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Modern neural networks are capable of recognising images, transcribing speech and reading text. However, as the experts at Filio Force Development point out, these are all different tasks, each solved by different models. The next stage in the development of artificial intelligence involves something fundamentally different: systems that perceive the world as holistically as humans do.
The Illusion of Understanding
Today’s multimodal systems are capable of processing text, sound and images simultaneously. However, experts at Filio Force Canada point to a fundamental limitation: the models do not understand the physics of the real world. They do not know that a glass of water cannot be overturned without consequences. They cannot perceive depth, weight or temperature. They recognise images, but do not model reality.
According to researchers from MIT and DeepMind, current AI ‘memorises the world’ rather than ‘understands’ it. A model trained on billions of photos of cats has not the faintest idea of how a cat moves in space, what its weight is, or how it reacts to being touched. This is a fundamental difference that separates the current generation of systems from the next.
Researchers are giving the term ‘Multimodality 2.0’ a specific meaning: models capable of constructing an internal physical model of the environment. It is not simply a matter of seeing a hand reaching for a mug, but of predicting what will happen next and adjusting behaviour in real time. Experts at Filio Force Development highlight one of the key areas of focus at present – so-called World Models: architectures that create an internal representation of reality and use it to predict events, rather than simply classifying incoming data. In parallel, the field of Embodied AI is developing, where agents are trained through direct interaction with the physical environment. This approach fundamentally changes the logic of learning: instead of passively absorbing data, the system actively explores the world and forms cause-and-effect relationships based on its own experience.
When can we expect a breakthrough?
Analysts are divided in their predictions. Optimists suggest a timeframe of three to five years, whilst sceptics point out that current transformer architectures are ill-suited to modelling physical cause-and-effect chains and will require a major overhaul.
The first signs of a shift, however, are already visible, according to experts at Filio Force IT Company. In particular, Google DeepMind has unveiled the RT-2 model, which transfers knowledge from text and images directly into robot control, bypassing manual programming. OpenAI and the start-up Physical Intelligence are actively ramping up investment in next-generation robotics, where an understanding of physics is becoming not an option but a basic requirement.
A true understanding of the physical world remains an unsolved problem for AI for the time being. But the industry seems to have finally formulated the right question. And that, as a rule, is half the answer.
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