Generative Audio Models Just Had a Great Week
Was this email forwarded to you? Sign up here Generative Audio Models Just Had a Great WeekThree major generative audio released in the last seven days.Next Week in The Sequence:
You can subscribed to The Sequence below:📝 Editorial: Generative Audio Models Just Had a Great WeekAudio is rapidly becoming one of the most important frontiers in generative AI, a field that is advancing swiftly. Several factors contribute to this rapid evolution. Technically, generative audio poses a fundamentally simpler problem than video or 3D, which leads to faster iterations in research and implementation. Additionally, from a model standpoint, many of the techniques, such as diffusion that pioneered text-to-image generation, are quite applicable to audio. From a market perspective, there exists a rich set of audio datasets that can be used to train new models, and the impact on industries such as media, robotics, or home automation can be quite immediate. The pace of innovation in generative audio is accelerating at remarkable levels. Just a few days ago, OpenAI shared some details about Voice Engine, a new model for synthetic voice generation. Last week, we saw several major releases related to generative audio:
To these innovations, you need to add more established players such as Eleven Labs, which have been pushing the boundaries of generative audio for years. Generative audio might be inching towards its ChatGPT moment faster than we think." 🔎 ML ResearchJambaFollowing their open source release( which we covered last week) AI21 published the paper detailing Jamba. The model presents a unique transformer-Mamba MoE architecture that looks the leverage the best of both approaches —> Read more. Many-Shot JailbreakingAnthropic published a paper detailing many-shot jailbreaking, a technique that can bypass traditional guardrails in LLMs including Claude. The technique exploits large context windows in LLMs that allows an attacker to position malicious text in different positions —> Read more. GekkoGoogle DeepMind published a paper discussing Gekko, a hyper efficient text embedding model that can achieve knowledge generalization with relatively small data. Gekko uses a very simple architecture that leverages knowledge from LLMs into a retriever —> Read more. MambaMixerResearchers from Cornell University published a paper outlining MambaMixer an architecture block that could be added to State Space Models(SSMs). MambaMixer uses a dual selection mechanism that combines tokens across different modalities —> Read more. Mixture-of-DepthGoogle DeepMind published a paper detailing a technique for allocating compute to specific positions in a sequence instead of the entire sequence. This approach really optimizes compute in transformer models to the point of capping the number of tokens that can participate in the attention layers —> Read more. ReFTResearchers from Stanford University published a paper introducing representation fine-tuning(ReFT), a technique that looks to edit representations in LLMs via fine-tuning. ReFT banks on the idea that representations encode rich semantic information which can lead to more effective fine-tuning —> Read more. 🤖 Cool AI Tech ReleasesStable Audio 2.0Stability AI released Stable Audio 2.0 which can generate musical tracks up to 3 mins long —> Read more. Universal-1AssemblyAI launched Universal-1, its multilingual speech-to-text model —> Read more. Command R+Cohere introduced Command R+, the new version of its LLM optimized for RAG and tool usage —> Read more. OpenAI Custom ModelsOpenAI announced enhacements to its training API as well as new mechanisms for building custom models —> Read more. Ressemble EnhanceResemble AI released Ressemble Enhance, a high quality speech resolution model —> Read more. 🛠 Real World MLText-To-SQL at PinterestThe Pinterest team shared details about their use of text-to-sql models for analytics workflows —> Read more. ML Lifecycle Management at SalesforceSalesforce discusses details about their ML Console for managing the lifecycle of internal ML workloads —> Read more. 📡AI Radar
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