AI’s Obstacles In Chemical Innovation: Polyani Paradox Impact – Healthcare – United States
As reported here, AI has been transformative for many industries in 2023, including biotechnology, healthcare, finance, education, and more. That being said, AI also faces many challenges and the Nature editorial below discusses the slow progress of using AI and automated systems for chemical synthesis. This discussion has important lessons for using AI in general and may suggest that the Polyani paradox is a significant obstacle to AI-driven innovation.
The main challenges of using AI in the development of new and improved chemical synthesis processes include the following:
Future developments in robotics will certainly provide automatic systems that can test more comprehensive ranges of chemical reactions, and the amount of data available for training AI systems is continuously increasing. The general need for more data for hungry AI models may also be solved by developing specialized AI systems such as AlphaFold.
However, the lack of negative data problem may be tough to handle because negative data is rarely published by scientific journals. Chemists are addressing this issue through efforts like the Open Reaction Database, but it remains a significant hurdle.
The negative data problem points to a deeper problem for AI in scientific innovation that is often referred to as the Polyani Paradox, after the science philosopher with the same name. According to Polyani, scientific discovery relies on personal knowledge that is acquired by experience and internalized unconsciously. The Polyani Paradox can be summarized as We can know more than we can tell.
Negative data points are often internalized and unexpressed experiences that become part of the personal knowledge of individuals or groups of scientists. So, information, insights, and experiences critical for innovation may never be expressed verbally or in a tangible propositional form that AI models can learn from. Negative data and the Polyani Paradox may consequently be crucial blind spots of certain applications of AI and essential to be aware of when using AI models, whether for scientific discovery or any human endeavor such as legal problem-solving. To become better than a human chemist, as requested by the Nature editorial, AI models must somehow overcome the Polyani Paradox.
The field of chemical innovation is currently grappling with the challenges posed by AI and automated systems. While AI has revolutionized various industries, its progress in chemical synthesis has been slow, emphasizing a need to address the Polyani Paradox—a significant hurdle that limits AI-driven innovation.
Developing more advanced robotics can enable automatic systems capable of testing a wider range of chemical reactions. Additionally, the continuously increasing availability of data for training AI systems shows promise. Specialized AI systems like AlphaFold can help fulfill the hungry appetite for data required by AI models.
However, one major obstacle is the lack of negative data. Scientific journals rarely publish negative results, making it difficult for AI models to learn from unsuccessful experiments. Efforts such as the Open Reaction Database aim to address this issue, but it remains a significant challenge for the field.
The Polyani Paradox, named after science philosopher Michael Polanyi, poses a deeper problem for AI in scientific innovation. According to Polanyi, scientific discovery relies on personal knowledge acquired through experience and internalized unconsciously. The paradox can be summarized as We can know more than we can tell.
Negative data points, representing internalized and unexpressed experiences, form part of the personal knowledge of scientists or groups. This information, critical for innovation, is often never expressed in ways that AI models can learn from. The Polyani Paradox and the scarcity of negative data are blind spots that need to be considered when utilizing AI models in scientific discoveries and other human efforts, like legal problem-solving.
Overcoming the Polyani Paradox is crucial for AI models to surpass human chemists, as emphasized by the Nature editorial. By finding ways to capture and utilize implicit knowledge effectively, AI can truly unlock its potential as a transformative tool in the realm of chemical innovation.
In conclusion, while AI has made significant strides across various industries, the field of chemical innovation still faces challenges in harnessing its full potential. The Polyani Paradox and the scarcity of negative data pose hurdles that need to be addressed to ensure AI-driven innovation flourishes. By acknowledging and overcoming these obstacles, the scientific community can unlock the true power of AI in revolutionizing chemical synthesis and advancing healthcare, among other sectors.
