Nanocarrier delivery has huge potential in agriculture; however, there are significant scientific and societal barriers to overcome. In this Review, the authors explore the state of the field, what lessons can be learned from nanomedicine, and discuss what scientific and societal issues need to be addressed.
Primordial black holes are hypothetical objects formed during the earliest moments of the universe. According to the models, they formed from micro-fluctuations in matter density and spacetime to become sand grain-sized mountain-massed black holes.
It would be interesting and scary if a superintelligent system already existed and was incontrol of all other lesser AIs.
“I’m scared. I’d be crazy not to be,” one former employee tells Vox.
The 25th Annual S. Dexter Squibb Distinguished Lecture Series in ChemistryFeaturing: Dr. Theodore Goodson IIIThe Richard Barry Bernstein Collegiate Professor…
Imagine if the world’s response to climate change relied solely on speculative predictions from pundits and CEOs, rather than the rigorous—though still imperfect—models of climate science. “Two degrees of warming will arrive soon-ish but will change the world less than we all think,” one might say. “Two degrees of warming is not just around the corner. This is going to take a long time,” another could counter.
This is more or less the world we’re in with artificial intelligence, with OpenAI CEO Sam Altman saying that AI systems that can do any task a human can will be developed in the “reasonably close-ish future,” while Yann LeCun, Chief AI Scientist at Facebook, argues that human-level AI systems are “going to take a long time.”
Jaime Sevilla, a 28-year-old Spanish researcher, is trying to change that. It is far from clear whether and how the capabilities of the most advanced AI systems will continue to rapidly progress, and what the effects of those systems will be on society. But given how important AI already is, it’s worth trying to bring a little of the rigor that characterizes climate science to predicting the future of AI, says Sevilla. “Even if AI innovation stopped, this is already a technology that’s going to affect many people’s lives,” he says. “That should be enough of an excuse for us to get serious about it.”
This tutorial aims to provide a survey of the Bayesian perspective of causal inference under the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects, and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low and high dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples.
Instructor:
Four mathematicians have estimated the chances that there’s a clear path through a random maze.
Actually, nothing is wrong with it if you are a computer science major. It’s just that it has no place in the philosophy department.
From the point of anyone wanting to work in natural language, symbolic logic has all of the vices of mathematics and none of its virtues. That is, it is obscure to the point of incomprehensibility (given the weak neurons of this English major at any rate), and it leads to no useful outcome in the domain of human affairs. This would not be so bad were it not for all those philosophy major curricula that ask freshmen to take a course in it as their “introduction” to philosophy. For anyone looking to explore the meaning of life, this is a complete turnoff.
What were the philosophy mavens thinking?
