Liquid AI
Liquid AI is an MIT spin-off company that develops innovative Liquid Foundation Models (LFMs) using non-transformer architecture to achieve state-of-the-art AI performance with smaller memory footprint and more efficient inference.
https://www.liquid.ai/
Product Information
Updated:Dec 9, 2024
Liquid AI Monthly Traffic Trends
Liquid AI experienced a 60.1% decline in traffic, with visits dropping to 123.7K. Despite the recent launch of Liquid Foundation Models (LFMs), which outperform traditional large language models, the significant traffic drop suggests that the market may not have fully embraced these new models yet. Market competition from established players like Google and Nvidia, as well as broader industry trends such as supply chain issues and investor concerns, might have contributed to this decline.
What is Liquid AI
Founded by MIT CSAIL researchers Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus, Liquid AI is a Boston-based AI company that emerged from stealth mode with $37.6 million in seed funding. The company specializes in creating a new generation of foundation models that go beyond traditional Generative Pre-trained Transformers (GPTs). Their approach is grounded in the integration of fundamental principles across biology, physics, neuroscience, mathematics, and computer science, leading to the development of their flagship product - Liquid Foundation Models (LFMs).
Key Features of Liquid AI
Liquid AI is an MIT spin-off company that has developed a new generation of AI models called Liquid Foundation Models (LFMs), which are built on dynamical systems, numerical linear algebra, and signal processing principles rather than traditional transformer architecture. These models achieve state-of-the-art performance while maintaining a smaller memory footprint and more efficient inference, capable of handling various types of sequential data including text, audio, images, video, and signals.
Novel Architecture: Uses a non-transformer based architecture grounded in dynamical systems that allows parameters to adapt and change over time through experience
Efficient Resource Usage: Maintains a significantly smaller memory footprint compared to traditional LLMs, requiring less computational power and storage
Adaptive Computation: Features custom computational units with targeted weight sharing and feature sharing capabilities that can modulate based on input context
Multi-Modal Capabilities: Can process and understand various types of sequential data including text, audio, images, video, and time series data
Use Cases of Liquid AI
Autonomous Vehicles: Can be used for reliable steering and navigation in complex outdoor environments without extensive fine-tuning
Weather Forecasting: Capable of processing and analyzing complex time series data for accurate weather predictions
Enterprise AI Integration: Enables businesses to implement AI solutions with existing infrastructure due to its efficient resource usage and scalability
Multilingual Processing: Supports multiple languages including English, Spanish, French, German, Chinese, Arabic, Japanese, and Korean
Pros
Significantly smaller memory footprint and more efficient resource usage
Ability to adapt and learn from experience over time
Better interpretability and explainability compared to traditional models
Multi-modal capabilities with various data types
Cons
Relatively new technology with limited real-world implementation history
Not open-source, limiting community development and verification
Limited language support compared to some established models
How to Use Liquid AI
Note: Limited Access Currently: Based on the available information, Liquid AI's models are not yet publicly accessible. Users can only access them through specific platforms like Liquid's inference playground, Lambda Chat, or Perplexity AI.
Wait for Official Launch: The company is planning a launch event on October 23 at MIT Kresge in Cambridge where they will discuss LFMs and applications in various industries.
Monitor Development Updates: Liquid AI plans to release a series of technical blog posts leading up to the product launch event to provide more details about using their technology.
Consider Enterprise Solutions: Liquid AI plans to provide on-premises and private AI infrastructure for enterprise customers, along with a platform to build custom models. Interested organizations should contact Liquid AI directly.
Participate in Testing: The company encourages users to participate in red-teaming efforts to test and improve their models. Interested users can reach out to join these testing initiatives.
Liquid AI FAQs
Liquid AI is an MIT spinoff company that builds capable and efficient general-purpose AI systems using a new generation of models called Liquid Foundation Models (LFMs), which are non-transformer based AI models.
Official Posts
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Analytics of Liquid AI Website
Liquid AI Traffic & Rankings
123.8K
Monthly Visits
#346106
Global Rank
#6360
Category Rank
Traffic Trends: Sep 2024-Nov 2024
Liquid AI User Insights
00:01:21
Avg. Visit Duration
2.35
Pages Per Visit
42.48%
User Bounce Rate
Top Regions of Liquid AI
US: 22.49%
BR: 10.77%
AU: 6.17%
FR: 5.06%
TH: 4.02%
Others: 51.48%
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