Each year without proper AI instruction is another generation paying the price through diminished cognitive development.
But Educators face a challenge: how do you teach something you haven't been trained in yourself?
@chris_walker_ It's a tempting idea, but more complicated than it sounds.
The tl;dr is that what you learn with AI you learn how to do with AI. Like with calculators, you have to build the foundation before introducing the tech.
Here's the post @lwsnbaker teased: https://t.co/OQ6xXvHkx4
Check out the full post exploring the future of biological research, automation, and the hidden forces shaping our experiments here: https://t.co/MDUd9htE3W
Automating cell culture isn't about making science easier - it's about making it better.
By acknowledging rather than denying natural selection in our labs, we're opening new frontiers in biological research.
Automating cell culture isn't about making science easier - it's about making it better.
By acknowledging rather than denying natural selection in our labs, we're opening new frontiers in biological research.
As labs modernize and bring in automation, there's an opportunity to do better. We need to rethink cell culture from the ground up, just like the alarm clock reinvented wake-up calls.
Enter: self-driving cell culture systems.
As labs modernize and bring in automation, there's an opportunity to do better. We need to rethink cell culture from the ground up, just like the alarm clock reinvented wake-up calls.
Enter: self-driving cell culture systems.
This problem has been known for over 50 years, yet most labs still avoid regular cell verification.
Some argue time and cost, but fundamentally it's a workflow problem.
This problem has been known for over 50 years, yet most labs still avoid regular cell verification.
Some argue time and cost, but fundamentally it's a workflow problem.
In part because of the mechanisms of selection at play, 5-46% of cell lines in research labs are misidentified.
Even samples from originating labs are wrong 18% of the time. We're often not working with what we think we are.
In part because of the mechanisms of selection at play, 5-46% of cell lines in research labs are misidentified.
Even samples from originating labs are wrong 18% of the time. We're often not working with what we think we are.
Biologists face a paradox: they want to minimize variability to more easily analyze their results, BUT they are studying systems that evolved to have high variability as an evolutionary survival strategy.
Biologists face a paradox: they want to minimize variability to more easily analyze their results, BUT they are studying systems that evolved to have high variability as an evolutionary survival strategy.
Take HeLa cells as an example. Did you know these immortal cells are so good at surviving that they literally invade other cell lines?
One study found that 67% of misidentified cells had been overtaken by HeLa cells.
Take HeLa cells as an example. Did you know these immortal cells are so good at surviving that they literally invade other cell lines?
One study found that 67% of misidentified cells had been overtaken by HeLa cells.
High school bio told you about the peppered moth and it's changes due to pollution during the industrial revolution.
Lab specimens are doing the same thing under our noses, but the consequences for research are far more complicated.
High school bio told you about the peppered moth and it's changes due to pollution during the industrial revolution.
Lab specimens are doing the same thing under our noses, but the consequences for research are far more complicated.
Prediction 3: New statistical approaches will
1) attempt to quantify and measure the probability of validity
2) assess the resiliency of the proposed model
3) assess the resiliency of the biological system
4) result in numerical-based assessments of modulatory impact
The scientific method you were taught is outdated - itβs time to move beyond the enlightenment and into the modern era with automation and AI
https://t.co/zqoCekXjmZ
Prediction 2: The structure of hypotheses will change, rejecting the model of a single null and alternate hypotheses pair searching for causality in favor of
1) exploring the number of alternate hypotheses explored
2) increased focus and sensitivity to modulatory effects