Google mimics brain flexibility in a new AI that learns nonstop

In a move that redefines the concept of machine learning, Google is mimicking the brain's flexibility in a new artificial intelligence capable of constantly rebuilding itself and developing its knowledge. Instead of fixed models that forget information over time, this model comes to work as the human brain does: it learns, adapts, and expands its neural networks autonomously.

This is where the real transformation begins: not just a model that reacts to data, but a dynamic learning system that evolves with every experience. Here are the key points that reveal how this happens in Google's new model.

What is neuroplasticity and why have current systems neglected it? It is the unique biological ability of the human brain to reorganize itself and change its internal structure in response to new learning and experiences.

The brain can form new neural connections, reroute signals through alternative pathways, and strengthen or weaken links between neurons as needed. This mechanism is fundamental to all forms of learning, remembering, and adapting to a new environment.

The real problem is that current Large Language Models (LLMs) suffer from catastrophic forgetting; their knowledge is confined either to the immediate context or to the fixed information they learned during the training phase, and once the training is over, the model becomes fixed and incapable of real development.

The role of Google DeepMind and engineering innovation Google DeepMind is not just a traditional research laboratory. After the merger of Google Brain Lab with DeepMind in 2025, it became the parent organization for the world’s most ambitious artificial intelligence research . Its team works to combine artificial intelligence and neuroscience into one framework, using discoveries of the human brain to improve algorithms and develop new architectures.

As a result of this intensive scientific collaboration between Google researchers and the DeepMind team, a completely new technology called Nested Learning was born. Google developed a special model for this technology called Hope Architecture.

The key and important difference: This model does not process information in a single linear way like the old models, but rather relies on a complex system of multiple and overlapping layers that operate at different times and at different speeds; that is, some layers react quickly for quick decisions, and others work more slowly to form long-term knowledge. This mimics the way the real human brain learns, which uses a completely different processing of information.

Google mimics brain plasticity in new artificial intelligence: How does it work? Nested Learning fundamentally rethinks artificial intelligence . Instead of a single, simple machine, Google has developed a model that works like hundreds of small machines working together, much like the human brain works with millions of neurons. This is achieved through three integrated layers:

Multiple layers of memory: They operate at different speeds, some remembering immediate information such as the sentence you just read, and others storing long-term information such as what you learned weeks ago, to ensure that the model does not forget what it learned while focusing on what is important now. Self-regulating systems: Whenever the model learns something new, it changes its internal structure on its own, like your brain rewiring itself when learning. This means that the model gets smarter with each new experience and does not remain static. The new smart enhancers: They manage the learning process themselves. Instead of a teacher always giving you the same instructions, these enhancers learn how to teach better over time, remembering what worked and improving themselves based on the results. Experimental results: The numbers speak for themselves Practical tests have proven the strength of Google's new technology:

Long context handling: The new model achieves high accuracy even with very long contexts of up to millions of symbols. Continuous learning without forgetting: the ability to learn new knowledge without losing old knowledge. Improved computational efficiency: Less computational resource consumption compared to models of the same size. Superior performance on memory tasks: Successfully performed in tests of searching for information amidst huge amounts of data. Google mimics brain plasticity in new AI: future practical applications Evolution of digital services : Google apps and similar services will move beyond the stage of static performance. Instead of repeatedly providing the same responses and functions, these systems will become smarter with each interaction, learning from user behavior and preferences to continuously personalize the experience. Enhancing the online shopping experience: Recommendation systems in online stores can evolve from simply displaying products similar to those previously purchased to a deeper and more accurate understanding of customer preferences. The systems will learn from every click, every search, and every product overlooked, to offer future recommendations that seem carefully selected by an expert who knows you personally. Increased document processing efficiency: Long-text analysis applications, such as legal contracts or financial reports, will undergo a quantum leap, and will be able to understand complex contexts and identify key information with speed and accuracy that surpasses previous capabilities, which reduces the time and effort required to review and analyze huge amounts of written data. A new generation of robots and automation: Robots will move from being mere machines that execute pre-programmed commands to entities capable of truly learning and adapting to their environment. For example, an industrial robot can learn how to handle new and unfamiliar components through trial and error, or a household robot can learn the best cleaning route in your home based on people's movements and furniture placement. The step towards artificial general intelligence (AGI) Researchers believe this development represents a real step towards artificial general intelligence (AGI), which means artificial intelligence capable of performing almost any task that humans do, rather than specializing in only one area.

Solving the problem of catastrophic forgetfulness is one of the biggest remaining obstacles for AGI, and Nested Learning offers a real solution to this problem through its advanced memory system and continuous learning.

Remaining challenges and ongoing research Despite the remarkable success, challenges remain:

Compatibility with existing systems: Integrating Nested Learning with existing architectures requires additional work. Computational costs: More flexible models may require greater computational resources in some cases. Regulation and transparency: New laws such as the EU AI Act require transparency in continuous learning algorithms. Privacy and security: Ensuring that private data is not leaked during continuous learning . Frequently Asked Questions What is the layer of artificial intelligence that mimics the human brain? Hidden layers are the closest to simulating the brain, containing nodes that represent neurons, and Google's new innovation adds multi-level memory layers that operate at different time points.

What is Google's DeepMind AI? It is a giant research institution specializing in artificial intelligence and is owned by Alphabet Inc. It was founded as a British startup in 2010 and acquired by Google in 2014. It merged with Google Brain Lab in 2023 to become the current Google DeepMind.

The company is known for its groundbreaking innovations, most notably AlphaGo, which outperformed world champions in the game of Go, AlphaFold, which revealed the structure of proteins with high accuracy, in addition to the Virtual Rat Brain project, which provides a digital simulation of the functions of the mouse brain. What distinguishes its approach is the integration of artificial intelligence and neuroscience within a single, integrated vision.