sayzardMark Gadala-Maria (@markgadala)
Nano Banana 2가 출시된 지 24시간 만에 사용자들이 다양한 극한 실험을 시도하고 있다는 보고입니다. 예로 존재하지 않았던 포켓몬 게임을 새로 생성하는 등 창의적·실험적 사용 사례 10가지를 공유하고 있다는 내용입니다.
Vaibhav SharmaMachine learning is transforming Android apps, from smarter user experiences to real-time predictions.
But how do you actually apply ML in Android development?
This guide breaks it down step-by-step, from tools and frameworks to real use cases and optimization.
Build smarter, faster, and with confidence. Future-ready Android ML starts here. Check out the blog now!
https://ripenapps.com/blog/machine-learning-in-android-app-development/
#MachineLearning #MLInAndroidAppDevelopment #MLModels #ArtificialIntelligence #AIinMobileApp #MLinAndroid
Emily (@IamEmily2050)
작성자는 Google/DeepMind가 Seedance V2에 대한 해결책을 내놓길 희망한다며, Seedance V2를 써본 뒤 다른 모델은 쓰기 어렵다고 평가합니다. 과거 Opus 4.5가 완벽하진 않았지만 큰 반향을 일으켰던 사례를 비유로 들며 Seedance V2의 영향력을 암시하고 있습니다.
Emily (@IamEmily2050) on X
I really hope Google/DeepMind has a solution for Seedance V2, because it's impossible to use any other model after trying it. It's like when Opus 4.5 came out: it wasn't perfect, had a lot of problems, but got people hyped because it had crossed the point of being just another
Mahmoud ZaltWhat actually powers LinearRegression under the hood? This piece digs into the hidden engine behind it and why that internal design matters for your models.
Read More: https://zalt.me/blog/2026/01/hidden-linear-engine
#LinearRegression #MachineLearning #MLModels #SoftwareDesign
Hacker NewsLaunch HN: Plexe (YC X25) – Build production-grade ML models from prompts
Victoria Stuart 🇨🇦 🏳️⚧️[2506.21734] Hierarchical Reasoning Model
https://arxiv.org/abs/2506.21734
https://news.ycombinator.com/item?id=44699452
Hierarchical Reasoning Model
Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primarily employ Chain-of-Thought (CoT) techniques, which suffer from brittle task decomposition, extensive data requirements, and high latency. Inspired by the hierarchical and multi-timescale processing in the human brain, we propose the Hierarchical Reasoning Model (HRM), a novel recurrent architecture that attains significant computational depth while maintaining both training stability and efficiency. HRM executes sequential reasoning tasks in a single forward pass without explicit supervision of the intermediate process, through two interdependent recurrent modules: a high-level module responsible for slow, abstract planning, and a low-level module handling rapid, detailed computations. With only 27 million parameters, HRM achieves exceptional performance on complex reasoning tasks using only 1000 training samples. The model operates without pre-training or CoT data, yet achieves nearly perfect performance on challenging tasks including complex Sudoku puzzles and optimal path finding in large mazes. Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for measuring artificial general intelligence capabilities. These results underscore HRM's potential as a transformative advancement toward universal computation and general-purpose reasoning systems.
HabileDataWhy Is Data Annotation a Game Changer for AI/ML Enhancement?
Accurate data annotation is the cornerstone of any successful AI or machine learning model. Without well-labeled data, even the most advanced algorithms can fail to deliver results.
#DataAnnotation #MachineLearning #AI #ComputerVision #NLP #ArtificialIntelligence #MLModels #Annotation #DataLabeling
Low responsiveness of ML models to critical or deteriorating health conditions
https://www.nature.com/articles/s43856-025-00775-0
#HackerNews #LowResponsiveness #MLModels #HealthTech #DeterioratingConditions #AIInHealthcare
Low responsiveness of machine learning models to critical or deteriorating health conditions - Communications Medicine
Pias et al. evaluate machine learning models designed to predict in-hospital mortality and 5-year cancer survivability. Multiple classification models are unable to recognize critical health conditions or deteriorating patient conditions.
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#AIandML #TechServices #AIsoftware #MachineLearningSolutions #InnovationInTech #AIdevelopment #MLservices #SmartTech #DataDriven #AutomationTech #FutureTech #ArtificialIntelligence #MachineLearningExperts #AIpowered #TechInnovation #MLmodels
KAN: Kolmogorov-Arnold Networks
https://arxiv.org/abs/2404.19756
Kolmogorov-Arnold Neural Networks Shake Up How AI Is Done
https://spectrum.ieee.org/kan-neural-network
A new type of neural network is more interpretable
https://news.ycombinator.com/item?id=41162676
https://news.ycombinator.com/item?id=40219205
Kolmogorov–Arnold representation theorem: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_representation_theorem
KAN: Kolmogorov-Arnold Networks
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.