IBM Research | How can generative models fuel scientific discovery? @ibmresearch | Uploaded March 2022 | Updated October 2024, 4 days ago.
Learn more about Generative Modeling for Scientific Discovery research.ibm.com/blog/generative-models-toolkit-for-scientific-discovery
Check out the toolkit github.com/GT4SD/gt4sd-core
Did you know that on average, it takes 10 years of research and between $10 million and $100 million to discover and develop a new material? In this new video, John R. Smith, IBM Fellow and Discovery Technology Foundations Leader at IBM Research, explores how generative models can reduce these constraints and fuel newer and faster discoveries. John walks viewers through the traditional, time-intensive steps surrounding hypothesis creation, followed by the potential of generative models to streamline this process compared to discriminative models seen in traditional machine learning. He also highlights multiple areas, including climate change and drug discovery, that stand to benefit from the power of generative modeling.
00:00 - What’s behind a scientific discovery?
1:00 - Where does creativity fit within the steps of the scientific method?
3:20 - Why is it difficult to come up with a good hypothesis?
4:50 - What’s the difference between generative and discriminative AI models?
6:30 - How can you apply generative models to help accelerate discovery
#science #AI #scientificmethod #hypothesis
Learn more about Generative Modeling for Scientific Discovery research.ibm.com/blog/generative-models-toolkit-for-scientific-discovery
Check out the toolkit github.com/GT4SD/gt4sd-core
Did you know that on average, it takes 10 years of research and between $10 million and $100 million to discover and develop a new material? In this new video, John R. Smith, IBM Fellow and Discovery Technology Foundations Leader at IBM Research, explores how generative models can reduce these constraints and fuel newer and faster discoveries. John walks viewers through the traditional, time-intensive steps surrounding hypothesis creation, followed by the potential of generative models to streamline this process compared to discriminative models seen in traditional machine learning. He also highlights multiple areas, including climate change and drug discovery, that stand to benefit from the power of generative modeling.
00:00 - What’s behind a scientific discovery?
1:00 - Where does creativity fit within the steps of the scientific method?
3:20 - Why is it difficult to come up with a good hypothesis?
4:50 - What’s the difference between generative and discriminative AI models?
6:30 - How can you apply generative models to help accelerate discovery
#science #AI #scientificmethod #hypothesis
