Exclusive: $38 billion data and AI darling Databricks acquires security startup Okera
Around this time, a number of customers began asking Databricks about how to safely integrate large language models, or LLMs, internally and into their products, and how to maintain data privacy, security, and governance when training LLMs on their private data, Ghodsi said.
Okera solves these issues by using AI to automatically filter out sensitive data before it even makes it to the model-training stage, Ghodsi told Insider. In the past, Databricks has scooped up smaller startups like DataJoy, Cortex Labs, 8080 Labs, and Redash to improve its data analytics tooling and machine learning capabilities. It's funded these deals with a hefty coffer — to date, the company has raised $3.5 billion, with its 2021 Series H investment landing it anOkera will be a larger transaction for the acquirer.
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Gender differences in peer reviewed grant applications, awards, and amounts: a systematic review and meta-analysis - Research Integrity and Peer ReviewBackground Differential participation and success in grant applications may contribute to women’s lesser representation in the sciences. This study’s objective was to conduct a systematic review and meta-analysis to address the question of gender differences in grant award acceptance rates and reapplication award acceptance rates (potential bias in peer review outcomes) and other grant outcomes. Methods The review was registered on PROSPERO (CRD42021232153) and conducted in accordance with PRISMA 2020 standards. We searched Academic Search Complete, PubMed, and Web of Science for the timeframe 1 January 2005 to 31 December 2020, and forward and backward citations. Studies were included that reported data, by gender, on any of the following: grant applications or reapplications, awards, award amounts, award acceptance rates, or reapplication award acceptance rates. Studies that duplicated data reported in another study were excluded. Gender differences were investigated by meta-analyses and generalized linear mixed models. Doi plots and LFK indices were used to assess reporting bias. Results The searches identified 199 records, of which 13 were eligible. An additional 42 sources from forward and backward searches were eligible, for a total of 55 sources with data on one or more outcomes. The data from these studies ranged from 1975 to 2020: 49 sources were published papers and six were funders’ reports (the latter were identified by forwards and backwards searches). Twenty-nine studies reported person-level data, 25 reported application-level data, and one study reported both: person-level data were used in analyses. Award acceptance rates were 1% higher for men, which was not significantly different from women (95% CI 3% more for men to 1% more for women, k = 36, n = 303,795 awards and 1,277,442 applications, I2 = 84%). Reapplication award acceptance rates were significantly higher for men (9%, 95% CI 18% to 1%, k = 7, n = 7319 applications and 3324 awards, I2 = 63%
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