#artificial-intelligence learning platform

[eighth-heroine]

if i could just content block everything AI from my life that would be great

[happywebdesign]

https://chirpley.ai

[dcdesmond]

The potential impact for machine learning was there, considering the number of datacenters that Google has to keep cooled and powered, and we were already collecting streaming sensor data for our existing monitoring platforms.

Engineers and Alphabet's deep mind saw this as an opportunity to ingest that sensor data and train machine learning model to optimize cooling better than existing systems could.

The model they implemented reduced the cooling energy by 40 percent, and boosted the overall power effectiveness by 15 percent.

I find this example particularly inspirational, because it's a machine learning model trained on machine learning specialized hardware in a datacenter, telling the datacenter how hard it can run the machine learning specialized that the model is training on. Powerful inception level stuff.

Lak Lakshmanan, Coursera Google Cloud Platform Big Data and Machine Learning Fundamentals

[withluvvenus]

hi so to all the ai users specifically ! who have been saying the climate change wont affect them ! news flash it will and it has already begun and one way or another this will affect you directly x so i’m not going to sit here and complain and criticize people who turn to ai . instead i’m also going to provide resources that you can use to quit using platforms like chatgpt or character.ai to connect with your realities or manifestations . this is specifically for those two things , other things like education and artwork , you have the internet and books to get the same information ai has been retrieving !

shifting ( connect to your dr ) :

- pinterest ! make boards . scroll through pins . visualize yourself in that pin .

- music ! spotify , apple music , youtube . i don’t know go crazy . make different playlists for different realities . make playlists for different scenarios in that reality .

- discord ! join shifting based servers that allow you to speak about your realities . share your favorite experiences with others on there . make friends that you can talk to instead of chatgpt .

- games ! video games like minecraft where you can build , sims , tomodachi , etc . if you are looking for free content , genshin impact , sims , roblox , etc . create your dr homes , visualize how it would feel to be there in the flesh .

- writing ! post on tumblr , post on tiktok , hell , even write fanfics about yourself on wattpad . who gives a shit , at least it isnt killing anyone . just pour your heart out in your writing , immerse yourself in it .

&& list of some apps w/o ai !

channeling :

- shufflemancy ! use spotify , connect with your friends and family from your realities . ask questions and get answers in terms of music . decipher songs through lyrics or intentions behind the song .

- tarot ! learn how to do tarot , make your own diy deck , practice with it . channel your highest self or people from your realities . pay for tarot readings or find people online who would be willing to help . there are many on tiktok , just find the most reliable ones .

- pinterest ! here's a how to guide .

&& a note . . . instead of ai , channel people from your drs . it’s better because it’s actually the real person and not some artificial intelligence who is the generalized and basic version . just saying .

manifesting :

- law of assumption ! learn more about loa & how it is so helpful when it comes to manifesting your dream life and even helps with shifting .

- pinterest ! make different boards to help visualize . make a moodboard for your dream life , a desired wardrobe , wishlist , dream body , the list goes on .

- guidance ! you really don’t need a lot for manifesting if you say you don’t . robotic affirmations , sure . affirming once , sure . anything . you just have to learn , and if you need help learning there are many resources on tumblr ( shiftblr & loablr ) , tiktok , and even pinterest . if you don’t want to pay for lessons , i’m sure a good handful of creators will answer questions through comment sections on tiktok or inboxes of tumblr ( mine are always open ! )

- journaling ! journal your thoughts and ideas instead of telling some app on what to do next . i’ve seen many apps that utilize ai for manifesting ( marketed through tiktok ) and you do not need them . trust me .

i know this is going to fly over many people’s heads but seriously , manifesting and shifting are two things even ai can’t open your eyes for all this , you need to do on your own . so instead of asking some bot to help you , let’s go back to how we did it before ai existed ! let’s do it the normal and safe way . and the debate of ‘ if i am the creator of my reality , i can use ai and get whatever i want because i can manifest it won’t affect the environment ‘ ( trust me i’ve seen someone along the lines of this ) . . . if you are the creator of the reality then why do you even need ai in the first place ? just let the bot rest guys . also !! all this energy and time that goes into using ai , could be fueled into shifting or manifesting .

　　　　　　　　　　　　　 𝘄𝗶𝘁𝗵𝗹𝘂𝘃 ✶ 𝘃𝗲𝗻𝘂𝘀

[mariacallous]

McDonald’s AI Hiring Bot Exposed Millions of Applicants’ Data to Hackers Who Tried the Password ‘123456’

Basic security flaws left the personal info of tens of millions of McDonald’s job-seekers vulnerable on the “McHire” site built by AI softwa

If you want a job at McDonald’s today, there’s a good chance you'll have to talk to Olivia. Olivia is not, in fact, a human being, but instead an AI chatbot that screens applicants, asks for their contact information and résumé, directs them to a personality test, and occasionally makes them “go insane” by repeatedly misunderstanding their most basic questions.

Until last week, the platform that runs the Olivia chatbot, built by artificial intelligence software firm Paradox.ai, also suffered from absurdly basic security flaws. As a result, virtually any hacker could have accessed the records of every chat Olivia had ever had with McDonald's applicants—including all the personal information they shared in those conversations—with tricks as straightforward as guessing that an administrator account's username and password was “123456."

On Wednesday, security researchers Ian Carroll and Sam Curry revealed that they found simple methods to hack into the backend of the AI chatbot platform on McHire.com, McDonald's website that many of its franchisees use to handle job applications. Carroll and Curry, hackers with a long track record of independent security testing, discovered that simple web-based vulnerabilities—including guessing one laughably weak password—allowed them to access a Paradox.ai account and query the company's databases that held every McHire user's chats with Olivia. The data appears to include as many as 64 million records, including applicants' names, email addresses, and phone numbers.

Carroll says he only discovered that appalling lack of security around applicants' information because he was intrigued by McDonald's decision to subject potential new hires to an AI chatbot screener and personality test. “I just thought it was pretty uniquely dystopian compared to a normal hiring process, right? And that's what made me want to look into it more,” says Carroll. “So I started applying for a job, and then after 30 minutes, we had full access to virtually every application that's ever been made to McDonald's going back years.”

When WIRED reached out to McDonald’s and Paradox.ai for comment, a spokesperson for Paradox.ai shared a blog post the company planned to publish that confirmed Carroll and Curry’s findings. The company noted that only a fraction of the records Carroll and Curry accessed contained personal information, and said it had verified that the administrator account with the “123456” password that exposed the information “was not accessed by any third party” other than the researchers. The company also added that it’s instituting a bug bounty program to better catch security vulnerabilities in the future. “We do not take this matter lightly, even though it was resolved swiftly and effectively,” Paradox.ai’s chief legal officer, Stephanie King, told WIRED in an interview. “We own this.”

In its own statement to WIRED, McDonald’s agreed that Paradox.ai was to blame. “We’re disappointed by this unacceptable vulnerability from a third-party provider, Paradox.ai. As soon as we learned of the issue, we mandated Paradox.ai to remediate the issue immediately, and it was resolved on the same day it was reported to us,” the statement reads. “We take our commitment to cyber security seriously and will continue to hold our third-party providers accountable to meeting our standards of data protection.”

Carroll says he became interested in the security of the McHire website after spotting a Reddit post complaining about McDonald's hiring chatbot wasting applicants' time with nonsense responses and misunderstandings. He and Curry started talking to the chatbot themselves, testing it for “prompt injection” vulnerabilities that can enable someone to hijack a large language model and bypass its safeguards by sending it certain commands. When they couldn't find any such flaws, they decided to see what would happen if they signed up as a McDonald's franchisee to get access to the backend of the site, but instead spotted a curious login link on McHire.com for staff at Paradox.ai, the company that built the site.

On a whim, Carroll says he tried two of the most common sets of login credentials: The username and password “admin," and then the username and password “123456.” The second of those two tries worked. “It's more common than you'd think,” Carroll says. There appeared to be no multifactor authentication for that Paradox.ai login page.

With those credentials, Carroll and Curry could see they now had administrator access to a test McDonald's “restaurant” on McHire, and they figured out all the employees listed there appeared to be Paradox.ai developers, seemingly based in Vietnam. They found a link within the platform to apparent test job postings for that nonexistent McDonald's location, clicked on one posting, applied to it, and could see their own application on the backend system they now had access to. (In its blog post, Paradox.ai notes that the test account had “not been logged into since 2019 and frankly, should have been decommissioned.”)

That's when Carroll and Curry discovered the second critical vulnerability in McHire: When they started messing with the applicant ID number for their application—a number somewhere above 64 million—they found that they could increment it down to a smaller number and see someone else's chat logs and contact information.

The two security researchers hesitated to access too many applicants' records for fear of privacy violations or hacking charges, but when they spot-checked a handful of the 64-million-plus IDs, all of them showed very real applicant information. (Paradox.ai says that the researchers accessed seven records in total, and five contained personal information of people who had interacted with the McHire site.) Carroll and Curry also shared with WIRED a small sample of the applicants' names, contact information, and the date of their applications. WIRED got in touch with two applicants via their exposed contact information, and they confirmed they had applied for jobs at McDonald's on the specified dates.

The personal information exposed by Paradox.ai's security lapses isn't the most sensitive, Carroll and Curry note. But the risk for the applicants, they argue, was heightened by the fact that the data is associated with the knowledge of their employment at McDonald's—or their intention to get a job there. “Had someone exploited this, the phishing risk would have actually been massive,” says Curry. “It's not just people's personally identifiable information and résumé. It's that information for people who are looking for a job at McDonald's, people who are eager and waiting for emails back.”

That means the data could have been used by fraudsters impersonating McDonald's recruiters and asking for financial information to set up a direct deposit, for instance. “If you wanted to do some sort of payroll scam, this is a good approach,” Curry says.

The exposure of applicants' attempts—and in some cases failures—to get what is often a minimum-wage job could also be a source of embarrassment, the two hackers point out. But Carroll notes that he would never suggest that anyone should be ashamed of working under the Golden Arches.

“I have nothing but respect for McDonald’s workers,” he says. “I go to McDonald's all the time.”

[optimisticgrey]

On AI Detectors and the never ending accusations of people using AI in their art

Recently, a dear friend of mine was accused of using generative AI in their writing.

It was a deeply unfair way to handle a concern, to make such an accusation and then disappear, rather than opening an honest conversation about what was actually troubling them.

TLDR: No currently available AI system embeds watermarks, hidden characters, or any kind of traceable signature into the text it produces. There is no technical way to determine whether a piece of writing was generated by AI unless the author explicitly says so. Please don’t assume or accuse people of using AI based on “vibes”, or the use of our beloved em-dash or simple suspicion alone.

It’s also important to remember that many AI models were trained on large amounts of scraped text, including data from platforms like AO3. That makes false accusations especially painful and ironic.

Please don’t accuse anyone, especially in fandom spaces where people share their work freely, of using AI. It harms the atmosphere and creates a climate of suspicion rather than trust, and that hurts the very communities we’re all trying to be part of.

I'd also like to highlight @allthingswhumpyandangsty's very well put post from October 2025.

Longer explanation/rant below the cut.

AI and AI Detectors

The term AI detector is fundamentally misleading. These tools do not detect artificial intelligence in the way a scanner detects radiation or a forensic test detects DNA. They do not identify watermarks, embedded signatures, or any hidden metadata left behind by generative models. No contemporary AIs (including ChatGPT, Gemini, Claude, or open-source equivalents) inserts any such traceable markers into its text output. Once generated, the text exists as ordinary language, indistinguishable in form from human-authored writing.

Because there is nothing intrinsic to detect, so-called AI detectors instead rely on statistical inference. They are themselves AIs trained on large corpora of text labeled as “human-written” or “AI-generated.” Through this training, the model learns patterns of word distribution, syntactic regularity, sentence length, lexical predictability, punctuation frequency, and other probabilistic features. When a user submits a text, the detector computes how closely that text resembles the statistical patterns of the AI-generated samples in its training set.

The result is therefore not a determination, but a probability estimate: a statement about how similar the submitted text is to what the model has learned to associate with machine-generated language. This process is no different in principle from how generative models themselves operate : both rely on pattern recognition across large textual datasets.

Crucially, this approach is inherently unstable. Human writing is highly diverse, and creative, edited, genre-specific, or non-native-speaker prose often overlaps strongly with the statistical profiles learned by these detectors. As a result, false positives are common and unavoidable. For this reason, in academic and forensic contexts, AI-detection tools are not regarded as reliable evidence of authorship; at best they provide weak heuristics, not proof.

Moreover, AI detection tools are commercial products. Their outputs are designed to appear authoritative in order to encourage continued use or paid upgrades, despite the underlying uncertainty of their predictions. You should therefore understand that submitting text to an AI detector is not performing an objective test, but rather feeding that text into yet another AI whose output is probabilistic and commercially motivated.

Conclusion: AI detectors do not “find” artificial intelligence within a text. They apply another AI model to estimate similarity to prior training data. Treating such estimates as definitive proof is a category error; one that risks misidentifying human authorship and undermining trust in creative communities.

AO3 Dataset

Source: https://www.reddit.com/r/AO3/comments/1k6ie6v/ao3s_data_was_scraped_for_ai_what_to_know/

In April 2025, Archive of Our Own (AO3) became the subject of an unauthorized scrape in which a registered user collected a large dataset of publicly available works from the Archive. This included fanfiction and artwork that was accessible without login. AO3 administrators were made aware of the incident and took action, including issuing a DMCA takedown request against a version of the dataset that had been uploaded to Hugging Face. That particular upload was made temporarily inaccessible, though copies of the dataset had also been posted elsewhere. Importantly, only publicly available works were scraped (works that were visible to anyone without account login). Archive-locked works (restricted to registered users) were not included in the dataset. AO3 staff and volunteers have long expressed concern about widespread data scraping, including how it can feed into large language model training, and have implemented measures like rate limiting and opting out of major crawls used for training datasets.

While some of the scraped datasets have since been taken down or removed from public distribution, the possibility that this material was used to train additional models remains entirely plausible.

This leads to an important conclusion: AI-generated writing can resemble fanfiction not because fanfiction is “becoming AI-like,” but because AI systems have been trained on fanfiction.

The resemblance runs in one direction only. It is therefore a fundamental error to assume that a fic “sounds like AI,” when in reality AI has learned to sound like us.

Showcasing with my own writing

Because I couldn’t help myself, I tested it. I deliberately copied and pasted my own writing into several different AI detectors to see what they would report. A piece of writing I know for a fact, has never been touched by AI.

The results were striking. The same passage of text produced wildly different outcomes depending on which detector I used. One flagged it as highly likely to be AI-generated, another judged it to be entirely human, and a third fell somewhere in between. None of these tools agreed, not on percentages, not on confidence, and not even on the basic classification. (See results below)

This is not a flaw of any single detector; it is a structural limitation of the entire approach. Each detector has been trained on different datasets and tuned to different models. Some are calibrated primarily against ChatGPT-style outputs, others against Gemini-like prose, others against mixtures of synthetic and human-edited text. As a result, they are not measuring an objective property of the writing itself, they are measuring similarity to whatever their own training data happens to look like.

In practice, this means that AI detection is not reproducible. A scientific test must yield consistent results when applied to the same input under the same conditions. These tools do not. They produce unstable, contradictory outputs because they are not detecting anything intrinsic to the text, they are guessing based on pattern resemblance.

What this demonstrates is that AI detectors do not provide evidence. They provide opinions: automated, statistically informed opinions, but opinions nonetheless. Treating those numbers as proof of AI use is therefore methodologically unsound, and in creative communities it becomes actively harmful, because it encourages suspicion based on tools that cannot reliably distinguish human authorship at all.

Quillbot:

ZeroGPT

GPTZero

Scribbr AI Detector

aidetector.com

mydetector.ai

copyleaks.com

reilaa.com

grammarly.com/ai-detector

Links

AO3 reddit thread of the scraping incident in April 2024: https://www.reddit.com/r/AO3/comments/1k6ie6v/ao3s_data_was_scraped_for_ai_what_to_know/

Paperdemon's analysis and updates: https://www.paperdemon.com/app/g/pdarpg/events/view/994/immediate-action-required-your-art-and-writing-has-been-scraped-and-published-in-an-ai-dataset/1

[rhysspiecez]

Hello everyone,

Recently, I was accused of using artificial intelligence to write my work — more specifically, that my writing was created with the assistance of AI.

Writing has always been something I take great pride in. Throughout high school, I received academic recognition for my writing abilities and dedication to the craft.

Fanfiction and creative writing have also always been an important part of my life, as I know they are for many others in this community.

I understand that technology has advanced significantly, and some creators choose to use AI as part of their writing process. Personally, I am not one of them, nor will I ever be. I will always remain an anti-AI creator and strongly value authenticity within creative spaces.

False accusations like these can be incredibly damaging — not only to a creator’s platform, but also to their credibility, reputation, and confidence as a writer. I stand by my work completely and will continue creating content that is entirely my own.

AI generated content has become so normalized online that it’s starting to replace the value of real human creativity.

Art, writing, music, and fan works come from lived experiences, emotions, practice, and passion — things a machine cannot genuinely feel or understand.

Many AI programs are trained on the work of actual artists and writers without consent, often copying styles and ideas from people who spent years developing their craft.

Supporting ‘ human-made’ content matters because creativity is more than just producing something quickly; it’s about connection, individuality, and the effort behind it.

Creative spaces should continue encouraging real people to learn, improve, and express themselves instead of rewarding shortcuts that remove the human element from art.

- Rhys💕

[signalcli]

AI is Going to Take Over the World… Or Is It?

The rise of artificial intelligence (AI) has sparked widespread excitement — and fear. Will AI revolutionize our lives for the better, or are we headed toward a dystopian future straight out of a science fiction novel?

Interestingly, this question isn’t entirely new. Nearly a century ago, visionary writer Isaac Asimov introduced what became famously known as the “Three Laws of Robotics”:

Now, to remind our younger generation (who may not know this yet, as Marvel hasn’t made movies about it), these laws state:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Later, Asimov introduced an even more profound concept — Law Zero:

A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

Fast forward to today — roughly 90 years after Asimov began exploring these ideas — and humanity now faces real-life implications of AI. We interact daily with AI systems designed to simplify our tasks, manage our schedules, and even help us trade cryptocurrencies profitably.

AI Today: Expectations vs. Reality

Despite its impressive capabilities, general-purpose AI frequently falls short when it comes to tasks requiring precision and strict adherence to instructions. Users often encounter frustration, instructing the AI to “modify exactly THIS letter” or “calculate THAT complex formula,” only to receive unexpected, arbitrary results. Even when following iterative refinement processes — as recommended by many AI systems — results are often inconsistent and unsatisfactory.

An earlier workaround, such as using one AI tool to instruct another, is also becoming increasingly ineffective. AI tools now seem to have an in-built confidence (or stubbornness?) in knowing what’s best — even when explicitly instructed otherwise.

So, should we be worried?

Custom-Built AI: The Silver Lining?

Ironically, the limitations of general-purpose AI have opened the door to specialized, custom-built AI solutions. Unlike broad-spectrum AI, these specialized systems handle complex tasks — like analyzing hundreds of crypto indicators to generate precise trading signals (SignalCLI, as an example) — with remarkable accuracy, consistency, and reliability. They meticulously follow instructions, avoid arbitrary changes, and adapt dynamically through sophisticated self-learning processes. But exploring the potential of custom-built crypto signal platforms is another story altogether. For now, let’s return to the central question: Should we be worried? Does our growing dependence on general-purpose AI make us, well, not-so-smart after all?

AI: Empowering or Weakening Human Intelligence?

The debate surrounding AI’s impact on human intelligence is diverse. Some critics argue that AI “makes us stupid,” citing anecdotal evidence of students passing exams solely due to AI assistance. Others maintain that AI’s effectiveness ultimately depends on the intelligence of its user — essentially, how well a human directs the AI.

A Crypto Parallel

Let’s draw a parallel with cryptocurrency. The idea of digital currency isn’t new; some argue it’s a concept nearly 40 years old. Yet, Bitcoin officially launched in 2009 — just over 15 years ago — and has rapidly become a major force in global finance. Within less than two decades, crypto transformed from niche digital experimentation to a heavily regulated, widely accepted financial asset class.

Similarly, AI today might still be in its relative infancy. Despite its widespread application, we haven’t fully understood its ultimate potential or ramifications. If crypto’s journey is any indication, AI technology may undergo similar accelerated growth and profound impact within the next few decades.

Should We Expect the Terminator?

Not quite yet. While AI has made significant strides, it is still fundamentally dependent on human-developed rules, parameters, and limitations. Even sophisticated AI requires continuous human oversight, especially to navigate tasks that involve nuanced judgment or ethical considerations.

Yet, the next frontier — quantum computing — might drastically alter AI capabilities. Quantum computing could unlock previously unimaginable processing power, vastly amplifying AI’s potential. This leap could, in theory, accelerate AI’s autonomy to unprecedented levels.

And that’s precisely why Asimov’s laws (including the profound “Law Zero”) must be considered deeply and embedded rigorously within AI development. Without careful guidelines, we risk unintended consequences far beyond simple miscalculations or incorrect edits.

Conclusion: A Responsible Path Forward

So, will AI take over the world? The answer lies in our collective approach. Like cryptocurrency’s evolution from speculative curiosity to regulated financial asset, AI’s future will hinge on thoughtful development, strict guidelines, and proactive oversight.

We aren’t headed toward a science-fiction apocalypse — at least not yet. AI remains a powerful tool, one capable of extraordinary good when carefully managed. But to keep it that way, it’s up to us to establish the right guardrails now, long before quantum computing takes AI into uncharted and possibly risky territories.

Good luck — and happy trading!

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