DATA

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Delta Lake UniForm GA: Unified Data Access Across Tools

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@databricks

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09 July 2024 18h50

Delta Lake UniForm is now GA! UniForm combines Delta Lake’s superior price-performance with access to every tool in your stack. Write a single copy of your data and make it available to any engine that supports the primary open table formats. https://
dbricks.co/3XSy9q3

→ View original post on X — @futurepedia_io

AI Speech Classifier Optimizes Content Categorization Workflow

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@futurepedia_io

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09 July 2024 18h01

AI Speech Classifier automatically categorizes and labels speech content efficiently. How could this optimize your workflow? #SpeechRecognition #DataAnalysis

→ View original post on X — @alteryx

AI-Powered Analytics for Marketing Data Consolidation and Attribution

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@alteryx

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09 July 2024 17h02

Get our e-book to learn how anyone on your marketing team can use #AIpowered analytics for intelligent data consolidation, customer segmentation, attribution, and #CampaignPerformance reports. Go from data to decisions now: https://
ow.ly/5fCJ50SxxIn @marketingdive #analytics

Data Poisoning Removal: Hypotheses on Model Parameter Impact

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09 July 2024 15h33

We present a few hypotheses as to why data poisoning is so tricky to remove. Some hypotheses include that data poisoning moves the model parameters a lot, or in a subspace orthogonal to the clean data. Would love to see more exploration here! 6/n

MUL Method Mitigates Data Poisoning Effects in Training

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09 July 2024 15h33

But MUL has also been proposed as a method to remove the effect of data poisoning. Such effects are *indirect* — they manifest in other datapoints besides the specific training point. 4/n

Machine Unlearning: Removing Training Data Influence Efficiently

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09 July 2024 15h33

MUL is pretty well studied by now: given a trained model, can we "remove" all influence of some points in the training set? Naive way: retrain from scratch without those points. But this is slow. A number of faster methods have been proposed. 2/n

Evaluating Machine Unlearning with Membership Inference Attacks

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09 July 2024 15h33

The most common way to evaluate an MUL method is using "membership inference attacks." These try to directly test whether the "unlearned" point was in the dataset used to train the model or not. 3/n

→ View original post on X — @officialindiaai

India Launches AI Data Platform for Global Readiness Assessment

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@officialindiaai

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09 July 2024 11h44

Meet the speaker @ #GlobalIndiaAISummit – Ms. Kavita Bhatia, GC & COO IndiaAI, as she highlights how the Government of India is launching the IndiaAI data platform to enable researchers worldwide to assess India's AI readiness index.@abhish18 @AshwiniVaishnaw @GPAI_PMIA… pic.twitter.com/SRQUO1SHQ3
— IndiaAI (@OfficialINDIAai) 9 juillet 2024

Meet the speaker @ #GlobalIndiaAISummit – Ms. Kavita Bhatia, GC & COO IndiaAI, as she highlights how the Government of India is launching the IndiaAI data platform to enable researchers worldwide to assess India's AI readiness index. @abhish18 @AshwiniVaishnaw @GPAI_PMIA