#large language model services

[atcuality1]

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[rosemarry-06]

Large Language Model Development Company

Large Language Model Development Company (LLMDC) is a pioneering organization at the forefront of artificial intelligence research and development. Specializing in the creation and refinement of large language models, LLMDC leverages cutting-edge technologies to push the boundaries of natural language understanding and generation. The company's mission is to develop advanced AI systems that can understand, generate, and interact with human language in a meaningful and contextually relevant manner. 

With a team of world-class researchers and engineers, LLMDC focuses on a range of applications including automated customer service, content creation, language translation, and more. Their innovations are driven by a commitment to ethical AI development, ensuring that their technologies are not only powerful but also aligned with principles of fairness, transparency, and accountability. Through continuous collaboration with academic institutions, industry partners, and regulatory bodies, LLMDC aims to make significant contributions to the AI landscape, enhancing the way humans and machines communicate.

Large language model services offer powerful AI capabilities to businesses and developers, enabling them to integrate advanced natural language processing (NLP) into their applications and workflows. 

The largest language model services providers are industry leaders in artificial intelligence, offering advanced NLP solutions that empower businesses across various sectors. Prominent among these providers are OpenAI, Google Cloud, Microsoft Azure, and IBM Watson. OpenAI, renowned for its GPT series, delivers versatile and powerful language models that support a wide range of applications from text generation to complex data analysis. Google Cloud offers its AI and machine learning tools, including BERT and T5 models, which excel in tasks such as translation, sentiment analysis, and more. 

Microsoft Azure provides Azure Cognitive Services, which leverage models like GPT-3 for diverse applications, including conversational AI and content creation. IBM Watson, with its extensive suite of AI services, offers robust NLP capabilities for enterprises, enabling advanced text analytics and language understanding. These providers lead the way in delivering scalable, reliable, and innovative language model services that transform how businesses interact with and utilize language data.

Expert Custom LLM Development Solutions offer tailored AI capabilities designed to meet the unique needs of businesses across various industries. These solutions provide bespoke development of large language models (LLMs) that are fine-tuned to specific requirements, ensuring optimal performance and relevance. Leveraging deep expertise in natural language processing and machine learning, custom LLM development services can address complex challenges such as industry-specific jargon, regulatory compliance, and specialized content generation.

[bullet-proof-gay]

I have bad news for everyone. Customer service (ESPECIALLY tech support) is having AI pushed on them as well.

I work in tech support for a major software company, with multiple different software products used all over the world. As of now, my team is being tasked with "beta testing" a generative AI model for use in answering customer questions.

It's unbelievably shit and not going to get better no matter how much we test it, because it's a venture-capital company's LLM with GPT-4 based tech. It uses ChatGPT almost directly as a translator (instead of, you know, the hundreds of internationally-spread employees who speak those languages. Or fucking translation software).

We're not implementing it because we want to. The company will simply fire us if we don't. A few months ago they sacked almost the entire Indian branch of our team overnight and we only found out the next day because our colleagues' names no longer showed up on Outlook. I'm not fucking touching the AI for as long as physically possible without getting fired, but I can't stop it being implemented.

Even if you manage to contact a real person to solve your problem, AI may still be behind the answer.

Not only can you not opt out, you cannot even ensure that the GENUINELY real customer service reps you speak to aren't being forced to use AI to answer you.

[albertpeter]

What Is the Role of AI Ethics in Custom Large Language Model Solutions for 2025?

The rapid evolution of artificial intelligence (AI) has led to significant advancements in technology, particularly in natural language processing (NLP) through the development of large language models (LLMs). These models, powered by vast datasets and sophisticated algorithms, are capable of understanding, generating, and interacting in human-like ways. As we move toward 2025, the importance of AI ethics in the creation and deployment of custom LLM solutions becomes increasingly critical. This blog explores the role of AI ethics in shaping the future of these technologies, focusing on accountability, fairness, transparency, and user privacy.

Understanding Custom Large Language Models

Before delving into AI ethics, it is essential to understand what custom large language models are. These models are tailored to specific applications or industries, allowing businesses to harness the power of AI while meeting their unique needs. Custom Large Language Model solutions can enhance customer service through chatbots, streamline content creation, improve accessibility for disabled individuals, and even support mental health initiatives by providing real-time conversation aids.

However, the deployment of such powerful technologies also raises ethical considerations that must be addressed to ensure responsible use. With the potential to influence decision-making, shape societal norms, and impact human behavior, LLMs pose both opportunities and risks.

The Importance of AI Ethics

1. Accountability

As AI systems become more integrated into daily life and business operations, accountability becomes a crucial aspect of their deployment. Who is responsible for the outputs generated by LLMs? If an LLM generates misleading, harmful, or biased content, understanding where the responsibility lies is vital. Developers, businesses, and users must collaborate to establish guidelines that outline accountability measures.

In custom LLM solutions, accountability involves implementing robust oversight mechanisms. This includes regular audits of model outputs, feedback loops from users, and clear pathways for addressing grievances. Establishing accountability ensures that AI technologies serve the public interest and that any adverse effects are appropriately managed.

2. Fairness and Bias Mitigation

AI systems are only as good as the data they are trained on. If the training datasets contain biases, the resulting LLMs will likely perpetuate or even amplify these biases. For example, an LLM trained primarily on texts from specific demographics may inadvertently generate outputs that favor those perspectives while marginalizing others. This phenomenon, known as algorithmic bias, poses significant risks in areas like hiring practices, loan approvals, and law enforcement.

Ethics in AI calls for fairness, which necessitates that developers actively work to identify and mitigate biases in their models. This involves curating diverse training datasets, employing techniques to de-bias algorithms, and ensuring that custom LLMs are tested across varied demographic groups. Fairness is not just a legal requirement; it is a moral imperative that can enhance the trustworthiness of AI solutions.

3. Transparency

Transparency is crucial in building trust between users and AI systems. Users should have a clear understanding of how LLMs work, the data they were trained on, and the processes behind their outputs. When users understand the workings of AI, they can make informed decisions about its use and limitations.

For custom LLM solutions, transparency involves providing clear documentation about the model’s architecture, training data, and potential biases. This can include detailed explanations of how the model arrived at specific outputs, enabling users to gauge its reliability. Transparency also empowers users to challenge or question AI-generated content, fostering a culture of critical engagement with technology.

4. User Privacy and Data Protection

As LLMs often require large volumes of user data for personalization and improvement, ensuring user privacy is paramount. The ethical use of AI demands that businesses prioritize data protection and adopt strict privacy policies. This involves anonymizing user data, obtaining explicit consent for data usage, and providing users with control over their information.

Moreover, the integration of privacy-preserving technologies, such as differential privacy, can help protect user data while still allowing LLMs to learn and improve. This approach enables developers to glean insights from aggregated data without compromising individual privacy.

5. Human Oversight and Collaboration

While LLMs can operate independently, human oversight remains essential. AI should augment human decision-making rather than replace it. Ethical AI practices advocate for a collaborative approach where humans and AI work together to achieve optimal outcomes. This means establishing frameworks for human-in-the-loop systems, where human judgment is integrated into AI operations.

For custom LLM solutions, this collaboration can take various forms, such as having human moderators review AI-generated content or incorporating user feedback into model updates. By ensuring that humans play a critical role in AI processes, developers can enhance the ethical use of technology and safeguard against potential harms.

The Future of AI Ethics in Custom LLM Solutions

As we approach 2025, the role of AI ethics in custom large language model solutions will continue to evolve. Here are some anticipated trends and developments in the realm of AI ethics:

1. Regulatory Frameworks

Governments and international organizations are increasingly recognizing the need for regulations governing AI. By 2025, we can expect more comprehensive legal frameworks that address ethical concerns related to AI, including accountability, fairness, and transparency. These regulations will guide businesses in developing and deploying AI technologies responsibly.

2. Enhanced Ethical Guidelines

Professional organizations and industry groups are likely to establish enhanced ethical guidelines for AI development. These guidelines will provide developers with best practices for building ethical LLMs, ensuring that the technology aligns with societal values and norms.

3. Focus on Explainability

The demand for explainable AI will grow, with users and regulators alike seeking greater clarity on how AI systems operate. By 2025, there will be an increased emphasis on developing LLMs that can articulate their reasoning and provide users with understandable explanations for their outputs.

4. User-Centric Design

As user empowerment becomes a focal point, the design of custom LLM solutions will prioritize user needs and preferences. This approach will involve incorporating user feedback into model training and ensuring that ethical considerations are at the forefront of the development process.

Conclusion

The role of AI ethics in custom large language model solutions for 2025 is multifaceted, encompassing accountability, fairness, transparency, user privacy, and human oversight. As AI technologies continue to evolve, developers and organizations must prioritize ethical considerations to ensure responsible use. By establishing robust ethical frameworks and fostering collaboration between humans and AI, we can harness the power of LLMs while safeguarding against potential risks. In doing so, we can create a future where AI technologies enhance our lives and contribute positively to society.

[everydeviceneedstoknow]

United States Secret Service large language models being relied upon as knowing complete information are actually deficiently informed on many topics.

[rosemarry-06]

large language model companies in India

Large Language Model Development Company (LLMDC) is a pioneering organization at the forefront of artificial intelligence research and development. Specializing in the creation and refinement of large language models, LLMDC leverages cutting-edge technologies to push the boundaries of natural language understanding and generation. The company's mission is to develop advanced AI systems that can understand, generate, and interact with human language in a meaningful and contextually relevant manner. 

With a team of world-class researchers and engineers, LLMDC focuses on a range of applications including automated customer service, content creation, language translation, and more. Their innovations are driven by a commitment to ethical AI development, ensuring that their technologies are not only powerful but also aligned with principles of fairness, transparency, and accountability. Through continuous collaboration with academic institutions, industry partners, and regulatory bodies, LLMDC aims to make significant contributions to the AI landscape, enhancing the way humans and machines communicate.

Large language model services offer powerful AI capabilities to businesses and developers, enabling them to integrate advanced natural language processing (NLP) into their applications and workflows. 

The largest language model services providers are industry leaders in artificial intelligence, offering advanced NLP solutions that empower businesses across various sectors. Prominent among these providers are OpenAI, Google Cloud, Microsoft Azure, and IBM Watson. OpenAI, renowned for its GPT series, delivers versatile and powerful language models that support a wide range of applications from text generation to complex data analysis. Google Cloud offers its AI and machine learning tools, including BERT and T5 models, which excel in tasks such as translation, sentiment analysis, and more. 

Microsoft Azure provides Azure Cognitive Services, which leverage models like GPT-3 for diverse applications, including conversational AI and content creation. IBM Watson, with its extensive suite of AI services, offers robust NLP capabilities for enterprises, enabling advanced text analytics and language understanding. These providers lead the way in delivering scalable, reliable, and innovative language model services that transform how businesses interact with and utilize language data.

Expert Custom LLM Development Solutions offer tailored AI capabilities designed to meet the unique needs of businesses across various industries. These solutions provide bespoke development of large language models (LLMs) that are fine-tuned to specific requirements, ensuring optimal performance and relevance. Leveraging deep expertise in natural language processing and machine learning, custom LLM development services can address complex challenges such as industry-specific jargon, regulatory compliance, and specialized content generation.

[techdriveplay]

What is the rabbit r1? The Future of Personal Technology

In the rapidly evolving landscape of technology, a groundbreaking device has emerged that aims to revolutionize the way we interact with our digital world. Meet the rabbit r1, an innovative gadget that blends simplicity with sophistication, offering a unique alternative to the traditional smartphone experience. This article delves into the essence of the rabbit r1, exploring its features,…

[directactionforhope]

"Starting this month [June 2024], thousands of young people will begin doing climate-related work around the West as part of a new service-based federal jobs program, the American Climate Corps, or ACC. The jobs they do will vary, from wildland firefighters and “lawn busters” to urban farm fellows and traditional ecological knowledge stewards. Some will work on food security or energy conservation in cities, while others will tackle invasive species and stream restoration on public land. 

The Climate Corps was modeled on Franklin D. Roosevelt’s Civilian Conservation Corps, with the goal of eventually creating tens of thousands of jobs while simultaneously addressing the impacts of climate change. 

Applications were released on Earth Day, and Maggie Thomas, President Joe Biden’s special assistant on climate, told High Country News that the program’s website has already had hundreds of thousands of views. Since its launch, nearly 250 jobs across the West have been posted, accounting for more than half of all the listed ACC positions. 

“Obviously, the West is facing tremendous impacts of climate change,” Thomas said. “It’s changing faster than many other parts of the country. If you look at wildfire, if you look at extreme heat, there are so many impacts. I think that there’s a huge role for the American Climate Corps to be tackling those crises.”  

Most of the current positions are staffed through state or nonprofit entities, such as the Montana Conservation Corps or Great Basin Institute, many of which work in partnership with federal agencies that manage public lands across the West. In New Mexico, for example, members of Conservation Legacy’s Ecological Monitoring Crew will help the Bureau of Land Management collect soil and vegetation data. In Oregon, young people will join the U.S. Department of Agriculture, working in firefighting, fuel reduction and timber management in national forests. 

New jobs are being added regularly. Deadlines for summer positions have largely passed, but new postings for hundreds more positions are due later this year or on a rolling basis, such as the Working Lands Program, which is focused on “climate-smart agriculture.”  ...

On the ACC website, applicants can sort jobs by state, work environment and focus area, such as “Indigenous knowledge reclamation” or “food waste reduction.” Job descriptions include an hourly pay equivalent — some corps jobs pay weekly or term-based stipends instead of an hourly wage — and benefits. The site is fairly user-friendly, in part owing to suggestions made by the young people who participated in the ACC listening sessions earlier this year...

The sessions helped determine other priorities as well, Thomas said, including creating good-paying jobs that could lead to long-term careers, as well as alignment with the president’s Justice40 initiative, which mandates that at least 40% of federal climate funds must go to marginalized communities that are disproportionately impacted by climate change and pollution. 

High Country News found that 30% of jobs listed across the West have explicit justice and equity language, from affordable housing in low-income communities to Indigenous knowledge and cultural reclamation for Native youth...

While the administration aims for all positions to pay at least $15 an hour, the lowest-paid position in the West is currently listed at $11 an hour. Benefits also vary widely, though most include an education benefit, and, in some cases, health care, child care and housing. 

All corps members will have access to pre-apprenticeship curriculum through the North America’s Building Trades Union. Matthew Mayers, director of the Green Workers Alliance, called this an important step for young people who want to pursue union jobs in renewable energy. Some members will also be eligible for the federal pathways program, which was recently expanded to increase opportunities for permanent positions in the federal government...

 “To think that there will be young people in every community across the country working on climate solutions and really being equipped with the tools they need to succeed in the workforce of the future,” Thomas said, “to me, that is going to be an incredible thing to see.”"

-via High Country News, June 6, 2024

--

Note: You can browse Climate Corps job postings here, on the Climate Corps website. There are currently 314 jobs posted at time of writing!

Also, it says the goal is to pay at least $15 an hour for all jobs (not 100% meeting that goal rn), but lots of postings pay higher than that, including some over $20/hour!!

[albertpeter]

How Do Large Language Model Development Services Assist in Predictive Analytics?

In recent years, the explosion of data and advancements in artificial intelligence (AI) have transformed various industries, enabling organizations to harness the power of data like never before. One of the most groundbreaking developments in AI is the creation and utilization of Large Language Models (LLMs). These models have not only revolutionized natural language processing (NLP) but have also emerged as crucial tools for predictive analytics. In this blog, we will explore how large language model development services assist businesses in enhancing their predictive analytics capabilities.

Understanding Predictive Analytics

Predictive analytics refers to the practice of using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on past behaviors and trends. Organizations across various sectors, including finance, healthcare, retail, and marketing, leverage predictive analytics to make informed decisions, optimize operations, and improve customer experiences. Traditional predictive analytics methods often rely on structured data, but with the advent of LLMs, organizations can now analyze unstructured data, such as text, to enhance their predictive capabilities.

The Role of Large Language Models

Large Language Models, such as GPT-3 and its successors, are trained on vast datasets containing diverse text sources. These models can understand, generate, and manipulate human language in ways that were previously unimaginable. The key characteristics of LLMs that make them particularly effective in predictive analytics include:

Natural Language Understanding (NLU): LLMs can comprehend context, semantics, and sentiment in language, enabling them to extract meaningful insights from unstructured text data.

Contextual Learning: By processing vast amounts of information, LLMs can recognize patterns and relationships that may not be apparent in traditional datasets, allowing for more accurate predictions.

Generative Capabilities: LLMs can create human-like text, which can be valuable in generating scenarios, forecasts, and narratives based on predictive analysis.

How LLM Development Services Enhance Predictive Analytics

1. Enhanced Data Processing

One of the most significant advantages of LLMs in predictive analytics is their ability to process and analyze unstructured data. Traditional predictive analytics often struggles with data that is not neatly organized in tables or spreadsheets. However, LLMs excel in extracting insights from textual data, such as customer reviews, social media posts, and open-ended survey responses.

LLM development services can create customized models that understand specific terminologies, industry jargon, and user intent, enabling organizations to derive valuable insights from vast amounts of textual data. For example, a retail company can analyze customer feedback to predict trends in consumer behavior, identifying which products are likely to become popular.

2. Improved Accuracy of Predictions

LLMs are trained on extensive datasets, allowing them to recognize patterns and correlations within the data that may go unnoticed by conventional analytics methods. This ability to analyze diverse data sources can lead to more accurate predictions.

By incorporating LLMs into predictive analytics, organizations can enhance their forecasting models. For instance, a financial institution can use LLMs to analyze news articles, social media sentiment, and market trends to predict stock price movements more effectively. The model’s contextual understanding allows it to incorporate factors that traditional models may overlook, leading to more reliable predictions.

3. Sentiment Analysis and Market Trends

Sentiment analysis is a critical component of predictive analytics, particularly in understanding customer opinions and market trends. LLMs can be employed to analyze sentiment in customer reviews, social media discussions, and news articles, providing valuable insights into public perception.

LLM development services can create models that not only assess sentiment but also correlate it with potential outcomes. For example, a company can analyze customer sentiment regarding a product launch to predict its success. By understanding how customers feel about the product, businesses can make data-driven decisions about marketing strategies and resource allocation.

4. Scenario Simulation and Forecasting

Predictive analytics often involves simulating various scenarios to understand potential outcomes. LLMs can assist in this process by generating text-based scenarios based on historical data and current trends.

For instance, in healthcare, predictive analytics can be used to simulate the spread of diseases based on previous outbreaks and current health data. LLMs can generate narratives that describe potential future scenarios, helping healthcare providers prepare for different outcomes and allocate resources accordingly.

5. Personalized Recommendations

In the realm of e-commerce and marketing, personalized recommendations are crucial for enhancing customer experiences and driving sales. LLMs can analyze customer behavior and preferences to generate personalized recommendations based on predictive analytics.

LLM development services can create tailored models that learn from user interactions, predicting which products or services a customer is likely to be interested in. By leveraging both structured and unstructured data, businesses can provide a more personalized shopping experience, leading to increased customer satisfaction and loyalty.

6. Real-Time Decision Making

In today's fast-paced business environment, organizations need to make decisions quickly. LLMs can facilitate real-time predictive analytics by processing data streams in real-time, allowing businesses to react to emerging trends and changes in customer behavior promptly.

For example, in finance, LLMs can analyze market news and social media in real time to provide instant insights on market fluctuations. This capability enables traders and financial analysts to make informed decisions based on the latest data, enhancing their competitive edge.

7. Integration with Existing Systems

LLM development services can seamlessly integrate large language models into existing predictive analytics frameworks and business systems. This integration allows organizations to leverage the strengths of LLMs while maintaining their established processes.

By connecting LLMs to existing databases and analytics tools, businesses can enhance their predictive capabilities without overhauling their entire systems. This approach enables organizations to transition gradually to more advanced predictive analytics without significant disruptions.

Conclusion

Large Language Models have emerged as powerful tools that significantly enhance predictive analytics capabilities. Their ability to process unstructured data, improve prediction accuracy, analyze sentiment, simulate scenarios, and provide personalized recommendations makes them indispensable for organizations looking to harness the power of data effectively.

As businesses continue to evolve and adapt to a data-driven landscape, the role of LLM development services will become increasingly vital. By investing in LLMs, organizations can not only improve their predictive analytics but also gain a competitive edge in their respective industries. The future of predictive analytics lies in the innovative use of large language models, paving the way for more informed decision-making and enhanced business outcomes.

[probablyasocialecologist]

It is, as many have already pointed out, incredibly ironic that OpenAI, a company that has been obtaining large amounts of data from all of humankind largely in an “unauthorized manner,” and, in some cases, in violation of the terms of service of those from whom they have been taking from, is now complaining about the very practices by which it has built its company.  The argument that OpenAI, and every artificial intelligence company who has been sued for surreptitiously and indiscriminately sucking up whatever data it can find on the internet is not that they are not sucking up all of this data, it is that they are sucking up this data and they are allowed to do so.  OpenAI is currently being sued by the New York Times for training on its articles, and its argument is that this is perfectly fine under copyright law fair use protections.

[...]

OpenAI and Microsoft are essentially now whining about being beaten at its own game by DeepSeek. But additionally, part of OpenAI’s argument in the New York Times case is that the only way to make a generalist large language model that performs well is by sucking up gigantic amounts of data. It tells the court that it needs a huge amount of data to make a generalist language model, meaning any one source of data is not that important. This is funny, because DeepSeek managed to make a large language model that rivals and outpaces OpenAI’s own without falling into the more data = better model trap. Instead, DeepSeek used a reinforcement learning strategy that its paper claims is far more efficient than we’ve seen other AI companies do.

29 January 2025

[republicansaredomesticterrorists]

They are trying to illegally fire tens of thousands of Federal workers at the worst possible time of year. These are middle class to lower middle class people in DC and probably about a third or so of them voted for Trump. Contrary to publican opinion nobody gets rich working for the Federal government, especially in a major city where the cost of living is very high as are mortgages and rents. A disproportionate number of them are African-American, women, and other marginalized people since government employment is a safe haven for them with the added safety of being largely Unionized. To be honest there aren’t a lot of jobs open in the DC area with the pay and benefits of a unionized government job.

Musk is trying to run the government as if it were a business and that model does not apply. Government is not supposed to make a profit or pay dividends to shareholders. The primary purpose of government is to SPEND money to improve the lives and safety of all Americans.

Mass firings will not only ruin the lives of those removed from employment but it will have a ripple effect throughout the greater DC, Virginia, and Maryland area. All retail outlets will suffer immediately from lost business. Community and social services will be strained to the breaking point by hordes of people becoming unemployed all at once. The housing and rental market will collapse. Banks and Credit Unions will be stressed by loss of revenue. Families will dissolve and suicides will increase and so on. For those of you who don’t have sympathy for Federal employees, wait until the inconvenience of having almost no government services available to you strikes home. Think of the benefits that will be cut off. Think of the aid you won’t be able to receive when something goes wrong in your. Wait until you try to call a government agency to correct something to find out it doesn’t exist or is run by a skeleton crew.

You can look up the salaries of the rank and file workers and see it’s not great especially for one of the priciest markets in the country. This is a cold and heartless move which will have a devastating impact on large numbers of real people. It won’t just be in DC because they plan on spreading to field offices around the country so the pain will begin to seep into every county in the nation.

Government is not as simplistic as a business and can’t be run like one or by business people, entrepreneurs, oligarchs, CEO’s etc. Republicans, and other thoughtless people, need to separate themselves from the notion that someone who runs a business can run a country, or even a government agency. Diplomacy for example is much more complicated than a simple business deal. Diplomats spend lifetimes working on treaties and international agreements that will be in effect for decades or even centuries. It takes detailed knowledge of the past, current demographics and their needs, and years of forethought to play out every possible outcome of a treaty. Each word and phrase is excruciatingly analyzed for months or even years to achieve the desired effect and avoid any misinterpretations, misunderstandings, or vagaries of translation.

Treaties and national policy can’t be rationally drawn up by amateurs over a drunken round of golf, or a drunken steak dinner, or an amateurish conference call with the complexities of foreign languages which required highly skilled diplomatic translators who know the particularities of not only the mother tongue but each regional accent and dialect to avoid any posssible faux pas.

Trump and his henchmen are arrogant, poorly educated, unqualified, and often inebriated bigots and racists trying to run the world’s largest and most complex governing body as if it were a chain restaurant franchise.

Have you noticed yet nobody is talking about cutting aid to big energy companies, airlines, big pharma, or virtually any big corporate enterprise. Felon Muskrat says he’s uncovered billions in corruption and waste at every federal agency, in only a week. Felon hasn’t offered one scrap of documentation or proof though and neither had Trump. The media reports their claims of billions in waste and the Republican voters accept it as true. After a few weeks of hearing it in the news many of you will accept the lie as truth simply because you’ve heard it so often.

They are deliberately trying to overwhelm you, distract you, and wear you out. Democratic lawmakers trying to enter the very Federal agencies they fund have been locked out and kept at bay by armed private security wearing no identification and unwilling to state their names. Unwilling to say anything except you can’t enter.

They are pushing back but most of you seem to have forgotten that they became the minority party in both houses of Congress and the minority doesn’t have the ability to win a vote and pass anything or do anything other than protest and try to delay. Fortunately majority labor unions are out protesting in front of all the major agencies being targeted so far. Protests are also happing in some big cities but you’d never know it because it is virtually ignored as always by tv news.

[mariacallous]

It feels like no one should have to say this, and yet we are in a situation where it needs to be said, very loudly and clearly, before it’s too late to do anything about it: The United States is not a startup. If you run it like one, it will break.

The onslaught of news about Elon Musk’s takeover of the federal government’s core institutions is altogether too much—in volume, in magnitude, in the sheer chaotic absurdity of a 19-year-old who goes by “Big Balls” helping the world’s richest man consolidate power. There’s an easy way to process it, though.

Donald Trump may be the president of the United States, but Musk has made himself its CEO.

This is bad on its face. Musk was not elected to any office, has billions of dollars of government contracts, and has radicalized others and himself by elevating conspiratorial X accounts with handles like @redpillsigma420. His allies control the US government’s human resources and information technology departments, and he has deployed a strike force of eager former interns to poke and prod at the data and code bases that are effectively the gears of democracy. None of this should be happening.

It is, though. And while this takeover is unprecedented for the government, it’s standard operating procedure for Musk. It maps almost too neatly to his acquisition of Twitter in 2022: Get rid of most of the workforce. Install loyalists. Rip up safeguards. Remake in your own image.

This is the way of the startup. You’re scrappy, you’re unconventional, you’re iterating. This is the world that Musk’s lieutenants come from, and the one they are imposing on the Office of Personnel Management and the General Services Administration.

What do they want? A lot.

There’s AI, of course. They all want AI. They want it especially at the GSA, where a Tesla engineer runs a key government IT department and thinks AI coding agents are just what bureaucracy needs. Never mind that large language models can be effective but are inherently, definitionally unreliable, or that AI agents—essentially chatbots that can perform certain tasks for you—are especially unproven. Never mind that AI works not just by outputting information but by ingesting it, turning whatever enters its maw into training data for the next frontier model. Never mind that, wouldn’t you know it, Elon Musk happens to own an AI company himself. Go figure.

Speaking of data: They want that, too. DOGE agents are installed at or have visited the Treasury Department, the National Oceanic and Atmospheric Administration, the Small Business Administration, the Centers for Disease Control and Prevention, the Centers for Medicare and Medicaid Services, the Department of Education, the Department of Health and Human Services, the Department of Labor. Probably more. They’ve demanded data, sensitive data, payments data, and in many cases they’ve gotten it—the pursuit of data as an end unto itself but also data that could easily be used as a competitive edge, as a weapon, if you care to wield it.

And savings. They want savings. Specifically they want to subject the federal government to zero-based budgeting, a popular financial planning method in Silicon Valley in which every expenditure needs to be justified from scratch. One way to do that is to offer legally dubious buyouts to almost all federal employees, who collectively make up a low-single-digit percentage of the budget. Another, apparently, is to dismantle USAID just because you can. (If you’re wondering how that’s legal, many, many experts will tell you that it’s not.) The fact that the spending to support these people and programs has been both justified and mandated by Congress is treated as inconvenience, or maybe not even that.

Those are just the goals we know about. They have, by now, so many tentacles in so many agencies that anything is possible. The only certainty is that it’s happening in secret.

Musk’s fans, and many of Trump’s, have cheered all of this. Surely billionaires must know what they’re doing; they’re billionaires, after all. Fresh-faced engineer whiz kids are just what this country needs, not the stodgy, analog thinking of the past. It’s time to nextify the Constitution. Sure, why not, give Big Balls a memecoin while you’re at it.

The thing about most software startups, though, is that they fail. They take big risks and they don’t pay off and they leave the carcass of that failure behind and start cranking out a new pitch deck. This is the process that DOGE is imposing on the United States.

No one would argue that federal bureaucracy is perfect, or especially efficient. Of course it can be improved. Of course it should be. But there is a reason that change comes slowly, methodically, through processes that involve elected officials and civil servants and care and consideration. The stakes are too high, and the cost of failure is total and irrevocable.

Musk will reinvent the US government in the way that the hyperloop reinvented trains, that the Boring company reinvented subways, that Juicero reinvented squeezing. Which is to say he will reinvent nothing at all, fix no problems, offer no solutions beyond those that further consolidate his own power and wealth. He will strip democracy down to the studs and rebuild it in the fractious image of his own companies. He will move fast. He will break things.

[covid-safer-hotties]

Also preserved in our archive

A new study by researchers at Zhejiang University has highlighted the disproportionate health challenges faced by sexual and gender-diverse (SGD) individuals during the COVID-19 pandemic. By analyzing over 471 million tweets using advanced natural language processing (NLP) techniques, the study reveals that SGD individuals were more likely to discuss concerns related to social connections, mask-wearing, and experienced higher rates of COVID-19 symptoms and mental health issues than non-SGD individuals. The study has been published in the journal Health Data Science.

The COVID-19 pandemic has exposed and intensified health disparities, particularly for vulnerable populations like the sexual and gender-diverse (SGD) community. Unlike traditional health data sources, social media provides a more dynamic and real-time reflection of public concerns and experiences. Zhiyun Zhang, a Ph.D. student at Zhejiang University, and Jie Yang, Assistant Professor at the same institution, led a study that analyzed large-scale Twitter data to understand the unique challenges faced by SGD individuals during the pandemic.

To address this, the research team used NLP methods such as Latent Dirichlet Allocation (LDA) models for topic modeling and advanced sentiment analysis to evaluate the discussions and concerns of SGD Twitter users compared to non-SGD users. This approach allowed the researchers to explore three primary questions: the predominant topics discussed by SGD users, their concerns about COVID-19 precautions, and the severity of their symptoms and mental health challenges.

The findings reveal significant differences between the two groups. SGD users were more frequently involved in discussions about "friends and family" (20.5% vs. 13.1%) and "wearing masks" (10.1% vs. 8.3%). They also expressed higher levels of positive sentiment toward vaccines such as Pfizer, Moderna, AstraZeneca, and Johnson & Johnson. The study found that SGD individuals reported significantly higher frequencies of both physical and mental health symptoms compared to non-SGD users, underscoring their heightened vulnerability during the pandemic.

"Our large-scale social media analysis highlights the concerns and health challenges of SGD users. The topic analysis showed that SGD users were more frequently involved in discussions about 'friends and family' and 'wearing masks' than non-SGD users. SGD users also expressed a higher level of positive sentiment in tweets about vaccines," said Zhiyun Zhang, the lead researcher. "These insights emphasize the importance of targeted public health interventions for SGD communities."

The study demonstrates the potential of using social media data to monitor and understand public health concerns, especially for marginalized communities like SGD individuals. The results suggest the need for more tailored public health strategies to address the unique challenges faced by SGD communities during pandemics.

Moving forward, the research team aims to develop an automated pipeline to continuously monitor the health of targeted populations, offering data-driven insights to support more comprehensive public health services.

More information: Zhiyun Zhang et al, Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing, Health Data Science (2024). DOI: 10.34133/hds.0127 spj.science.org/doi/10.34133/hds.0127

[yellow-yarrow]

(copied from the Summer Eternal website for better readability:)

Summer Eternal Manifesto

On this day the 11th of October 2024, we announce SUMMER ETERNAL. We recognize we are writing the opening words of our story at a time of apocalyptic material conditions for game creators across the world.

Our art has been dressed down into an industry, and this industry has been pilfered by corrupt executives, by the vulgar profiteering of corporate bodies moving like leviathans in the dark, burning human fuel in their insatiable lust for money.

It is not pencil-pushers and moneylenders who make games. It is the relentless passion of the workers that creates an art form capable of saying something true.

As creators and game makers, we have too long been led away from the truth, away from the right to define ourselves as artists in service of the definitive art form of the future, one that has made us dream since we were children.

Instead, the disposability culture operating at the ruthless core of this industry wants us to think of ourselves as cogs in the machine: rudimentary craftsmen, disposable career workers, inert producers of made-to-order marketing-driven "content" — empty calories leaving the soul hungry.

The Profiteer knows that by keeping your dignity low, he will keep you crawling on the treadmill of passion until he lays you off for the sake of the red number in his book.

We make games because we have to. It is our calling. Because we have no choice but to see the transformative potential of this youngest medium of human interaction. You can't turn away once you've seen the light, Or it will always feel like everyone else in the world is doing something without you, there in the light you try to abandon but can't, because — oh, the horror — it comes from inside you.

All art is communication — dialogue across time, space and thought. In its rawest, it is one mind’s ability to provoke emotion in another. Large language models — simulacra, cold comfort, real-doll pocket-pussy, cyberspace freezer of an abandoned IM-chat — which are today passed off for “artificial intelligence”, will never be able to offer a dialogue with the vision of another human being.

Machine-generated works will never satisfy or substitute the human desire for art, as our desire for art is in its core a desire for communication with another, with a talent who speaks to us across worlds and ages to remind us of our all-encompassing human universality. There is no one to connect to in a large language model. The phone line is open but there’s no one on the other side.

The peddlers who aim to get rich quick from this scheme will always PLAY THE FOOL to any ethical or artistic argument. This is why we must push back against Big Tech's encroachment on the territory of our art, against increasing corporatization and alienation of game creators from their work, against the robbery of rights from workers, performers, artists and all contributors to this complicated and MULTI-FACETED medium.

Our mission is to unite world-class artists and creatives in a truly independent game studio which will always prioritize artistic integrity over personal comfort, profit margin, short term interests and Big Tech profit-bubbles.

There we will be able to embark on that treacherous road of building a cultural megaproject, a Role Playing Game with complexity and ambition worthy to rival our wretched and wonderful world.

In this we are committed to pursue the highest caliber of literary quality.

Here we stand, bound by our love for games and devotion to our craft, ready to bleed and weep and ride the cavalry into machine gun fire one more time.

For this we will need all the support and help from you, our readers, colleagues and future visionaries. Come walk the desert with us.

We will not take any of your support for granted. We have seen the suffering wrought by the hunger for power, the terror of greed and envy, the complicity of the averted eye. We have also seen the triumph of the human spirit, of solidarity in the striving, of making something never before seen and seeing its miracle unfold in the world. We have seen poverty, and we have seen plenty. We will make mistakes, we will win and we will fail, but we will never forget what we are doing this for. Yours in every season,

SUMMER ETERNAL

[albertpeter]

How Can Large Language Model Development Services Simplify Legal Document Review?

In the fast-paced world of law, the need for efficiency and accuracy is paramount. Legal professionals are tasked with reviewing vast amounts of documentation, including contracts, case law, and other legal documents. This traditionally labor-intensive process can be daunting and time-consuming. However, advancements in technology, particularly through large language models (LLMs), are revolutionizing the way legal document review is conducted. In this blog, we will explore how LLM development services can simplify legal document review, enhance productivity, and improve the accuracy of legal work.

Understanding Large Language Models

Before delving into their application in the legal sector, it's essential to understand what large language models are. LLMs are advanced artificial intelligence systems trained on massive datasets of text. They utilize deep learning techniques to understand, generate, and manipulate human language. Notable examples include OpenAI’s GPT-3 and GPT-4, which can perform various language tasks such as translation, summarization, question-answering, and more. Their capability to comprehend context, identify nuances, and generate coherent text makes them particularly useful in legal applications.

The Challenges of Legal Document Review

Legal document review involves scrutinizing documents for relevant information, inconsistencies, and compliance with legal standards. It is an essential part of various legal processes, including litigation, contract negotiations, and regulatory compliance. However, several challenges hinder the efficiency of this process:

Volume of Documents: Legal professionals often deal with an overwhelming number of documents. The volume can easily exceed thousands of pages, making manual review not only tedious but also prone to human error.

Complexity of Language: Legal documents are characterized by dense language, complex terminology, and specific jargon. This complexity can lead to misunderstandings and misinterpretations.

Time Constraints: Legal professionals frequently face tight deadlines, necessitating quick and accurate reviews of documents. Rushed reviews can result in missed details that could have significant implications.

Resource Intensity: Reviewing documents requires substantial manpower, which can strain budgets and limit the resources available for other critical tasks.

How LLM Development Services Can Simplify Legal Document Review

1. Automated Document Analysis

LLMs can analyze large volumes of documents in a fraction of the time it would take a human reviewer. By leveraging natural language processing (NLP) capabilities, these models can quickly scan through legal documents, extracting relevant information and identifying key phrases. This automation significantly reduces the workload on legal professionals, allowing them to focus on more strategic tasks that require human judgment and expertise.

2. Contextual Understanding

One of the significant advantages of LLMs is their ability to understand context. Unlike traditional keyword-based search methods, which may overlook nuances, LLMs can interpret the meaning behind the text. This capability is particularly valuable in legal settings where the implications of specific wording can change the document's meaning. For instance, LLMs can discern the difference between “shall” and “may,” understanding their legal significance in contract language.

3. Enhanced Search and Retrieval

LLMs can enhance the search and retrieval process for legal documents. They can be trained to recognize and categorize legal terms, concepts, and precedents, making it easier for legal professionals to find relevant information quickly. This improved search capability streamlines the research process, enabling lawyers to access necessary data and cases efficiently.

4. Risk Assessment and Compliance Checking

In legal practice, ensuring compliance with regulations is crucial. LLMs can assist in compliance checking by analyzing documents against relevant laws and regulations. They can flag potential issues or inconsistencies, helping legal professionals mitigate risks associated with non-compliance. This proactive approach not only saves time but also protects clients from legal repercussions.

5. Contract Review and Management

Contracts are a fundamental aspect of legal work, and reviewing them for potential risks and liabilities is essential. LLMs can automate the contract review process by identifying clauses that may be unfavorable or ambiguous. They can also highlight inconsistencies across multiple contracts, ensuring that terms are aligned and compliant. This functionality allows legal teams to manage contracts more effectively and respond to client needs swiftly.

6. Summarization and Comparison

Legal professionals often need to summarize lengthy documents or compare multiple versions of a contract. LLMs can generate concise summaries that capture essential points, enabling lawyers to grasp key information quickly. Additionally, they can compare different versions of documents to identify changes and discrepancies, streamlining the revision process.

7. Cost Efficiency

Implementing LLM development services can lead to significant cost savings for law firms and legal departments. By automating routine tasks, firms can reduce the number of billable hours spent on document review, allowing them to allocate resources more effectively. The time saved on document review can be redirected toward more complex legal work that requires human expertise, enhancing overall productivity.

8. Training and Customization

LLMs can be trained on specific legal corpuses to improve their accuracy and relevance to particular legal contexts. By customizing the model to understand the unique language and practices of a law firm or a specific area of law, organizations can enhance the effectiveness of LLMs in document review processes. Customization allows for improved understanding of specific legal jargon and case law, leading to more accurate results.

Conclusion

The integration of large language model development services into the legal document review process marks a significant advancement in legal technology. By automating routine tasks, enhancing accuracy, and streamlining workflows, LLMs empower legal professionals to work more efficiently and effectively. As the legal landscape continues to evolve, embracing technology will be essential for firms looking to stay competitive and deliver the best possible service to their clients.

With the ongoing development of LLMs and their increasing accessibility, law firms have a unique opportunity to revolutionize their document review processes. By leveraging these powerful tools, legal professionals can not only simplify their workflows but also enhance their decision-making capabilities, ultimately leading to better outcomes for clients and the legal industry as a whole.

[b4mpyre-k1zz3s]

The Jackass Guys Working in Fast Food HC’s!

Warnings: Suggestive content, crude language, drug use, tampering with food (and general bad food service practices)

An: This fic was largely inspired by this spot the guys did for the Arby’s Action Sports Awards, a concept which still eludes me to this day…

The awards show that invited the jackass guys to host had this sponsorship deal with some fast food company,

And, as written in tiny print on the contract, the guys ended up getting roped into something they’d never thought they’d have to deal with:

Working in food service.

Johnny

Given his position as the leader of the group, Johnny is kinda the manager by default

Partially because he’s so charismatic and partially because he just has pretty privilege so customers can’t get too mad at him

So when the drive through window gets stuck, guess who’s running orders outside?

He was the most responsible one and often takes up the job of cleaning up the dining area,

Even though he did have a tendency to clean off tables while people were eating or sweep a little too close to the patrons,

“Uh, scuse’ me, ma’am…Feet up, please.” And they never seemed to mind!

In fact, anytime someone got their order messed up, guess who they send in?

“I really am sorry for the inconvenience, sir,” Knoxville shoveled about twenty apple pies into a bag as turned to speak over his shoulder to the pissed off customer

“But I just wanted the order I paid for-”

“Shh…Just between you and me.” Johnny nudged the bag closer to him with a wink, “Go ahead- take it! I gotcha.”

And he actually took it.

Bam

“What’re you- some kinda wussy?” Bam had a tendency to shit talk customer’s orders, often pressuring them to size up,

“C’mon, be a man! You know what, dude? I’m just gonna put you down for a large combo…”

God forbid a customer is rude to him because holy shit. Bam is a master the guerrilla food terrorisim!

He has 100% spit in a guys onion rings because he yelled at him over the drive thru

And you bet he served them with a smile

Even though Bam has that whole line cook look, he’s maybe the worst person you want to have working at your restaurant.

It’s pretty rare that he gets sent out to register duty (due to the fact it takes him forever to make change)

But when he does, he just looks so disheveled from working in the kitchen

I’m talking condiments on his apron, pieces of meat just…hanging off of him, which obviously raised a couple eyebrows

“I mean- I was in the kitchen. I was workin’ hard back there! Can’t you tell?”

Steve-O

Steve couldn’t help but grin to himself when the angry customer over the drive through sarcastically asked him if he was ‘on something’

“Yes, sir- I am.”

Completely opposite to Bam, Steve is the closest thing they have to a model employee due to his experience working shitty jobs

If you order a four piece nugget, and he’s making it, count on getting a fifth one every time because he knows he would be pumped if he got one.

Point is, Steve is the fast food employee everyone loves, and that extends to his work at the counter

When all the guys are hustling to get orders out on time during a rush, guess who’s out there doing clown tricks to keep customers entertained?

Doing backflips off of the counter and juggling condiment packages to keep people happy people while whistling that one circus theme

“If you like the condiment stuff, wait till you see what I do with the drinks!”

Chris

“Welcome to Arby’s! Can I tempt you with my- I mean, our meat?”

Him and Steve have competitions as to who can say the most out of pocket thing over the drive thru speaker. He’s in the lead (for obvious reasons).

One of the best ones he came up with was when he was told to advertise the new dessert offerings,

“Are you sure you don’t wanna try one of our pies? The cream is delicious.”

Him and Steve are inseparable, usually spending more time fucking around in the kitchen than actually preparing food

So when, in the middle of a rush, the mayo gun Steve was using gets jammed and (despite his very skillful efforts to fix it) explodes all over him, Chris has a lot to say,

“Oh my god-” He turned to where his buddy was standing there, stunned, “Steve. Is this your man-aise?”

The customers could hear their laughter from the kitchen.

And speaking of Steve, Chris came up with a few tricks of his own to pull when he’s on register duty

Like walking out with two burgers stuffed in the top of his apron like boobs,

“Can I take anybody’s order?” He looked around the restaurant like nothing was amiss as he adjusted the twins.

Ryan

“Welcome to Arby’s, where the world’s a better place…” Ryan sighed, reading off the drive thru script for the fiftieth time that day,

“Whaddya want?”

Ryan hates dealing with customers and, in the middle of a rush, went out for a “smoke break”, which really meant he was going to hide in the freezer until his shift was nearly over

“Really, Ry?” Bam raised an eyebrow at the ice crystals in his beard, which only tipped him off that something was amiss because it was June.

Kinda similar to how Steve and Chris have their drive thru routine, him and Bam tag team on food sabotage, only Ryan’s arguably less gross

Like the worst he’s ever done was take a sip out of a guy’s milkshake before he gave it to him.

It isn’t that hard to believe given the fact he introduced the guys to using “God’s Tongs”

(if you don’t know, is a nice way to say picking up food with your hands)

In fact, everyone remembers that one day a customer was complaining to him that their burger arrived without a bun, holding out the bare patty to show him,

“Alright- I gotcha.” Ryan took a few steps back, grabbing a top bun from the back, and he just chucked the thing at the guy!

That top bun landed perfectly on top of that burger.