Imperialismo computacional. Recopilación de enlaces, marzo 2015, 3.

Destacamos de ésta edición, entre 10 puntos,  todos muy interesantes: la visión de la izquierda americana, una izquierda diferente, sobre el fenómeno del imperialismo computacional; un artículo (con conclusiones sorprendentes en mi opinión) que intenta excavar más profundamente de lo habitual sobre los fundamentos teóricos del deep learning;  y un nuevo artículo  con base empírica sobre el impacto de la robótica sobre el empleo.

1. ¿Debemos temer a las máquinas?.


A pesar de todo lo anterior, otros expertos relevantes no ven ninguno de estos riesgos ni de lejos, como tampoco los logros que nos muestra la ciencia-ficción. Para el director del Instituto de Investigación en Inteligencia Artificial (IIIA) del CSIC, Ramón López de Mántaras,

tanto las expectativas sobre el desarrollo de la IA como la percepción de sus riesgos están sobredimensionadas. Si los dos asesores de Ex Machina admiten que aún estamos muy lejos de la IA que nos muestra la película, la visión de López de Mántaras es aún más prudente.

Este científico fue uno de los primeros firmantes de la carta del FLI, y lo hizo en pro de la investigación en IA hacia fines socialmente responsables y beneficiosos. Pero no quiere ni oír hablar de la singularidad: “No, esto no va a ocurrir; es absolutamente impensable, al menos en el plazo de décadas del que hablan los optimistas tecnológicos”. López de Mántaras hace notar que, “exceptuando algún caso, ninguno de los que hablan de la singularidad es un experto en IA”.

2. Crítica de un libro sobre imperialismo computacional: Rise of the Robots: Technology and the Threat of a Jobless Future, by Martin Ford, Basic Books, 352 pages,

Ya nos hicimos eco de la publicación de este libro. Ahora enlazamos a una crítica. El autor de la crítica es escéptico con la tesis de que todo esto va a ocurrir muy rápidamente: If I’m not persuaded by Ford’s omens, what would persuade me? Well, I take betting odds seriously. Since automation might reduce employment, I’ve expressed my skepticism about big automation progress soon by betting $1,200 at 12–1 odds that the Bureau of Labor Statistics’ measurement of the labor fraction of U.S. income won’t go below 40 percent by 2025. And since better computer software should increase the demand for computer hardware, I’ve bet $1,000 at 20–1 odds that computers and electronics hardware won’t be over 5 percent of U.S. GDP by 2025.

Más extractos.

Now, Ford wants to reassure you that he isn’t crazy. He isn’t one of those people who see robots with human-level intelligence coming soon and superintelligent terminators killing us all soon after. No, Ford just thinks that dumb robots specialized for particular jobs are quite enough reason to panic.

Like many others today, Ford says this time really is different. He gives four reasons.

After all, there isn’t a fundamental connection between automation and wage inequality; in past eras more automation was associated with less inequality. If there’s a connection now, it may be temporary and change again. More important, if we want to increase transfers because we dislike inequality, we don’t need to discuss robots at all. It wouldn’t matter why inequality is high; we’d just increase transfers when we saw more inequality than we liked. Or set up a system, like a basic income guarantee, to do this automatically.

I’d also like to see a time series of the rates at which jobs were displaced by automation in the past. If this rate were unusually high and rising, that would be an omen worth noticing. But if it’s too hard to say which past jobs were lost to automation, what hope could we have of predicting which future jobs will be so lost?

3. Allanamiento en las diferencias salariales globales. 

Como se demuestra una vez más en el punto anterior, el tema de la desigualdad salarial sale siempre asociado a la temática del imperialismo computacional. La realidad es que aunque la desigualdad dentro de las economías desarrolladas se está incrementando, a nivel global está decreciendo. Y esto por las razones que apuntan en el siguiente artículo.

Título. Why are American Workers getting Poorer? China, Trade and Offshoring

We suggest that the impact of globalization on wages has been missed because its effects must be captured by analyzing occupational exposure to globalization. In this paper, we extend our previous work to include recent years (2003-2008), a period of increasing import penetration, China’s entry into the WTO, and growing US multinational employment abroad. We find significant effects of globalization, with offshoring to low wage countries and imports both associated with wage declines for US workers. We present evidence that globalization has led to the reallocation of workers away from high wage manufacturing jobs into other sectors and other occupations, with large declines in wages among workers who switch, explaining the large differences between industry and occupational analyses. While other research has focused primarily on China’s trade, we find that offshoring to China has also contributed to wage declines among US workers. However, the role of trade is quantitatively much more important. We also explore the impact of trade and offshoring on labor force participation rates. While offshoring to China has a negative impact on US labor force participation, other factors such as increasing computer use and substitution of capital for labor are significantly more important determinants of US employment rates across occupations. 

4. Poniendo Deep Learning en contexto.

Deep stuff about deep learning?

Se trata de una entrada en un blog  de un investigador vinculado a Microsoft Research, y que ve el tema como teórico computacional.


So what is deep learning? Well, from my current limited understanding, it seems to be a pretty simple supervised learning technique which can be explained in a few lines. Of course, simple problems can be very deep (ever heard of the Collatz conjecture?), and it looks like this may be the case with deep learning. After explaining what is deep learning (or more accurately what is my understanding of deep learning), I will point out some recent theoretical progress towards understanding the basic phenomenona at play. Let me be clear right away: it seems to me that no one has a real clue about what’s going on.

I think it’s fair to say that we do not understand why deep nets work at all, and we understand even less why they are doing so much better than anything else. The three main questions are: (i) why is SGD meaningful for this non-convex problem, (ii) why is dropout enough to avoid overfitting, (iii) can we explain why ConvNets are a sensible way to learn representations?

La entrada termina proporcionando literatura relevante para la contestación a estas preguntas.

5. Una entrada en un blog con múltiples enlaces sobre Deep Learning.

P.s visto en los comentarios de la entrada anterior.

6. Imperialismo computacional. Otro artículo sobre la temática. 

Los autores son dos economistas. Muy interesante pues se basan en un estudio empírico.

Robots may be dangerous not only to the action heroes of cinema, but also to the average manufacturing worker. This column analyses the effect robots have had in 14 industries across 17 developed countries from 1993 to 2007. Industrial robots increase labour productivity, total factor productivity, and wages. While they don’t significantly change total hours worked, they may be a threat to low- and middle-skilled workers.

Sus conclusiones:

Our findings on the aggregate impact of robots are interesting given recent concerns in the macroeconomic literature that productivity gains from technology in general may have slowed down. Gordon (2012, 2014) expresses a particularly pessimistic view, and there are broader worries about secular macroeconomic stagnation (Summers 2014, Krugman 2014), although others remain more optimistic (Brynjolfsson and McAfee 2014). We expect that the beneficial effects of robots will extend into the future as new robot capabilities are developed, and service robots come of age. Our findings do come with a note of caution: there is some evidence of diminishing marginal returns to robot use, or congestion effects, so robots are not a panacea for growth.

Although we do not find evidence of a negative impact of robots on aggregate employment, we see a more nuanced picture when we break down employment (and the wage bill) by skill groups. Robots appear to reduce the hours and the wage bill shares of low-skilled workers, and to a lesser extent also of middle skilled workers. They have no significant effect on the employment of high-skilled workers. This pattern differs from the effect that recent work has found for ICT, which seems to benefit high-skilled workers at the expense of middle-skilled workers (Autor 2014, Michaels et al. 2014).

In further results, we find that industrial robots increased total factor productivity and wages. At the same time, we find no significant effect of these robots on the labour share.

In summary, we find that industrial robots made significant contributions to labour productivity and aggregate growth, and also increased wages and total factor productivity.  While fears that robots destroy jobs at a large scale have not materialized, we find some evidence that robots reduced low- and middle-skilled workers’ employment.

Es decir a medida que los robots van asumiendo tareas humanas, el empleo humano se va concentrando en las  tareas más complejas, mejor pagadas. Primera pregunta: si el número de trabajadores humanos que pueden asumir tareas complejas se incrementa, manteniéndose igual la oferta de empleo de este nivel, ¿ no debería de bajar los salarios ?. Entiendo que si, pero de momento, si he entendido bien, se han creado tantos empleos para humanos (en niveles altos) como se han destruido (en niveles bajos, por el uso de robots). Segunda pregunta: modulo efecto congestión (no se a que se refieren) si en sucesivas iteraciones los robots van asumiendo más  y más tareas, cada vez más complejas, ¿ cual es el fin del proceso ?.

Téngase en cuenta que el artículo ha tenido en cuenta solo los efectos de la robótica, no de la informática. A estudiar los efectos de congestión.

7. Imperialismo computacional. Sobre los luditas (historia de).

8. Imperialismo computacional. El punto de vista sobre este tema de la izquierda de EEUU.

Es un artículo en la revista Jacobin, que se autodescriben como a leading voice of the American left, offering socialist perspectives on politics, economics, and culture. The print magazine is released quarterly and reaches over 10,000 subscribers, in addition to a web audience of 600,000 a month.

Si no recuerdo mal, la izquierda americana nunca se llevó del todo bien con el comunismo, nunca fue marxista pese al macartismo.

El autor es Peter Frase (sociologo). No he leído el artículo de momento.


Is Google making us stupid? Is Facebook making us lonely? Are robots going to steal our jobs? These, it seems, are the anxieties that afflict many today.

9. Toda esa edición de Jacobin es sobre tecnología.

Muy interesante conocer el punto de vista de una iquierda diferente sobre estos temas.

10. Sociedades del Ocio. ¿ Que viene después del capitalismo ? 

Ya que hablamos de iquierda, de marxismo, que nace como una teoría económica que pretende predecir la dinámica del capitalismo, parece oportuno ofrecer un enlace sobre como ven dos economistas destacados (Phelps y Summers) el futuro  (en una entrevista). Quizás muy indirectamente relacionado con la temática del imperialismo computacional (no lo se, no he leído las respuestas).

Algunas de las preguntas que les hacen:

Q: What is capitalism?

Q: What have been the greatest benefits to society from free-market capitalism?

Q: What are the key problems with modern-capitalism?

Q: What is the impact of economic stagnation?

Q: Has globalisation and the advent of multi-nationals impinged on competition and innovation?

Q: What is the future of economics as a discipline?


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