Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into deep learning methods with a focus on end-to-end models for core vision tasks, alongside modern approaches such as transformers, diffusion models, and visual-language models that power today’s AI systems. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. https://online.stanford.edu/courses/cs231n-deep-learning-computer-vision
Category: robotics/AI – Page 52
How everyday devices could train AI faster while keeping personal data on-device
A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81%. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.
The MIT researchers boosted the efficiency of a technique known as federated learning, which involves a network of connected devices that work together to train a shared AI model.
In federated learning, the model is broadcast from a central server to wireless devices. Each device trains the model using its local data and then transfers model updates back to the server. Data are kept secure because they remain on each device.
How AI is finding cancer treatments with fewer side-effects | BBC News
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Designing better quantum circuits with AI
Researchers from the group of theoretical physicist Hans Briegel have collaborated with NVIDIA to develop an AI method that automatically generates efficient quantum circuits, a key bottleneck in making quantum computers practically useful.
The work was published in Machine Learning: Science and Technology, in a paper titled “Synthesis of discrete–continuous quantum circuits with multimodal diffusion models.”
Before a quantum computer can perform any useful task, a quantum algorithm needs to be translated into a sequence of elementary quantum operations, known as quantum gates. Writing these quantum circuits efficiently is one of the hardest open problems in the field.
The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise
We examine how artificial intelligence transforms the core pillars of collaboration— performance, expertise sharing, and social engagement—through a pre-registered field experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings reveal that AI significantly enhances performance: individuals with AI matched the performance of teams without AI, demonstrating that AI can effectively replicate certain benefits of human collaboration. Moreover, AI breaks down functional silos. Without AI, R&D professionals tended to suggest more technical solutions, while Commercial professionals leaned towards commerciallyoriented proposals.
Dr. Stuart Hameroff: Consciousness is More than Computation!
13 years ago, I walked into Dr. Stuart Hameroff’s operating room with a camera, a microphone, and a single stubborn question:
Is consciousness computation?
Hameroff, an anesthesiologist and professor at the University of Arizona, and co-author with Sir Roger Penrose of the Orch OR theory, said no.
Emphatically. Unfashionably. Against the entire weight of mainstream neuroscience and Silicon Valley orthodoxy.
At the GF2045 conference, where I first met him, Ray Kurzweil went out of his way to declare Orch OR “totally wrong.” Others called it speculative. Untestable. Unscientific.
Today, in the age of large language models, that argument is no longer a niche dispute among philosophers and physicists. It is the decisive question of our century.
Modern Transformer Architecture Explained
The course covers the key architecture designs of modern Transformers that power today’s generative AI revolution.
