AI behaves more like a brain than a database – cognitive science’s role in its origin story helps explai...

Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-10-07T12:35:34.316Z • Permanent link

Recent advancements in artificial intelligence have sparked intense interest in the scientific community, with many speculating about the true nature of large language models. This phenomenon owes its existence to a convergence of cognitive and computer science. The story begins with the work of Geoffrey Hinton, a renowned cognitive scientist and professor at the University of Toronto. Hinton's research on backpropagation, a type of neural network algorithm, laid the groundwork for the development of deep learning models.

In 2014, Hinton collaborated with Yann LeCun and Yoshua Bengio on the IBM Watson project, which aimed to create a question-answering computer system. The team's efforts led to the creation of a language model that could understand and respond to natural language inputs. This breakthrough marked the beginning of a new era in AI research, with many institutions and companies investing heavily in the development of large language models.

Google's BERT (Bidirectional Encoder Representations from Transformers) model, released in 2018, marked a significant milestone in the development of large language models. BERT's ability to understand context and nuances in language paved the way for the creation of more sophisticated models. The model's impact was felt across various industries, from natural language processing to healthcare, and has been credited with revolutionizing the field of AI research.

The rise of large language models has significant implications for the Social & Behavioral domain. Companies such as Google, Amazon, and Facebook are leveraging these models to improve their natural language processing capabilities, leading to more accurate customer service and more effective targeted advertising. Research communities are also benefiting from the advancements in language models, with many using them to analyze and understand complex social dynamics and behavior.

The impact of large language models can be seen in various markets, from healthcare to finance. For instance, the use of language models in medical diagnosis has improved accuracy and reduced the need for human intervention. In the financial sector, language models are being used to detect and prevent financial crimes, such as money laundering and insider trading. These advancements have far-reaching consequences, with many organizations and policymakers taking notice.

The development of large language models is part of a larger pattern of technological advancements in the field of AI. The 2010s saw a surge in the development of deep learning models, with many researchers and institutions investing heavily in this area. This was also a time of significant investment in cognitive science, with many researchers exploring the intersection of human and artificial intelligence.