Superhumans and the Race for AI Supremacy | Stephen Hsu
Feb 5, 2024
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Demetri Kofinas speaks with Stephen Hsu, Professor of Theoretical Physics and Computational Mathematics, about advancements in genomic science and AI, the competitive landscapes in both industries, and the risks and opportunities associated with these technologies.
Genomic Prediction offers advanced genotyping and prediction services for disease risks in embryos, standing out in the IVF industry.
Advances in genomic science enable the analysis of genotypes to predict phenotypes like height, intelligence, and disease risks.
Machine learning algorithms can compute polygenic scores to predict complex traits, but the limitation lies in obtaining enough high-quality data.
The intersection of genomics, wearable technology, and AI presents opportunities for personalized medicine but raises challenges in privacy, data accuracy, and interpretation.
Deep dives
Genomic Prediction and Embryo Selection
Genomic Prediction, founded in 2017, is a company focused on embryo selection in in vitro fertilization (IVF). They offer services related to pre-implantation genetic screening, where a small biopsy is taken from the embryo to genotype it. The company can analyze the entire genome of the embryo and compute various predictions, including the risk for certain diseases. Their testing is more accurate than traditional methods and provides more information from the genome. Genomic Prediction competes with other companies in the IVF space, such as Natera and Cooper Genomics, but stands out with their advanced genotyping process and ability to compute polygenic scores.
Understanding Genomic Variation
Genomic variation refers to the differences in DNA sequences among individuals. The human genome consists of about three billion base pairs split into 23 chromosomes. The genotype refers to the specific DNA sequence of an individual, while the phenotype encompasses the observable characteristics determined by the genotype. Understanding the relationship between genotype and phenotype is a fundamental unsolved problem in biology. Advances in genomic science and computational genomics are enabling researchers to analyze genotypes and predict phenotypes, such as height, cognitive abilities, and disease risks. High-quality data is crucial for accurate predictions and improving our understanding of the genetic architecture behind complex traits.
Polygenic Traits and Predictive Algorithms
Polygenic traits, like height and intelligence, are influenced by many genes working together. Algorithms based on machine learning can compute polygenic scores that predict phenotypic outcomes based on genomic data. The accuracy of these predictions largely depends on the volume and quality of data available. While the algorithms are becoming more sophisticated, the main limitation currently lies in obtaining enough high-quality data with labeled phenotypic information. Certain companies, like Genomic Prediction, are leading the way in accurately predicting complex traits by genotyping embryos and analyzing their whole genomes for more precise predictions.
Ethics and Regulation in Genomic Science
As technology advances, important ethical considerations arise in the field of genomics. Currently, regulations and guidelines govern what information can be shared with patients and how certain traits and diseases can be screened during embryo selection and pre-implantation genetic testing. The science of making intentional changes to DNA to create designer babies presents unique social and ethical dilemmas. Various factors, including public opinion, scientific progress, and regulatory frameworks, will shape the future of genomic science and its applications in reproductive technologies. Balancing scientific advancements with ethical considerations will be crucial as the field continues to develop.
Intersections of Genomics, Wearable Tech, and AI
The intersection of genomics, wearable technology, and artificial intelligence presents new opportunities and challenges. Wearable devices, such as fitness trackers and smartwatches, can provide longitudinal data for individuals, contributing to a better understanding of health and behavior patterns. Coupling this data with genomic information could revolutionize personalized medicine and enhance preventive healthcare efforts. However, the availability of accurate and diverse data remains a significant challenge. Additionally, AI plays a crucial role in analyzing and interpreting the vast amount of genomic and wearable tech data. Understanding how AI influences privacy, security, and decision-making processes is essential to navigate this rapidly evolving landscape.
Dating Apps, Genomics, and AI
The future of dating apps may involve incorporating genomic insights, creating a complex landscape with social, ethical, and privacy implications. While the extent of data collection by dating apps is often secretive, the potential for connecting genomic information with dating profiles raises questions about consent, genetic discrimination, and unintended consequences. The broader field of artificial intelligence is also influencing dating apps, shaping algorithms for matches and predictions. The impact of genomics and AI on the dating landscape will depend on public perception, cultural norms, and regulatory frameworks, ensuring that technology respects individual autonomy, privacy, and diversity.
The Broader Implications of AI in Genomics
AI plays a significant role in genomics, driving advancements in data analysis, predictive algorithms, and personalized medicine. However, the use of AI in the private sector, geopolitics, and national security raises complex challenges. AI carries risks such as algorithmic bias, security vulnerabilities, and potential unintended consequences. The discussion around AI in genomics should encompass questions of ownership, access, ethics, and the responsible development and deployment of these technologies. Balancing innovation, regulation, and public trust will be essential to harness the potential of AI in genomics while mitigating its risks.
In Episode 351 of Hidden Forces, Demetri Kofinas speaks with Stephen Hsu, a Professor of Theoretical Physics and Computational Mathematics, Science, and Engineering at Michigan State University. Stephen is also the co-founder of multiple companies, including Genomic Prediction, which provides preimplantation genetic screening services for human embryos, and SuperFocus.ai, which builds large language models for narrow enterprise use cases.
This is a conversation about some of the most important advancements and trends in genomic science and artificial intelligence, including the social and ethical dilemmas arising from implementing these technologies at scale. Stephen and I discuss the competitive landscapes in both industries, how America’s geostrategic competition with China is driving tradeoffs between innovation and safety, the risks and opportunities that these revolutionary technologies pose, and how the world’s largest companies, economies, and military powers can work together to reap the benefits of this revolution while averting some of their most disastrous potential consequences.
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