#Ai driven

[vengoai]

With AI reshaping industries, Zeal Designs is leading the charge in remodeling. As 80% of websites prepare to adopt AI agents, see how Zeal is already accelerating timelines, personalizing designs, and redefining customer satisfaction. Read more: https://t.ly/gBe-i

[spinnrblog]

With AI reshaping industries, Zeal Designs is leading the charge in remodeling. As 80% of websites prepare to adopt AI agents, see how Zeal is already accelerating timelines, personalizing designs, and redefining customer satisfaction. Read more: https://t.ly/hnyP1

[nando161mando]

"Race science group say they accessed sensitive UK health data" https://theguardian.com/world/2024/oct/17/race-science-group-say-they-accessed-sensitive-uk-health-data

In 'Resisting AI' I specifically warned about UK Biobank, genome-wide association studies and the emergence of AI-driven eugenics.

[marketinsightsenterprises]

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[ladyhusle]

Cut Through the Noise: Embrace Authentic Marketing to Build Lasting Connections

Discover the secrets to authentic marketing with Chelsey's latest post: Cut Through the Noise! Dive into the power of authenticity, transparency, and real connections to build lasting relationships in the digital age. 🌟 #MarketingTips #DigitalMarketing

Chelsey’s blog post emphasizes returning to marketing basics—authenticity, transparency, and genuine connections—to cut through digital overload. Using examples like Patagonia and TOMS Shoes, she illustrates how these principles build trust and loyalty, creating lasting impacts in the digital age. BY Chelsey’s Curations July 28,…

[s10safecare]

#1 AI-Driven Medical Scribing

The Evolution Of Medical Scribing

Medical scribing has been a crucial aspect of healthcare documentation for years, capturing patient encounters in written or electronic form. Traditionally, medical scribes were human individuals who accompanied healthcare providers during patient visits and documented the encounter in real time. However, with the rapid advancements in technology, the landscape of medical scribing is evolving, paving the way for AI-driven medical scribing. Efficiency, accuracy, and productivity are more crucial than ever in the hectic healthcare environment of today. 

Healthcare providers constantly seek innovative solutions to streamline workflow, reduce documentation errors, and improve patient care. This has led to the emergence of AI-driven medical scribing, which promises to revolutionize how healthcare providers document patient encounters.In this blog post, we will explore the concept of AI-driven medical scribing, how it works, and the benefits it offers to healthcare providers and facilities. We will delve into the intricacies of this cutting-edge technology, its potential impact on the healthcare industry, and the challenges it may face. Let's embark on a journey to discover how AI-driven medical scribing is shaping the future of healthcare documentation.

What Is AI-Driven Medical Scribing?

AI-driven medical scribing is a technological advancement in healthcare documentation that uses artificial intelligence (AI) and natural language processing (NLP) algorithms to capture, transcribe, and analyze patient encounters in real time. This innovative approach leverages the power of machine learning and automation to generate accurate and comprehensive medical notes, reducing the reliance on human scribes for documentation tasks.

With AI-driven medical scribing, a virtual assistant or a scribebot, powered by sophisticated AI algorithms, accompanies healthcare providers during patient visits, listens to the conversation, and transcribes the encounter in real time. The AI software captures relevant information, such as patient history, symptoms, diagnoses, treatments, and other clinical data, and automatically generates a detailed and structured medical note. The generated note can then be reviewed, edited, and electronically signed by the healthcare provider before being added to the patient's electronic health record (EHR).AI-driven medical scribing can be implemented in various healthcare settings, including hospitals, clinics, urgent care centers, and telemedicine platforms. It can be integrated with existing EHR systems, allowing seamless documentation and improved clinical workflow.Let's delve deeper into how AI-driven medical scribing works and the advantages it offers to healthcare providers and facilities.

How Does AI-Driven Medical Scribing Work?

AI-driven medical scribing involves a sophisticated process that combines machine learning algorithms and natural language processing (NLP) to capture, transcribe, and analyze patient encounters. Here's how it typically works:

Audio Recording: The healthcare provider speaks with the patient during a patient encounter while the AI-powered scribebot records the conversation. The conversation can be recorded using various methods, such as a voice recognition device or a dedicated microphone.

Speech Recognition: The recorded audio is then processed by the AI algorithm, which uses speech recognition technology to convert the spoken words into text. The algorithm identifies and separates the voices of the healthcare provider and the patient, transcribing their conversation into a written format.

NLP Analysis: The transcribed text is then analyzed using natural language processing (NLP) algorithms, which can identify and extract relevant information, such as patient history, symptoms, diagnoses, treatments, and other clinical data. The NLP algorithms use pattern recognition, semantic analysis, and contextual understanding to accurately identify and extract the relevant information from the transcribed text.

Structured Medical Note Generation: The AI algorithm automatically generates a structured medical note once the relevant information is extracted. The generated note includes the relevant information in a structured format, following a predefined template or format. The structured medical note can consist of sections such as chief complaint, history of present illness, review of systems, physical examination findings, diagnoses, treatments, and recommendations.

Review and Editing: The generated medical note is then reviewed by the healthcare provider, who can edit, add, or modify the information as needed to ensure accuracy and completeness. The healthcare provider can also electronically sign the medical note, indicating their review and approval.

Integration with EHR: The finalized medical note is then integrated with the patient's electronic health record (EHR), allowing for seamless documentation and continuity of care. The medical note becomes a part of the patient's permanent medical record, which can be accessed by other healthcare providers for future reference and coordination of care.

Data Analysis and Insights: The AI-driven medical scribing software can also analyze the captured data to generate insights and trends, such as patient demographics, common diagnoses, treatment patterns, and outcomes. This data can be used for quality improvement, research, and clinical decision-making.

Overall, AI-driven medical scribing streamlines the documentation process reduces the reliance on human scribes, and improves the accuracy and efficiency of capturing patient encounters. Let's now explore the benefits of AI-driven medical scribing for healthcare providers and facilities.

Benefits Of AI-Driven Medical Scribing

Implementing AI-driven medical scribing offers many benefits for healthcare providers and facilities. Let's delve into some of the critical advantages of using AI-driven medical scribing in clinical practice:

Enhanced Documentation Efficiency: AI-driven medical scribing automates capturing patient encounters in real-time, reducing the time and effort required for manual documentation.This enables healthcare professionals to concentrate more on providing patient care and less on office duties.

With the help of AI-powered scribebots, medical notes can be generated quickly and accurately, improving the efficiency of the documentation process.

Improved Accuracy and Quality: AI algorithms are designed to capture and transcribe patient encounters accurately, reducing the chances of errors and omissions that can occur with manual documentation. NLP algorithms used in AI-driven medical scribing can accurately extract relevant information from spoken words, ensuring that the generated medical notes are comprehensive and complete. This can lead to improved accuracy and quality of clinical documentation, reducing the risk of misinterpretation or miscommunication.

Standardized Documentation: AI-driven medical scribing software can follow predefined templates or formats for generating medical notes, ensuring standardized documentation across healthcare providers and facilities. This can help maintain consistency and uniformity in the documentation process, making it easier to review, analyze, and compare medical notes for quality improvement, research, and regulatory compliance.

Increased Provider Productivity: With AI-driven medical scribing, healthcare providers can focus more on patient care and less on documentation tasks. This can increase their productivity and allow them to see more patients in a given timeframe. Providers can also review and edit the generated medical notes quickly, further enhancing their productivity and reducing the backlog of pending documentation tasks.

Enhanced Patient Interaction: AI-driven medical scribing can improve patient-provider interaction during clinical encounters. Since healthcare providers do not need to spend excessive time on documentation, they can give more attention to patients, actively listen to their concerns, and engage in meaningful conversations. This can enhance the patient experience and lead to better patient satisfaction.

Enhanced Data Analysis and Insights: AI-driven medical scribing software can capture and analyze a large volume of clinical data, providing insights and trends that can be used for quality improvement, research, and clinical decision-making. The data captured by the AI algorithm can be used to identify patterns, trends, and outcomes, leading to improved patient care, better treatment decisions, and enhanced patient outcomes.

Time-saving and Productivity: With AI-driven medical scribing, healthcare providers can save time and improve productivity. Instead of manually documenting patient encounters, providers can focus more on patient care, leading to improved patient satisfaction and better healthcare outcomes.

Enhanced Workflow and Documentation: AI-driven medical scribing can seamlessly integrate into existing clinical workflows, enhancing documentation practices. The software can automatically capture relevant patient data and create comprehensive medical notes, which healthcare providers can quickly review, edit, and sign. This can streamline the documentation process, reduce administrative burden, and improve documentation quality.

Access to Real-time Information: AI-driven medical scribing allows healthcare providers real-time access to patient information. The software can automatically extract relevant data from patient encounters and populate electronic health records (EHRs), providing up-to-date and accurate patient information for informed decision-making and improved patient care.

Cost-effective and Scalable: Implementing AI-driven medical scribing can be cost-effective and scalable. Once the software is trained and implemented, it can operate 24/7 without needing breaks or vacations, reducing the need for additional staffing resources. This can result in cost savings for healthcare facilities, especially in the long run.

Standardization and Compliance: AI-driven medical scribing can help standardize documentation practices and ensure compliance with regulatory requirements. The software can adhere to predefined templates, guidelines, and protocols, reducing variability and ensuring consistent documentation practices across healthcare providers, which is critical for compliance with regulatory standards.

AI-driven medical scribing presents significant advantages in accuracy, efficiency, time-saving, productivity, workflow integration, real-time information access, cost-effectiveness, standardization, compliance, and improved data analytics. By leveraging the power of AI in clinical documentation, healthcare providers can enhance patient care, streamline workflows, and improve overall healthcare outcomes. It is essential to carefully consider these benefits in implementing AI-driven medical scribing and address any potential challenges or limitations to ensure successful integration into clinical workflows.

Recommended Reading : Is Technology Disrupting Medic Scribe Industry?

Advancing Healthcare Documentation With S10.AI Robot Medical Scribe

As healthcare continues evolving, AI-driven medical scribing, such as the S10.AI Robot Medical Scribe, rapidly transforms clinical documentation practices. The integration of AI technology in medical scribing can revolutionize how healthcare providers capture, process, and utilize patient information, ultimately leading to improved patient care and outcomes.With the S10.AI Robot Medical Scribe, healthcare providers can use advanced natural language processing (NLP) algorithms to transcribe patient encounters accurately, automatically populate electronic health records (EHRs), and streamline documentation processes. This can increase accuracy, efficiency, and productivity, allowing healthcare providers to focus more on patient care and less on administrative tasks.

The benefits of S10.AI Robot Medical Scribe go beyond just documentation accuracy and efficiency. Real-time access to patient information, standardized documentation practices, compliance with regulatory requirements, improved data analytics, and research capabilities are all valuable advantages that can contribute to evidence-based medicine, clinical decision support, and better patient outcomes.However, it is essential to acknowledge that there may be challenges and limitations associated with AI-driven medical scribings, such as the need for continuous updates and training of the AI algorithms, the potential for errors in NLP algorithms, and concerns about data privacy and security. Therefore, it is crucial for healthcare providers to carefully implement and monitor the use of AI-driven medical scribing solutions and address any potential challenges or limitations to ensure successful integration into clinical workflows.

In conclusion, S10.AI Robot Medical Scribe, along with other AI-driven medical scribing technologies, has the potential to significantly improve healthcare documentation practices, enhance patient care, and contribute to better healthcare outcomes. As technology continues to advance, the use of AI in healthcare is expected to grow, and S10.AI Robot Medical Scribe is at the forefront of this innovation, empowering healthcare providers with cutting-edge tools to advance the field of clinical documentation.

[clarkkantagain]

timothee chalamet by tony liam

[casualavocados]

"我們是兄弟..."

Chiang Tien as AI DI KISEKI: DEAR TO ME Ep. 9

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

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

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

 — 

Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

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

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

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

[bloggingboutburgers]

I honestly thought the censoring of words like Sex were because sites like YouTube and TikTok are little bitches about people being "family friendly" and will demonetize and repress vids that use that word /gen

No it is. It absolutely is. But a- that's what I'm pissed at too, that's what I'm pissed at especially, that's why I had a line in that comic that was like "let them use the damn word", it's infuriating and b- some people will actually take that even outside outside of the platforms that apply that kind of censorship, or on those platforms even though they don't rely on views or much less monetization as a livelihood. It's just copying a stupid status quo imposed by platforms that actively kill any literacy or critical thinking by wanting everyone to think like machines, and... Ye idk it sucks that it's working, in a way

Edit: Tbh no wait now that I think about it that shit also existed in some social spaces before such platforms. These platforms are just doing more shit that was already done before and that already sucked back then. Makes it all the more severe that we as a society keep accepting it continuously.

[luxraydyne]

what have you done

[marketinsightsenterprises]

AI-Driven Innovations in Drug Discovery Market Trends and Growth Opportunities

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[sciderman]

How do you feel about the increase in really weird NSFW ads on here (advertising panels that look like sexual encounters, and AI art apps that pride themselves on porn) but will take down NSFW posts from their users, even if it isn't technically sexual.

i hate all social media and it's consistent prioritising the advertisers over the users and the internet simply was a better place before capitalism sunk its hooks into it

[tvckerwash]

I hc wash and south (and by proxy, north) as all being ODSTs prior to pfl and one thing I really like about hcing them as such is that it adds another layer of depth to why they were all chosen to be a part of the recovery force.

ODSTs are a special forces unit within the marines, and they're generally used as force amplifiers and in high risk or sensitive operations. two such scenarios include the recovery or recapture of personal and high level assets behind enemy lines, as well as deep reconnaissance and intelligence gathering. ODSTs are also used in politically sensitive operations, which pfl was following the crash of the moi.

so basically, who better to be on the recovery force than former ODSTs who already have a background doing the kind of work that would need to be done?

this also adds to some of the tension between north and south as well imo, as while they're a great team who are capable of working together they clearly have two very different skillsets—south is not portrayed as someone who has the patience necessary for long reconnaissance missions, and part of the reason why team b failed so spectacularly is because two snipers and an intelligence operative are not a good choice for a smash and grab mission. had north been replaced with south things would've probably went way better for them, because south is actually fairly similar to wash in her "get in, get it done, get out" mentality, though where wash comes off as more methodical and is willing to take that "wait and see" approach, south throws caution to the wind and has a "we'll cross that bridge when we get there" approach.

this is probably why wash and south were (on paper at least) going to get eta and iota—south would've benefited greatly from having an ai that was afraid and anxious as it would force her to slow down and think things through more, and wash getting an ai that was happy and cheerful would force him to loosen up a bit and be less high-strung and serious.

[chrysalizzm]

“ranboo has broken free” SHUT UP AUSTIN AND JERMA WERE TOTALLY ABOUT TO KISS

[mittensmorgul]

There’s another post going around about this, but tumblr won’t let me reblog it but...

When I read a story written by a human being, I’m not just reading it because I want to read a coffee shop AU with a specific plot description. I’m reading it because it’s making a connection to another human storyteller and seeing a piece of them carved into the words. Storytelling is a human act of sharing joy, angst, tension, resolution, satisfaction. It’s an act of love.

Writing and reading a story isn’t just an act of creation and consumption. I hate that commercialism and AI are reducing it to that sort of transaction. Like oh, you need words on this subject and that’s the end of it. Like what we really needed was just a vending machine we can push buttons on to get a fix, as if the human creating the story wasn’t a factor. That the author’s life experience and views and feelings haven’t infused the words with their own unique touches.

I’ve read hundreds of coffee shop AU’s over the years (and thousands of fics in general). I’ve seen many similar tropes reused across stories, and just like an AI would, I’ve learned things about writing them that I will always carry with me. But unlike an AI, a human author is not just the sum total of coffee shop AU’s we’ve consumed. Even if we used the same prompt, the same sets of tropes, the same characters. I will always choose the human-crafted story over the computer generated one.

Because again, I’m not just looking for a very specific fix via a series of words. I’m looking for a human connection through story.

Unlike an AI, I have BEEN to a coffee shop. I’ve had experiences in coffee shops. I’ve had funny little meet-cutes with people. I’ve accidentally spilled coffee on myself and knocked heads with someone as we both rushed to wipe it up. I know what it FEELS like. The machine doesn’t.

I’ve also read millions of things that aren��t fanfic, or coffee shop AU’s. I’ve experienced things OTHER than going to coffee shops and having meet-cutes. And I know what all those things feel like when processed through my personal human lens of experience, which is different from every other personal human lens of experience.

All the machine can do is spit out what it THINKS a human experience is, and I honestly don’t care about that at all. Fic is not a “product” to be “generated.” It’s an art form that connects us to other people who share the same love of a thing that we do.

People who, even when all writing the same characters in the same setting to the exact same prompt, will all add something or have a viewpoint about something or bring a completely different personality and life experience to the story that no one else on the planet could. That’s what I’m actually reading.