Russian Scientists Simplify Neural Networks: New Method Makes AI Safer and More Reliable

Modern neural networks are highly effective at image recognition, text analysis, and event prediction. However, their excessive confidence can lead to dangerous outcomes. For instance, a system trained to distinguish cats from dogs might confidently classify a photo of a giraffe as a dog. Such mistakes are harmless in entertainment services, but in autopilot or medical systems, they can be extremely costly. The key challenge is teaching artificial intelligence to recognize when it encounters unknown or unusual data.

Professionals refer to this as the task of detecting out-of-distribution (OOD) data. It’s considered one of the most pressing challenges in AI safety. Traditional methods, such as Bayesian approaches, require enormous computational resources. Simpler solutions, like Laplace approximation, analyze the structure of the model’s parameter space to assess confidence in predictions. However, these methods also have their limitations.

The Precision Paradox: When Complexity Gets in the Way

A group of Russian scientists has challenged the established belief that accurately measuring parameter curvature always helps detect anomalies. Their experiments revealed that when data classes are easily distinguishable—for example, trucks versus airplanes—the decision space becomes too “sharp.” In such cases, standard methods mistakenly assume the model is entirely confident in its predictions and stop recognizing new, unfamiliar objects.

This paradox stood out most on simple datasets where classes are clearly separated. Instead of increasing reliability, complex curvature computations only made the problem worse. The researchers concluded that excessive geometric detail in decision-making not only fails to help but also prevents the system from detecting anomalies in time.

Radical simplification: a new perspective on uncertainty

In search of a solution, the scientists decided to abandon complex calculations and proposed an extremely simple model. Rather than analyzing the intricate shape of the parameter space, they used a symmetrical sphere, described by a unit matrix. This approach greatly simplified the computations and made the algorithm more adaptable.

The main advantage of the new method is its ability to automatically adjust the scale of this sphere for a specific task. This made it possible to achieve high efficiency without losing accuracy on known data. The new algorithm has been named ICLA and has demonstrated excellent results on popular benchmark datasets such as CIFAR-10, CIFAR-100, and ImageNet.

Advantages and prospects: where the new technology can be applied

Testing has confirmed that ICLA outperforms traditional methods in anomaly detection without reducing the accuracy of recognizing familiar objects. The model maintains the integrity of its predictions and does not require significant computational resources. This makes it especially attractive for implementation in systems where responsiveness and reliability are critical, such as in autonomous vehicles or medical diagnostic complexes.

The innovation lies in the fact that, to improve AI safety, it is not necessary to complicate the model—on the contrary, it should be simplified. This approach enables neural networks to better define the boundaries of their knowledge and promptly signal when they encounter something new. This paves the way for the development of more reliable and predictable artificial intelligence systems.

In case you didn’t know: what is MIPT and why are its innovations important?

Moscow Institute of Physics and Technology (MIPT) is one of Russia’s leading research and educational centers, founded in 1946. The university is renowned for its strong programs in physics, mathematics, computer science, and engineering. MIPT graduates traditionally hold key positions in research institutes, tech companies, and startups worldwide. The institute is actively involved in international research projects, and its laboratories regularly publish work in prestigious scientific journals. MIPT pays special attention to the development of artificial intelligence, robotics, and quantum technologies. In recent years, the university has become a venue for major conferences and hackathons focused on new technologies. Many innovative solutions developed at MIPT have found applications in industry, medicine, and transportation. The institute maintains close ties with leading universities and research centers around the world. Thanks to this, MIPT remains one of the main drivers of technological progress in Russia and beyond. If you are interested in the latest achievements in AI, MIPT’s developments deserve special attention.