To make AI more accessible to all. Not just for the chosen ones.
We are a team of scientists and engineers applying the principles of neurobiology to machine intelligence research. Our neurobiology research code is publicly disclosed and available.
We share events from the life of our organization, so that everyone knows that we cannot be stopped.
Today, on 29/07/2020, The Neurosphere Organising Committee decided to stop blogging. In almost six years, it has fulfilled its role in promoting and developing artificial intelligence and neural networks. Over the years, the resource, as well as the occasional Internet dialogues that appear on it, has significantly expanded both its understanding and saturation, as well […]
Read DetailsAstrophysicists have decided to use the most modern artificial intelligence (AI) in order to create a three-dimensional site model of the Universe. The project was called the Deep Density Displacement Model (D3M) and researchers don’t understand how the AI was able to create something like that. Astrophysicists were impressed not only that the D3M turned […]
Read DetailsMany of the technologies are already used in robotics, not only in prototypes, but also in mass production. The biggest journey so far has been in the areas of computer vision and natural language processing, in other words, in the recognition of visual and text information. Robotic systems are already in place, successfully applying some […]
Read DetailsThe SYPWAI start-up has already attracted over $90 million in investments. Life Asapa and existing investors have invested in it. In total the company has already attracted $110 million of investments. And today The Neurosphere announced the launch of its start-up. As previously reported, the SYPWAI platform has already been for a long time tested […]
Read DetailsComputer vision This is the processing of visual information to obtain knowledge. The basic task inside this technology is to detect the object in images and video, i.e. to recognise that one picture in a corner shows a car and the other one shows a computer, keyboard and phone. In robotics, the results of object […]
Read DetailsRobotics has developed separately from artificial intelligence (AI) for a long time, but now automation has no prospects without it. Analysts have considered the practice of using AI to create robots. The concept of artificial intelligence is inseparably related to robotics. In essence, a robot is a machine that is able to take the environment […]
Read DetailsWe are a team of scientists and engineers applying the principles of neurobiology to machine intelligence research. Our neurobiology research code is publicly disclosed and available.
The Neurosphere has developed the first public platform for learning neural networks for all interested parties and now we are learning how to incorporate key theory principles into the field of machine intelligence.
We are one of the few teams that have developed a large-scale platform to study sections of the brain that are biologically restricted, tested and implemented in software. We believe that the SYPWAI platform will be the basis for creating really intelligent systems.
Over the last decade, machine learning and artificial intelligence have succeeded in performing many tasks that were previously unimaginable. Such problems as identifying a cat in an image or recognizing someone’s speech have become commonplace in our lives. However, the way in which these technologies solve problems is fundamentally different from how our brain performs the same tasks. For example, a machine learning system that recognizes a cat can be trained with more than a million labeled training images to reliably identify a cat, while a human child will only learn about a cat by a few examples. Furthermore, this child knows that the cat can purr, the cat can scratch and the cat can jump high, whereas all the machine knows is what the cat looks like. The machine doesn’t understand the cat; the child does.
The limitations of modern artificial intelligence systems are generally agreed upon in the machine learning community. Such systems require huge amounts of time and resources for learning, they are fragile to noise, cannot learn continuously and do not summarize, but rather achieve narrow, specific goals. We are still far from a robot that performs tasks that a child can easily do.
We do not believe that these limitations can be overcome simply by following the same path with a large amount of data and more power. A fresh approach is needed, and we believe that it should include what we have learned from the brain.
We have a reliable road map that applies the principles of neurobiology to direct us towards machine intelligence. In the near future, we will apply our model to the existing CNN and AI models to enable systems that are more like the brain, such as systems that are noise-resistant and capable of continuous learning. In the long term, we will continue to create more parts of our model with the ultimate goal of creating intelligent sensor-based systems that can learn, plan and act.