Chloé AzencottIt's tomorrow!
Gwenn has done stellar work and taken things much farther than I've been able to follow — good thing that Véronique Stoven was her main advisor — and I'm very impressed by all that she has achieved and how much she's grown.
Dash RemoverCan’t wait for the keynote where Apple announces LidGPT — the world’s most advanced hinge-based emotion model. Uses your opening angle to detect mood swings. 👁️🧠📐 #AI #AppleEvent #MachineLeaning
Wikidata Taiwan 臺灣維基數據社群
Srijit Kumar Bhadra@marjon
I am assuming that you are limiting your opinion to LLM, and more broadly to Generative AI.
Otherwise machine learning, with artificial neural networks, strongly influenced 2024 Nobel prize winning work in both Physics and Chemistry.
#AI #NobelPrize #MachineLeaning #DeepNeuralNetwork #LLM #GenerativeAI
I am assuming that you are limiting your opinion to LLM, and more broadly to Generative AI.
Otherwise machine learning, with artificial neural networks, strongly influenced 2024 Nobel prize winning work in both Physics and Chemistry.
#AI #NobelPrize #MachineLeaning #DeepNeuralNetwork #LLM #GenerativeAI
Concordia School of HealthSeveral Concordia researchers are among the authors of this new paper on the potential of autonomous ultrasound solutions: "Comprehensive review of reinforcement learning for medical ultrasound imaging"
#ultrasound #ai #machineleaning #deeplearning
https://link.springer.com/article/10.1007/s10462-025-11268-w
Comprehensive review of reinforcement learning for medical ultrasound imaging - Artificial Intelligence Review
Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents through rewarded interactions with their environments. Several existing surveys on advancements in US imaging predominantly focus on partially autonomous AI solutions. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this survey proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline -including data preparation, problem formulation, simulation environment, RL training, validation and finetuning- and reviews current research efforts under this taxonomy. This work aims to highlight the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.
glenux
just a💧in my 🍺Bill Toulas wrote a summary about the drama with #Ultralytics #YOLO at bleepingcomputer.com
In the meantime, I learned that there is a #GitHub repo about dramas at GitHub:
https://github.com/neodrama/github-drama/issues/2
#ComputerVision #CyberSecurity #securitybreach #MachineLeaning #machine_learning
Haojia WuNVIDIA AI Releases cuPyNumeric: A Drop-in Replacement Library for NumPy Bringing Distributed and Accelerated Computing for Python #python #cuda #ai #MachineLeaning
NVIDIA AI Releases cuPyNumeric: A Drop-in Replacement Library for NumPy Bringing Distributed and Accelerated Computing for Python
One of the long-standing bottlenecks for researchers and data scientists is the inherent limitation of the tools they use for numerical computation. NumPy, the go-to library for numerical operations in Python, has been a staple for its simplicity and functionality. However, as datasets have grown larger and models more complex, NumPy’s performance constraints have become evident. NumPy operates solely on CPU resources and isn't optimized for the massive datasets often processed today. The limited computing power of a single CPU core leads to bottlenecks, extending computational times and restricting scalability. This gap has created a need for more efficient tools
Thomas 🇪🇺Interesting Neural Network for covid detection in ct scans.
CTNETDive into the world of Large Language Models! Discover their benefits, challenges, and how they can transform learning and creativity. #AI #MachineLeaning #Blog
https://www.ctnet.co.uk/introductory-guide-to-large-language-model/
