๐๐ก๐ ๐๐ ๐ซ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐๐๐ญ๐๐ฅ๐ฒ๐ณ๐ข๐ง๐ ๐๐ง ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ ๐ฌ๐ญ๐๐๐ค ๐
Machine learning has dramatically advanced in recent years โ since the 2017 breakout paper โAttention is all you need,โ which laid the foundation for the transformer deep learning architecture, we have now reached a Cambrian explosion of AI research, with new papers being published every day and compounding at an astonishing pace.
๐๐ก๐ข๐ฌ ๐ญ๐๐๐ญ๐จ๐ง๐ข๐ ๐ฌ๐ก๐ข๐๐ญ ๐ข๐ง ๐๐ ๐ข๐ง๐ง๐จ๐ฏ๐๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐๐๐ญ๐๐ฅ๐ฒ๐ณ๐ข๐ง๐ ๐๐ง ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐ข๐ง ๐๐๐ญ๐ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ ๐๐๐ซ๐จ๐ฌ๐ฌ ๐ฆ๐๐ง๐ฒ ๐ฏ๐๐๐ญ๐จ๐ซ๐ฌ.
First, AI is powering the modern data stack, and incumbent data infrastructure companies have started incorporating AI functionalities for synthesis, retrieval, and enrichment within data management. Additionally, recognizing the strategic importance of the AI wave as a business opportunity, several incumbents have even released entirely new products to support AI workloads and AI-first users. For instance, many database companies now support embeddings as a data type, either as a new feature or standalone offering.
Next, data and AI are inextricably linked. Data continues to grow at a phenomenal rate to push the limits on current infrastructure tooling. The volume of generated data, especially unstructured data, is projected to skyrocket to 612 zettabytes by 2030, driven by the wave of ML/AI excitement and synthetic data produced by generative models across all modalities. (One zettabyte = one trillion gigabytes or one billion terabytes.) In addition to volume, data types and sources continue to grow in complexity and variety. Companies are responding by developing new hardware including more powerful processors (e.g., GPUs, TPUs), better networking hardware to facilitate efficient data movement, and next-gen storage devices.
Lastly, building on recent progress in ML and hardware, a new wave of AI-native and AI-embedded startups is emergingโthese companies either leverage AI/ML from the ground up or use it to augment their existing capabilities. Unfortunately, much of current data infrastructure and tooling is still not optimized for AI use cases. Similar to forcing a square peg into a round hole, AI engineers have had to create workarounds or hacks within their current infrastructure.
๐๐'๐ซ๐ ๐ฌ๐๐๐ข๐ง๐ ๐๐ง ๐๐ฆ๐๐ซ๐ ๐ข๐ง๐ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ ๐ฌ๐ญ๐๐๐ค ๐ฉ๐ฎ๐ซ๐ฉ๐จ๐ฌ๐-๐๐ฎ๐ข๐ฅ๐ญ ๐๐จ๐ซ ๐๐.
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Product Management, Marketing & Business Professional | Entrepreneur | Angel Investor
1moI suppose individual angel investors (who can write $10K to $500K cheques) can participate.