New Generative AI Innovations from Google and Salesforce
Was this email forwarded to you? Sign up here New Generative AI Innovations from Google and SalesforceSundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.🗓 Next week in TheSequence Edge:Edge #261: Our series about ML interpretability continues with an overview of the famous LIME method, Meta’s research about how interpretable neurons can affect the accuracy of neural networks and the Alibi Explain framework. Edge #262: Explores NVIDIA’s Get3D generative model. 📝 EditorialGenerative AI is at the center of the activity in the AI market these days. While the top headlines are captured by high profile models/apps like DALL-E or ChatGPT, there is a tremendous levels of activity in research and applications across the board. This week, we saw some interesting developments with new generative AI models. Google Brain has been super active in the text-to-image space with the creation of models like Imagen or Parti. This week, Google unveiled Muse, a new text-to-image transformer model that is able to achieve state-of-the-art results while being more efficient than diffusion and autoregressive methods. Muse’s efficiency gains relative to diffusion techniques is based on requiring fewer iterations. Compared to autoregressive models, Muse seems to improve based on its use of parallel decoding techniques. Salesforce Research has also been active in the text-to-image area. A few days ago, the research lab unveiled a new algorithm that allows text guidance to edit images. Named Exact Diffusion Inversion via Coupled Transformations (EDICT), the technique can be super relevant for the inpainting and outpainting capabilities of text-to-image foundation models. Just another week in generative AI! 🔎 ML ResearchNew Text-to-Image ModelGoogle introduced Muse, a new text-to-image generation and editing transformer model —> Read more. Text Guided DiffusionSalesforce Research published a paper detailing Exact Diffusion Inversion via Coupled Transformations (EDICT), a model to guide text-to-image generation to achieve consistency —> Read more. Language Models and DisinformationOpenAI and Georgetown University published a report quantifying the used of large language models in disinformation campaigns —> Read more. DP for Speech RecognitionAmazon Science published a paper detailing Private aggregation of teacher ensembles (PATE), a differential privacy technique for speech recognition methods —> Read more. 🤖 Cool AI Tech ReleasesPyTorch HTAPyTorch released Holistic Trace Analysis (HTA), a framework developed by Meta to debug performance issues in large scale models —> Read more. TensorFlow for Intel ProcessorsTensorFlow released a new build optimized for Intel Xeon processors —> Read more. 🛠 Real World MLAccelerating PyTorch TrainingEngineers from Hugging Face discuss how to accelerate PyTorch training jogs using a cluster of Intel Rapid servers on AWS —> Read more. 💸 Money in AI
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Edge 261: Local Model-Agnostic Interpretability Methods: LIME
Friday, January 20, 2023
LIME, Meta AI research on interpretable neurons and the Alibi Explain framework.
Edge 262: NVIDIA’s Get3D is a Generative AI Model for 3D Shapes
Friday, January 20, 2023
The model is actively used in NVIDIA's Omniverse platform.
Edge 259: Local Model-Agnostic Interpretability Methods: SHAP
Tuesday, January 10, 2023
SHAP method, MIT taxonomy for ML interpretability and BAIR's iModels framework.
NVIDIA Latest Push in Generative AI the Metaverse
Sunday, January 8, 2023
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.
Edge 258: Inside OpenAI's Point-E: The New Foundation Model Able to Generate 3D Representations from Language
Thursday, January 5, 2023
The new model combines GLIDE with image-to-3D generation models is a very clever and efficient architecture.
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