Building AI for Science

From Neuroscience to Artificial Intelligence

A Founder With a Long-Term Vision

Demis Hassabis co-founded DeepMind in 2010. His goal was clear. Build artificial general intelligence inspired by the human brain. He combined neuroscience, psychology, and computer science.

Early work focused on reinforcement learning. The team trained AI systems to learn through trial and error. This approach helped machines improve without explicit programming. As a result, DeepMind created systems that learned complex tasks efficiently.

In 2014, Google acquired DeepMind. This gave access to massive computing power. It also allowed the team to scale research faster.

Breakthrough With Alpha Systems

Solving Problems Once Considered Impossible

DeepMind gained global attention with AlphaGo. It defeated world champion Lee Sedol in the game of Go. This marked a major milestone in AI capability.

However, the bigger impact came later. DeepMind introduced AlphaFold. This system predicted protein structures with high accuracy. Scientists had struggled with this problem for decades.

As a result, AlphaFold accelerated drug discovery and biological research. Thousands of researchers now use its database. This shifted AI from a tech tool to a scientific engine.

Scaling Impact Beyond Research

Applying AI Across Industries

DeepMind applied AI to energy efficiency in data centers. It reduced cooling costs significantly. This showed real-world value beyond labs.

Moreover, its models improved healthcare diagnostics and climate research. Each application followed the same principle. Use AI to solve high-impact problems.