How natural language and logical reasoning are used to develop cancer drugs

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In 2015, David Ferrucci, the award-winning artificial intelligence (AI) researcher who led the development of IBM Watson, which won the 2011 Jeopardy game show against two of the game's top champions, remarked that most AI systems failed. understand the meaning of language. This meant that they could not provide detailed and reasoned explanations for the results.

It was then that Ferrucci founded Elemental Cognition, a New York-based AI research and technology company, to address one of the toughest challenges facing the future of AI: developing the ability to reason and understand beyond statistical machine learning and data analysis, overcome bias and deliver insights at scale.

"Elemental Cognition's mission is to go beyond traditional AI to help humans understand complex content, reason about it, and find and explain plausible answers," Ferrucci told VentureBeat .

Now the company is applying those efforts to cancer research. For example, last week the company announced a partnership with the Philadelphia neuro-oncology research center, Penn Medicine Brain Tumor Center, to accelerate the development of therapeutic drugs.

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Seven years in development, the company's hybrid AI platform combines natural language understanding, machine learning, explicit knowledge models and automated reasoning to enable a new class of AI applications. AI capable of learning and providing explainable intelligence. What makes the platform interesting is its three-pronged approach.

First is Cogent, Elemental Cognition's expert Engagement solution that captures critical knowledge to power expert applications and enables users to share, maintain, and extend their natural language expertise.

Next, the Cordial engine introduces "reasoning" to the platform. Scalable APIs can empower chatbots, websites, or applications with interactive problem-solving intelligence and provide expert interactions at a fraction of the cost. This can help users customize and evaluate alternatives, allocate resources, optimize schedules, diagnose outages, apply and explain policies, etc., enabling them to handle overwhelming situations in time. real.

The company's Collaborative Research Assistant (CORA) is a software-as-a-service (SaaS) application based on a hybrid AI platform that aims to accelerate research by obtaining fast, unbiased answers and logic to complex research questions. It uses natural language understanding and transparent logical reasoning to interactively help discover, prove and connect knowledge.

CORA helps researchers aggregate information from various data sources by performing semantic search on concepts and relationships that are automatically inferred from the data. It codifies expert knowledge, creates repeatable research models and develops reasoning about data to surface new insights, highlights evidence to support or refute any discovery, and automatically summarizes findings logically with evidence and references.

While CORA can be deployed in multiple industries, it specifically adds great value to biomedical research through its ability to analyze content, identify links...