The idea of imperfect competition leading to technological advancement can be mapped onto education/early learning and social determinants of health. /2

Market forces lead to suboptimal investments in health and education, especially social determinants of health and early learning. (Invest early, invest often for biggest rerurns.) /3
Public policy can provide the "monopolistic" investment that optimizes (at least improves) productive potential of every person in society, not discounting the potential of the poors and the marginalized. /4
This is exactly what we are going to need more of in the next few years, as the Global North ages rapidly and we are faced with dependency ratios that look like the 1960s, but with half the growth. That working are population will need all hands on deck. /5
In short, after a lifetime of being told that markets can do the job if you just get out of the way, we are entering an era where we will need more public policy interventions to optimize growth and well being./6
That means turning the policy mantra of the last three decades (less government, more market) on its head. To be the best we can be, to maximize potential and boost growth, we will need more government. /7
That will likely mean that government as % of GDP will rise. But more spending (and, critically, will mean, also, higher quality of life and technological breakthroughs in arenas like climate change. Needed because the planet's health is the starting point for all production.
3 tweets back: "That working age population will need all hands on deck." (not "working are", damn autocorrect)
last tweet: more spending (and, critically, *more revenues as a % of GDP) ...

This makes GBA+ analysis more important than ever because it links outcomes to outlays
GBA+=gender budget analysis, + = fiscal choices on all equity seeking groups.(Indigenous,newcomers, racial minorities, disabled)
GBA+ links $ spending to the *benefits* of spending. What change is that new $100M buying?
Radical. Indispensable if the future needs more gov't.
FIN

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https://t.co/6cRR2B3jBE
Viruses and other pathogens are often studied as stand-alone entities, despite that, in nature, they mostly live in multispecies associations called biofilms—both externally and within the host.

https://t.co/FBfXhUrH5d


Microorganisms in biofilms are enclosed by an extracellular matrix that confers protection and improves survival. Previous studies have shown that viruses can secondarily colonize preexisting biofilms, and viral biofilms have also been described.


...we raise the perspective that CoVs can persistently infect bats due to their association with biofilm structures. This phenomenon potentially provides an optimal environment for nonpathogenic & well-adapted viruses to interact with the host, as well as for viral recombination.


Biofilms can also enhance virion viability in extracellular environments, such as on fomites and in aquatic sediments, allowing viral persistence and dissemination.
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How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)
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