This would be my choice too. He's a good storyteller.
Although like all good storytellers, don't believe everything he says. I looked up a couple of his more bizarre anecdotes, and they usually turn out not to be true. The one example I can remember is women living close together sync'ing up their menstrual period. That turned out to be probably a case of the researchers underestimating the intricacies of the statistics necessary to show that cyclic phenomenon of not quite the same length adjust towards each other.
I have this thing when lecturers are engaging that I have even more trouble believing them going forward if they make an error.
I came to that point about the cycle synchronization in his lecture series and I had to stop. Not entirely because of the error, but because the phenomenon he cited was controversial well before he gave those lectures. That tells me he was not one to a) update his beliefs b) predisposed to falsifying his beliefs/seeking out contrary evidence or c) acknowledge disagreement about a phenomenon
Any one or combination of those qualities makes me skeptical when one is teaching a "science". So rather than spend the rest of the series second guessing everything he said, I stopped watching. It's a shame, he's really a great lecturer.
This is one of the great disconnects between computer scientists and biologists. Computers are man-made, and fundamentally knowable - answers will be definitively, provably, right or wrong.
Biology is different. Sure, at a molecule level, you can make definite conclusions. "This drug binds to that receptor."
But the kind of biology that is immediately useful to humans - where it touches on psychology or sociology - is too complex to get 100% right. So how do you do 'science' in these fields?
The answer is that you make up some cohesive theory based on existing research and do studies in that direction. You try to prove yourself right.
And it works! Theories that come out of this sort of research can turn out to be 100% true. Or 90% true - where they are wrong under some conditions, but still very useful. Or they can be complete bunk - not predictive, and a waste of time.
When anyone presents a cohesive theory of a complex system, they are probably not 100% right. Doesn't make them entirely wrong, though, and certainly not useless.
I have trouble trusting information from sources that aren't self-critical or aware of the conversation associated with the research they're citing.
And probably hypocritically, I also want to kick back, turn off the critical side of my brain and enjoy the lectures and get some learning for free while not questioning every claim. Edutainment so to speak. But that requires a lot of trust, and if that trust seems threatened, I can't in good conscience continue my lazy learning.
That’s a common misconception, especially among people who have only shallow acquaintances with really complex computer systems, amplified by deeper exposure to biological systems.
Any sufficiently complex system, be it biological or man made, exhibits characteristics of difficult predictability, even to the point of unpredictable.
Although like all good storytellers, don't believe everything he says. I looked up a couple of his more bizarre anecdotes, and they usually turn out not to be true. The one example I can remember is women living close together sync'ing up their menstrual period. That turned out to be probably a case of the researchers underestimating the intricacies of the statistics necessary to show that cyclic phenomenon of not quite the same length adjust towards each other.