CPA Australia Survey: Practical AI adoption and easier financing drive Taiwan SME confidence to a five year high - Mid Florida Newspapers
Hey there, little superstar! Guess what?
Imagine a bunch of small shops, like your favorite toy store or the yummy ice cream place. These shops are called "SMEs" – that's just a grown-up word for small businesses!
Now, these shops in a faraway place called Taiwan are feeling super happy, like when you get a new toy! Why?
Two big reasons! First, they're using smart computer helpers, like little robot friends, to make their work easier and faster. That's called "AI"! It's like having a magic helper!
Second, it's easier for them to get money to buy new things for their shops, like more toys or ice cream.
Because of these two cool things, the shop owners are smiling a lot! They feel super good about their shops, more than they have in a very, very long time! Yay for happy shops!
CPA Australia Survey: Practical AI adoption and easier financing drive Taiwan SME confidence to a five year high Mid Florida Newspapers
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surveypaper![[P] MCGrad: fix calibration of your ML model in subgroups](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-earth-satellite-QfbitDhCB2KjTsjtXRYcf9.webp)
[P] MCGrad: fix calibration of your ML model in subgroups
Hi r/MachineLearning , We’re open-sourcing MCGrad , a Python package for multicalibration–developed and deployed in production at Meta. This work will also be presented at KDD 2026. The Problem: A model can be globally calibrated yet significantly miscalibrated within identifiable subgroups or feature intersections (e.g., "users in region X on mobile devices"). Multicalibration aims to ensure reliability across such subpopulations. The Solution: MCGrad reformulates multicalibration using gradient boosted decision trees. At each step, a lightweight booster learns to predict residual miscalibration of the base model given the features, automatically identifying and correcting miscalibrated regions. The method scales to large datasets, and uses early stopping to preserve predictive performanc
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[D] KDD Review Discussion
KDD 2026 (Feb Cycle) reviews will release today (4-April AoE), This thread is open to discuss about reviews and importantly celebrate successful reviews. Let us all remember that review system is noisy and we all suffer from it and this doesn't define our research impact. Let's all prioritise reviews which enhance our papers. Feel free to discuss your experiences submitted by /u/BomsDrag [link] [comments]
Chinese surveillance tech rolled out in Africa with ZTE, Hikvision and Huawei at the helm
Across major African cities such as Nairobi, Lusaka, and Abuja, governments are using surveillance technologies and credit lines from China to monitor public spaces and curb crime, a new survey shows. The UK-based Institute of Development Studies (IDS) said Chinese banks were increasingly funding African governments to build and maintain digital infrastructure – including surveillance cameras and command and control centres – under the “safe city” project, also known as “smart city”, which is...
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ciflow/trunk/179003: Thread compile_region_name through AOTAutograd cache hit path
On AOTAutograd cache hit, _compile_fx_inner is skipped entirely, so compile_region_name was never stamped onto the cached CompiledFxGraph. This caused name-dependent tests to see name=None when a prior test with the same graph shape (but no name) populated the cache first. Thread compile_region_name through fx_config so it reaches the cache hit path in wrap_post_compile. The name is excluded from cache keys since it doesn't affect compiled output — it's just a debug label. This replaces the previous workaround of disabling autograd cache for name visibility tests.
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