Businesses focus on cyber risk reduction and data value. They employ secure collaboration, multi-party computation, and encrypted cloud processing for data safety. Secure data monetization in compliant marketplaces ensures privacy, building customer trust. Microblog @antgrasso
DATA
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AI Drives Business Transformation: Data to Real-World Impact
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From data to impact: IDC shows how AI fuels #businesstransformation. Text, audio, video, and code converge in #AI models, driving industry advancements and productivity
Lead the AI wave with insights from @ingliguori and #TheDigitalEdge Get the ebook https://
bit.ly/3u4pILl -
LLM Size vs Genome: Information Compression Limits
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Not really.
That would have to be compressed in a tiny amount of information.
A small 7B LLM requires 14GB.
Your entire genome fits in 800MB (uncompressed).
The difference between human and chimp genome is 1% of that, or 8MB.
Not enough to encode a significant structure. -
8 Real-World MQTT Use Cases for Industrial IoT
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8 Real-World MQTT Use Cases
>> https://
ow.ly/jrZa50Q4BxJ #sponsored #influxdata_iiot #InfluxDB #industrialiot #digitaltransformation #mqtt #iiot #awsreinvent #reinvent23 #reinvent -
MLflow 2.8 LLM-as-a-Judge Evaluation for RAG Applications
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Get out-of-the-box metrics like latency, tokens and more, using #MLflow 2.8 with LLM-as-a-judge Discover how it can save you time and money and best practices for #LLM evaluation in RAG applications https://
bit.ly/3FHCWAg -
Ulta Beauty builds guest loyalty through responsible data use
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Don’t miss seeing Melissa Berscheid share how @ultabeauty created a foundation of trust with guests by responsibly collecting and using guest data to drive unmatched loyalty. #NRF2024 http://
2.sas.com/6011uFBGc #retail -
Snowflake, Activeloop, and Truera: Data and AI Infrastructure Leaders
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@SnowflakeDB – powerhouse in data warehousing, esp mobilizing data with scale and performance @activeloop – database for AI, targeting multi-modal, low latency use cases @truera_ai – scalable ML monitoring to minimize AI risks like toxicity, hallucinations, bias
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LangSmith Benchmarks: Evaluate LLM Performance on Your System
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More to come! Check out the blog post for more information on how to get started, or see the benchmark docs to run these on your own system. https://
blog.langchain.dev/public-langsmi
th-benchmarks/
… https://
langchain-ai.github.io/langchain-benc
hmarks/index.html
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LangChain Benchmarks Package for LLM and Embedding Comparison
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Try it yourself We've published the new langchain-benchmarks package, which provides tooling to easily compare LLMs, embeddings, indexing techniques, and more across these datasets, so you can find the optimal solution for each task.
https://
langchain-ai.github.io/langchain-benc
hmarks/notebooks/retrieval/langchain_docs_qa.html
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