How we can bridge the AI divide with accessible AI data scientists
AI data scientists automate data processing, making advanced analytics accessible to all organizations – driving innovation, efficiency and actionable insights.
Entrepreneur and software engineer known for pioneering work in causal artificial intelligence (AI) – a new category of intelligent machines that reason like humans. Co-Founder and Chief Executive Officer of causaLens, a $50 million venture-backed scale-up. Holds a PhD in Artificial Intelligence and an MBA from the University of Southampton. Prior to causaLens, made transformative contributions to AI research at the National Physical Laboratory and Man Group.
AI data scientists automate data processing, making advanced analytics accessible to all organizations – driving innovation, efficiency and actionable insights.
Where genAI relies on recognizing correlations and patterns in events, causal AI is rooted in a deeper understanding of the cause and the effects behind them.
Black box approaches to AI, including large language models and other generative techniques, are unlikely to comply with regulations for many uses. Causal AI offers a solution.