This is a topic close to my heart since the above slide was part of my Rules of Life slide deck while I was running NSF’s Biological Sciences Directorate. Well, turns out Venus may harbor life. Lay version of the story here. So I may need to revise my thinking.
Marvelous Op Ed by the language AI, GPT-3, here. She/he’s trying to persuade us not to be afraid of AI. Success?
The problem is as follows: the ribosome is a specialized macromolecular complex with the specific function of translating messenger RNA into proteins. So how would it have evolved prior to proteins in an RNA world (or even a metabolite world)?
Selection pressure is lacking. There are no proteins that need to be made.
So the thought is, it must have been doing something else. What might that else be? And can we glean that from its current form?
My introduction to this fascinating question came from my reading of Eric Smith and Harold Morowitz’s 2013 book, The Origin of Life on Earth: The Emergence of the Fourth Geosphere. My copy of which, is currently at my university office–probably the one book I am really missing here at home.
- Face-to-face impromptu chats with colleagues
- Students knocking on my office door
- My microscope
- Taking the bus or metro
- ADHH on Tuesdays
- Walking across the Fairfax campus in the Fall or Spring
- Journal club in person
- Upgrading to business on my points
- All things Alaska, especially Toolik Field Station
- Friday Evening Lectures in Lillie Auditorium
The application in question is powering a spacecraft without solar or the radioisotope thermoelectric generator used on the Curiosity Mars Rover and some of the existing deep space probes.
Tom Philpot’s excellent analysis in The Guardian here. Both the Central Valley of California and soil-rich Iowa. There are many pieces to solving these issues–they include diversification, cover-crops, re-thinking the role of soil microbiomes and water use–but the driver for the challenges is pretty clear: the climate is changing.
The piece is here, behind their firewall I presume. But worth reading if you are interested in the bigger picture without the scientific detail.
The latest call emphasizes computer science areas such as AI. There is what I liken to an immune antibody response from the community to something new–this is the norm for when NSF changes gears (which it normally always does). The science press writes an article with juicy quotes. Perhaps there are Zoom meetings to prepare talking points for the agency leadership. And life goes on.
To my mind, the program (GRFP) is doing just fine. Smart and diverse graduate students from all STEM fields will continue to get funding for their doctoral research–from economics to theoretical physics. And perhaps one of them will make the a crucial discovery in quantum computing that changes everything–NSF funded discoveries have been at the center of practical advances for society since I can remember. From basic curiosity-driven science great things often happen.
I think the virus isn’t under control and college age young adults aren’t going to stop getting close to one another. So what we have is a recipe for campus hotspots spread across the country. And when these campuses inevitably undergo the so-called “pivot” to on-line…. a lot of those students are likely to return home to spread infection. In short, I think it’s a bad idea.
As a former university administrator, I recognize the financial challenges to all the stakeholders. But in my view, first the country needs to get COVID-19 under control and then it can deal with the myriad of other challenges that it faces. From what I hear, there’s a lot of progress on the vaccine front. And I’m optimistic that treatment for the acute cases of the disease are getting more effective. But we’re not where we need to be as far as packing thousands of folks onto college campuses.
So the neuroscientist in me is constantly amazed by what our brains can do effortlessly. Among my favorite brain features are: unconscious control of walking movements, recognizing music from a very short time series of notes, and most of all the creation of a stable egocentric mental image of our environment from the chaotic images built up on our retinas. So those are among the features. But certainly there are bugs also.
If we were designing a humanoid robot with general artificial intelligence what might we augment or delete from the human repertoire of mental characteristics? This is an interesting question for which knowledge of neuroscience is not a prerequisite. I’m certain that my economist friends would look to tweaking the time discounting bias. And my statistically-knowledgable colleagues might push our robot to be more Bayesian in its decision frameworks. And probably my psychologist collaborators might push for less dopamine-rush from social networking apps. But those are the obvious ones. What might the more subtle design changes consist of?