s large, foundational models get larger and larger, they start to act the same. The differentiator is in the data.
Alembic Technologies, a San Francisco-based AI lab, is dedicated to solving the problem of AI homogeneity, making models that are actually distinct from one another, founder and CEO Tomas Puig told The Deep View.
“As we see the capabilities of these models converge … this creates a very large problem for corporations,” said Puig. “While I think generalized intelligence is really good, where we've really focused on is building the best intelligence in the world from private data sets.”
The startup, which develops custom AI models for enterprises using their proprietary data, announced a $145 million Series B funding round. The round multiplies the company’s valuation more than 15-fold, bringing it to $645 million, Puig said.
Alembic’s focus lies specifically in causal AI models, or those that think using cause and effect.
- For example, using a company’s own data, a causal model may analyze which kinds of marketing perform best for a company and why those tactics do well.
- “The benefits of the cause and effect side of the house is you actually know what you can affect and what you cannot, what is worth pursuing and what's not worth pursuing,” Puig said.
Additionally, Alembic announced that it is deploying a DGX AI Supercomputing cluster running the NVIDIA AI Enterprise software suite. The architecture, to be constructed in California, marks the company’s second such cluster, the first being in Virginia. Given that it’s building models with private and sensitive data, owning its own hardware is key in ensuring privacy, Puig said.
“For our clients at their security level, they want to know that literally anything we compute never leaves our own private house,” said Puig. “We work with the type of data that nobody in the world wants to give somebody else access to.”