Viewpoint from the Data & AI Center of Excellence team at Amaris Consulting.
The world of AI is moving quickly, sometime too quickly to take stock of what truly matters. Behind the headlines and hype, there are quieter breakthroughs happening in labs and research centers that deserve our attention. They might not always make the front page, but they have the potential to change how we build, use, and think about intelligent systems.
At Amaris Consulting we spend a lot of time following these developments, not to chase trends, but to understand where the field is heading and how it might translate into meaningful impact for people, businesses, and society.
In this article, we’ve chosen to highlight a few standout research efforts that point to smarter, more thoughtful ways of working with AI.
Liquid neural networks: where AI learns to flow and adapt
Among the more intriguing developments in recent AI research is the concept of liquid neural networks, introduced by Liquid AI. These models are designed to adapt continuously to changing data, a feature that could make them particularly useful in unpredictable or fast-moving environments.
Unlike traditional neural networks, which rely on fixed parameters once trained, liquid neural networks adjust their internal weights dynamically. This flexibility allows them to respond more effectively to real-time changes in data streams. While still early in their development, they open promising paths in fields like robotics, autonomous systems, and healthcare, where conditions often shift faster than static models can keep up with.
What stands out to us is not just their potential performance, but their efficiency. Early research suggests they can achieve strong results while using fewer computational resources, making them an interesting option for real-world deployment.
There’s still a lot to learn about how these models scale and perform across different use cases. But the idea of neural networks that keep learning and adapting on the fly feels like a step toward making AI more responsive and perhaps more human in how it interacts with the world around it.
Stable Diffusion 3.5: creativity with AI-driven art
The release of Stable Diffusion 3.5 by Stability AI is a step forward in making high-quality image generation tools more accessible. This suite of models provides an improved platform for creators, whether they’re artists, designers, or businesses, looking to generate visuals with more precision and flexibility.
While previous versions of Stable Diffusion introduced the world to the power of AI-generated art, version 3.5 enhances those capabilities. It improves image coherence, reduces visual artifacts, and offers better detail generation. These upgrades allow for more realistic and diverse artistic styles, enabling users to produce images that can rival traditional digital design methods.
But beyond the technical improvements, what we find particularly exciting is the potential for democratization. AI tools like this one make it easier for people with limited technical expertise to create professional-grade visuals, opening up new possibilities in design and digital content creation.
Of course, the rise of AI-generated art also brings challenges related to authenticity, intellectual property, and ethics. Stability AI is actively addressing these issues by developing transparency features, so users can clearly differentiate between human-generated and AI-generated content. This is an important step toward ensuring responsible use of AI in creative industries.
FunnelRAG: refining data for more accurate AI
The quality of the data that feeds into a system can often make or break the accuracy of its output. FunnelRAG, developed by researchers from Harbin Institute of Technology and Peking University, offers an interesting approach to improving the retrieval-augmented generation (RAG) process.
Unlike traditional RAG models, which sometimes pull in a lot of unnecessary or irrelevant data, FunnelRAG refines the data retrieval process, ensuring that only the most useful and relevant information makes its way into AI responses. This results in AI systems that are not only more efficient but also more reliable in providing accurate and helpful information.
We believe this could be a real asset in industries where precision is key, such as customer service, legal research, or technical support. As AI tools continue to play a larger role in day-to-day tasks, making sure the information they provide is trustworthy and relevant is essential for ensuring their effectiveness.
MCP: a step toward seamless AI integration
One of the persistent challenges in the adoption of AI is how to integrate these systems smoothly with existing infrastructure. MCP (Model Context Protocol), introduced by Anthropic, aims to address this issue by offering an open-source standard for connecting AI models with external data sources and applications.
For many organizations, the lack of standardized methods for AI integration can lead to inefficiencies, delays, and complexities when trying to implement AI systems across different platforms. MCP simplifies this by providing a universal framework that ensures compatibility across a wide range of AI applications, reducing the time and effort involved in deployment.
What’s particularly encouraging about MCP is its focus on security and data privacy. As more businesses look to integrate AI into their operations, having a standardized approach that prioritizes these concerns is critical for ensuring responsible, trustworthy AI deployment.
Though we’re still in the early stages of seeing how MCP will be adopted at scale, it represents an opportunity to make AI more accessible and reliable for businesses of all sizes.
It’s about how we guide its development
Looking ahead, it’s clear that AI will continue to evolve in ways we can’t fully predict. The breakthroughs we’ve seen so far are just the beginning. What matters now is how we approach the next steps. As AI becomes more integrated into everyday life, the challenge will be to ensure that its growth aligns with the needs and values of people everywhere. We believe that the future of AI is shaped by more than just the technology itself; it’s about how we, as a global community, responsibly guide its development.
The next phase of AI innovation will require a careful balance of creativity, ethics, and collaboration. As researchers, practitioners, and users, we all have a role to play in building AI systems that enhance, rather than replace, human potential. At Amaris Consulting, we’re committed to learning alongside our partners and contributing to the thoughtful, transparent development of AI, one step at a time.
Discover the work of our Data & AI Center of Excellence and see how we’re contributing to a smarter, more sustainable approach to AI.
