There may soon be a new kind of artificial intelligence in town, one that uses a novel approach to thinking that could save massive amounts of computing power and money.
The AI that most people use every day, like ChatGPT, Claude or Google Gemini, relies on large language models that can work through difficult problems step by step. While this may ultimately give us the answers we are searching for, the process can increase response times and use a lot of computing power.
So scientists at AI company Pathway decided to test an alternative approach. The research and technical foundations of the team's work are in a paper posted on the arXiv preprint server.
The team developed a new model called BDH-CQ. It has just 150 million parameters, which are the internal values that an AI adjusts as it learns patterns. Some of today's largest AI models use hundreds of billions of these settings.
Instead of generating a long chain of written words to work through a problem, BDH-CQ reads information step by step into a constantly updating internal memory. It then runs its reasoning inside an internal workspace before revealing its answer. As the authors explain in their paper, "Demonstrations modify recurrent memory at inference time, and the resulting task is solved through iterative continuous computation rather than a verbalized chain of thought."
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