The TL podcast for February 5th, 2023: Cloud growth slows, but tech growth overall is not falling off a cliff https://
thetechnologyletter.com/the-posts/the-
tl-podcast-for-february-5th-2023-cloud-growth-slows-but-tech-growth-overall-is-not-falling-off-a-cliff
… // $AMZN $AAPL $AMD $QCOM $META $SNAP $MSFT $TSLA $CLFD $ZI $NEWR #investing #stocks #TL20 #technology #earningsseason
AI
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Cloud Growth Slows But Tech Sector Remains Resilient
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Build AI-Powered Search with Cohere AI Platform
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What are you waiting for? Get started with building your AI-powered search using Cohere AI. https://
dashboard.cohere.ai/welcome/regist
er?utm_source=influencer&utm_medium=social&utm_campaign=shubham
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Building AI-Powered Search Frontend with Streamlit Components
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Step-5: Add @streamlit components to build a frontend for the search. Let's break it down what all we would need: 1. We need users to be able to input their search query.
2. Cohere's model to convert text to embedding.
3. Finding similar sentences based on Cosine similarity. -
Export Python Code from Playground to IDE
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Step-4: Export the Python code from the Playground and paste it into an IDE.
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Visualizing Embeddings for Semantic Search Understanding
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Step-3: Visualize the embeddings in the playground to understand how semantic search works. Semantically similar sentences would be dots closer to each other and vice-versa.
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Upload Datasets for AI Search in Chohere Playground
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Step-2: Upload the dataset which you want to make searchable either in CSV form or upload it directly via Chohere's Playground UI.
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Building Twitter Thread Search with Cohere Embed
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Let's build a simple search example for finding twitter thread topics related to Y-Combinator. Step-1: Go to the "Embed" Section in Cohere's Playground. Here is the link –
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Semantic Search: Understanding Meaning Beyond Keywords
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Semantic search is based on the idea of understanding the meaning and context of the words used in a query, rather than just matching the exact keywords to the documents. It focuses on finding relevant results that match the user's intention, rather than matching the keywords.
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Cohere AI Embeddings: Convert Text to Numerical Representations
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Interested in building AI-powered search but don't know where to start? Introducing Embeddings by Cohere AI Embeddings are a way to represent the meaning of text in a numerical form. Cohere's model can convert texts to embeddings out-of-the-box. https://
dashboard.cohere.ai/welcome/regist
er?utm_source=influencer&utm_medium=social&utm_campaign=shubham
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