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A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company, founded in 2013 by the original creators of Apache Spark. [1] [4] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models.
DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. [1] [2] [3] It is a mixture-of-experts transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. [4]
A significant aspect of the Pareto frontier in economics is that, at a Pareto-efficient allocation, the marginal rate of substitution is the same for all consumers. [5] A formal statement can be derived by considering a system with m consumers and n goods, and a utility function of each consumer as = where = (,, …,) is the vector of goods, both for all i.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Databricks’ $43 billion valuation is up from the last time the company sought capital. In 2021, Databricks collected $1.6 billion in a series H round led by Counterpoint Global. It was valued at ...
Price-vector: a vector assigning a price (a real number) to each item. Bang-for-buck ratio: for an agent i and an object o, it is the ratio of the agent's valuation of the item, to the item price: v ij / p j. Maximum bang-for-buck (MBB) set: for an agent i, it is the set of objects maximizing his bang-for-buck ratio (given a price-vector p).
The seven tools are: [3] [4] [5] Cause-and-effect diagram (also known as the "fishbone diagram" or Ishikawa diagram) Check sheet; Control chart; Histogram; Pareto chart; Scatter diagram; Stratification (alternatively, flow chart or run chart) The designation arose in postwar Japan, inspired by the seven famous weapons of Benkei. [6]