Geoengineering vs. Greenhouse gasses

Geoengineering vs. Greenhouse gasses- Who would win?

This week is apparently a week where the UN  has met to discuss the state of the oceans. Since it is one of our most pressing issues, I figured I’d learn more and haphazardly blog them as part of my initiative to kaizenize my blog. But given the huge interest in climate change- I have been wondering where the news covfefe about this has been? *snort* It’s sad that the US is not a member of the Paris agreement, but the people still can be.

Climate engineering would cool down the planet — but it may not save West Antarctica  is a critical view of geoengineering which as a nerd I of course would want to propose. I learned that the West Antarctica ice sheet is melting not only from above but from below because of how CO2 is affecting the currents. I thought about it and considered that here is a
reminder that when we think about climate, we should be picturing a circulatory system….
We thinkScreen Shot 2017-06-08 at 7.55.48 PM

But we should also think

Really grateful to my bestie’s husband for alerting me to the awesome work of suspicious0bservers… probably 3ish years ago? That’s how I know these maps even exists. There are always a copious amount of links under their videos. A true ‘web site’ there is a node devoted to the climate – Earth changes.  If you’re in the spirit to learn more about the situation we’re in and like to watch videos.

For me to read later: New Insights on the Physical Nature of the Atmospheric Greenhouse Effect Deduced from an Empirical Planetary Temperature Model

I for one, welcome our new robot overlords

When Does Labor Scarcity Encourage Innovation?

This paper studies whether labor scarcity encourages technological advances, that is, technology adoption or innovation, for example, as claimed by Habakkuk in the context of nineteenth-century United States. I define technology as strongly labor saving if technological advances reduce the marginal product of labor and as strongly labor complementary if they increase it. I show that labor scarcity encourages technological advances if technology is strongly labor saving and will discourage them if technology is strongly labor complementary. I also show that technology can be strongly labor saving in plausible environments but not in many canonical macroeconomic models


I analyze an economy in which firms can undertake both labor- and capital-augmenting technological improvements. In the long run, the economy resembles the standard growth model with purely labor-augmenting technical change, and the share of labor in GDP is constant. Along the transition path, however, there is capital-augmenting technical change and factor shares change. Tax policy and changes in labor supply or savings typically change factor shares in the short run, but have no or little effect on the long-run factor distribution of income.

The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment

The advent of automation and the simultaneous decline in the labor share and employment among advanced economies raise concerns that labor will be marginalized and made redundant by new technologies. We examine this proposition using a task-based framework in which tasks previously performed by labor can be automated and more complex versions of existing tasks, in which labor has a comparative advantage, can be created. We characterize the equilibrium in this model and establish how the available technologies and the choices of firms between producing with capital or labor determine factor prices and the allocation of factors to tasks. In a static version of our model where capital is fixed and technology is exogenous, automation reduces employment and the share of labor in national income and may even reduce wages, while the creation of more complex tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research towards automation and the creation of new complex tasks. Under reasonable conditions, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. An increase in automation reduces the cost of producing using labor, and thus discourages further automation and encourages the faster creation of new complex tasks. The endogenous response of technology restores the labor share and employment back to their initial level. Although the economy contains powerful self-correcting forces, the equilibrium generates too much automation. Finally, we extend the model to include workers of different skills. We find that inequality increases during transitions, but the self-correcting forces in our model also limit the increase in inequality over the long-run.

Excerpt from /r/machinelearning AMA with JuergenSchmidhuber

While a problem solver is interacting with the world, it should store the entire raw history of actions and sensory observations including reward signals. The data is ‘holy’ as it is the only basis of all that can be known about the world. If you can store the data, do not throw it away! Brains may have enough storage capacity to store 100 years of lifetime at reasonable resolution [1].

As we interact with the world to achieve goals, we are constructing internal models of the world, predicting and thus partially compressing the data history we are observing. If the predictor/compressor is a biological or artificial recurrent neural network (RNN), it will automatically create feature hierarchies, lower level neurons corresponding to simple feature detectors similar to those found in human brains, higher layer neurons typically corresponding to more abstract features, but fine-grained where necessary. Like any good compressor, the RNN will learn to identify shared regularities among different already existing internal data structures, and generate prototype encodings (across neuron populations) or symbols for frequently occurring observation sub-sequences, to shrink the storage space needed for the whole (we see this in our artificial RNNs all the time). Self-symbols may be viewed as a by-product of this, since there is one thing that is involved in all actions and sensory inputs of the agent, namely, the agent itself. To efficiently encode the entire data history through predictive coding, it will profit from creating some sort of internal prototype symbol or code (e. g. a neural activity pattern) representing itself [1,2]. Whenever this representation becomes activated above a certain threshold, say, by activating the corresponding neurons through new incoming sensory inputs or an internal ‘search light’ or otherwise, the agent could be called self-aware. No need to see this as a mysterious process — it is just a natural by-product of partially compressing the observation history by efficiently encoding frequent observations.

[1] Schmidhuber, J. (2009a) Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. SICE Journal of the Society of Instrument and Control Engineers, 48 (1), pp. 21–32.

[2] J. Schmidhuber. Philosophers & Futurists, Catch Up! Response to The Singularity. Journal of Consciousness Studies, Volume 19, Numbers 1-2, pp. 173-182(10), 2012.

Two strands functional


I have successfully created two sections of LED strip out of the total nine sections of strip required. So as to not get overwhelmed my plan is one strand soldered daily. I hooked up the mic and tried to get some “sound-reactive” code to work, but it did not. I may have to end up doing way more work than I expected to get the level of sound reaction I desire. Oh well, if by the beach trip I just have funky lights thats OK.