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.

Each generation has its own task

Kierkegaard believed “each generation has its own task and need not trouble itself unduly by being everything to previous and succeeding generations”. In an earlier book he had said, “to a certain degree every generation and every individual begins his life from the beginning”, and in another, “no generation has learned to love from another, no generation is able to begin at any other point than the beginning”, “no generation learns the essentially human from a previous one”.

We are how we eat

I was reading in an Anthropology text and learned that malaria was likely resultant from the invention of slash and burn farming. I think this is a great history lesson for the conditions we find ourselves in today.
Today, we know global warming is in large part contributed to by factory farming, particularly of animals. Although, some say that we require factory farming in order to feed people, perhaps something farmers who slash and burn might have argued too, there is a lot of evidence that merely addressing food waste and informing the agricultural practice with permacultural principals is the way to achieve symbiosis with our planet. Permacultural principals would result in meat becoming an expensive delicacy, but overall would lead to even more diversity in the diet.
It is important to have regulation, because we don’t want to allow the economical advantage to be with the short-sighted, low-investment methods of agriculture, slash and burn and factory farms. Yet, it is important to have correct regulation, because sometimes good intentions can have big consequences, such as the UK banning feeding swill to pigs, exasperating the food waste problem, while diverting food products from people livestock humans invented to turn food scraps into food.