Month: September 2019

GMO Eggplant

Conventionally grown brinjal is one of the most heavily sprayed crops in South Asia. Historically, farmers have sprayed as many as 84x in a growing season to protect their crops. Genetically modified insect-resistant eggplant (Bt brinjal) had a 39% reduction of pesticides and a 51% reduction in the number of times that farmers applied pesticides.

2020-11-19:

Bt eggplant offers a 51% increase in yield, a 37.5% decrease in pesticide use, increased farmer profits and decreased farmer sickness. Wow!

RNA-DNA chimeras

Origin-of-Life Study Points to Chemical Chimeras, Not RNA

research is beginning to show that starting with the right kind of mess is not only more realistic, but more effective at generating the materials vital to life, while also doing away with problems that have plagued purer systems. “There are times when we have mixtures, rather than just the isolated reactants that people typically use, and we get better results”. When mixtures are taken into consideration, the emergence of life on Earth in some ways “is not as hard as we might think it is.” What if the chimeric instability was, instead, secretly beneficial and offered a more natural way to get to a world of pure RNA and pure DNA right out of the gate?

Because the nucleic acids with mixed backbones formed weaker 2-strand systems, they didn’t succumb to the strand separation problem that prevented replication for pure RNA. Moreover, during their replication process, the RNA-DNA chimeras preferentially synthesized strands of pure RNA and pure DNA rather than new chimeric molecules — and they produced more of those pure compounds than pure nucleic acid templates did.

Chickadee memory

Mountain chickadees remember the location of 80K seeds

Despite weighing less than 15g, mountain chickadees are able to survive harsh winters complete with subzero temperatures, howling winds and heavy snowfall. How do they do it? By spending the fall hiding as many as 80K individual seeds, which they then retrieve — by memory — during the winter. Their astounding ability to keep track of that many locations puts their memory among the most impressive in the animal kingdom.

Proton Radius

Muon-orbited protons are 0.84 femtometers in radius — 4% smaller than those in regular hydrogen. If the discrepancy was real, meaning protons really shrink in the presence of muons, this would imply unknown physical interactions between protons and muons — a fundamental discovery. 100s of papers speculating about the possibility have been written. After Pohl’s muonic hydrogen result, a team of physicists set out to remeasure the proton in regular, “electronic” hydrogen. Finally, the results are in: The proton’s radius is 0.833 femtometers, give or take 0.01, a measurement exactly consistent with Pohl’s value. Both measurements are more precise than earlier attempts, and they suggest that the proton does not change size depending on context; rather, the old measurements using electronic hydrogen were wrong.

Existential risk and growth

Technological innovation can create or mitigate risks of catastrophes—such as nuclear war, extreme climate change, or powerful artificial intelligence run amok—that could imperil human civilization. What is the relationship between economic growth and these existential risks? In a model of endogenous and directed technical change, with moderate parameters, existential risk follows a Kuznets-style inverted Ushape. This suggests we could be living in a unique “time of perils,” having developed technologies advanced enough to threaten our permanent destruction, but not having grown wealthy enough yet to be willing to spend much on safety. Accelerating growth during this “time of perils” initially increases risk, but improves the chances of humanity’s survival in the long run. Conversely, even short-term stagnation could substantially curtail the future of humanity. Nevertheless, if the scale effect of existential risk is large and the returns to research diminish rapidly, it may be impossible to avert an eventual existential catastrophe.

AI-generating algorithms

Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the “manual AI approach”. This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA), which automatically learns how to produce general AI. 3 Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments. I argue that either approach could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path. Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach. To encourage such research, I describe promising work in each of the 3 Pillars. I also discuss AI-GA-specific safety and ethical considerations. Because it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.

Self-locating uncertainty

Self-locating uncertainty is a different kind of epistemic uncertainty from that featured in pilot-wave models. You can know everything there is to know about the universe, and there’s still something you’re uncertain about, namely where you personally are within it. Your uncertainty obeys the rules of ordinary probability, but it requires a bit of work to convince yourself that there’s a reasonable way to assign numbers to your belief. In one sense, all of these notions of probability can be thought of as versions of self-locating uncertainty. All we have to do is consider the set of all possible worlds — all the different versions of reality one could possibly conceive. Some such worlds obey the rules of dynamical-collapse theories, and each of these is distinguished by the actual sequence of outcomes for all the quantum measurements ever performed. Other worlds are described by pilot-wave theories, and in each one the hidden variables have different values. Still others are many-worlds realities, where agents are uncertain about which branch of the wave function they are on. We might think of the role of probability as expressing our personal credences about which of these possible worlds is the actual one.