Events
Learn about the latest events and where we will be showcasing our latest developments.
QuantMinds – Why Causal AI Prevents Overfitting
The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future.
Read moreAn Intro to Causal AI for Asset Management
causaLens' Ben Steiner will be speaking on Causal AI and its uses in asset management.
Read moreAI in Finance Summit
A financial industry event to discover cutting-edge advancements in AI & Machine Learning and their adoption in financial services to increase efficiency & solve challenges.
Read moreActual causality, responsibility, explanations, and fairness – a bird’s eye view
causaLens' own Hana Chockler speaks on her leading research on actual causality, and its relationship to core concepts such as responsibility, explanation, and fairness.
Read moreAI Humans Can Trust
Leaders who make the most transcendent decisions for our society are unable to trust current AI systems to help them make those decisions.
Read moreBattleFin
With the market valued at over $1B currently and expected to continue growing at 40% yearly, the world of alternative data is changing rapidly.
Read moreLondon AI Summit: Meet the scaleups shaping the future of AI
The AI Summit is the world's foremost event to look at the practical implications of AI for enterprise organizations: the actual solutions that are transforming business productivity.
Read moreQuantitative Finance Conference – Why Causal AI Prevents Overfitting
The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future.
Read moreAdvanced Alpha Testing Techniques
Our CEO Dr. Darko Matovski gives a presentation on Causal AI powered alpha testing techniques and participate in a panel with fellow industry leaders.
Read moreAdapting ML strategies during a pandemic
The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future. This approach can work in static environments and for closed problems with fixed rules.
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