Is Generative AI Under or Overhyped? (Vol. 3)
Gartner says AI is about to turn the corner to overhyped; Seth Godin says AI is underhyped and wildly misunderstood. Know both sides now in {The, Weekly, Vector}.
Each week, Vector Database Central shares short yet big ideas about vector databases and generative AI.
Is Generative AI OVERHYPED?
Gartner and the Wall Street Journal recently reported that AI may soon turn the corner from high expectations to early disillusionment based on "VC propaganda" that user experience hasn't yet matched – and that the air coming out of this hype cycle is reflected in a reality check for AI startup funding. ChatGPT and Midjourney have seen a drop in monthly visitors, and the GenAI writing tool Jasper had a round of layoffs in July. One VC noted that "We've moved from a moment of 'How big can this be?' to 'How do we make it work?'"
Read AI Startup Buzz Is Facing a Reality Check in the Wall Street Journal and the latest Gartner emerging technology hype cycle here.
Is Generative AI UNDERHYPED?
Seth Godin thinks AI is underhyped and wildly misunderstood.
The best-selling author of The Purple Cow says that while many dismiss AI as “just like something else before it,” it’s underhyped because few understand its disruptive power due to its scale, speed, and price.
Godin says that AI is misunderstood because it’s persistent in the same way that we went from using our phones 4 minutes a day to 4 hours a day. That persistence of use is the key to why it remains misunderstood.
Watch Seth Godin on the AI Revolution. For more on “the persistent use of generative AI for creativity and leadership,” read The Joy of Generative AI Cocreation.
WHY NOT USE A CONVENTIONAL DATABASE FOR VECTORS?
“Why can’t I use a standard database for vectors? A standard database has rows and columns; in a vector database, you have arrays of numbers clustered together based on similarity, which you can query at low latency. This makes them ideal for generative AI applications. Relational databases like Postgres now have tools like PGVector to support vector functionality, and we will see many more jumping on this bandwagon.”
From Vector Databases - Are You On Board? by Robert Merlicek, CTO.
VECTOR INDEXES ARE A ROADMAP FOR VECTOR QUERIES
“Road maps are helpful when traveling because they take dense geographical information from the real world and condense it into an easily navigable sheet of paper (or smartphone display). In the same way travelers benefit from maps, vector databases benefit from the vector index.”
Read Vector Indexing: Roadmaps to Search in VDC Tech Corner.
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