#large language model development services

[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.

[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.

[atcuality1]

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[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

[mariacallous]

AI’s energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining.

The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

“The money that bitcoin miners had to get to where they are today is peanuts compared to the money that Google and Microsoft and all these big tech companies are pouring in [to AI],” he says. “This is just escalating a lot faster, and it’s a much bigger threat.”

The development of AI is already having an impact on Big Tech’s climate goals. Tech giants have acknowledged in recent sustainability reports that AI is largely responsible for driving up their energy use. Google’s greenhouse gas emissions, for instance, have increased 48 percent since 2019, complicating the company’s goals of reaching net zero by 2030.

“As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute,” Google’s 2024 sustainability report reads.

Last month, the International Energy Agency released a report finding that data centers made up 1.5 percent of global energy use in 2024—around 415 terrawatt-hours, a little less than the yearly energy demand of Saudi Arabia. This number is only set to get bigger: Data centers’ electricity consumption has grown four times faster than overall consumption in recent years, while the amount of investment in data centers has nearly doubled since 2022, driven largely by massive expansions to account for new AI capacity. Overall, the IEA predicted that data center electricity consumption will grow to more than 900 TWh by the end of the decade.

But there’s still a lot of unknowns about the share that AI, specifically, takes up in that current configuration of electricity use by data centers. Data centers power a variety of services—like hosting cloud services and providing online infrastructure—that aren’t necessarily linked to the energy-intensive activities of AI. Tech companies, meanwhile, largely keep the energy expenditure of their software and hardware private.

Some attempts to quantify AI’s energy consumption have started from the user side: calculating the amount of electricity that goes into a single ChatGPT search, for instance. De Vries-Gao decided to look, instead, at the supply chain, starting from the production side to get a more global picture.

The high computing demands of AI, De Vries-Gao says, creates a natural “bottleneck” in the current global supply chain around AI hardware, particularly around the Taiwan Semiconductor Manufacturing Company (TSMC), the undisputed leader in producing key hardware that can handle these needs. Companies like Nvidia outsource the production of their chips to TSMC, which also produces chips for other companies like Google and AMD. (Both TSMC and Nvidia declined to comment for this article.)

De Vries-Gao used analyst estimates, earnings call transcripts, and device details to put together an approximate estimate of TSMC’s production capacity. He then looked at publicly available electricity consumption profiles of AI hardware and estimates on utilization rates of that hardware—which can vary based on what it’s being used for—to arrive at a rough figure of just how much of global data-center demand is taken up by AI. De Vries-Gao calculates that without increased production, AI will consume up to 82 terrawatt-hours of electricity this year—roughly around the same as the annual electricity consumption of a country like Switzerland. If production capacity for AI hardware doubles this year, as analysts have projected it will, demand could increase at a similar rate, representing almost half of all data center demand by the end of the year.

Despite the amount of publicly available information used in the paper, a lot of what De Vries-Gao is doing is peering into a black box: We simply don’t know certain factors that affect AI’s energy consumption, like the utilization rates of every piece of AI hardware in the world or what machine learning activities they’re being used for, let alone how the industry might develop in the future.

Sasha Luccioni, an AI and energy researcher and the climate lead at open-source machine-learning platform Hugging Face, cautioned about leaning too hard on some of the conclusions of the new paper, given the amount of unknowns at play. Luccioni, who was not involved in this research, says that when it comes to truly calculating AI’s energy use, disclosure from tech giants is crucial.

“It’s because we don’t have the information that [researchers] have to do this,” she says. “That’s why the error bar is so huge.”

And tech companies do keep this information. In 2022, Google published a paper on machine learning and electricity use, noting that machine learning was “10%–15% of Google’s total energy use” from 2019 to 2021, and predicted that with best practices, “by 2030 total carbon emissions from training will reduce.” However, since that paper—which was released before Google Gemini’s debut in 2023—Google has not provided any more detailed information about how much electricity ML uses. (Google declined to comment for this story.)

“You really have to deep-dive into the semiconductor supply chain to be able to make any sensible statement about the energy demand of AI,” De Vries-Gao says. “If these big tech companies were just publishing the same information that Google was publishing three years ago, we would have a pretty good indicator” of AI’s energy use.

[thoughtportal]

SoundCloud — a music sharing platform once so beloved by artists it spawned sub-genres named after it — updated its terms of service (TOS), forcing artists who use SoundCloud to let their music train AI.

It looks like the change went into effect during SoundCloud's last TOS policy update in February 2024, and it's just coming to light now.

The updated terms read that "in the absence of a separate agreement that states otherwise," creators who upload content to the site "explicitly agree that your Content may be used to inform, train, develop or serve as input to artificial intelligence or machine intelligence technologies or services as part of and for providing the services."

In short, the language in the update suggests that musicians, artists, and other creators who use SoundCloud for distribution can expect their uploaded work to be funneled into AI models as training data.

It's a sweeping provision. And how SoundCloud is actually applying it isn't exactly clear. But since early 2024, SoundCloud has integrated several generative AI tools and services into its platform that largely center on creating or producing new music.

These changes started in late January 2024 (just before the TOS update), with the integration of three "assistive AI" products designed to help SoundCloud's creators. According to a press release, these tools are designed to "allow artists to upload songs created with assistive AI tools directly to SoundCloud."

[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.

[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.

[canmom]

more on art production ~under capitalism~

reading Who Owns This Sentence?, a very engaging and fiercely critical history of the concept of copyright, and it's pretty fire. there's all sorts of fascinating intricacies in the way the notion of IP formed around the world (albeit so far the narrative has mainly focused on Europe, and to a limited extent China), and the different ideologies that justified the types of monopolies that it granted. the last chapter i read skewers the idea that the ability to exploit copyright and patents is what motivates the writing of books and research/invention, and I'll try and pull out the shape of the argument tomorrow. so far I'm only up to the 18th century; I'm looking forward to the rest of their story of how copyright grew from the limited forms of that period into the monster it is today.

it's on libgen if you wanna read it! i feel like the authors would be hypocrites to object :p

it is making me think about the differences between the making of books and other media, from (since this has been rattling around my head lately) an economic angle...

writing books, at least in the case of fiction is usually done on a prospective, spec-work kind of basis (you write your novel with no guarantee it will get published unless you're already an established author under contract). admittedly, a lot of us probably read books by authors who managed to 'make it' as professional authors and write full time - but this is not a lucrative thing to do and to make it work you need truly exceptional luck to get a major hit, or to be extremely prolific in things people want to read.

the films and games of the types most of us play are, by contrast, generally made by teams of salaried people - and thus do rarely get made without the belief it will be profitable. if you went on about your 'monetisation model' when writing a book, people would look at you funny and rightly so, but it's one of the first questions that gets asked when pitching a game.

open source software is a notable comparison here. a lot of it is done for its own sake without any expectation of profit, taking untold hours, but large free software projects tend to sprout foundations, which take donations (typically from companies that use the software) to pay for full time developers. mozilla, notably, gets a huge part of its funding from google paying for their search engine to be the default in Firefox; this in turn drives development of not just Firefox itself but also the Rust programming language (as discussed in this very enlightening talk by Evan Czaplicki). Blender is rightly celebrated as one of the best open source projects for its incredibly fast development, but they do have an office in amsterdam and a number of full time devs.

what money buys in regards to creative works is not motivation, but time - time to work on a project, iterate and polish and all that. in societies where you have to buy food etc. to survive, your options for existence are basically:

work at a job

own capital

rely on someone else (e.g. a parent or partner)

rely on state benefits if you can get them

beg

steal

if you're working at a job, this takes up a lot of your time and energy. you can definitely make art anyway, loads of people do, but you're much more limited in how you can work at it compared to someone who doesn't have to work another job.

so again, what money buys in art is the means of subsistence for someone, freeing them to work fully on realising a project.

where does the money come from that lets people work full time on art? a few places.

one is selling copies of the work itself. what's remarkable is that, when nearly everything can be pirated without a great deal of effort, it is still possible to do this to some degree - though in many ways the ease of digital copying (or at least the fear if it) has forced new models for purely digital creations, which either trade on convenience (streaming services) or in the case of games, find some way to enforce scarcity like requiring connection to a central server and including 'in-app purchases', where you pay to have the software display that you are the nebulous owner of an imaginary thing, and display this to other players. anyway, whichever exact model, the idea is that you turn the IP into capital which you then use to manufacture a product like 'legal copies', 'subscriptions' or 'accounts with a rare skin unlocked'.

the second is using the work to promote some other, more profitable thing - merchandising, an original work, etc. this is the main way that something like anime makes money (for the production committee, if not the studio) - the anime is, economics-wise, effectively an ad for its own source manga, figurines, shirts etc. the reason why there is so much pro media chasing the tastes of otaku is partly because otaku spend a lot on merch. (though it's also because the doujin scene kind of feeds into 'pro' production)

the third is some kind of patronage relationship, notably government grants, but also academic funding bodies, or selling commissions, or subscriptions on a streaming platform/patreon etc.

grants are how most European animated films are funded, and they often open with the logos of a huge list of arts organisations in different countries. the more places you can get involved, the more funds you can pull on. now, instead of working out how to sell your creation to customers who might buy a copy, under this model you need to convince funding bodies that it fits their remit. requesting grants involves its own specialised language.

in general the issue with the audience patronage model is that it only really pays enough to live on if you're working on a pretty huge scale. a minority make a fortune; the vast majority get a pittance at most, and if they do 'make it', it takes years of persistence.

the fourth is, for physical media, to sell an original. this only works if you can accumulate enough prestige, and the idea is to operate on extreme scarcity. the brief fad of NFTs attempted to abstract the idea of 'owning' an original from the legal right to control the physical object to something completely nebulous. in practice this largely ended up just being a speculative bubble - but then again, a lot of the reason fine art is bought and sold for such eye watering sums is pretty much the same, it's an arbitrary holder of an investment.

the fifth is artworks which are kind of intrinsically scarce, like live performances. you can only fit so many people in the house. and in many cases people will pay to see something that can be copied in unique circumstances, like seeing a film at a cinema or festival - though this is a special case of selling copies.

the sixth is to sell advertising: turn your audience into the product, and your artwork into the bait on the hook.

the alternative to all of these options is unpaid volunteer work, like a collab project. the participants are limited to the time and energy they have left after taking care of survival. this can still lead to great things, but it tends to be more unstable by its nature. so many of these projects will lose steam or participants will flake and they'll not get finished - and that's fine! still, huge huge amounts of things already get created on this kind of hobby/indie/doujin basis, generally (tho not always) with no expectation of making enough money to sustain someone.

in every single one of these cases, the economic forces shape the types of artwork that will get made. different media are more or less demanding of labour, and that in turn shapes what types of projects are viable.

books can be written solo, and usually are - collaborations are not the norm there. the same goes for illustrations. on the other hand, if you want to make a hefty CRPG or an action game or a feature length movie, and you're trying to fit that project around your day job... i won't say it's impossible, I can think of some exceptional examples, but it won't be easy, and for many people it just won't be possible.

so, that's a survey of possibilities under the current regime. how vital is copyright really to this whole affair?

one thing that is strange to me is that there aren't a lot of open source games. there are some - i have memories of seeing Tux Racer, but a more recent example would be Barotrauma (which is open source but not free, and does not take contributions from outside the company). could it work? could you pay the salaries of, say, 10 devs on a 'pay what you can' model?

it feels like the only solution to all of this in the long run is some kind of UBI type of thing - that or a very generous art grants regime. if people were free to work on what they wanted and didn't need to be paid, you wouldn't have any reason for copyright. the creations could be publicly archived. but then the question i have is, what types of artwork would thrive in that kind of ecosystem?

I've barely talked about the book that inspired this, but i think it was worth the trouble to get the contours of this kind of analysis down outside my head...

[vague-humanoid]

The ongoing harms of AI

In the early days of the chatbot hype, OpenAI CEO Sam Altman was making a lot of promises about what large language models (LLMs) would mean for the future of human society. In Altman’s vision, our doctors and teachers would become chatbots and eventually everyone would have their own tailored AI assistant to help with whatever they needed. It wasn’t hard to see what that could mean for people’s jobs, if his predictions were true. The problem for Altman is that those claims were pure fantasy.

Over the 20 months that have passed since, it’s become undeniably clear that LLMs have limitations many companies do not want to acknowledge, as that might torpedo the hype keeping their executives relevant and their corporate valuations sky high. The problem of false information, often deceptively termed “hallucinations,” cannot be effectively tackled and the notion that the technologies will continue getting infinitely better with more and more data has been called into question by the minimal improvements new AI models have been able to deliver.

However, once the AI bubble bursts, that doesn’t mean chatbots and image generators will be relegated to the trash bin of history. Rather, there will be a reassessment of where it makes sense to implement them, and if attention moves on too fast, they may be able to do that with minimal pushback. The challenge visual artists and video game workers are already finding with employers making use of generative AI to worsen the labor conditions in their industries may become entrenched, especially if artists fail in their lawsuits against AI companies for training on their work without permission. But it could be far worse than that.

Microsoft is already partnering with Palantir to feed generative AI into militaries and intelligence agencies, while governments around the world are looking at how they can implement generative AI to reduce the cost of service delivery, often without effective consideration of the potential harms that can come of relying on tools that are well known to output false information. This is a problem Resisting AI author Dan McQuillan has pointed to as a key reason why we must push back against these technologies. There are already countless examples of algorithmic systems have been used to harm welfare recipients, childcare benefit applicants, immigrants, and other vulnerable groups. We risk a repetition, if not an intensification, of those harmful outcomes.

When the AI bubble bursts, investors will lose money, companies will close, and workers will lose jobs. Those developments will be splashed across the front pages of major media organizations and will receive countless hours of public discussion. But it’s those lasting harms that will be harder to immediately recognize, and that could fade as the focus moves on to whatever Silicon Valley places starts pushing as the foundation of its next investment cycle.

All the benefits Altman and his fellow AI boosters promised will fade, just as did the promises of the gig economy, the metaverse, the crypto industry, and countless others. But the harmful uses of the technology will stick around, unless concerted action is taken to stop those use cases from lingering long after the bubble bursts.

[kanguin]

Prometheus Gave the Gift of Fire to Mankind. We Can't Give it Back, nor Should We.

AI. Artificial intelligence. Large Language Models. Learning Algorithms. Deep Learning. Generative Algorithms. Neural Networks. This technology has many names, and has been a polarizing topic in numerous communities online. By my observation, a lot of the discussion is either solely focused on A) how to profit off it or B) how to get rid of it and/or protect yourself from it. But to me, I feel both of these perspectives apply a very narrow usage lens on something that's more than a get rich quick scheme or an evil plague to wipe from the earth.

This is going to be long, because as someone whose degree is in psych and computer science, has been a teacher, has been a writing tutor for my younger brother, and whose fiance works in freelance data model training... I have a lot to say about this.

I'm going to address the profit angle first, because I feel most people in my orbit (and in related orbits) on Tumblr are going to agree with this: flat out, the way AI is being utilized by large corporations and tech startups -- scraping mass amounts of visual and written works without consent and compensation, replacing human professionals in roles from concept art to story boarding to screenwriting to customer service and more -- is unethical and damaging to the wellbeing of people, would-be hires and consumers alike. It's wasting energy having dedicated servers running nonstop generating content that serves no greater purpose, and is even pressing on already overworked educators because plagiarism just got a very new, harder to identify younger brother that's also infinitely more easy to access.

In fact, ChatGPT is such an issue in the education world that plagiarism-detector subscription services that take advantage of how overworked teachers are have begun paddling supposed AI-detectors to schools and universities. Detectors that plainly DO NOT and CANNOT work, because the difference between "A Writer Who Writes Surprisingly Well For Their Age" is indistinguishable from "A Language Replicating Algorithm That Followed A Prompt Correctly", just as "A Writer Who Doesn't Know What They're Talking About Or Even How To Write Properly" is indistinguishable from "A Language Replicating Algorithm That Returned Bad Results". What's hilarious is that the way these "detectors" work is also run by AI.

(to be clear, I say plagiarism detectors like TurnItIn.com and such are predatory because A) they cost money to access advanced features that B) often don't work properly or as intended with several false flags, and C) these companies often are super shady behind the scenes; TurnItIn for instance has been involved in numerous lawsuits over intellectual property violations, as their services scrape (or hopefully scraped now) the papers submitted to the site without user consent (or under coerced consent if being forced to use it by an educator), which it uses in can use in its own databases as it pleases, such as for training the AI detecting AI that rarely actually detects AI.)

The prevalence of visual and lingustic generative algorithms is having multiple, overlapping, and complex consequences on many facets of society, from art to music to writing to film and video game production, and even in the classroom before all that, so it's no wonder that many disgruntled artists and industry professionals are online wishing for it all to go away and never come back. The problem is... It can't. I understand that there's likely a large swath of people saying that who understand this, but for those who don't: AI, or as it should more properly be called, generative algorithms, didn't just show up now (they're not even that new), and they certainly weren't developed or invented by any of the tech bros peddling it to megacorps and the general public.

Long before ChatGPT and DALL-E came online, generative algorithms were being used by programmers to simulate natural processes in weather models, shed light on the mechanics of walking for roboticists and paleontologists alike, identified patterns in our DNA related to disease, aided in complex 2D and 3D animation visuals, and so on. Generative algorithms have been a part of the professional world for many years now, and up until recently have been a general force for good, or at the very least a force for the mundane. It's only recently that the technology involved in creating generative algorithms became so advanced AND so readily available, that university grad students were able to make the publicly available projects that began this descent into madness.

Does anyone else remember that? That years ago, somewhere in the late 2010s to the beginning of the 2020s, these novelty sites that allowed you to generate vague images from prompts, or generate short stylistic writings from a short prompt, were popping up with University URLs? Oftentimes the queues on these programs were hours long, sometimes eventually days or weeks or months long, because of how unexpectedly popular this concept was to the general public. Suddenly overnight, all over social media, everyone and their grandma, and not just high level programming and arts students, knew this was possible, and of course, everyone wanted in. Automated art and writing, isn't that neat? And of course, investors saw dollar signs. Simply scale up the process, scrape the entire web for data to train the model without advertising that you're using ALL material, even copyrighted and personal materials, and sell the resulting algorithm for big money. As usual, startup investors ruin every new technology the moment they can access it.

To most people, it seemed like this magic tech popped up overnight, and before it became known that the art assets on later models were stolen, even I had fun with them. I knew how learning algorithms worked, if you're going to have a computer make images and text, it has to be shown what that is and then try and fail to make its own until it's ready. I just, rather naively as I was still in my early 20s, assumed that everything was above board and the assets were either public domain or fairly licensed. But when the news did came out, and when corporations started unethically implementing "AI" in everything from chatbots to search algorithms to asking their tech staff to add AI to sliced bread, those who were impacted and didn't know and/or didn't care where generative algorithms came from wanted them GONE. And like, I can't blame them. But I also quietly acknowledged to myself that getting rid of a whole technology is just neither possible nor advisable. The cat's already out of the bag, the genie has left its bottle, the Pandorica is OPEN. If we tried to blanket ban what people call AI, numerous industries involved in making lives better would be impacted. Because unfortunately the same tool that can edit selfies into revenge porn has also been used to identify cancer cells in patients and aided in decoding dead languages, among other things.

When, in Greek myth, Prometheus gave us the gift of fire, he gave us both a gift and a curse. Fire is so crucial to human society, it cooks our food, it lights our cities, it disposes of waste, and it protects us from unseen threats. But fire also destroys, and the same flame that can light your home can burn it down. Surely, there were people in this mythic past who hated fire and all it stood for, because without fire no forest would ever burn to the ground, and surely they would have called for fire to be given back, to be done away with entirely. Except, there was no going back. The nature of life is that no new element can ever be undone, it cannot be given back.

So what's the way forward, then? Like, surely if I can write a multi-paragraph think piece on Tumblr.com that next to nobody is going to read because it's long as sin, about an unpopular topic, and I rarely post original content anyway, then surely I have an idea of how this cyberpunk dystopia can be a little less.. Dys. Well I do, actually, but it's a long shot. Thankfully, unlike business majors, I actually had to take a cyber ethics course in university, and I actually paid attention. I also passed preschool where I learned taking stuff you weren't given permission to have is stealing, which is bad. So the obvious solution is to make some fucking laws to limit the input on data model training on models used for public products and services. It's that simple. You either use public domain and licensed data only or you get fined into hell and back and liable to lawsuits from any entity you wronged, be they citizen or very wealthy mouse conglomerate (suing AI bros is the only time Mickey isn't the bigger enemy). And I'm going to be honest, tech companies are NOT going to like this, because not only will it make doing business more expensive (boo fucking hoo), they'd very likely need to throw out their current trained datasets because of the illegal components mixed in there. To my memory, you can't simply prune specific content from a completed algorithm, you actually have to redo rhe training from the ground up because the bad data would be mixed in there like gum in hair. And you know what, those companies deserve that. They deserve to suffer a punishment, and maybe fold if they're young enough, for what they've done to creators everywhere. Actually, laws moving forward isn't enough, this needs to be retroactive. These companies need to be sued into the ground, honestly.

So yeah, that's the mess of it. We can't unlearn and unpublicize any technology, even if it's currently being used as a tool of exploitation. What we can do though is demand ethical use laws and organize around the cause of the exclusive rights of individuals to the content they create. The screenwriter's guild, actor's guild, and so on already have been fighting against this misuse, but given upcoming administration changes to the US, things are going to get a lot worse before thet get a little better. Even still, don't give up, have clear and educated goals, and focus on what you can do to affect change, even if right now that's just individual self-care through mental and physical health crises like me.

[praeobscura]

Cross-posting from my mention of this on Pillowfort.

Yesterday, Draft2Digital (which now includes Smashwords) sent out an email with a, frankly, very insulting survey. It would be such a shame if a link to that survey without the link trackers were to circulate around Tumblr dot Com.

The survey has eight multiple choice questions and (more importantly) two long-form text response boxes.

The survey is being run from August 27th, 2024 to September 3rd, 2024. If you use Draft2Digital or Smashwords, and have not already seen this in your associated email, you may want to read through it and send them your thoughts.

Plain text for the image below the cut:

D2D AI Rights Licensing Survey:

This survey is going to all authors and publishers of Draft2Digital and Smashwords. We seek feedback from our community regarding potential rights licensing opportunities.

This survey is anonymous and should take only about five minutes to complete. Survey responses will be accepted until Tuesday, September 3, 2024.

Introduction:

In recent months, a growing number of AI developers have begun approaching large publishers, seeking to license books for the purpose of training Large Language Models (LLMs).

Books – both fiction and non-fiction – are highly prized for LLM training due to their long form narrative structures which teach Natural Language Processing.

Common uses for these LLMs include powering personal productivity applications such as customer service chatbots, virtual assistants, and the drafting of written communications for marketing, customer service, and internal communications.

What are your AI training rights worth? There’s no hard and fast rule to answer this question because each licensing deal is different.

Some early compensation models for news publishers suggest the equivalent of about $100 per license for LLM training rights for a 75,000-word novel, which works out to a little over 1/10th of a cent per word.

Some experts believe training rights for long-form book content justifies higher compensation for training rights than news content.

[bharatpatel1061]

Beyond Scripts: How AI Agents Are Replacing Hardcoded Logic

Introduction: Hardcoded rules have long driven traditional automation, but AI agents represent a fundamental shift in how we build adaptable, decision-making systems. Rather than relying on deterministic flows, AI agents use models and contextual data to make decisions dynamically—whether in customer support, autonomous vehicles, or software orchestration. Content:

This paradigm is powered by reinforcement learning, large language models (LLMs), and multi-agent collaboration. AI agents can independently evaluate goals, prioritize tasks, and respond to changing conditions without requiring a full rewrite of logic. For developers, this means less brittle code and more resilient systems.

In applications like workflow automation or digital assistants, integrating AI agents allows systems to "reason" through options and select optimal actions. This flexibility opens up new possibilities for adaptive systems that can evolve over time.

You can explore more practical applications and development frameworks on this AI agents service page.

When designing AI agents, define clear observation and action spaces—this improves interpretability and debugging during development.

[mariacallous]

Stories about AI-generated political content are like stories about people drunkenly setting off fireworks: There’s a good chance they’ll end in disaster. WIRED is tracking AI usage in political campaigns across the world, and so far examples include pornographic deepfakes and misinformation-spewing chatbots. It’s gotten to the point where the US Federal Communications Commission has proposed mandatory disclosures for AI use in television and radio ads.

Despite concerns, some US political campaigns are embracing generative AI tools. There’s a growing category of AI-generated political content flying under the radar this election cycle, developed by startups including Denver-based BattlegroundAI, which uses generative AI to come up with digital advertising copy at a rapid clip. “Hundreds of ads in minutes,” its website proclaims.

BattlegroundAI positions itself as a tool specifically for progressive campaigns—no MAGA types allowed. And it is moving fast: It launched a private beta only six weeks ago and a public beta just last week. Cofounder and CEO Maya Hutchinson is currently at the Democratic National Convention trying to attract more clients. So far, the company has around 60, she says. (The service has a freemium model, with an upgraded option for $19 a month.)

“It’s kind of like having an extra intern on your team,” Hutchinson, a marketer who got her start on the digital team for President Obama’s reelection campaign, tells WIRED. We’re sitting at a picnic table inside the McCormick Place Convention Center in Chicago, and she’s raising her voice to be heard over music blasting from a nearby speaker. “If you’re running ads on Facebook or Google, or developing YouTube scripts, we help you do that in a very structured fashion.”

BattlegroundAI’s interface asks users to select from five different popular large language models—including ChatGPT, Claude, and Anthropic—to generate answers; it then asks users to further customize their results by selecting for tone and “creativity level,” as well as how many variations on a single prompt they might want. It also offers guidance on whom to target and helps craft messages geared toward specialized audiences for a variety of preselected issues, including infrastructure, women’s health, and public safety.

BattlegroundAI declined to provide any examples of actual political ads created using its services. However, WIRED tested the product by creating a campaign aimed at extremely left-leaning adults aged 88 to 99 on the issue of media freedom. “Don't let fake news pull the wool over your bifocals!” one of the suggested ads began.

BattlegroundAI offers only text generation—no AI images or audio. The company adheres to various regulations around the use of AI in political ads.

“What makes Battleground so well suited for politics is it’s very much built with those rules in mind,” says Andy Barr, managing director for Uplift, a Democratic digital ad agency. Barr says Uplift has been testing the BattlegroundAI beta for a few weeks. “It’s helpful with idea generation,” he says. The agency hasn’t yet released any ads using Battleground copy yet, but it has already used it to develop concepts, Barr adds.

I confess to Hutchinson that if I were a politician, I would be scared to use BattlegroundAI. Generative AI tools are known to “hallucinate,” a polite way of saying that they sometimes make things up out of whole cloth. (They bullshit, to use academic parlance.) I ask how she’s ensuring that the political content BattlegroundAI generates is accurate.

“Nothing is automated,” she replies. Hutchinson notes that BattlegroundAI’s copy is a starting-off point, and that humans from campaigns are meant to review and approve it before it goes out. “You might not have a lot of time, or a huge team, but you’re definitely reviewing it.”

Of course, there’s a rising movement opposing how AI companies train their products on art, writing, and other creative work without asking for permission. I ask Hutchinson what she’d say to people who might oppose how tools like ChatGPT are trained. “Those are incredibly valid concerns,” she says. “We need to talk to Congress. We need to talk to our elected officials.”

I ask whether BattlegroundAI is looking at offering language models that train on only public domain or licensed data. “Always open to that,” she says. “We also need to give folks, especially those who are under time constraints, in resource-constrained environments, the best tools that are available to them, too. We want to have consistent results for users and high-quality information—so the more models that are available, I think the better for everybody.”

And how would Hutchinson respond to people in the progressive movement—who generally align themselves with the labor movement—objecting to automating ad copywriting? “Obviously valid concerns,” she says. “Fears that come with the advent of any new technology—we’re afraid of the computer, of the light bulb.”

Hutchinson lays out her stance: She doesn’t see this as a replacement for human labor so much as a way to reduce grunt work. “I worked in advertising for a very long time, and there's so many elements of it that are repetitive, that are honestly draining of creativity,” she says. “AI takes away the boring elements.” She sees BattlegroundAI as a helpmeet for overstretched and underfunded teams.

Taylor Coots, a Kentucky-based political strategist who recently began using the service, describes it as “very sophisticated,” and says it helps identify groups of target voters and ways to tailor messaging to reach them in a way that would otherwise be difficult for small campaigns. In battleground races in gerrymandered districts, where progressive candidates are major underdogs, budgets are tight. “We don’t have millions of dollars,” he says. “Any opportunities we have for efficiencies, we’re looking for those.”

Will voters care if the writing in digital political ads they see is generated with the help of AI? “I'm not sure there is anything more unethical about having AI generate content than there is having unnamed staff or interns generate content,” says Peter Loge, an associate professor and program director at George Washington University who founded a project on ethics in political communication.

“If one could mandate that all political writing done with the help of AI be disclosed, then logically you would have to mandate that all political writing”—such as emails, ads, and op-eds—“not done by the candidate be disclosed,” he adds.

Still, Loge has concerns about what AI does to public trust on a macro level, and how it might impact the way people respond to political messaging going forward. “One risk of AI is less what the technology does, and more how people feel about what it does,” he says. “People have been faking images and making stuff up for as long as we've had politics. The recent attention on generative AI has increased peoples' already incredibly high levels of cynicism and distrust. If everything can be fake, then maybe nothing is true.”

Hutchinson, meanwhile, is focused on her company’s shorter-term impact. “We really want to help people now,” she says. “We’re trying to move as fast as we can.”

[govindhtech]

Pegasus 1.2: High-Performance Video Language Model

Pegasus 1.2 revolutionises long-form video AI with high accuracy and low latency. Scalable video querying is supported by this commercial tool.

TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock will soon provide Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models. Amazon Bedrock, a managed service, lets developers access top AI models from leading organisations via a single API. With seamless access to TwelveLabs' comprehensive video comprehension capabilities, developers and companies can revolutionise how they search for, assess, and derive insights from video content using AWS's security, privacy, and performance. TwelveLabs models were initially offered by AWS.

Introducing Pegasus 1.2

Unlike many academic contexts, real-world video applications face two challenges:

Real-world videos might be seconds or hours lengthy.

Proper temporal understanding is needed.

TwelveLabs is announcing Pegasus 1.2, a substantial industry-grade video language model upgrade, to meet commercial demands. Pegasus 1.2 interprets long films at cutting-edge levels. With low latency, low cost, and best-in-class accuracy, model can handle hour-long videos. Their embedded storage ingeniously caches movies, making it faster and cheaper to query the same film repeatedly.

Pegasus 1.2 is a cutting-edge technology that delivers corporate value through its intelligent, focused system architecture and excels in production-grade video processing pipelines.

Superior video language model for extended videos

Business requires handling long films, yet processing time and time-to-value are important concerns. As input films increase longer, a standard video processing/inference system cannot handle orders of magnitude more frames, making it unsuitable for general adoption and commercial use. A commercial system must also answer input prompts and enquiries accurately across larger time periods.

Latency

To evaluate Pegasus 1.2's speed, it compares time-to-first-token (TTFT) for 3–60-minute videos utilising frontier model APIs GPT-4o and Gemini 1.5 Pro. Pegasus 1.2 consistently displays time-to-first-token latency for films up to 15 minutes and responds faster to lengthier material because to its video-focused model design and optimised inference engine.

Performance

Pegasus 1.2 is compared to frontier model APIs using VideoMME-Long, a subset of Video-MME that contains films longer than 30 minutes. Pegasus 1.2 excels above all flagship APIs, displaying cutting-edge performance.

Pricing

Cost Pegasus 1.2 provides best-in-class commercial video processing at low cost. TwelveLabs focusses on long videos and accurate temporal information rather than everything. Its highly optimised system performs well at a competitive price with a focused approach.

Better still, system can generate many video-to-text without costing much. Pegasus 1.2 produces rich video embeddings from indexed movies and saves them in the database for future API queries, allowing clients to build continually at little cost. Google Gemini 1.5 Pro's cache cost is $4.5 per hour of storage, or 1 million tokens, which is around the token count for an hour of video. However, integrated storage costs $0.09 per video hour per month, x36,000 less. Concept benefits customers with large video archives that need to understand everything cheaply.

Model Overview & Limitations

Architecture

Pegasus 1.2's encoder-decoder architecture for video understanding includes a video encoder, tokeniser, and big language model. Though efficient, its design allows for full textual and visual data analysis.

These pieces provide a cohesive system that can understand long-term contextual information and fine-grained specifics. It architecture illustrates that tiny models may interpret video by making careful design decisions and solving fundamental multimodal processing difficulties creatively.

Restrictions

Safety and bias

Pegasus 1.2 contains safety protections, but like any AI model, it might produce objectionable or hazardous material without enough oversight and control. Video foundation model safety and ethics are being studied. It will provide a complete assessment and ethics report after more testing and input.

Hallucinations

Occasionally, Pegasus 1.2 may produce incorrect findings. Despite advances since Pegasus 1.1 to reduce hallucinations, users should be aware of this constraint, especially for precise and factual tasks.

[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.