Source URL: https://www.tomtunguz.com/partnering-with-lance/
Source: Tomasz Tunguz
Title: The Multimodal Lake House : Partnering with Lance
Feedly Summary: Remember when you took a family photo & Ghibli-styled it?
Or that vibe coding session, when you pasted a screenshot of the browser so the AI can help you debug some Javascript?
Today, we expects AI to be able to hear, see, & read. This is why multimodal is the future of AI.
Multimodal data means using text, images, video, sound, even three-dimensional shapes with AI.
These are magical user experiences. But they aren’t easy to build. Data pipelines must be built to manage larger files. Embeddings need to be extracted from these unstructured files in ways that don’t explode compute costs.
Multimodal data is orders of magnitude larger than text : the average PDF is 10x larger than a text file & a YouTube video is roughly a million times larger.
Plus, multimodal data doesn’t change one part of the data pipeline : engineers must process the data at each step of the AI stack, from model training to real-time serving & downstream analysis at petabyte scale.
We kept hearing about these problems from builders & in the same breath, about a company that solves them.
Founded by Chang She, creator of the Pandas Library, & Lei Xu, core contributor to HDFS, the Hadoop file system, LanceDB has a tremendous heritage within the data ecosystem.
RunwayML, Midjourney, WorldLabs, ByteDance, UBS, Harvey, & Hex use Lance. We admire the technology so much, we are using it internally at Theory as part of our AI stack & we’re excited to partner with Chang & Lei to bring multimodal AI to builders & users everywhere.
Read more about the multimodal lake house & the kinds of AI it can enable.
AI Summary and Description: Yes
Summary: The text discusses the future of AI through the lens of multimodal data, emphasizing its potential to enhance user experiences. It highlights the challenges of managing large data pipelines and introduces LanceDB as a solution, showcasing its significance within the broader data ecosystem and its adoption by notable companies.
Detailed Description: The content describes the emerging trend of multimodal AI, where artificial intelligence integrates various data types such as text, images, audio, and videos to enrich user experiences. This text is particularly relevant for professionals involved in AI and infrastructure security as it touches on the complexities of handling large datasets and the technology designed to address these challenges.
– **Multimodal AI**: The future of AI is focused on processing and utilizing multimodal data, which encompasses multiple forms of information, enhancing the engagement and capabilities of AI systems.
– **Data Size Challenges**: The average size of multimodal data is significantly larger than traditional text data, posing issues for data management and processing. For instance:
– A PDF can be approximately 10 times larger than a text file.
– A single YouTube video can be nearly a million times larger than a text document.
– **Data Pipeline Efficiency**: The complexity of multimodal data necessitates robust data pipelines to manage these larger files effectively, ensuring that engineers can efficiently process data at each step of the AI stack from model training to real-time analysis.
– **LanceDB’s Role**: The introduction of LanceDB, a product founded by notable figures in the data ecosystem, serves as a significant technological advancement aimed at solving these data management issues. This tool is already being utilized by various industry leaders, indicating its effectiveness and reliability.
– **Industry Adoption**: Companies such as RunwayML, Midjourney, and ByteDance are leveraging LanceDB, underscoring its practical importance in the current landscape of AI development.
In summary, the discussion surrounding multimodal AI and the introduction of a specialized tool like LanceDB provides critical insights into the ongoing evolution of AI technologies and the corresponding security, privacy, and compliance challenges that arise from managing such extensive and varied datasets. This information is vital for professionals tasked with ensuring security measures within AI applications and data management frameworks.