The French AI startup gunning for Workday, Oracle, and SAP - Fortune
The French AI startup gunning for Workday, Oracle, and SAP Fortune
Could not retrieve the full article text.
Read on GNews AI France →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
b8660
ggml-webgpu: move from parameter buffer pool to single buffer with offsets ( #21278 ) Work towards removing bitcast Move rest of existing types over Add timeout back to wait and remove synchronous set_tensor/memset_tensor move to unpackf16 for wider compatibility cleanup Remove deadlock condition in free_bufs Start work on removing parameter buffer pools Simplify and optimize further simplify profile futures Fix stride Try using a single command buffer per batch formatting macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CU

“Following the incentives”
A few years ago I listened to a fascinating podcast interview featuring former Democratic presidential candidates Andrew Yang and Marianne Williamson. They agreed that politics is a mess and politicians are constantly doing bad things that harm the people they are supposed to serve. But they couldn’t agree on how bad that made the politicians as people . Yang wanted to view the politicians as normal people responding to bad incentives, but Williamson wanted to call them evil for failing to exercise courage in the face of these bad incentives. Morally, the notion that you can’t blame people when they are following incentives is akin to the “just following orders” excuse that Nazis tried to use at the Nuremberg trials. But what’s the alternative? In practice, we can’t and don’t expect people

Synthetic Population Testing for Recommendation Systems
Offline evaluation is necessary for recommender systems. It is also not a full test of recommender quality. The missing layer is not only better aggregate metrics, but better ways to test how a model behaves for different kinds of users before launch. TL;DR In the last post, I argued that offline evaluation is useful but incomplete for recommendation systems. After that, I built a small public artifact to make the gap concrete. In the canonical MovieLens comparison, the popularity baseline wins Recall@10 and NDCG@10 , but the candidate model does much better for Explorer and Niche-interest users and creates a very different behavioral profile. I do not think this means “offline evaluation is wrong.” I think it means a better pre-launch evaluation stack should include some form of synthetic




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!