Simon Willison, a practical expert on open source large language models (LLMs), brings clarity to their workings and limitations. The podcast explores misconceptions surrounding open source AI, ethical concerns of open source training, and democratizing access to LLMs. It also discusses the impact of AI on various professions and the benefits of open source in search engines. The hosts express admiration for Simon and delve into the complexity of the web three and crypto space. Simon's work with Data Set, an open source tool for exploring and publishing data, is also discussed.
Read more
AI Summary
Highlights
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Open source AI models increase accessibility and prevent monopolization, but security and privacy concerns persist for both open and closed models.
Playfulness and creativity in experimenting with AI models help understand their capabilities, vulnerabilities, and limitations.
The New York Times lawsuit highlights the challenge of balancing fair use, copyright, and ethical boundaries in training language models.
Balancing user privacy, intellectual property rights, and open access is crucial for the democratization and responsible development of open source AI models.
Deep dives
The Rise of Open Source AI Models
Open source AI models, such as llama and chat GPT, have become more accessible, allowing individuals to run them on their own devices. This increase in accessibility is crucial to prevent the technology from being monopolized by a small group of companies. However, concerns about security and privacy have arisen, with some labeling open source AI as 'unsecured AI.' The reality is that vulnerabilities and prompt injection attacks can occur in both open source and closed models, highlighting the need for robust security measures and ethical training practices.
The Importance of Play and Creativity
Playfulness and creativity have played significant roles in the development and testing of open source AI models. Researchers and users have discovered the models' capabilities, as well as vulnerabilities and limitations, through experimentation and inventive prompts. This allows for a better understanding of the technology and helps debunk misconceptions about AI's infallibility. The creative approaches taken to interact with the models highlight the need for careful consideration of security and potential misuses.
The Need for Fair Use and Ethical Training
The New York Times lawsuit against OpenAI raises important questions about fair use and the training of language models. It emphasizes the challenge of training models without infringing on copyright or licensing restrictions. While the lawsuit specifically addresses the use of copyrighted content, it also reveals the difficulty of defining fair use and the ethical boundaries of data usage. Striking a balance between user privacy, intellectual property rights, and open access to knowledge remains a key challenge in the AI community.
The Future of Open Source AI Models
The availability of open source AI models has democratized access to this technology and fosters innovation. However, challenges regarding security, training ethics, and fair use need to be addressed. Balancing user privacy, intellectual property rights, and creative exploration within the AI community is crucial to ensuring inclusivity and preventing monopolization. Continued research, responsible development, and collaborative efforts are necessary to shape the future of open source AI models.
The Power of Open Source Data and Vegan Models
Open source data, including Project Gutenberg, Wikipedia, and GitHub, contains a vast amount of information that can be used to train language models. Some people prefer to use models trained on public domain data, while others are comfortable with models trained on copyrighted data. There is a moral component to this, with some individuals choosing to be AI vegans and only use models trained on publicly available data. However, the challenge lies in fine-tuning these models with high-quality data and ensuring they learn to have high-quality conversations. There are efforts to collect and curate such data, but it is important to strike a balance between openness and copyright restrictions. It is anticipated that in the near future, models trained on public domain data will continue to advance, allowing for more exploration and experimentation.
The Democratization of AI and the Rise of Personal AI Assistants
AI models, like chat GPT, are becoming powerful personal assistants and tools for learning and exploration. They can assist with tasks such as searching the internet, brainstorming ideas, writing code, and answering specific questions. The key is to understand the limitations of these models and approach them with a sense of skepticism and creativity. User experience design and innovation will play a crucial role in harnessing the full potential of these models, as the current chat interface can be limiting and lacks discoverability. Personal AI assistants have the potential to enhance various human disciplines, providing people with an always-available teaching assistant that assists in tackling more ambitious tasks.
Challenges and Opportunities in Working with Language Models
Working with language models presents a unique set of challenges and opportunities. The current interface, such as chat, may not be ideal for certain tasks, and there is a need for user experience design innovation to make interactions more effective and intuitive. Developing a deep technical understanding of how these models work, combined with intuition gained through extensive usage, is key to harnessing their power effectively. Balancing the fine line between over-anthropomorphizing the models and understanding their limitations is crucial. The space of AI-powered language models is ever-evolving, morally ambiguous, fascinating, and provides ample room for experimentation and discovery.
Simon Willison joined Bryan and Adam to discuss a recent article maligning open source large language models. Simon has so much practical experience with LLMs, and brings so much clarity to what they can and can’t do. How do these systems work? How do they break? What are open and proprietary LLMs out there?
Recorded 1/15/2024
We've been hosting a live show weekly on Mondays at 5p for about an hour, and recording them all; here is the recording.
Simon posted a follow up blog article where he explains using MacWhisper and Claude to make his LLM pull out a few of his favorite quotes from this episode:
If we got something wrong or missed something, please file a PR! Our next show will likely be on Monday at 5p Pacific Time on our Discord server; stay tuned to our Mastodon feeds for details, or subscribe to this calendar. We'd love to have you join us, as we always love to hear from new speakers!
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
