Navigating the AI Future: ARTA's Financial Model, LANTA Supercomputers, and the Impact of LLaMa 2
Updated: Jul 31
In a world that is increasingly driven by the advances of Artificial Intelligence, the way we do business and navigate the digital terrain is constantly evolving. The boundaries of what we once thought possible are being redrawn as we find new ways to leverage these powerful technologies. This week, in light of recent rate limit restrictions on Twitter's API affecting our regular trend monitoring, we're shifting our lens to focus on three groundbreaking developments in the field of AI: the emergence of ARTA's financial model, the development of LANTA Supercomputers, and the transformational impact of open-source LLaMa 2.
ARTA: Paving the Way for Modern AI Businesses in the Internet Era
The evolution of the digital landscape has transformed the way we think about the economics of service provision. A pivotal moment in this evolution was the emergence of the Average Revenue Per User (ARPU) metric in the telecommunications industry. The ARPU metric has allowed service providers to pivot from traditional sales models towards more sustainable, subscription-based business models that emphasize customer retention and quality of service.
Fast forward to today, in the era of modern internet businesses, we're witnessing a new financial cornerstone emerge — the Average Revenue per Tokenized API Interaction (ARTA). Much like its predecessor, ARPU, ARTA is reshaping the way we understand and leverage technology.
In the sphere of AI services, APIs serve as critical connectors, enabling seamless communication between systems. Monetizing these essential tools, however, has often been a complex task due to the multifaceted nature of API interactions that span multiple users, services, and data types.
ARTA steps in to bridge this gap. By focusing on the revenue generated per tokenized API interaction, this novel financial model allows businesses to track and monetize each API call effectively. It casts a light on the interaction's economic value, narrowing the gap between operational realities and financial implications.
But ARTA doesn't stop there. It borrows from the subscription-based principles of ARPU, grounding its approach in the generation of a stable and recurring revenue stream. The aim is to maintain a low churn rate and roll over revenue, prioritizing quality control and service uptime to build user trust. This strategy fosters a more economically sustainable and technologically integrated future for AI and internet-based businesses.
Average Cost on Our PulsarWave Project
Daily expenses on OpenAI's API average around $1, leveraging GPT-3.5 and GPT-4 iterations for six hours daily. Costs fluctuate based on the API processing success rate, a variable we're strategizing to optimize in the upcoming PulsarWave version. Notably, our initial OpenAI API subscription and the hackathon period have brought us a total credit of $43.
Our usage of OpenAI's API reached its peak on July 2, 2023, as we employed a GPT-4 API call via our local machine, utilizing a ChatBot frontend known as "hydrabot". The hydrabot has the unique advantage of being able to leverage multiple AI model backend APIs. Specifically, while OpenAI's ChatGPT imposes a limit of 25 GPT-4 prompts every 3 hours, the hydrabot enables us to bypass this constraint due to an important project that necessitated continuous GPT-4 prompting.
Unlike the regular ChatGPT, the hydrabot does not impose a usage cap, although its per-token charge may render it more expensive compared to a fixed monthly rate. For instance, with our usage amounting to $4 a day, the total monthly cost would be approximately $120, substantially higher than the $20 flat rate offered by the monthly pro account.
Since its original deployment in May 2023, PulsarWave's primary cloud platform saw daily expenses rise from approximately $0 (due to the waiver period) to $4, a result of the end of the waived period on our compute engine. As of June 4, 2023, we transitioned to the current E2 platform, effectively reducing our daily expenses to $0.84. Additionally, we received a generous credit of $100 during the hackathon period.
Thanks to its ongoing waiver period, our Amazon Web Services cloud platform hasn't incurred significant usage expenses yet. Additionally, we were granted a $200 credit during the hackathon period.
As previously addressed, Twitter has imposed a rate limit, making it impossible for the free API tier to retrieve any information unless we upgrade to the basic tier at a cost of $100 a month (around $3.33 daily). However, we are working on techniques to bypass the need for Twitter, while ensuring that in the final version of PulsarWave, Twitter sourcing remains an available option for users.
Optimized ARTA: The New Efficiency Frontier in Digital Interactions
In the ever-evolving digital landscape, efficiency and value remain vital guiding principles. In a world where Application Programming Interfaces (APIs) mediate a significant part of our digital interactions, measuring and enhancing the financial return from each of these interactions has become crucial. Enter the concept of Average Revenue per Tokenized API Interaction, or ARTA – a novel way of looking at digital efficiency and effectiveness.
ARTA is not just a metric; it represents a paradigm shift in the digital world's economic dynamics. More than measuring the revenue generated from each interaction processed through an API, ARTA encapsulates the relentless pursuit of value, efficiency, and customer success in each digital interaction.
The journey towards optimized ARTA is not a linear one; it requires a relentless commitment to innovation and customer-centricity. Optimizing ARTA means striking the right balance between cost-effectiveness and delivering exceptional value. Every increment in ARTA optimization translates into a higher value for customers and better returns for businesses.
While minimizing costs to maximize margins has always been an essential business principle, it's not our primary objective. Moreover, driving efficiency without considering the service cost and environmental impact isn't responsible or sustainable. This is where optimizing Average Revenue per Tokenized API Interaction (ARTA) becomes important. We're striving to strike the right balance between operational efficiency, cost-effectiveness, and environmental responsibility. In our PulsarWave project, for instance, we've leveraged genetic algorithms, Elixir, to determine the optimal parameters and combinations of AI fleets. This approach aims to deliver the most sustainable and efficient services to our users, thereby optimizing ARTA in a responsible and sustainable way.
The path to optimized ARTA is paved with continuous innovation and a dedicated focus on maximizing the value of every tokenized interaction. This journey isn't merely about making processes more efficient; it's about transforming the way we perceive and deliver value in the digital world.
As we move further into a future marked by digital ubiquity, the significance of optimizing ARTA is expected to amplify. Organizations that can derive maximum value from every tokenized interaction will be the ones leading the charge in this new era of digital efficiency.
Carbon Footprint and Power Consumption in LLaMa2: [source]
Carbon Footprint and Power Consumption Comparative Study between OPT-175B, BLOOM-175B and LLaMa (version 1) several models: [source]
Our commitment to optimizing ARTA isn't just about delivering superior, cost-effective services; it's about upholding a promise embodied in the tagline "Your Goals, Our Commitment: Optimized ARTA". It symbolizes the unyielding dedication to helping customers extract the most value out of every interaction, delivering better outcomes at lower costs.
The concept of ARTA and its optimization isn't just transforming how we view digital interactions, it's redefining success in the digital world. Embracing ARTA means embracing a future where every interaction counts, every token is valuable, and every effort is geared towards driving success in this new efficiency frontier.
The pursuit of optimized ARTA is more than a business strategy; it's a mission to set new benchmarks in digital interaction efficiency and value. With ARTA at the center, a future is envisioned where every interaction is not just a routine process, but a step towards higher efficiency, greater value, and enhanced success. This is the power and promise of optimized ARTA.
Updating on the Development of our LANTA Supercomputer Project and Outlining Plans to Employ LLaMa 2 as our Core Model for Geopolitical Analysis
In a move to open-source AI, Meta has unveiled LLaMa 2, a free-to-use, large language model aimed at competing with OpenAI's popular ChatGPT. The release comprises a suite of AI models, including different sizes of LLaMa 2 and a chatbot variant similar to ChatGPT. Unlike its competitors, however, LLaMa 2 must be downloaded through Meta’s launch partners - Microsoft Azure, Amazon Web Services, and Hugging Face, offering both closed-source and open-source options to users.
The shift to open-source presents opportunities but is not without challenges. Meta has not disclosed the data set used to train LLaMa 2, raising questions about the presence of copyrighted works or personal data. Moreover, like other large language models, LLaMa 2 is susceptible to generating false and offensive content. However, the company believes that by letting developers and companies experiment with the model, it can glean vital insights to enhance the model's safety, efficiency, and lessen its bias.
Even though LLaMa 2's performance does not yet match that of OpenAI's latest language model, GPT-4, experts believe its open-source nature and customizability could lead to quicker development of products and services. Furthermore, its open-source status allows researchers and developers to probe the model for security flaws, potentially making it safer than proprietary models, and opens doors for in-depth study of AI models' biases, ethics, and efficiency.
Jupyter notebook testing performance between CPU vs GPU on LANTA
At the heart of our technological strategy lies the intention to utilize LLaMa 2 as our base model for a Large Language Model (LLM) specialized in geopolitical analysis. We believe this novel approach will allow us to harness the advanced capabilities of AI for a more insightful understanding of global political dynamics.
Our current efforts have been centered on putting our LANTA Supercomputer project to the test. We have utilized Jupyter notebooks to conduct a comparative study between CPU and GPU performance in AI tasks. The results have been overwhelmingly in favor of GPU, which demonstrated a staggering speed of processing that was 500 times faster than CPU.
Snapshot of the Local File Directory for LLaMa 7b Ready for Submission to the LANTA Supercomputer
In a major development, we have successfully downloaded the base model of LLaMa 2 (7b) into our LANTA Supercomputer. This marks a significant step as we delve into the exploration of a production environment, all the while assessing the model's capabilities.
As we progress, our journey of testing and retraining the LLaMa 2 model will be documented comprehensively. The aim is to construct a clear, concise guide on how to most effectively utilize Large Language Models in social science objectives, specifically in the realm of geopolitical risk analysis. We are optimistic that these advances will significantly enhance our understanding of geopolitical dynamics and further our ability to provide astute and timely insights.
We wish to inform you that our usual weekly trend monitoring radar is on a brief hiatus this week. This pause is a necessary step to ensure we continue providing you with the best possible insights.
But don't worry! We won't leave you without exciting updates. Instead of the regular trend snapshot, we are thrilled to share an exclusive report detailing our progress in the exciting realm of AI experimentation. We are pushing the boundaries of what's possible, and we can't wait to bring you along on this journey.
We appreciate your understanding and look forward to resuming our weekly trend monitoring radar in the near future. Until then, stay tuned for the latest on our AI advancements!