Podcast Summary
Cartagia's rebellion against Transformers: Co-founders Karen Gull and Albert Gu challenge Transformers' dominance with fast models S4 and Mamba, showing significant potential in low-latency applications like gaming and voice agents.
Karen Gull and Albert Gu, the co-founders of Cartagia, are leading a rebellion against the dominant architecture of Transformers with their innovative models S4 and Mamba. They have seen significant excitement in their technology in the areas of gaming and voice agents, where low latency is crucial. Their fast text-to-speech engine, Sonic, is already shaving off 150 milliseconds from typical response times, with the goal of reaching the next 600 milliseconds. Both Karen and Albert have a research background from Stanford, where they worked on sequence modeling and alternate recurrent models. They became interested in these models due to their elegance and effectiveness in various applications. Their most recent model, Mamba, has shown impressive results in language modeling. Karen is now a professor at Carnegie Mellon University, where her research lab continues to explore these questions academically, while Cartagia puts the technology into production. Karen grew up in India with a family of engineers and initially aimed to be a doctor before switching to engineering. She started her PhD journey with no clear focus, eventually settling on reinforcement learning. Her PhD advisor, Chris Ray, was skeptical of the field, leading to an interesting transition period where they explored various projects together.
Google Cloud project collaboration: Collaborating on a Google Cloud project led the speaker and their collaborator to a shared interest in State Space Models, resulting in advancements in their field.
The speaker's professional relationship began when they collaborated on a project involving filling up and expanding a disk on Google Cloud. They worked together on this project, and the speaker's insistence on only adding a terabyte at a time caused some frustration. After the project, the speaker joined the team working on S4 push at Albert, initially to help out during a NeurIPS deadline. The speaker then went on to explain the concept of State Space Models (SSMs), which they had become interested in due to its sequential processing nature. SSMs are a type of model that processes data one piece at a time, updating the belief state with new information. The speaker found this approach fundamental and was inspired by connections to other dynamical systems. SSMs can be applied to various types of data, and the speaker mentioned that the first models they worked on were particularly effective for modeling perceptual signals, such as text data. They also mentioned that there are different advantages for different types of data and various variants of SSMs. The speaker's professional relationship with their collaborator began with a project that required them to work together and led to a shared interest in SSMs. They went on to apply this approach to various types of data, leading to advancements in their field.
Model architecture selection: The choice of machine learning model architecture depends on the nature of the data and desired trade-offs. Transformers excel at text but struggle with raw data and have a quadratic scaling problem. Alternative models like state space models can be efficient but lack exact retrieval capabilities. Consider factors like natural fit, computational efficiency, and trade-offs.
The choice of machine learning model architecture depends on the nature of the data and the desired trade-offs between modeling capabilities and computational efficiency. The discussion highlighted the strengths and limitations of transformers and other models in handling different types of data, such as raw waveforms, raw pixels, and text. Transformers excel at modeling text due to their ability to capture long-range dependencies and context, but they struggle with raw data and have a quadratic scaling problem, making them less efficient for large datasets. Alternative models like state space models, which are more efficient due to their linear scaling, can serve as effective fuzzy compressors but lack the exact retrieval capabilities of transformers. The development of these models has been interconnected with advancements in data processing techniques, such as tokenization, which can influence the effectiveness of different modeling assumptions. When considering the advantages of various architectures, it's essential to weigh factors like natural fit for specific data types, computational efficiency, and the trade-offs between modeling capabilities and processing speed.
Audio technology edge computing: Audio technology, specifically SSN, is gaining importance in commercial applications due to real-time capabilities and edge computing is expected to make it more accessible and cost-effective by reducing the need for large data centers and expensive GPUs.
Audio technology is gaining significant importance in various commercial applications, particularly in voice agents and gaming, due to its real-time capabilities with signals and sensor data. SSN, a specific technology, is making strides in this field by offering an efficient model that can be implemented on smaller hardware and pushed towards the edge. This shift towards edge computing is expected to reduce the need for large data centers and expensive GPUs, making the technology more accessible and cost-effective. The second wave of development in this field is focusing on efficiency, following the initial wave of exploration and discovery. Companies like Apple are already demonstrating the potential of running large models on devices, and SSN is poised to contribute to this trend by making the technology more edge-oriented.
On-device AI models: The future of AI lies in making models more capable and accessible on-device, enabling new applications and improved quality without the cost and ongoing computing issues.
The future of AI models lies in making them more capable while keeping the cost low, enabling their use at scale. Current 3D models have potential but are not yet capable enough. The challenge with Transformers is their high resource consumption, which is a concern even in data centers. The shift towards making intelligence more accessible everywhere requires infrastructure and technology that can run models on-device, leading to new applications and improved quality without the cost and ongoing computing issues. As an investor, the assumption that running models on hardware will become possible opens up a world of opportunities for full-stack companies. In the same dollars, they can do way more intelligent computation, leading to different applications and a focus on quality. For instance, having a music model on-device could result in a personal musician that generates music based on user input, eliminating the need to go to the cloud. Cartagena's recent launch of its Texas speech product showcases impressive performance and fast shipping. The company is focusing on building efficient systems to perform tasks like voice and audio generation using fairly general models. This approach allows for conditioning on various inputs, such as text transcripts, and paves the way for further advancements in AI technology.
Text-to-Speech Challenges: Despite advancements, creating high-quality, engaging Text-to-Speech systems remains challenging due to the need for efficient model stacks, effective training methods, emotional depth, and nuanced speech patterns. Multimodal models with language understanding capabilities are necessary to perfect TTS and adapt to various roles and contexts.
While significant progress has been made in text-to-speech (TTS) technology, it is not yet solved. The efficiency and real-time capabilities of audio generation are crucial, requiring robust model stacks and effective training methods. The goal is to create a high-quality, engaging experience that can hold a user's attention for more than 30 seconds. Current TTS systems lack the emotional depth and nuanced speech patterns found in human interaction. The evaluation of TTS systems is challenging due to their qualitative nature and the subjective perception of users. Emotion and the embodiment of societal roles through speech are particularly difficult to replicate. Even basic evaluations, such as recognizing words or pronouncing them correctly, require a more comprehensive understanding of language. To truly perfect TTS, multimodal models with language understanding capabilities are necessary. The ultimate goal is to create a system that can adapt to various roles and contexts, mimicking the unique speech patterns of professions and regions. This would significantly enhance user engagement and bring TTS technology closer to the ceiling of its potential.
Real-time multimodal model improvement: The team is improving their real-time audio model, Sonic, and exploring other modalities, aiming to create versatile building blocks for various applications, with a focus on enabling conversational capabilities and real-time processing on devices.
The team is focusing on improving their real-time audio model, Sonic, while also exploring other modalities. Multi-modality, specifically the combination of speech and text, has presented new challenges but has not been the primary motivation for their work. Instead, they aim to create versatile and general building blocks for various applications. The team is excited about the potential of Sonic, which can generate a signal in real-time and capture the idea of generating a response. They plan to continue improving Sonic while also addressing the need for real-time processing on devices, such as laptops. Additionally, they aim to enable conversational capabilities with these models, allowing for intelligent responses to user inputs and reasoning over data and context. Long-term goals include building a large-scale multimodal language model, but the team is developing unique techniques to make this a reality. Overall, their focus is on creating a powerful multimodal model that is easy to run on devices.
Unified and elegant speech synthesis models: The future of speech synthesis models lies in creating unified, efficient solutions with improved audio quality and reduced latency, inspired by mathematical and computational elegance, aiming for a single, unified model for all tasks, and bringing SSMs closer to on-device and edge computing.
The future of speech synthesis models (SSMs) lies in making them more unified, efficient, and elegant. The focus should be on improving the audio quality and reducing latency by minimizing the need for multimodality and model orchestration. The speaker expresses a preference for simple, elegant solutions to complex problems, drawing inspiration from the world of mathematics and computer science. This aesthetic approach has driven the development of SSMs, and the goal is to continue finding elegant solutions while addressing the engineering challenges. The eventual goal is to have a single, unified model that can handle all speech synthesis tasks, making the systems and engineers obsolete. The speaker also emphasizes the importance of bringing SSMs closer to on-device and edge computing for better accessibility and performance.
Trust in local data processing technology: The team behind SSM advocates for trusting their local real-time data processing technology, emphasizing the importance of faith in the system's capabilities, and comparing it to trusting a book over a deity. They are expanding their team and encourage engagement with their community.
The team behind SSM, a company specializing in real-time data processing, emphasizes the importance of trusting their technology, which provides the same feature set as the cloud version but runs locally. Radesh, a mathematician from Hungary, highlights the need for faith in the system's capabilities, comparing it to believing in a book rather than a deity. The team, consisting of 15 members and several interns, is actively hiring for model roles to expand their modeling team and contribute to the future of their technology, which they refer to as "overthrowing the empire" or "the rebellion." SSM's offerings include real-time audio streaming and they encourage engagement with their community through social media channels and their website.