The complexity of living organisms cannot be reduced to mere mechanical processes; they are dynamic systems with interdependent parts that evolve and adapt in ways machines cannot. No machine can autonomously write its own program; biological organisms have had billions of years to develop complexity and abilities that machines have yet to achieve.

Human beings are not machines; they possess consciousness, emotions, and the ability to create meaning, which machines cannot replicate or understand.

It's time to reconsider the notion of organisms designed solely for human-centric applications, as we have much to learn from bacteria and their roles on this planet.

The development of AI models without the evolutionary pressures that shaped human consciousness presents a significant objection to their potential consciousness.

AI's evolution is rapid and inorganic, potentially advancing from primitive forms to highly sophisticated systems in just a couple of decades, unlike biological evolution which took billions of years.

While there are data constraints in machine learning, we can still make significant progress in biology with the existing data, and there's a massive opportunity to generate new data tailored for machine learning applications.

Machine learning for biology is not limited to drug design; it encompasses a broader potential that significantly impacts human lives.

Machines taking over is a significant concern, and we should consider how less intelligent beings can control more intelligent ones, as history shows this is possible. AI has developed even faster than I thought.

While we are approaching human-level computing power with supercomputers, we still lack a clear understanding of how to replicate human intelligence.