As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
I Know We’re in an AI Bubble Because Nobody Wants Me 😭
I first got into deep learning in 2012, when AlexNet came out. I was CTO of Jetpac, a startup that aimed to provide information about bars, hotels, and restaurants by analyzing public photos, for e…
Detection of Deepfake Videos and Audios on Social Media Platforms
Deepfake technology, a rapidly evolving application of artificial intelligence, has enabled the creation of highly realistic yet synthetic multimedia content. While this innovation offers potential benefits in areas such as entertainment and education, its misuse has raised significant ethical and security concerns, including misinformation and financial fraud. This study evaluates the effectiveness of current deepfake detection methods, focusing on the Xception model for video detection and the LCNN model for audio detection, using a dataset composed of real-life and deepfake content. The dataset includes deepfakes generated by tools such as the Deepfake Offensive Toolkit and Haotian AI, a cutting-edge provider known for its high-quality outputs. Our findings reveal that the Xception model, while achieving 89.1% accuracy on control datasets, struggled to detect Haotian AI-generated deepfakes, misclassifying nearly all samples as authentic. This performance gap highlights the need for more diverse training datasets and advanced detection frameworks capable of addressing the nuances of emerging deepfake tools. Additionally, metadata changes caused by uploading and downloading content on social media platforms were found to have minimal impact on detection accuracy, challenging the feasibility of metadata-based detection approaches. This research underscores the limitations of current deepfake detection models and emphasizes the necessity for multimodal approaches and broader datasets to enhance robustness. The study’s implications call for continued advancements in detection methods to keep pace with the growing sophistication of deepfake technologies.
Google Chrome Dear Sophie
ml5 - A friendly machine learning library for the web.
ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.
YouTube Music adds new labels for AI music
We’re introducing new labels and details in YouTube Music to give you more transparency when songs are created primarily using generative AI.
DeepL AI Platform: Translation, Voice & API
Explore our AI suite and get more done: Translate speech, text, and media, or integrate the DeepL API.
Nenex: A Neural Personal Wiki Idea
Proposal for a personal wiki built on neural nets: all edits are logged & used to finetune a NN assistant in realtime.
Google for Developers Blog - News about Web, Mobile, AI and Cloud
EmbeddingGemma 2 is a compact, open-source multimodal embedding model that maps text, code, images, video, and audio into a unified 768-dimensional space. Developers can use the sentence-transformers library to selectively load modular modality encoders—ranging from 270M to 740M parameters—to optimize memory usage. Additionally, Matryoshka Representation Learning enables dynamic dimension truncation down to 128d, significantly reducing vector database storage requirements while maintaining high retrieval performance.
DR Tulu: An open, end-to-end training recipe for long-form deep research | Ai2
We introduce Deep Research Tulu (DR Tulu), an open post-training recipe and framework for long-form deep research agents.
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
View recent discussion. Abstract: Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.
Distilling the Knowledge in a Neural Network
Our teams advance the state of the art through research, systems engineering, and collaboration across Google.
The Beginner’s Guide to Text Embeddings & Techniques | deepset Blog
Text embeddings represent human language to computers, enabling tasks like semantic search. Here, we introduce sparse and dense vectors in a non-technical way.
OpenAI Is A Systemic Risk To The Tech Industry
Before we go any further: I hate to ask you to do this, but I need your help — I'm up for this year's Webbys for the best business podcast award. I know it's a pain in the ass, but can you sign up and vote for Better
ARahim3/DigUp
Search your Mac by what's in your files: photos, PDFs, recordings, videos and code. On-device, built on Google DeepMind's EmbeddingGemma 2.