This is the key to everything else. There is built in reproducibility and amplification of new, functional ideas in the ML community.
For the most part in life sciences, papers are published to achieve current grant aims and write future grants that will be funded. You can be an academic and love your research area and be ultra-passionate about it, but at the end of the day, grants are the end product that you are working for.
Your science does not have to work or be replicated, all you need to do is publish papers that make grant reviewers think you are reliable enough to not waste federal grant money. Nobody on the grant review board has time to look carefully to see if you papers are not fraudulent.
Let’s look at physics on the early 20th century, which had progress even faster than today’s machine learning research. Massive upheavals and rapid progress in understanding our world, including 4 different models of the atom (including the most correct one we still use today) and general relativity. What’s the difference to today’s life sciences? At the important epicenters of the day, working in the field was 1) contributing new observations, 2) directly testing somebody else’s theories with an experiment.
In today’s world, very rarely will somebody contribute new observations without an underlying motivation (get new grant money, advance current grant claims). And nobody has the time or resources to test other people’s ideas with new experiments. Why? Cause research is expensive and you would need a grant to fund a replication. And no government body funds those grants.
Disclaimer: there’s people in life sciences in some fields doing good work.
This is the key to everything else. There is built in reproducibility and amplification of new, functional ideas in the ML community.
For the most part in life sciences, papers are published to achieve current grant aims and write future grants that will be funded. You can be an academic and love your research area and be ultra-passionate about it, but at the end of the day, grants are the end product that you are working for.
Your science does not have to work or be replicated, all you need to do is publish papers that make grant reviewers think you are reliable enough to not waste federal grant money. Nobody on the grant review board has time to look carefully to see if you papers are not fraudulent.
Let’s look at physics on the early 20th century, which had progress even faster than today’s machine learning research. Massive upheavals and rapid progress in understanding our world, including 4 different models of the atom (including the most correct one we still use today) and general relativity. What’s the difference to today’s life sciences? At the important epicenters of the day, working in the field was 1) contributing new observations, 2) directly testing somebody else’s theories with an experiment.
In today’s world, very rarely will somebody contribute new observations without an underlying motivation (get new grant money, advance current grant claims). And nobody has the time or resources to test other people’s ideas with new experiments. Why? Cause research is expensive and you would need a grant to fund a replication. And no government body funds those grants.
Disclaimer: there’s people in life sciences in some fields doing good work.