The Three Horsemen Of The Machine Learning Apocalypse

an article by Ashutosh Jogalekar in The Curious Wavefunction brought to us by S. Abbas Raza of 3 Quarks Daily

My colleague Patrick Riley from Google has a good piece in Nature in which he describes three very common errors in applying machine learning to real world problems. The errors are general enough to apply to all uses of machine learning irrespective of field, so they certainly apply to a lot of machine learning work that has been going on in drug discovery and chemistry.

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Hazel’s comment

Artificial intelligence is only as “good” as the person writing the code. Biases and skewed rhinking will come into the mix because we are all human and not machines.

I had hoped for an image of some kind that would fit the article -- the three horsemen of

1. biases in the training data
2. hidden variables
3. ensuring your model is fit for purpose (at least I think that is what it says my science being a bit rusty).

but no such luck 

So I went looking for the four horsemen of the Book of Revelation Thank you Wikipedia


Four Horsemen of the Apocalypse, an 1887 painting by Viktor Vasnetsov. Depicted from left to right are 

1. Death, 
2. Famine, 
3. War, and 
4. Conquest.

The Lamb is visible at the top.

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Why Are There Still So Many Jobs? The History and Future of Workplace Automation

an article by David H Autor (Massachusetts Institute of Technology, Cambridge, Massachusetts) published in Journal of Economic Prospects Volume 29 Number 3 (Summer 2015)

Abstract

In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay.

In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future.

The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.

JEL Codes: E24, J22, J23, J24, J31, O31

Full text (PDF 29pp)

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