WE ARE ON A MISSION

 
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OUR PURPOSE

Using Thinking Networks to scale reasoning has become a necessity. Human linear thinking, which can only processes 5-9 concepts at the same time, can’t handle the exponential growth of knowledge we are witnessing in this century.

While humans are very good at formalizing and connecting pieces of knowledge on a relatively small scale, thinking machines are capable of exponentially exploring all the possible inference combinations between them. With 7.4 billion people on the planet, our problems are becoming increasingly challenging. The use of Reasoning Computing will give us unprecedented power for solving complicated problems, particularly in domains such as medicine and life science.

 
 

“WITH OUR TECHNOLOGY, WE ENABLE INDIVIDUALS & ORGANIZATIONS TO USE THE FULL POTENTIAL OF THEIR KNOWLEDGE”

KHAI MINH PHAM, FOUNDER & CEO

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THE LIMITS OF BIG DATA

Humans solve problems by using two main cognitive approaches: Pattern Recognition and Reasoning. Unfortunately, we cannot scale to handle the ever-­­increasing volumes of data and knowledge. Similarly,Artificial Intelligence (AI) is composed of two main schools: Pattern Recognition(Data Mining/Machine Learning using Big Data) and Problem Solving(Reasoning Computing using Complex Knowledge).

The AI market today is mainly focused on data, scaling Pattern Recognition using Machine Learning (ML).  The results have been amazing in applications ranging from self-driving cars to skin cancer diagnosis through image recognition, where extracting correlations from Big Data is the goal. These Pattern Recognition systems work extremely well when “digesting” large amounts of data to discover patterns and to build predictive systems, but they are “black boxes”, meaning they can’t provide an explanation for their results. This is an obvious problem for any decision support system in health, finance, biotech, etc, where explanation is vital to both justify decisions and to improve upon a system. Equally problematic, the reasoning capabilities of Pattern Recognition systems are limited to pattern matching while sophisticated reasoning is necessary for solving complex analytical problems.
 

REASONING COMPUTING:
THE NEXT BIG MARKET IN AI

ThinkingNode™ can be used to model and run simulations of cellular dynamics, the genome, metabolic networks, the immune system, the drug compendium with their mechanism of action, diagnostic approaches, therapeutic procedures, etc. Once the models have been formalized into Reasoning Networks, scientists can make sophisticated hypotheses and test them using as many parameters and concepts as needed. Imagine how much faster progress could be made if scientists were able to access all available knowledge and compute them at any level of complexity - way beyond the 5 to 9 concepts that a human mind can handle.

In addition, the ThinkingNode™ platform enables scientists to easily collaborate by sharing these models as they currently share spreadsheets. This approach will break down silos and accelerate drug and biomarker discovery, drug safety analysis, clinical trial design, production of compounds, confirmation of diagnoses, design of combination therapies, and more.  In fact, ThinkingNode™ can be used in any domain where exponential thinking is needed.

ThinkingNode unlocks the promise of Reasoning Computing by providing the exponential thinking capability we need to solve complex problems.

 
 
 
 

THINKINGNODE
CAN REVOLUTIONIZE YOUR INDUSTRY

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