operator;

Adding more training data to an AI will make it stop hallucinating, so that it can take over high-stakes, high-value tasks without a "human in the loop."

These are dubious propositions. There's a universe of low-stakes, low-value tasks – political disinformation, spam, fraud, academic cheating, nonconsensual porn, dialog for video-game NPCs – but none of them seem likely to generate enough revenue for AI companies to justify the billions spent on models, nor the trillions in valuation attributed to AI companies:

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

The proposition that increasing training data will decrease hallucinations is hotly contested among AI practitioners. I confess that I don't know enough about AI to evaluate opposing sides' claims, but even if you stipulate that adding lots of human-generated training data will make the software a better guesser, there's a serious problem. All those low-value, low-stakes applications are flooding the internet with botshit. After all, the one thing AI is unarguably very good at is producing bullshit at scale. As the web becomes an anaerobic lagoon for botshit, the quantum of human-generated "content" in any internet core sample is dwindling to homeopathic levels:

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

This means that adding another order of magnitude more training data to AI won't just add massive computational expense – the data will be many orders of magnitude more expensive to acquire, even without factoring in the additional liability arising from new legal theories about scraping:

https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

That leaves us with "humans in the loop" – the idea that an AI's business model is selling software to businesses that will pair it with human operators who will closely scrutinize the code's guesses. There's a version of this that sounds plausible – the one in which the human operator is in charge, and the AI acts as an eternally vigilant "sanity check" on the human's activities.

For example, my car has a system that notices when I activate my blinker while there's another car in my blind-spot. I'm pretty consistent about checking my blind spot, but I'm also a fallible human and there've been a couple times where the alert saved me from making a potentially dangerous maneuver. As disciplined as I am, I'm also sometimes forgetful about turning off lights, or waking up in time for work, or remembering someone's phone number (or birthday). I like having an automated system that does the robotically perfect trick of never forgetting something important.

There's a name for this in automation circles: a "centaur." I'm the human head, and I've fused with a powerful robot body that supports me, doing things that humans are innately bad at.

That's the good kind of automation, and we all benefit from it. But it only takes a small twist to turn this good automation into a nightmare. I'm speaking here of the reverse-centaur: automation in which the computer is in charge, bossing a human around so it can get its job done. Think of Amazon warehouse workers, who wear haptic bracelets and are continuously observed by AI cameras as autonomous shelves shuttle in front of them and demand that they pick and pack items at a pace that destroys their bodies and drives them mad:

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

Automation centaurs are great: they relieve humans of drudgework and let them focus on the creative and satisfying parts of their jobs. That's how AI-assisted coding is pitched: rather than looking up tricky syntax and other tedious programming tasks, an AI "co-pilot" is billed as freeing up its human "pilot" to focus on the creative puzzle-solving that makes coding so satisfying.

But an hallucinating AI is a terrible co-pilot. It's just good enough to get the job done much of the time, but it also sneakily inserts booby-traps that are statistically guaranteed to look as plausible as the good code (that's what a next-word-guessing program does: guesses the statistically most likely word).

This turns AI-"assisted" coders into reverse centaurs. The AI can churn out code at superhuman speed, and you, the human in the loop, must maintain perfect vigilance and attention as you review that code, spotting the cleverly disguised hooks for malicious code that the AI can't be prevented from inserting into its code. As "Lena" writes, "code review [is] difficult relative to writing new code":

https://twitter.com/qntm/status/1773779967521780169

Why is that? "Passively reading someone else's code just doesn't engage my brain in the same way. It's harder to do properly":

https://twitter.com/qntm/status/1773780355708764665

There's a name for this phenomenon: "automation blindness." Humans are just not equipped for eternal vigilance. We get good at spotting patterns that occur frequently – so good that we miss the anomalies. That's why TSA agents are so good at spotting harmless shampoo bottles on X-rays, even as they miss nearly every gun and bomb that a red team smuggles through their checkpoints:

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

"Lena"'s thread points out that this is as true for AI-assisted driving as it is for AI-assisted coding: "self-driving cars replace the experience of driving with the experience of being a driving instructor":

https://twitter.com/qntm/status/1773841546753831283

In other words, they turn you into a reverse-centaur. Whereas my blind-spot double-checking robot allows me to make maneuvers at human speed and points out the things I've missed, a "supervised" self-driving car makes maneuvers at a computer's frantic pace, and demands that its human supervisor tirelessly and perfectly assesses each of those maneuvers. No wonder Cruise's murderous "self-driving" taxis replaced each low-waged driver with 1.5 high-waged technical robot supervisors:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

AI radiology programs are said to be able to spot cancerous masses that human radiologists miss. A centaur-based AI-assisted radiology program would keep the same number of radiologists in the field, but they would get less done: every time they assessed an X-ray, the AI would give them a second opinion. If the human and the AI disagreed, the human would go back and re-assess the X-ray. We'd get better radiology, at a higher price (the price of the AI software, plus the additional hours the radiologist would work).

But back to making the AI bubble pay off: for AI to pay off, the human in the loop has to reduce the costs of the business buying an AI. No one who invests in an AI company believes that their returns will come from business customers to agree to increase their costs. The AI can't do your job, but the AI salesman can convince your boss to fire you and replace you with an AI anyway – that pitch is the most successful form of AI disinformation in the world.

An AI that "hallucinates" bad advice to fliers can't replace human customer service reps, but airlines are firing reps and replacing them with chatbots:

https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know

An AI that "hallucinates" bad legal advice to New Yorkers can't replace city services, but Mayor Adams still tells New Yorkers to get their legal advice from his chatbots:

https://arstechnica.com/ai/2024/03/nycs-government-chatbot-is-lying-about-city-laws-and-regulations/

The only reason bosses want to buy robots is to fire humans and lower their costs. That's why "AI art" is such a pisser. There are plenty of harmless ways to automate art production with software – everything from a "healing brush" in Photoshop to deepfake tools that let a video-editor alter the eye-lines of all the extras in a scene to shift the focus. A graphic novelist who models a room in The Sims and then moves the camera around to get traceable geometry for different angles is a centaur – they are genuinely offloading some finicky drudgework onto a robot that is perfectly attentive and vigilant.

But the pitch from "AI art" companies is "fire your graphic artists and replace them with botshit." They're pitching a world where the robots get to do all the creative stuff (badly) and humans have to work at robotic pace, with robotic vigilance, in order to catch the mistakes that the robots make at superhuman speed.

Reverse centaurism is brutal. That's not news: Charlie Chaplin documented the problems of reverse centaurs nearly 100 years ago:

https://en.wikipedia.org/wiki/Modern_Times_(film)

As ever, the problem with a gadget isn't what it does: it's who it does it for and who it does it to. There are plenty of benefits from being a centaur – lots of ways that automation can help workers. But the only path to AI profitability lies in reverse centaurs, automation that turns the human in the loop into the crumple-zone for a robot:

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

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/2024/04/01/human-in-the-loop/#monkey-in-the-middle

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

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

--

Jorge Royan (modified) https://commons.wikimedia.org/wiki/File:Munich_-_Two_boys_playing_in_a_park_-_7328.jpg

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

--

Noah Wulf (modified) https://commons.m.wikimedia.org/wiki/File:Thunderbirds_at_Attention_Next_to_Thunderbird_1_-_Aviation_Nation_2019.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

[bishopofstdiesis]

London, Down In The Underground c. 1899 (either one)

When I first learned of MidJourney, I did what anyone might do... I wasted time & image hours doing stupid things. Then I got it together & attempted to make a universe in a geode & it looked O.K. It was also sparkly & dark. So I thought, “Why not make London? But FALLEN London...”

Did it work? Kinda. I didn’t have the knowledge of prompts or aspects or art movements or whatever else made it do amazing things. I was armed with the knowledge it was Victorian London in a cavern far under the surface of the Earth.

So, this is what happened. Is this all of it? Oh, gods, no. I made so many of these before realising that isn’t how it works that I have like... piles & piles & most look the same but aren’t. Someday, I will not be a chaos dragon & I will clean out my horde. Until this, THIS IS MY CHAOS & YOU CANNOT HAVE IT!

(Yes, the tile is a direct reference to Labyrinth & if any of you f--kers say you never ONCE wanted to go to wherever “The Underground” was that got sung about, I’m gonna call you a liar. But only if you have seen & loved the movie, if you have not, you have no idea what the hell I mean & that’s O.K. But down, in the underground, you’ll find someone new...)

[helpimstuckposting]

My roommate and I like to joke that season 4, the teen wolf writers got a small team of interns to help and then just passed them all of the side stories. Then season 6 they completely stopped checking their work and let the interns have too much free range

[pneumanomads]

Decoding the Mind of Artificial Intelligence: Understanding How AI Thinks

#ArtificialIntelligence #MachineLearning #DeepLearning #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #NeuralNetworks #Data #Algorithm #AIlimitations #AImodel

Artificial Intelligence (AI) has come a long way in recent years. From the early days of rule-based systems to the current state-of-the-art machine learning models, AI has evolved to be able to tackle increasingly complex tasks. But have you ever wondered about how AI thinks? At the core of AI is the ability to learn from data. Machine learning algorithms are used to train AI models on large…

[mean-vampyre]

I keep feeding different AIs my drafts and pitting them against each other to see who can understand my scrambled ideas better. court jester fighting to get the best improv play for their queen

[francescolelli]

A No-nonsense Approach to Deep Learning, LLM, Supervised Learning, Generative AI, and Everything in Between

This is a short preview of the article: With this post I will share a few resources freely available in the internet that I believe can serve as an entry point for understanding the world around AI in a no-nonsense manner. The domain is relatively vast and we will cover topics like Deep Learning, Large Language Models, Supervised

If you like it consider checking out the full version of the post at: A No-nonsense Approach to Deep Learning, LLM, Supervised Learning, Generative AI, and Everything in Between

If you are looking for ideas for tweet or re-blog this post you may want to consider the following hashtags:

Hashtags: #DeepLearning, #GenerativeAI, #LLM, #SuperviseLearning

The Hashtags of the Categories are: #BigData, #MachineLearning

A No-nonsense Approach to Deep Learning, LLM, Supervised Learning, Generative AI, and Everything in Between is available at the following link: https://francescolelli.info/big-data/a-no-nonsense-approach-to-deep-learning-llm-supervise-learning-generative-ai-and-everything-in-between/ You will find more information, stories, examples, data, opinions and scientific papers as part of a collection of articles about Information Management, Computer Science, Economics, Finance and More.

The title of the full article is: A No-nonsense Approach to Deep Learning, LLM, Supervised Learning, Generative AI, and Everything in Between

It belong to the following categories: Big Data, Machine Learning

The most relevant keywords are: deep Learning, Generative AI, LLM, Supervise Learning

It has been published by Francesco Lelli at Francesco Lelli a blog about Information Management, Computer Science, Finance, Economics and nearby ideas and opinions

With this post I will share a few resources freely available in the internet that I believe can serve as an entry point for understanding the world around AI in a no-nonsense manner. The domain is relatively vast and we will cover topics like Deep Learning, Large Language Models, Supervised

Hope you will find it interesting and that it will help you in your journey

With this post I will share a few resources freely available in the internet that I believe can serve as an entry point for understanding the world around AI in a no-nonsense manner. The domain is relatively vast and we will cover topics like Deep Learning, Large Language Models, Supervised Learning, Generative AI, and a…

[davidaugust]

“…a deep truth about AI: that the story of AI being managed by a ‘human in the loop’ is a fantasy, because humans are neurologically incapable of maintaining vigilance in watching for rare occurrences.”

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

#AI #ArtificialIntelligence #machines #human #supervision #AIsafety

[govindhtech]

Define machine learning: 5 machine learning types to know

Machine learning (ML) can be used in computer vision, large language models (LLMs), speech recognition, self-driving cars, and many more use cases to make decisions in healthcare, human resources, finance, and other areas.

However, ML’s rise is complicated. ML validation and training datasets are generally aggregated by humans, who are biased and error-prone. Even if an ML model isn’t biased or erroneous, using it incorrectly can cause harm.

Diversifying enterprise AI and ML usage can help preserve a competitive edge. Distinct ML algorithms have distinct benefits and capabilities that teams can use for different jobs. IBM will cover the five main categories and their uses.

Define machine learning

ML is a computer science, data science, and AI subset that lets computers learn and improve from data without programming.

ML models optimize performance utilizing algorithms and statistical models that deploy jobs based on data patterns and inferences. Thus, ML predicts an output using input data and updates outputs as new data becomes available.

Machine learning algorithms recommend products based on purchasing history on retail websites. IBM, Amazon, Google, Meta, and Netflix use ANNs to make tailored suggestions on their e-commerce platforms. Retailers utilize chat bots, virtual assistants, ML, and NLP to automate shopping experiences.

Machine learning types

Supervised, unsupervised, semi-supervised, self-supervised, and reinforcement machine learning algorithms exist.

1.Supervised machine learning

Supervised machine learning trains the model on a labeled dataset with the target or outcome variable known. Data scientists constructing a tornado predicting model might enter date, location, temperature, wind flow patterns, and more, and the output would be the actual tornado activity for those days.

Several algorithms are employed in supervised learning for risk assessment, image identification, predictive analytics, and fraud detection.

Regression algorithms predict output values by discovering linear correlations between actual or continuous quantities (e.g., income, temperature). Regression methods include linear regression, random forest, gradient boosting, and others.

Labeling input data allows classification algorithms to predict categorical output variables (e.g., “junk” or “not junk”). Logistic regression, k-nearest neighbors, and SVMs are classification algorithms.

Naïve Bayes classifiers enable huge dataset classification. They’re part of generative learning algorithms that model class or category input distribution. Decision trees in Naïve Bayes algorithms support regression and classification techniques.

Neural networks, with many linked processing nodes, replicate the human brain and can do natural language translation, picture recognition, speech recognition, and image generation.

Random forest methods combine decision tree results to predict a value or category.

2. Unsupervised machine learning

Apriori, Gaussian Mixture Models (GMMs), and principal component analysis (PCA) use unlabeled datasets to make inferences, enabling exploratory data analysis, pattern detection, and predictive modeling.

Cluster analysis is the most frequent unsupervised learning method, which groups data points by value similarity for customer segmentation and anomaly detection. Association algorithms help data scientists visualize and reduce dimensionality by identifying associations between data objects in huge databases.

K-means clustering organizes data points by size and granularity, clustering those closest to a centroid under the same category. Market, document, picture, and compression segmentation use K-means clustering.

Hierarchical clustering includes agglomerative clustering, where data points are isolated into groups and then merged iteratively based on similarity until one cluster remains, and divisive clustering, where a single data cluster is divided by data point differences.

Probabilistic clustering group’s data points by distribution likelihood to tackle density estimation or “soft” clustering problems.

Often, unsupervised ML models power “customers who bought this also bought…” recommendation systems.

3. Self-supervised machine learning

Self-supervised learning (SSL) lets models train on unlabeled data instead of enormous annotated and labeled datasets. SSL algorithms, also known as predictive or pretext learning algorithms automatically classify and solve unsupervised problems by learning one portion of the input from another. Computer vision and NLP require enormous amounts of labeled training data to train models, making these methods usable.

4. Reinforcement learning

Dynamic programming dubbed reinforcement learning from human feedback (RLHF) trains algorithms using reward and punishment. To use reinforcement learning, an agent acts in a given environment to achieve a goal. The agent is rewarded or penalized based on a measure (usually points) to encourage good behavior and discourage negative behavior. Repetition teaches the agent the optimum methods.

Video games often use reinforcement learning techniques to teach robots human tasks.

5. Semi-supervised learning

The fifth machine learning method combines supervised and unsupervised learning.

Semi-supervised learning algorithms learn from a small labeled dataset and a large unlabeled dataset because the labeled data guides the learning process. A semi-supervised learning algorithm may find data clusters using unsupervised learning and label them using supervised learning.

Semi-supervised machine learning uses generative adversarial networks (GANs) to produce unlabeled data by training two neural networks.

ML models can gain insights from company data, but their vulnerability to human/data bias makes ethical AI practices essential.

Manage multiple ML models with watstonx.ai.

Whether they employ AI or not, most people use machine learning, from developers to users to regulators. Adoption of ML technology is rising. Global machine learning market was USD 19 billion in 2022 and is predicted to reach USD 188 billion by 2030 (a CAGR of almost 37%).

The size of ML usage and its expanding business effect make understanding AI and ML technologies a key commitment that requires continuous monitoring and appropriate adjustments as technologies improve. IBM Watsonx.AI Studio simplifies ML algorithm and process management for developers.

IBM Watsonx.ai, part of the IBM Watsonx AI and data platform, leverages generative AI and a modern business studio to train, validate, tune, and deploy AI models faster and with less data. Advanced data production and classification features from Watsonx.ai enable enterprises optimize real-world AI performance with data insights.

In the age of data explosion, AI and machine learning are essential to corporate operations, tech innovation, and competition. However, as new pillars of modern society, they offer an opportunity to diversify company IT infrastructures and create technologies that help enterprises and their customers.

Read more on Govindhtech.com

[aicognitech]

Machine Learning: Exploring the Main Components and Functions of this Powerful AI Technique

Delve into the sector of Machine Learning as we discover its fundamental additives and functions. Discover the intricacies of supervised learning, unsupervised getting to know, and reinforcement gaining knowledge of, and understand how Machine Learning is revolutionizing industries and using AI advancements.

Machine Learning

[usaii]

Supervised vs Unsupervised Machine Learning: Understanding the Contrasts | USAII®

Learn the nuances of supervised and unsupervised machine learning from the perspective of an AI professional. Delve deeper into their functioning, characteristics, and types of algorithms used; and pave a successful AI career.

Read more: https://bit.ly/3XGcm2W

Supervised Learning, supervised learning algorithms, supervised learning in machine learning, supervised and unsupervised machine learning, supervised learning models, unsupervised learning methods, Unsupervised Learning, unsupervised learning algorithms, unsupervised machine learning, AI applications, machine learning algorithms, machine learning techniques, supervised and unsupervised learning

[joeman-the-joeman]

"What's 2+2?"

"5?"

"Little lower"

"4,5?"

"... getting closer..."

And that's how AIs are made

[menjeet]

The risks and reward of using AI in stock market

Got invited to a talk on “A.I. for wealth creation in the stock market” at an investor’s meet in my hometown and came away completely bamboozled. The speaker, the CEO of a brokerage firm, seemingly an expert on stock markets had zero knowledge about how AI works. He threw up a few slides on AI that were incomprehensible to the largely local non-tech savvy attendees and then started demonstrating…

[qwikskills]

Unleashing the Power of Machine Learning in the 21st Century

Machine learning is one of the most talked about and rapidly growing fields in the tech industry. It is a branch of artificial intelligence that allows computers to learn and make predictions or decisions without explicit programming. The rise of big data and the increasing availability of computing power have made it possible for machine learning algorithms to handle vast amounts of data and provide valuable insights and predictions.

In recent years, machine learning has been applied in various industries, ranging from healthcare to finance, retail, and marketing. In healthcare, machine learning algorithms are used to analyze patient data and help doctors make more accurate diagnoses. In finance, machine learning is used to detect fraud, analyze financial markets, and make investment decisions. In retail, machine learning is used to personalize shopping experiences, recommend products, and optimize pricing.

One of the key benefits of machine learning is that it allows for automated decision-making, which can save time and resources. Machine learning algorithms can analyze large amounts of data and provide insights in real-time, enabling organizations to make data-driven decisions more efficiently. Additionally, machine learning algorithms are able to improve over time, becoming more accurate as they are exposed to more data.

Despite its many advantages, machine learning is not without its challenges. One of the main challenges is the lack of transparency in decision-making. It can be difficult to understand how machine learning algorithms arrived at a particular decision, making it difficult to explain the decision to stakeholders. Additionally, machine learning algorithms can be biased if the data used to train them is biased, leading to unfair or inaccurate decisions.

In conclusion, machine learning is a powerful tool that has the potential to transform the way we live and work. As the technology continues to evolve and improve, we can expect to see more and more applications of machine learning in various industries. However, it is important to approach machine learning with caution and ensure that the algorithms are developed and used in a transparent and ethical manner.

[honeywellbuildings]

Smart Home Security AI CCTV Camera from Honeywell

Honeywell's smart home security ai CCTV camera is proactive and equipped with cutting-edge detecting insights, allowing you to take prompt action.

Click to check: https://www.honeywellbuildings.in/intelligent-security/mass-mid-segment/video-system/ai-camera/smart-ai-supervision

[mostlysignssomeportents]

Supervised AI isn't

It wasn't just Ottawa: Microsoft Travel published a whole bushel of absurd articles, including the notorious Ottawa guide recommending that tourists dine at the Ottawa Food Bank ("go on an empty stomach"):

https://twitter.com/parismarx/status/1692233111260582161

After Paris Marx pointed out the Ottawa article, Business Insider's Nathan McAlone found several more howlers:

https://www.businessinsider.com/microsoft-removes-embarrassing-offensive-ai-assisted-travel-articles-2023-8

There was the article recommending that visitors to Montreal try "a hamburger" and went on to explain that a hamburger was a "sandwich comprised of a ground beef patty, a sliced bun of some kind, and toppings such as lettuce, tomato, cheese, etc" and that some of the best hamburgers in Montreal could be had at McDonald's.

For Anchorage, Microsoft recommended trying the local delicacy known as "seafood," which it defined as "basically any form of sea life regarded as food by humans, prominently including fish and shellfish," going on to say, "seafood is a versatile ingredient, so it makes sense that we eat it worldwide."

In Tokyo, visitors seeking "photo-worthy spots" were advised to "eat Wagyu beef."

There were more.

Microsoft insisted that this wasn't an issue of "unsupervised AI," but rather "human error." On its face, this presents a head-scratcher: is Microsoft saying that a human being erroneously decided to recommend the dining at Ottawa's food bank?

But a close parsing of the mealy-mouthed disclaimer reveals the truth. The unnamed Microsoft spokesdroid only appears to be claiming that this wasn't written by an AI, but they're actually just saying that the AI that wrote it wasn't "unsupervised." It was a supervised AI, overseen by a human. Who made an error. Thus: the problem was human error.

This deliberate misdirection actually reveals a deep truth about AI: that the story of AI being managed by a "human in the loop" is a fantasy, because humans are neurologically incapable of maintaining vigilance in watching for rare occurrences.

Our brains wire together neurons that we recruit when we practice a task. When we don't practice a task, the parts of our brain that we optimized for it get reused. Our brains are finite and so don't have the luxury of reserving precious cells for things we don't do.

That's why the TSA sucks so hard at its job – why they are the world's most skilled water-bottle-detecting X-ray readers, but consistently fail to spot the bombs and guns that red teams successfully smuggle past their checkpoints:

https://www.nbcnews.com/news/us-news/investigation-breaches-us-airports-allowed-weapons-through-n367851

TSA agents (not "officers," please – they're bureaucrats, not cops) spend all day spotting water bottles that we forget in our carry-ons, but almost no one tries to smuggle a weapons through a checkpoint – 99.999999% of the guns and knives they do seize are the result of flier forgetfulness, not a planned hijacking.

In other words, they train all day to spot water bottles, and the only training they get in spotting knives, guns and bombs is in exercises, or the odd time someone forgets about the hand-cannon they shlep around in their day-pack. Of course they're excellent at spotting water bottles and shit at spotting weapons.

This is an inescapable, biological aspect of human cognition: we can't maintain vigilance for rare outcomes. This has long been understood in automation circles, where it is called "automation blindness" or "automation inattention":

https://pubmed.ncbi.nlm.nih.gov/29939767/

Here's the thing: if nearly all of the time the machine does the right thing, the human "supervisor" who oversees it becomes incapable of spotting its error. The job of "review every machine decision and press the green button if it's correct" inevitably becomes "just press the green button," assuming that the machine is usually right.

This is a huge problem. It's why people just click "OK" when they get a bad certificate error in their browsers. 99.99% of the time, the error was caused by someone forgetting to replace an expired certificate, but the problem is, the other 0.01% of the time, it's because criminals are waiting for you to click "OK" so they can steal all your money:

https://finance.yahoo.com/news/ema-report-finds-nearly-80-130300983.html

Automation blindness can't be automated away. From interpreting radiographic scans:

https://healthitanalytics.com/news/ai-could-safely-automate-some-x-ray-interpretation

to autonomous vehicles:

https://newsroom.unsw.edu.au/news/science-tech/automated-vehicles-may-encourage-new-breed-distracted-drivers

The "human in the loop" is a figleaf. The whole point of automation is to create a system that operates at superhuman scale – you don't buy an LLM to write one Microsoft Travel article, you get it to write a million of them, to flood the zone, top the search engines, and dominate the space.

As I wrote earlier: "There's no market for a machine-learning autopilot, or content moderation algorithm, or loan officer, if all it does is cough up a recommendation for a human to evaluate. Either that system will work so poorly that it gets thrown away, or it works so well that the inattentive human just button-mashes 'OK' every time a dialog box appears":

https://pluralistic.net/2022/10/21/let-me-summarize/#i-read-the-abstract

Microsoft – like every corporation – is insatiably horny for firing workers. It has spent the past three years cutting its writing staff to the bone, with the express intention of having AI fill its pages, with humans relegated to skimming the output of the plausible sentence-generators and clicking "OK":

https://www.businessinsider.com/microsoft-news-cuts-dozens-of-staffers-in-shift-to-ai-2020-5

We know about the howlers and the clunkers that Microsoft published, but what about all the other travel articles that don't contain any (obvious) mistakes? These were very likely written by a stochastic parrot, and they comprised training data for a human intelligence, the poor schmucks who are supposed to remain vigilant for the "hallucinations" (that is, the habitual, confidently told lies that are the hallmark of AI) in the torrent of "content" that scrolled past their screens:

https://dl.acm.org/doi/10.1145/3442188.3445922

Like the TSA agents who are fed a steady stream of training data to hone their water-bottle-detection skills, Microsoft's humans in the loop are being asked to pluck atoms of difference out of a raging river of otherwise characterless slurry. They are expected to remain vigilant for something that almost never happens – all while they are racing the clock, charged with preventing a slurry backlog at all costs.

Automation blindness is inescapable – and it's the inconvenient truth that AI boosters conspicuously fail to mention when they are discussing how they will justify the trillion-dollar valuations they ascribe to super-advanced autocomplete systems. Instead, they wave around "humans in the loop," using low-waged workers as props in a Big Store con, just a way to (temporarily) cool the marks.

And what of the people who lose their (vital) jobs to (terminally unsuitable) AI in the course of this long-running, high-stakes infomercial?

Well, there's always the food bank.

"Go on an empty stomach."

Going to Burning Man? Catch me on Tuesday at 2:40pm on the Center Camp Stage for a talk about enshittification and how to reverse it; on Wednesday at noon, I'm hosting Dr Patrick Ball at Liminal Labs (6:15/F) for a talk on using statistics to prove high-level culpability in the recruitment of child soldiers.

On September 6 at 7pm, I'll be hosting Naomi Klein at the LA Public Library for the launch of Doppelganger.

On September 12 at 7pm, I'll be at Toronto's Another Story Bookshop with my new book The Internet Con: How to Seize the Means of Computation.

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/2023/08/23/automation-blindness/#humans-in-the-loop

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

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

--

West Midlands Police (modified) https://www.flickr.com/photos/westmidlandspolice/8705128684/

CC BY-SA 2.0 https://creativecommons.org/licenses/by-sa/2.0/

[oharabunny]

The Grass is Greener on the Other Side

Description: It's Miguel's birthday and you want to surprise him with his childhood favorite foods. With you living in his home, he has rules, and you broke the most important one.

Story is connected to my yandere!caretaker!Miguel fic.

Word Count: 5170

Warning: 18+, mdni, yandere!caretaker!Miguel, fem!afab!Reader, spanking, manipulation, slut-shaming, Stockholm Syndrome, infantilization, physical pain, non-con, not beta read

Please read warnings before proceeding. The following behaviors are abusive and I do not condone them.

You wake up one morning from Lyla’s alarm and see Miguel isn’t next to you in bed. That is typical of him because he has to go to work early or the rare times he stays there overnight (which he will always give you a heads up on). 

He will almost always come home every night so it doesn’t particularly bother you. (But it definitely bothers him because he just wants to spend an eternity in every waking hour caring for you, but alas the multiverse isn’t going to take care of itself. It’s not like he can entrust the fate and balance of it all to the other Spiders.)

You slowly begin to wake up and slide yourself out of bed. Another day without Miguel as your only friend and husband to keep you entertained. 

That is until you realize what day it is.

You memorize this specific date because it is one of the few things that Miguel will tell you about himself (you pestered him a long time to tell you).

Today is his birthday.

You feel saddened by the fact he is not home to celebrate, but that gives you the chance to surprise him if he comes back home tonight.

You ask Lyla what Miguel’s favorite cakes and birthday food are. 

Lyla says he really likes the pan dulce sold at this specific bakery downtown. Unfortunately they’re a prideful business that does not do delivery. 

That is a problem.

Ever since he took you under his wing to live in his apartment, he has many, many rules for you to follow. They only get stricter after marriage and childbirth. 

Rule number one is you do not leave the apartment for any reason (unless it’s for safety and Miguel is not there to save the day).

Lyla, his AI assistant, is also sure as hell not going to let you go either.

You have a child now, a daughter of 9 years, so there is even less incentive to let you go outside.

You think to yourself, wouldn’t your daughter also want to help set up his birthday surprise?

You immediately wash up and dress in one of Miguel’s favorite dresses that he likes on you. You even put on the style of makeup and hairdo the way he likes them.

You go to your daughter’s room and softly knock on her door. “Hey, Y/D/N, can I come in?”

She swings the door open, and says while yawning, “Hey mama, good morning.”

You step into her room and sit on her bed. You pat the bed to gesture to her to sit down next to you.

“It’s your papa’s birthday today. And I think we should surprise him with his favorite foods when he gets back.”

Her face lights up in excitement and bounces up to her toes. “It is?! Oh can we, mama?”

She pauses, “But wait, you can’t cook.”

It was another one of those rules Miguel set for you: you are not allowed to cook. You can at most use the microwave, with Lyla’s supervision. 

“I know, sweetie, but I know a few places we can stop by to pick up his favorite foods.” You counter.

“But papa says you’re not allowed to go outside, it’s too dangerous for you.” Your daughter looks to the side with uncertainty while playing with her fingers. She does this whenever she feels pressured.

You sigh, “I know…that papa can be protective of me. But sometimes…he doesn’t know what he’s talking about. I’m not as weak as he thinks. Besides, I want to spend time with you outside! I’ll promise to take you to your favorite ice cream spot, if you’ll show me.”

With just that, her face lights up again in glee. She has always wanted to bring you to her favorite places in the city that she usually goes with her papa. You never got the chance to see the outside world beyond that brief time in Spider Society and when he brought you to the hospital for her birth. 

You lean into your daughter’s ear, “But we’ll need to trick the alarm system and Lyla if we want to make it happen.”

You have to convince Lyla into disabling the alarm system without her alerting Miguel.

You also have to make your trip super quick because he likes to video call you randomly. 

There are very few ways to convince or even trick Lyla. None of which would pop up in your head if you aren’t particularly tech savvy. 

And you aren’t.

“Lyla.” You called her.

“Hey there sweetie. What’s up?” The small yellow woman appears on your shoulder and tilts her head in question.

You pause to gather your words and organize your mind. It doesn’t really work.

“It’s Miguel’s birthday today and I want to surprise him.” You start slowly to gauge her response.

“Ah, yes it is, and oh dear…” Lyla pauses, “He doesn’t particularly like his birthday, much less surprises.”

“Well, I still think he should have a little something. Maybe not like a party if he doesn’t like those, but something like getting his favorite foods. And before you tell me I’m not allowed to cook, I know. And… I need to go outside to pick them up.” You clasp your hands together and look down to help with your words.

You can see Lyla is already about to cut in.

“I KNOW, I know, rule number one. But, it’s close by. I’ll make it quick. Y/D/N will be with me.” 

Lyla sighs and readjusts her pink heart-shaped glasses, “You know Miguel is still not going to be happy about that.”

“You don’t have to tell him! I mean, you’ll be with me and if anything happens you can call him. But, I swear, nothing will happen to me!” 

“I’m sorry gal, I just can’t let you do that.” Lyla could only give you a sympathetic look.

“He deserves something special for a special day. Even setting aside his whole birthday, I just want to show him how much I appreciate him for everything he’s done for me.” You could feel yourself becoming dejected.

“You can paint him a picture.” Lyla suggests.

“I painted him a thousand.” You counter.

“You can crochet.”

“I do that every day.” You are getting frustrated and sigh, “And don’t you think it’s a little ridiculous that I’m not allowed outside. I can walk just fine. You can take your diagnostics. Even I need a change of scenery every now and then.”

“Girlie, you know you two have talked about this. Miguel set that rule in stone. I can’t do anything about it.” Lyla is still on the fence.

“I just… I just want to get him something that reminds him of home. I haven’t seen him have that pan dulce at all before. He deserves it for all his hard work. Don’t you think?” You plead and plead in hopes to appeal somewhere in her algorithm for Miguel’s sympathy.

“Well, I can ask one of our Spiders to fetch it for you.” Damn you Lyla.

“I can’t trouble the Spider people for this!” You quickly said.

And then you remembered something.

“Hey Lyla, don’t you have customization mods that you’ve been bugging Miguel to let you have?” Your eyes look devious.

“...no?” Lyla narrows her eyes in suspicion.

“Well… if I can get him the pan dulce, he’ll be very happy. And you know with happy Miguel, I can convince him to let you have your customization mods.” You wink. 

Lyla doesn’t immediately answer and looks up in thought. “Hmmm… Well, as long as you make it quick…”

“And he cannot know!”

“Deal. At least he won’t berate me about it.” Lyla twirls her hand. “But you have to make it quick!” She emphasizes and points at you.

You smile and nod. 

You quickly go and grab your laptop to order the pan dulce for pickup. You also map out a couple other stops to pick up his favorite empanadas, tamales, sopapillas, etc and your daughter’s ice cream spot as you promised. Nothing can go wrong. 

You tell her to get ready to go and Lyla to disable the alarm system.

“Hey Lyla, could you also temporarily disable the live tracking on my watch?” 

She gives you a bored look. “He quite literally checks every 30 minutes. Sometimes 5. Oh and including the camera feed in the apartment as well. He’s gonna notice.” 

“Could you, like, distract him at work? Maybe another anomaly case or what not.”

“Fortunately for you, he’s out in another universe catching an anomaly right now. But it’s an easy one. I can try and distract him a little, but he’s going to finish up pretty fast with this one.” Lyla conjures up her own digital screen to analyze all her possibilities. “I can probably shoot another case for him to do.”

Honestly anything is fine as long he’s distracted long enough for you to go to all of your stops. 

“I’ll try and be fast.” You promise her. “Oh and Lyla-”

“Hm?”

“Thank you.” You smile at her genuinely.

“Aw shucks.” Lyla smiles back.

–

On Miguel’s end, he is finishing up his capture on several anomalies and heading back to HQ. He just can’t wait to go home soon since today is slow and nothing else should be happening. All projects are being handled by the other Spiders, so he can take it easy and go see you.

That is until Lyla pops up on his shoulder and screams, “HELLO MIGUEL–!”

He flinches and covers his ears from the banshee levels of frequency. “What the shock Lyla! Don’t scream into my ear!”

“Haha sorry, sorry. I just wanted to let you know that there’s another case on Earth-2348 that needs your attention.”

“Send another Spider for that. I need to go home and check on Y/N.” Miguel raises his brow at her through his mask.

“You have a point, but this one requires your special attention.” Lyla shows him the data.

Miguel gives a gruff sigh and rolls his eyes, “Fine. Let’s get this over quick.”

–

You and your daughter are making your way downtown. Walking fast, faces pass and you’re… at Miguel’s childhood bakery!

The walk with your daughter has been a breath of fresh air. You’ve been trapped in that godforsaken apartment for the last 9 years, basically ever since your daughter was born. But even before then, Miguel wouldn’t let you go outside unless it was a date or state of an emergency. He hasn’t taken you out on a date since your daughter’s existence. And emergencies rarely ever do arise, if ever. 

But now, you get to have your own time with your daughter without being shackled to him and the shared apartment. It’s not like you hate him; you just wish he lets you have the freedom to choose and make your own decisions. 

Why can’t he see that?

Picking up the goods is quick and easy, even if there is a bit of a wait in some shops. You know you don’t have time to stall and admire your surroundings. 

You still take your daughter to the ice cream spot that she boasts about going with papa. You’re happy that you get to also share this moment with her as well.

“What flavor does papa get with you?” You ask your daughter. Maybe you can pick up a pint for him.

“He usually gets cinnamon-basil.” Your daughter scrunches up her face in disgust. “I usually get the peanut butter fudgesicle.”

Noted. 

You turn toward the male worker to place your order, “Um, hi there, I would like the peanut butter fudgesicle…”

You turn to your daughter, “On a cone?” She nods. 

“On a cone.”

You continue, “As for me, I would like the [your fav ice cream flavor] on a cone as well. Oh! And one pint each for the peanut butter fudgesicle and the cinnamon-basil.” 

The transaction goes smoothly and he hands you your order. He decides to add, “You are very pretty ma’am. I hope you and your daughter have a nice day.”

You blush at the compliment. Miguel is usually the one feeding you compliments, but it’s nice seeing someone else other than your husband acknowledging you. 

You smile back cheerfully, “Thank you!”

–

Miguel quickly finishes up on this “special” case that Lyla claims to be. Strange, she usually isn’t wrong with her calculations and data processing. Did something happen to her programming?

He fidgets his gizmo to check up on you since he hasn’t planned on taking on an extra case today. The camera feed of the apartment shows no signs of you or your daughter. Then, he pulls up his map of his Earth to find your pinpoint, but it’s not there. He searches for your ping frantically and it’s not there.

“Lyla.” He calls in a low tone.

She pops up and tries not to look guilty, mentally cursing you for not being fast enough.

“Why did you give me such an easy anomaly to take care of?” His voice is threatening. 

Lyla can’t take it anymore. “I’m sorry! Y/N wanted to surprise you for your birthday. She didn’t want you to find out because you know…you’d freak out.”

“You know the rules. You’re not programmed to respond to her commands.” He crosses his arms as he gives her a heated look.

“Well, you deserve a little something, and she really wants to show her appreciation for you. You can’t fault me for that!” Lyla protests.

He just glares at her in the most deadpan expression.

“Okaaaaay. It’s mostly because she promised me that she’ll help get me those mods you never let me get.” She rolls her eyes.

“Where. Is. She. Now.” He emphasizes each word, barely holding on to his anger and state of panic from breaking loose. He notes to himself to reprogram her, thoroughly.

“She should be on her way back to her apartment right now! She went to that bakery you grew up with and the ice cream spot you take Y/D/N with.”

And with that, he heads out.

–

You beeline towards your apartment as you check for the time. Luckily Miguel hasn’t called you all day or else you wouldn’t know what to say or how to react.

You and your daughter reach the second to last block of the apartment when suddenly you get approached by some shady, hooded figure.

“Hey there, pretty lady! Hope you can spare a few minutes with me~” He steps up towards you, a little too close for comfort.

You kind of freeze up in place, and are unsure of how to respond. You are too polite to tell him off. “U-Um, excuse me.” 

You take your daughter’s hand tightly, who is shooting daggers at him, and try to move past the stranger. 

He stops you by grabbing your shoulder and shoving you back into the alleyway behind you, causing you to lose your grip on both your daughter and bag of food. 

You hit against the brick wall aggressively, with your head smacking against it. You start to feel lightheaded and the area of impact pulsating.

He tries to reach for your purse, but is soon met with a loud, booming punch against the gut from your daughter. He is sent flying 50 feet away and smacks against the wall causing him to pass out. (She might have killed him.)

“Mama! Mama! Are you okay?!” Your daughter frantically rushes to your side, gripping the skirt of your dress.

“I-I’m fine. I just need a moment to collect myself.” You hold your head from the impending headache.

Not a second later, you are suddenly hoisted up like a potato sack causing you to scream and flail until you recognize whose back you’re seeing belongs to.

It’s your husband, and he wastes no time to leap to the top floor of the apartment building with not just you but your daughter also, one on each hand, without breaking a sweat. 

“Lyla, open the door.” He sternly commands.

The door opens on its own, and he gently sets both you and your daughter down. You are still shaken from the whole ordeal that your knees give out. He swiftly catches you, almost as if he expects you to. 

He carries you bridal style, and walks you to the living room to set you down on the couch. He takes off his mask, and you can see the tension contorting his face, stabbing you with guilt.

He grabs your chin to scan for any signs of obvious injury, and a quick visual across your body. 

“Lyla, scan her for vitals.”

Quickly, she does and concludes, “All vitals seem normal. Heart rate is 120, likely due to panic and stress. Increased blood flow to the back of her head due to external impact, but no signs of head trauma.”

He drops his head and leans in. He runs his fingers through your hair in the area of impact and massages your scalp. You can feel your headache already melting away, and you lean into his touch.

“Just why…” He whispers into your ear. “Why would you go outside?”

“I just wanted to surprise you for your birthday.” You put your hands on his wide shoulders and give him a light squeeze, trying to placate him.

It does nothing to sooth him. He shifts himself to sit beside you and pulls you into his embrace. You are led to sit on his lap with your face laying on the crook of his neck as he continues to massage your head. His other hand rests around your waist.

“Y/D/N, come here.” He doesn’t stop his ministrations.

She has been standing near the door fidgeting her fingers anxiously. She walks over to you two, and with the smallest voice she says, “Am I in trouble, papa?”

He sits up a little, but assures you that he won’t drop you by tightening his embrace. 

“No, but tell me what happened. Every last detail.” He says firmly, yet tactfully.

She tells him everything, including the part where the ice cream guy complimenting you. You can feel his grip getting harder and tighter as he grinds his jaw. His jealousy is apparent. 

“Thank you for being honest with me. You’re a very good girl for protecting your mother. You take after me which is why you are strong. You are also a smart girl. You must understand that your mother is in no shape to go outside without me. Never let her persuade you again.” He emphasizes “never” to drive home the point. “If she tries to go outside again, tell me.”

And at that, you pull yourself away from him. His arm around your waist doesn’t budge, still straddling you to his lap. The hand that was on your head now rests on your neck.

“That’s not fair, Miguel! I am a grown woman! Your wife, her mother! You can’t keep trapping me here in this apartment.” You protest.

“I’ll…I’ll go crazy.” You barely whisper whilst choking back a sob.

“We already had this discussion before. It’s just not safe. Look at what happened today! Do you really think you’re in any position to be demanding to go outside?!” He glares at you.

You don’t listen. You try to tear yourself from him but his grip is relentless. You push and kick with all your might, but you’re like a mouse fighting against a lion.

You turn your head to your daughter, “Y/D/N, I am your mother, please don’t listen to him.” You plead in hopes that she won’t bar you from ever going outside again too. 

Alas, Miguel is the one with authority here.

“Don’t drag her to your impulse. You also endangered her by taking her with you.” He chastises, and forces your head back down to lean into his, to look him in the eyes. “You may be her mother, but you can’t protect her.”

Somewhere in your heart breaks. You slump as all the energy in your muscles give out. 

Yes, considering today, you never would have been able to protect your child. Yes, it is in fact your own child, who hasn’t even reached puberty, that saved your life. What would have happened if she was taken while you were distracted? You have no survival skills.

“Y/D/N, go to your room. I need to talk to your mother in private.” He orders and watches as she scurries off.

With her bedroom door shut, he calls for Lyla, “Activate soundproofing.”

Your heart begins to race in anticipation for what’s about to happen. He gets up from the couch while holding you (causing you to koala hug him) with one hand on your bum and the other your back.

He carries you to your shared bedroom, and unceremoniously plops you to the bed. He flips you over so that you face down to the bed, and slides you towards the edge so your ass hangs off.

“I’m going to punish you now. This is your lesson for disobeying my most important rule.” He says in a cold tone. 

Goosebumps form and your body shivers in fear. You never would have expected to be here. He has always been so gentle, forceful at times, but gentle nonetheless. 

You fucked up big time.

“P-Please wait…!” You hold your arm out to stop him.

He swats it away and simply says, “Stop moving.”

He hikes up the skirt of your dress over your ass revealing your pretty lace panties, and grips the skirt in place on your lower back.

“You didn’t wear safety shorts under the dress? You’re either a slut or an idiot.”

Before you can answer, he gives you a hard smack on your left ass cheek. You yelp from the sudden impact. It stings and burns.

You squirm, but you don't move out of place from the heavy weight pressing you down your abdomen.

He gives you another smack, this time on the right cheek. His touch is not kind, not tender like you’ve been used to for all these years. Your heart races so intensely; you can feel it beating against the mattress. 

“P-Please…” You attempt to get the words out through your heavy pants. “I…just want to…give you a birthday present.”

He kneads your cheek harshly, and you instinctively hiss from the contrast of earlier hits and round your back to escape his hand. He pushes your abdomen back down.

“And yet, instead, you had not only made me worry, but you endangered yourself unnecessarily and for what? A couple baked goods? I can get them any time.” He hard smacks your cheeks a couple times earning him a scream. “Not very considerate of you on my birthday.”

You sob from his words and the pain from his strikes. He doesn’t loosen his hold and continues to strike your ass in rapid succession. 

You groan into the sheets while you grip them tightly to hold yourself in some form of leverage. 

He spreads your cheeks apart and pulls your panties up. The cloth wedging itself into your cunt.

“You’re getting wet.” He scoffs. “You’re getting off on this.”

He rubs the inner side of your ass near your cunt with his thumb. He pulls on it to spread your cunt out for him to see. He glides his pointer finger across your glistening hole.

“You’re very wet.” His husky voice is low and you can sense he’s beginning to feel aroused. 

He pulls your right leg up and anchors your foot down to the mattress. “Keep your ass up.”

You do as you’re ordered and he smacks the area close to your dripping pussy. He smacks again and again. You can barely hold on. That area is far too sensitive.

Especially when you’re becoming impossibly wet. 

Your cunt is clenching around nothing and you try to push down the neediness that’s building up in the pit of your stomach. Your clit won’t stop pulsating. 

He pushes your hiked up leg back down to focus the assault on just your ass. He forcefully pulls down your panties and inspects your pussy again. While it’s not the first time he’s ever seen it, for some reason, you’re so much more embarrassed being presented in this way. 

“I-I’m sorry Miguel. I w-won’t do it again.” You want to be out of this demeaning hold as soon as possible. You can’t contain your tears and sobs flowing into your bed.

He again kneads your ass, but in thought. As if considering your apology, “If you’re truly sorry, then you’ll continue to ride out your punishment.”

Your eyes widen as his hand crashes down on your ass again. 

Your skin is fiery hot and raw. Your mind is blanking out. Strings of saliva fly out of your mouth. 

He stops for a moment after minutes of nonstop assault on your poor ass to knead and console your sensitive skin and muscles. (If your skin is pale, your ass is beet red, almost glowing like his webs.) 

You make a guttural throat sound in response; the shock shooting your brain awake. 

“Forgive me! Please, forgive me. I didn’t know. I didn’t know. Please stop.” 

He stops but his hand does not leave your ass as he gives you a quizzical look. “You didn’t know? Like you didn’t know this would happen?” 

You make a poor attempt at a nod. “I’m sorry. I didn’t think someone would attack me.”

He lets go of your dress and grabs you by the bodice pulling you off of the mattress. He drags you to the front of the full size body mirror where you can clearly see the dramatic height difference between the two of you. 

He grabs your waist while holding your face out. “Look at yourself!” He yells.

You take a good look at yourself. You’re a mess. The tears streaming down your face ruined your makeup. Streams of black from your mascara stain your cheeks while your lipstick is smeared all over your mouth. Your hair is disheveled. Your eyes are red. Your dress is wrinkled.

You don’t quite understand what he’s looking for. All you see is a mess.

“Do you have any idea how captivating you are? Why do you think I love this specific dress on you? And your makeup? You can tempt any man around you. You can’t possibly think no one would try to take you?” He says while pressing his hard-on on your back.

Sure, the dress hugs your body in all the right places. It shows your cleavage. But still, nothing overly liberal and out of place for a casual stroll in the city. Not when other more scantily clad women are a dime a dozen. Especially in Nueva York, in a time that’s far more advanced and liberal than your own.

“I d-don’t understand what you’re saying. I’m not that pretty.” You struggle to stand, but his grip on your waist keeps you from falling.

“Don’t act like you don’t know.” He grips your jaw harder as he glares at you through the mirror.

“I’m really not. I don’t even know why you married me!” You sob. “I’m weak. I’m useless. There are tons of girls who are better and prettier than I am. Why did you choose me? All I do is give you reasons to do more work at home than you already have.” You can’t help but sulk and spill all your insecurities.

His gaze softens and drops his grip from your jaw. He spins you around and brings you close to his chest. He strokes your head like how he used to comfort you. Your gentle Miguel is back.

“Shhh, I know that you can’t do a lot of things like other people, but it doesn’t make me love you any less. Isn’t that enough?” 

You pull your face away from his chest and look up at him. “But I want to be your equal. I want to be worthy.”

“But you are worthy. You don’t have to be my ‘equal’ for me to love you.” He counters. Good point. He cups your cheek and you lean into his touch. He lightly wipes away your tears. 

You have nothing more to say. Perhaps you’ll never understand why he chose you, why he loves you. 

“I love you more than you can know.” He brushes your hair behind your ear. “We have a beautiful family now. I can’t risk losing you, any of you. I hope you understand that.”

His eyes darken. You can see that his words mean more than what he tells you. You don’t know what he really means.

You have no choice but to accept him anyway. He is your rock in this world. Your entire fiber of being and existence completely and solely hinges upon him and his will. Without him, you have nothing, you are nothing. 

He is your savior as much as he is your captor. He is your caretaker as much as he is your jailor. 

He is your God.

You two linger in each other’s hold as if time stood still. His scent calms you down, and you begin to relax more and more in each breath you take as you sink into his embrace. 

Until your stomach growls.

He chuckles as he lets you go slowly. “Looks like someone’s hungry. I’ll go whip up some dinner for us.”

He walks away for a second to grab a box of tissues. He wipes away all of your tears, makeup smears, and dripping nose (which he tells you to blow out while holding up the tissue for you). 

“B-But the pastries and the ice cream I got for you. They got left behind.” You sniffle. 

“Don’t worry about it.” He pauses to think. “How about this? I’ll take the next weekend off and we can go together, as a family.”

You smile up at him; you couldn’t be more happy. “Thank you, Miguel. Thank you.”

He softly smiles back and kisses your forehead, “Now, go rest, I’ll come back to get you when dinner is ready. I’ll wash you up after.”

You nod to that. You make your way to your bed and plop face down. Your ass is still stinging and burning so you can’t lay on your back. Your eyes flutter shut and begin to drift off.

Good grief is what he thought watching you pass out on the bed. You’re going to catch a cold. He lifts you up to untuck the blanket and covers you with it. He carefully tucks the blanket in every crevice around your neck, ensuring no part of you is bare to feel the cool air. 

He stays for a second to look at you and brush away the strands of hair from your face before walking out and closing the door behind him. 

A/N: Well...that was intense. The second part will be fluffier and smuttier. (づ￣ 3￣)づ I spent like at least 12 hours just on part 1 in both writing and proofreading, only to not even get to part 2 yet. Feedback is welcomed. This is not a comfortable read.

Also I want to thank @wreakingmarveloushavok for giving me the idea of what Mexican pastry that's eaten on birthdays! Everything else I googled, including any inaccurate health related mentions.