What should financial services know about AI decisioning? The moat is the data layer underneath it.
I spoke with David Mirfield, CPO at Provenir, 4 Billion decisions/year, 110+ customers and the discipline behind it comes down to three words: use case agnostic. Watch our full
DATA
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AI Decisioning in Financial Services: Data Layer Competitive Advantage
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Agent Memory Management: Filtering, Sharing, and Temporal Consistency
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Great questions. – No, not everything should be stored. The agent needs to filter what's actually useful vs what's not. – Yes agents can ahev shared memory, and most memory infra support multi tenancy. – For contradictions, timestamped facts let newer info override older
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Graph Traversal Enables Multi-Hop Queries Beyond Vector Search
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Good questions. Graph traversal adds a small overhead but makes multi-hop queries possible that vector search alone simply can't answer.
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Graph Search vs Vector Search: Beyond Similarity in AI
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Exactly. Vector search answers "what's similar" but not "how are these connected." The Alice-project-outage example in the post explains this. Most real questions need at least two hops, and that's where graphs become essential, not optional.
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The Process of Data Cleaning in Data Science and Big Data
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The Process of #Data Cleaning by @Python_Dv #DataScience #BigData
→ View original post on X — @ronald_vanloon, 2026-04-14 07:48 UTC
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Centralized Data Critical for FSD Robotaxi Networks
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For FSD to really get to the promised land the data needs to be centralized. I believe in 15 years literally every vehicle will be able to be put into a Robotaxi network, either Waymo, Zoox, Tesla, and maybe Uber if it survives. All of which need central data to optimize
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Efficient Cross-Domain Offline Reinforcement Learning with Data Filtering
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Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering Paper: https://
arxiv.org/pdf/2512.02435
Code: https://
github.com/zq2r/DVDF.git Our report: https://
mp.weixin.qq.com/s/ztE8GofcssuI
1PdkHx_kLg
… #PapersAccepted by Jiqizhixin -
Training AI Correctly: The DATA Method Explained
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I've published an episode on @ivoox
: "#1110: You're training your AI wrong… and the DATA method fixes it #podcast -
1847 Gaussian Machine Learning: BigData Analytics and AI Techniques
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1847 Gaussian Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/1847-Great-Minds
→ View original post on X — @gp_pulipaka, 2026-04-14 04:57 UTC
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Machine Learning Transforms IoT Computing with New Perspectives
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#MachineLearning Puts New Lens on #IoT Computing! by @gp_pulipaka! #BigData #Analytics #DataScience #AI #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/New-Lens-IoT
→ View original post on X — @gp_pulipaka, 2026-04-14 04:57 UTC