A latest hybrid AI approach can also considerably cut power use while enhancing reliability.
Artificial intelligence isn’t just converting software. It is likewise driving a sharp increase in electricity use. In the US alone, AI systems and data centers consumed about 415 terawatt-hours of electricity in 2024, as per the International Energy Agency. That amounts to more than 10% of the nation’s total energy output, and the figure is anticipated to double by 2030.
That trend is elevating a hard query for the future of AI: Can these systems become more capable without becoming considerably more costly to power?
Researchers on the Tufts University School of Engineering believe the solution may be yes. They have constructed a proof of concept for an AI approach that might use to a 100 times much less energy than nowadays’s preferred systems while also generating more right outcomes on certain tasks. In a field that regularly rewards ever large models and ever large computing infrastructure, that kind of development will be significant.
The work was evolved in the laboratory of Matthias Scheutz, Karol Family Applied Technology Professor. It centers on neuro-symbolic AI, which integrates standard neural networks with symbolic reasoning, just like how people break issues into steps and categories.
Rethinking How AI Systems Learn and Act
Scheutz and his team study robots that interact directly with people, so their work differs from screen-primarily based large language models (LLMs) which includes ChatGPT or Gemini. Rather than, they target on visible-language-action (VLA) models. These systems enlarge LLMs by adding vision and movement, permitting robots to interpret camera and language inputs and carry out physical actions which include moving wheels, arms, or fingers.
With conventional and resource-heavy VLA systems, even a easy task like stacking blocks may be error-inclined. A robot have to scan its surroundings, discover each block’s position, shape, and orientation, after which follow with instructions to stack them. Errors can rise up if shadows distort perception, if blocks are placed incorrectly, or if the final structure is unstable and collapses.
These mistakes resemble the well-known shortcomings of LLMs. Just as robots can fail in physical tasks, chatbots can generate incorrect or fabricated outputs, including inventing cases or producing images with unrealistic features like more fingers.
Symbolic reasoning providing a more efficient options. It lets in systems to apply general rules and abstract concepts, which includes of shape or center of mass, leading to more dependable planning with much less trial and errors.
How Neuro‑Symbolic Systems Work Better
“Like an LLM, VLA models act on statistical results from large training sets of same situations, but that may cause mistakes,” stated Scheutz. “A neuro-symbolic VLA can apply policies that restriction the amount of trial and error during learning and get to an solution a whole lot quicker. Not only does it whole the task much faster, more over the time spent on training the system is extensively reduced.”
In experiments using the classic Tower of Hanoi puzzle, the neuro-symbolic VLA system obtained a 95% success rate, compared to 34% for standard VLA models. When examined on a more complex version of the puzzle that the system had not encountered before, it still reached a 78% success rate, whilst conventional systems failed every attempt.
Training time also considerably decreased. The neuro-symbolic device needed just 34 mins to train, whilst a standard VLA model took more took more than a day and a half. Energy use decreased just as sharply. Training consumed only 1% of the energy needed by conventional models, and at some point of operation, the system used just 5% as much energy.
Scheutz compares these findings to acquainted LLMs like ChatGPT and Gemini. “These systems are just looking to predict the following word or action in a chain, however that can be imperfect, and they are able to come up with inaccurate result or hallucinations. Their energy cost is often disproportionate to the task. For example, while you search on Google, the AI summary at the top of the page consumes as much as 100 times more energy than the generation of the website listings.”
Toward a More Sustainable AI Future
As demand for AI keeps to grow and expands into industrial use, corporations are racing to construct larger data centers. These centers can need hundreds of megawatts of power, a ways exceeding the needs of many small cities.
The researchers claims that today’s LLMs and VLA systems, in spite of their quick adoption, won’t provide a sustainable or dependable long-term foundation. They suggest that hybrid neuro-symbolic AI offers a more efficient and reliable option, with the potential to ease mounting pressure on energy resources.
