Xebia
MARCH 28, 2024
API Security doesn’t start with penetration testing. This isn’t to say testing isn’t important – it is! The following advice is based on my years of testing and monitoring for issues as a security engineer, and implementing APIs as a developer. And at multiple phases.
AWS Machine Learning - AI
MARCH 28, 2024
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.
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CIO
DECEMBER 14, 2023
AI and machine learning (ML) can do this by automating the design cycle to improve efficiency and output; AI can analyze previous designs, generate novel design ideas, and test prototypes, assisting engineers with rapid, agile design practices. To avoid halting AI processing mid-stream, modern storage solutions are needed prior to running AI.
CIO
SEPTEMBER 7, 2023
Specifically, existing storage solutions are inadequate. Organizations need novel storage capabilities to handle the massive, real-time, unstructured data required to build, train and use generative AI. Assessments and investments must include generative AI’s specific storage and data management needs.
CIO
JANUARY 31, 2024
Use more efficient processes and architectures Boris Gamazaychikov, senior manager of emissions reduction at SaaS provider Salesforce, recommends using specialized AI models to reduce the power needed to train them. “Is He also recommends tapping the open-source community for models that can be pre-trained for various tasks. “All
TechCrunch
OCTOBER 18, 2021
The necessity of animal testing is a sad one for the process of drug discovery, but there’s seemingly no good alternative to mice, even though they’re not particularly accurate human analogues. That allows effective testing to be done with a handful of chips rather than, say, 500 mice.
CIO
FEBRUARY 1, 2024
A TCO analysis forces you to think about things such as data migration, employee training, and process re-engineering. And there could be ancillary costs, such as the need for additional server hardware or data storage capacity. Testing: Before I even deploy the hardware, what are the costs of quality assurance and testing?
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