The podcast discusses the risks and rewards of open sourcing large language models. They cover topics such as the potential harms of LLMs, downsizing responsible AI teams, auditing biased and harmful content, and prioritizing privacy and democratizing AI development.
Large language models (LLMs) pose risks such as generating disinformation and hate speech, impacting marginalized groups disproportionately.
Openness in LLMs is crucial for independent audits of data sets, but a balance is needed to prevent misuse and dissemination of problematic applications.
Deep dives
Risks and Rewards of Large Language Models
Large language models (LLMs) like ChachiPT and LAMA have shown great potential, but they come with risks. LLMs can be used to generate disinformation and hate speech on a large scale, posing a threat to the civic fabric of a country. Concerns are raised about the lack of understanding of LLM capabilities and the potential for misuse by intelligence agencies. The risks of exclusion and discrimination are also highlighted, as LLMs may disproportionately affect marginalized groups. The rewards of LLMs include their application in video games, virtual assistants, and increasing productivity in various industries. However, biases in LLMs can lead to unfair outcomes, like racial bias in loan evaluations by banks. The rush to develop LLMs and downsizing of responsible AI teams are causing concerns in the industry.
Challenges of Openness and Data Sets
The open nature of large language models (LLMs) raises questions about safety, privacy, and responsibility. Data sets used to train LLMs, such as the Common Crawl, contain both positive and negative elements of humanity, including explicit and harmful content. Auditing data sets and ensuring they are free from biases, racism, and sexism is vital. It's essential to make companies transparent about their data sources and to have regulations that allow independent scrutiny. While openness is important, there's a need to strike a balance to prevent misuse by bad actors and dissemination of problematic applications. The term 'data swamps' highlights the negative aspects of these large-scale data sets.
The Spectrum of Openness and Collaboration
The debate around large language models (LLMs) being open source versus closed source is better understood as a spectrum of openness. Policy makers need to consider what works for each model and take into account factors such as code, data sets, documentation, weighting, and usage terms. Openness can foster collaboration, democratization of access, and academic research. Initiatives like Big Science demonstrate the power of open communities working together to develop LLMs that are more representative of society. Privacy-preserving alternatives to widely used LLMs, such as GPT for All, are gaining traction to address privacy concerns and give users more control. The key is to find a balance between open and closed to ensure the positive potential of LLMs while mitigating risks.
Are today’s large language models too hot to handle? Bridget Todd digs into the risks and rewards of open sourcing the tech that makes ChatGPT talk.
In their competitive rush to release powerful LLMs to the world, tech companies are fueling a controversy about what should and shouldn’t be open in generative AI.
In this episode, we meet open source research communities who have stepped up to develop more responsible machine learning alternatives.
David Evan Harris worked at Meta to make AI more responsible and now shares his concerns about the risks of open large language models for disinformation and more.
Abeba Birhane is a Mozilla advisor and cognitive scientist who calls for openness to facilitate independent audits of large datasets sourced from the internet.
Sasha Luccioni is a researcher and climate lead at Hugging Face who says open source communities are key to developing ethical and sustainable machine learning.
Andriy Mulyar is co-founder and CTO of Nomic, the startup behind the open source chatbot GPT4All, an offline and private alternative to ChatGPT.
IRL: Online Life is Real Life is an original podcast from Mozilla, the non-profit behind Firefox. In Season 7, host Bridget Todd talks to AI builders that put people ahead of profit.
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