Responsible and Safe Large Language Models with AIQ.org
We are excited to introduce AIQ.org project, founded on Nebuli’s Augmented Intelligence Quotient (AIQ -pronounced â€œIQâ€) suite of cleansed and cited large language models (LLMs). AIQ is a critical element of our technology models that help businesses and institutions deploy responsible AI systems faster, significantly reducing bias, misinformation, and harmful content.
Businesses rely on AIQ to establish a standard that ensures their AI systems and data strategies are fair, transparent, and aligned with ethical codes. We are also integrating our LLMs with the upcoming AI regulatory frameworks, such as the EUâ€™s forthcoming legal framework on AI.
AIQ.org is our response to the increasing use of AI-based services, such as Generative AI and content recommendation systems, which pose dangerous outcomes through misinformation or bogus human-like interactions. Not to mention, the growing concerns over data privacy, security and the potential negative impact of AI bias on marginalised communities.
Understanding Large Language Models
LLMs are artificial intelligence systems that can process large amounts of text and speech data and generate human-like responses to prompts. Using machine learning techniques, these models are trained on enormous datasets of text data, such as books, articles, social media platforms and websites. Once trained, the model can generate reasonably coherent and grammatically correct language responding to specific human prompts.
How Do Large Language Models Solve Business Needs?
Well, this depends entirely on the application of your LLMs. For example, in SEO or consumer marketing campaigns, you may rely on a generic worldview that helps you produce content that appeals to a wider audience. Generative AI technologies like ChatGPT are quite good for such applications, though with ethical and accuracy concerns, which we discuss further below. However, these models are less comprehensive for highly specialised sectors, such as biotech and aerospace, or for business-critical applications. This is where Nebuli’s AIQ models come into play.
Most of the core team members have been involved in LLMs and NLUs for many years before launching Nebuli in 2019 through various academic and commercial projects, with a particular focus on expert-driven, nongeneric models. Since launch, we identified several duplicative data challenges and common problems businesses face, such as lack of explainability, limited ethical models, lack of citation scores with their LLMs and much more. Accordingly, we developed AIQ by building a suite of clean and fully cited large language models for specific verticals to support our customers and partners.
Our ambition has always been to open up AIQ into a public project as a fully independent, open-source framework that deals directly with the critical issues of the current models applied in LLMs and supports developers and startups globally. We have now started this process.
Merging Researcher.AI Project with AIQ.org Project
We launched our Researcher.AI project on April 24th 2023, focusing on the imminent COVID-19 crisis, to support researchers in monitoring and dealing with future outbreaks and other unforeseen emergencies, such as political instabilities and environmental catastrophes. COVID-19 will not be the last outbreak. Hence, we decided to add Researcher.AI datasets into AIQ so that it can be utilised within government and academic communities to observe emerging epidemiological trends that could support their efforts in preparing and planning well in advance, compared to what we have seen with COVID-19 to date.
Join and Support AIQ.org Project
AIQ.org‘s mission is to make AIQ readily available for individuals, teams and organisations of all sizes to assist them with their research and development projects, technology-based innovations, productivity challenges, augmented creativity and much more.
Our vision for AIQ.org is to evolve it over time into a decentralised network for repositories of large data models, LLMs, code libraries, design frameworks, tools, and algorithms that collectively contribute toward helping teams, startups, and enterprises build human-centric and responsible augmented intelligence applications.
As this is a community-led project, we invite your support and ideas. If you wish to participate or stay updated about this project, add your e-mail address below or follow us on LinkedIn, Twitter, Facebook, and Instagram.