Liquid Neural Networks: The Digital Brain That Understands the Context of Your Day
Lucas Magnus | Apr 23, 2026
Summary
- The report covers the technical ecosystem behind AI development, including Python libraries (Pandas, Scikit-learn, Matplotlib, Streamlit), deep learning frameworks (TensorFlow, PyTorch, JAX, Keras), vector databases (ChromaDB, FAISS, Pinecone), and pre-built AI services (Amazon SageMaker, Google Colab, Amazon Bedrock, Amazon Kendra, Predibase, Google Vertex AI).
- It explores real-world AI applications such as natural language processing, speech-to-text and text-to-speech, chatbots, content generation, recommendation systems, and predictive analytics, citing specific tools used in each area.
- The report breaks down AI pricing models — resource-based, compute unit, and generative model pricing — with examples from Amazon SageMaker and Amazon Bedrock to illustrate cost structures.
- It includes a case study on building a knowledge base chatbot using LangChain, ChromaDB, and Amazon Bedrock to answer questions about Cheesecake Labs' services.
Artificial Intelligence Beyond GenAI: State of Tech Research in 2024
Cheesecake Labs is delighted to share a new resource for tech executives: Artificial Intelligence Beyond GenAI: State of Tech Research in 2024.
Why you can’t miss this report
AI is a goldmine set to be worth $15.7 trillion by 2030. But with the flood of news and commentary around this technology, how do you identify what matters?
Our no-fuss, comprehensive report zeroes in on what you need to know as a tech leader:
- Discover the technical ecosystem powering AI innovation
- See AI at work in the real world to identify opportunities for your business
- Understand AI pricing models to gauge your project’s cost
Whether you’re new to AI or want to enhance AI-driven features within your existing products, this whitepaper can help you steer your initiatives.
Key tools driving AI’s growth
Our report demystifies the tools integral to AI projects to help you find your bearings. We guide you through the technical stack engineers and data scientists are using to develop AI models, including:
Primary language and core libraries
Python remains the primary AI development language because it has extensive libraries optimized for local, low-computation machine learning, including:
- Pandas for swift and intuitive data analysis and manipulation
- Scikit-learn for efficient data mining and data analysis
- Matplotlib for static, animated, and interactive visualizations
- Streamlit for building web applications and dashboards
Deep learning frameworks
Developers rely on these tools to build and train neural networks:
- TensorFlow for large-scale production deployments, especially TPU projects
- PyTorch for faster model training with GPU and CPU support, suitable for large models and real-time applications
- JAX for high-performance machine learning with accelerated linear algebra and automatic differentiation capabilities
- Keras for building and training neural networks on top of TensorFlow, PyTorch, or JAX
Vector databases
These databases handle the massive datasets that power today’s AI apps:
- ChromaDB for flexible storage tailored to Python and JavaScript developers
- FAISS (by Meta’s AI research team) for efficient similarity searches and dense vector clustering, accessible via a Python wrapper
- Pinecone for scalable cloud-based data management
Pre-built AI services
These ready-to-use models can be customized for specific tasks, allowing developers to quickly deploy AI solutions without extensive training:
- Amazon SageMaker for creating, training, and experimenting with new ML foundational and open-source models
- Google Colab for team coding, with free access to Jupyter Notebooks online
- Amazon Bedrock for connecting to a variety of foundational models from leading AI companies, allowing model training with custom data and on-demand/time-based provisioning
- Amazon Kendra for intelligible search solutions using LLMs
- Predibase for creating and fine-tuning instances with open-source foundational models using custom data
- Google Vertex AI for creating instances of open-source and third-party models, training custom models, and developing experimental code
Exploring real-world AI use cases
AI is everywhere, from virtual assistants to medical diagnoses. Our report explores some of the key AI developments and their practical uses:
- Natural language processing platforms that can generate human language (such as Google Cloud NL API and IBM Watson) are now widely applied in email filtering, smart assistants, predictive text, language translation, and data extraction.
- Speech-to-text technologies like Apple SiriKit convert spoken language into text for voice commands and transcriptions, while text-to-speech technologies like Apple’s AVSpeechSynthesizer transform written text into spoken output for voice-controlled devices.
- Chatbot platforms such as Dialogflow and Pandorabots simulate human-like conversations to provide automated responses and assistance in various industries.
- Content generation tools like OpenAI GPT make it quicker to produce marketing materials such as blog posts, social media content, and product descriptions.
- Recommendation systems like Amazon Personalize and Google Recommendations analyze user data to suggest targeted products, services, and content.
- Predictive analytics tools such as Google Cloud AI Platform and Microsoft Azure Machine analyze data and algorithms to forecast future outcomes, making them useful in fraud detection, customer segmentation, risk modeling, and healthcare.
The cost of AI innovation
What is the financial outlay for AI projects? Our report breaks down common AI pricing models to help you select cost-efficient technologies:
- Resource-based pricing sets charges based on the overall time and computational resources consumed during the model development process.
- Compute unit pricing applies fees for cloud resources based on the type of hardware used (TPU or GPU).
- Generative model pricing calculates charges based on the volume of data processed, factoring in elements like embedding outputs.
We also included real-world examples from Amazon SageMaker’s and Amazon Bedrock’s pricing to illustrate how costs are structured in practical scenarios.
A Cheesecake Labs exclusive case study
See how we built a knowledge base chatbot that uses insights from our blogs to handle questions about Cheesecake Labs’ services.
We walk you through the project’s technical journey — including how we used LangChain, ChromaDB, and Amazon Bedrock to develop and refine this LLM model. Maybe you’ll gain insights from our process to enhance your own AI development efforts!
Getting AI right for your business
Successful AI projects are built on a clear picture of where this technology is and where it’s going.
Download the full report to better understand the AI environment so you can start planning your tech stack and budget.
Do you have an AI-related project and need a development partner to make it happen? Let’s chat!
FAQ
What does the 'Artificial Intelligence Beyond GenAI: State of Tech Research in 2024' report cover?
The report covers the technical ecosystem powering AI innovation, real-world AI applications to help identify business opportunities, and AI pricing models to help gauge project costs. It is designed for tech leaders who are new to AI or want to enhance AI-driven features within existing products.
What key tools are highlighted as driving AI's growth?
The report highlights Python as the primary AI development language, along with core libraries like Pandas, Scikit-learn, Matplotlib, and Streamlit. It also covers deep learning frameworks (TensorFlow, PyTorch, JAX, Keras), vector databases (ChromaDB, FAISS, Pinecone), and pre-built AI services (Amazon SageMaker, Google Colab, Amazon Bedrock, Amazon Kendra, Predibase, Google Vertex AI).
What real-world AI use cases are explored in the report?
The report explores natural language processing platforms (Google Cloud NL API, IBM Watson), speech-to-text and text-to-speech technologies (Apple SiriKit, AVSpeechSynthesizer), chatbot platforms (Dialogflow, Pandorabots), content generation tools (OpenAI GPT), recommendation systems (Amazon Personalize, Google Recommendations), and predictive analytics tools (Google Cloud AI Platform, Microsoft Azure Machine).
What AI pricing models are discussed?
The report breaks down three common AI pricing models: resource-based pricing (charges based on time and computational resources used during model development), compute unit pricing (fees for cloud resources based on hardware type such as TPU or GPU), and generative model pricing (charges based on volume of data processed, including embedding outputs). Real-world examples from Amazon SageMaker and Amazon Bedrock pricing are included.
What case study is included in the report?
The report includes a case study on building a knowledge base chatbot that uses insights from blog content to handle questions about Cheesecake Labs' services. It walks through the technical journey of how LangChain, ChromaDB, and Amazon Bedrock were used to develop and refine the LLM model.
About the author.
