Imperialismo computacional. Recopilación de enlaces enero 2015, 2.

En ésta edición dos enlaces sobre Neurociencias (sobre la conciencia como capacidad que se aprende y sobre Sebastian Seung, impulsor del Proyecto Conectoma Humano); varios sobre la Inteligencia Artificial y sus críticos: los agentes artificiales ya juegan mejor al póker que los humanos; valor y error de la tecnología Big Data; Psicología del nodo: ¿ que tal te va la onlife, amigo inforg ?🙂; varios sobre historia del software; y uno sobre la  temática lapo azul / genealogía genética: ¡¡ x30 Full genomes a 2000 usd !!.

Cuanto más leo sobre neurociencias más claro lo tengo: en Ciencias, en general,  sólo hay una manera  correcta de mirar un determinado campo, una manera que nos hace avanzar; en Neurociencias, todavía no la hemos encontrado (no quiero decir con esto que se estén dando palos de ciego, ni mucho  menos). Pero ojo, como veremos, la Inteligencia Artificial no es una Ciencia, es una Tecnología, y no tiene porque duplicar los métodos del cerebro. Le basta con solucionar los problemas con igual o mejor eficacia y eficiencia que los humanos.

Termino esta introducción con una frase que he leído en algún sitio: lo que a la máquina inteligente le suministran de manera gratuita, es el gran problema para el cerebro humano. Así cualquiera ¿ no ?

1.Neurociencias. La conciencia es una capacidad mental que se obtiene no por nacimiento, sino por aprendizaje.

Título. Mirror-Induced Self-Directed Behaviors in Rhesus Monkeys after Visual-Somatosensory Training

Abstract.

Mirror self-recognition is a hallmark of higher intelligence in humans. Most children recognize themselves in the mirror by 2 years of age [ 1 ]. In contrast to human and some great apes, monkeys have consistently failed the standard mark test for mirror self-recognition in all previous studies [ 2–10 ]. Here, we show that rhesus monkeys could acquire mirror-induced self-directed behaviors resembling mirror self-recognition following training with visual-somatosensory association. Monkeys were trained on a monkey chair in front of a mirror to touch a light spot on their faces produced by a laser light that elicited an irritant sensation. After 2–5 weeks of training, monkeys had learned to touch a face area marked by a non-irritant light spot or odorless dye in front of a mirror and by a virtual face mark on the mirroring video image on a video screen. Furthermore, in the home cage, five out of seven trained monkeys showed typical mirror-induced self-directed behaviors, such as touching the mark on the face or ear and then looking at and/or smelling their fingers, as well as spontaneously using the mirror to explore normally unseen body parts. Four control monkeys of a similar age that went through mirror habituation but had no training of visual-somatosensory association did not pass any mark tests and did not exhibit mirror-induced self-directed behaviors. These results shed light on the origin of mirror self-recognition and suggest a new approach to studying its neural mechanism.

De acuerdo,  mi titular es un poco exagerado….

2. ¿ Y la capacidad de jugar al póker bien, se nace con ella o se aprende ? Se aprende, pero invertir tiempo en ello ya no sirve para nada.

Título. Heads-up limit hold’em poker is solved

Abstract. 

Poker is a family of games that exhibit imperfect information, where players do not have full knowledge of past events. Whereas many perfect-information games have been solved (e.g., Connect Four and checkers), no nontrivial imperfect-information game played competitively by humans has previously been solved. Here, we announce that heads-up limit Texas hold’em is now essentially weakly solved. Furthermore, this computation formally proves the common wisdom that the dealer in the game holds a substantial advantage. This result was enabled by a new algorithm, CFR+, which is capable of solving extensive-form games orders of magnitude larger than previously possible. 

La mano tiene ventaja, claro🙂. No tengo acceso al artículo original publicado en Science. En lo que sigue nos basamos en el siguiente, publicado en Nature, que recoge la noticia.

Título. Game theorists crack poker

An ‘essentially unbeatable’ algorithm for the popular card game points to strategies for solving real-life problems without having complete information.

Aunque he sido apasionado jugador de Póker en el lejano pasado, casi en otra vida (siempre con dinero, siempre con amigos, siempre con los mismos) nunca he seguido los intentos de desarrollar jugadores artificiales en este juego y por lo tanto poco puedo aportar al lector. No se si es un resultado que se esperaba o ha sorprendido a los expertos. Ahora sí ha captado mi  atención y si tengo tiempo lo miraré en detalle.

Extracto.

But poker is harder to solve than draughts. Chess and draughts are examples of perfect-information games, in which players have complete knowledge of all past events and of the present situation in a game. In poker, in contrast, there are some things a player does not know: most crucially, which cards the other player has been dealt. The class of games with imperfect information is especially interesting to economists and game theorists, because it includes practical problems such as finding optimal strategies for auctions and negotiations.

Sí recomendamos a los lectores que lean el artículo completo pues el titular (el del artículo y el de mi punto), como a veces el propio agente artificial,  va un poco de farol:  en realidad el juego que se ha resuelto es una variante limitada de póker abierto (cierto que de uso muy frecuente), pero sólo cuando juegan 2 jugadores  (la máquina y otro).

Extracto.

As part of its developing strategy, the computer learned to inject a certain dose of bluffing into its plays. Although bluffing seems like a very human, psychological element of the game, it is in fact part of game theory — and, typically, of computer poker. “Bluffing falls out of the mathematics of the game,” says Bowling, and you can calculate how often you should bluff to obtain best results.

Of course, no poker algorithm can be mathematically guaranteed to win every game, because the game involves a large element of chance based on the hand you’re dealt. But Bowling and his colleagues have demonstrated that their algorithm always wins in the long run.

The problem is only what the researchers call ‘essentially solved’, meaning that there is an extremely small margin by which, in theory, the computer might be beaten by skill rather than chance. But this margin is negligible in practice.

Personalmente a mi no me sorprende el resultado. En el póker hay mucho cálculo (todavía recuerdo la última partida que jugué, hace ahora más de 10 años: podía haber acabado ganando bastante, el que más de la mesa, y acabé perdiendo, el peor de la mesa por un único error de cálculo en la última mano…) y algo de psicología (faroles etc…). Si tenemos en cuenta que un ordenador es, más potente en el cálculo y más fuerte emocionalmente que un humano, no hay mucho margen para la sorpresa: era cuestión de  tiempo.

Me pregunto si a la larga éste  resultado no podrá terminar con las plataformas de juego a distancia u on-line. Como en ajedrez también hay competiciones de jugadores artificiales y es posible que, también como en ajedrez, haya métodos para detectar el uso de estos jugadores cuando juegan con humanos.

Extracto.

This procedure, known as counterfactual regret minimization, has been widely adopted in the Annual Computer Poker Competition, which has run since 2006. But Bowling and colleagues have improved it by allowing the algorithm to re-evaluate decisions considered to be poor in earlier training rounds.

The other crucial innovation was the handling of the vast amounts of information that need to be stored to develop and use the strategy, which is of the order of 262 terabytes. This volume of data demands disk storage, which is slow to access. The researchers figured out a data-compression method that reduces the volume to a more manageable 11 terabytes and which adds only 5% to the computation time from the use of disk storage.

Por cierto en el último campeonato de ajedrez con agentes artificiales sólo 11 partidas de 64 fueron decisivas. Y hubo cambio de campeón, creo…

¿ Y aplicaciones que vayan más allá del resultado ?

Extracto.

Why is this important? The only previous games of any difficulty that have been solved are perfect information games–games where each player knows everything that has previously happened. Tic-tac-toe and chess are perfect information games because everything that has happened is summarized in the state of the board. In imperfect games there is hidden information, such as in card games where the opposing players cards are hidden.

It’s clear that most games in the real world (and that includes “games” of nuclear strategy, bargaining, and detection and monitoring) are imperfect information games. Even though the sample space for HULHE is very large it’s smaller than these real world strategy games (and smaller than other forms of poker). Nevertheless, it’s clear that people are “solving” the real world games not by working through the sample space but by pruning it. A combination of search and heuristic pruning in the perfect information game of chess has already produced computers that are better than any human player. What the solution to this relatively small and somewhat unimportant imperfect information game indicates is that the computers are soon going to be better than you and I at the “human” capabilities of threat, bluff and deception.

Fuente. Una entrada en el blog MR.

3. Una historia en el NYT sobre Sebastian Seung.

Sebastian es el neurocientífico impulsor del Proyecto Conectoma Humano. En el artículo hablan más sobre este proyecto que sobre el propio Sebastian.

Como hemos visto en una entrada anterior, ya existe un conectoma completo (el de el gusano C. Elegans). Este resultado ha sido útil pero no ha aclarado, ni mucho menos, todos los misterios.

Extracto.

Scientists know roughly what individual neurons in C. elegans do and can say, for example, which neurons fire to send the worm wriggling forward or backward. But more complex questions remain unanswered. How does the worm remember? What is constant in the minds of worms? What makes each one individual? In part, these disappointments were a problem of technology, which has made connectome mapping so onerous that until recently nobody considered doing more. In science, it is a great accomplishment to make the first map, but far more useful to have 10, or a million, that can be compared with one another.

Correcto. Con las singularidades, casos únicos, no se avanza en ciencia. En mi opinión lo mejor sería profundizar y concentrarse en variantes del caso más sencillo, C. Elegans. Primero  enriquecer el conectoma con elementos que ahora no están, según vimos en una entrada anterior. Y segundo, y desconozco si esto es posible, obtener conectomas  de un mismo “individuo” en diferentes momentos de su ciclo vital (del tiempo en general) y de diferentes individuos en el mismo momento de su ciclo vital  sería sin duda informativo, para determinar lo que  hay de fijo o invariante y lo que hay de fluido o individual en los conectomas y cuan importantes son las diferencias individuales (salvo casos patológicos no lo pueden ser tanto pues el repertorio de estados internos y comportamientos externos de una especie es perfectamente identificable y repertoriable). Cuanto más leo sobre neurociencias más aflora la tensión entre lo fijo y lo plástico.

En paralelo se puede trabajar en conectomas de otros organismos, como ya se está realizando.

Extracto. 

Gerry Rubin, Janelia’s director, said his team hopes to have a complete catalog of high-resolution images­ of the fruit-fly brain in a year or two and a completely traced wiring diagram within a decade. Rubin is a veteran of genome mapping and saw how technological advances enabled a project that critics originally derided as prohibitively difficult and expensive.

Rubin admitted, “if we can’t do the fly in 10 years, there is no prayer for the field.”

En el artículo hablan también sobre la idea de programación neuronal.

Extracto.

The basic idea (which borrows from computer science) is that simple units, connected in the right way, can give rise to surprising abilities (memory, recognition, reasoning).

4. Psicología del nodo. Onlife, la  Cuarta Revolución.

El libro al que enlazamos no trata de una Revolución Industrial sino conceptual, o más bien psicológica.

Extracto de la presentación  del libro:

As the boundaries between life online and offline break down, and we become seamlessly connected to each other and surrounded by smart, responsive objects, we are all becoming integrated into an “infosphere”. Personas we adopt in social media, for example, feed into our ‘real’ lives so that we begin to live, as Floridi puts in, “onlife”. Following those led by Copernicus, Darwin, and Freud, this metaphysical shift represents nothing less than a fourth revolution.

“Onlife” defines more and more of our daily activity – the way we shop, work, learn, care for our health, entertain ourselves, conduct our relationships; the way we interact with the worlds of law, finance, and politics; even the way we conduct war. In every department of life, ICTs have become environmental forces which are creating and transforming our realities. How can we ensure that we shall reap their benefits? What are the implicit risks? Are our technologies going to enable and empower us, or constrain us? 

El autor del libro, Floridi, es filósofo de la información  y afirma que esta cuarta revolución cuyo protagonista principal ha sido Turing, es equivalente a las revoluciones copernicana, darwiniana y freudiana.

Extracto.

We are not at the center of the universe (Copernicus), of the biological kingdom (Darwin), or of the realm of rationality (Freud). After Turing, we are no longer at the center of the world of information either. We share the infosphere with smart technologies. These are not some unrealistic artificial intelligence, as the review would have me suggest, but ordinary artifacts that outperform us in ever more tasks, despite being no cleverer than a toaster. Their abilities are humbling and make us reevaluate our unique intelligence. Their successes largely depend on the fact that the world has become an IT-friendly environment, where technologies can replace us without having any understanding or semantic skills. We increasingly live online.

Por lo tanto revolución física, biológica y psicológica. ¿ Se puede identificar el dominio informacional con el sociocultural ? No creo que lo informacional constituya un dominio ontológico  al  mismo nivel que los físico, biológico, psicológico o sociológico. La información parece más bien una entidad transversal.

De cualquier manera yo estoy ahora viviendo un importante retorno a lo bioquímico y hecho de menos, en todas estas teorías, la bioquímica. Por ello soy receptivo a la crítica de Searle sobre la que hablamos en el siguiente punto.

5. What your computer can´t know.

El enlace los es a un artículo del NYT, escrito por el filósofo John Searle, en el que analizan críticamente el libro citado en el punto anterior y  el libro Superintelligence, de Nick Bostrom, sobre el que ya hemos hablado en anteriores entradas.

Extracto.

Both of these books are rich in facts and ideas. Floridi has a good chapter on privacy in the information age, and Bostrom has extensive discussions of technological issues, but for reasons of time and space, I am concentrating on the central claim of each author.

I believe that neither book gives a remotely realistic appraisal of the situation we are in with computation and information. And the reason, to put it in its simplest form, is that they fail to distinguish between the real, intrinsic observer-independent phenomena corresponding to these words, and to the observer-relative phenomena that also corresponds to these words but is created by human consciousness.

Suppose we took seriously the project of creating an artificial brain that does what real human brains do. 

In the project it is essential to remember that what matters are the inner mental processes, not the external behavior. If you get the processes right, the behavior will be an expression of those processes, and if you don’t get the processes right, the behavior that results is irrelevant. That is the situation we are currently in with Artificial Intelligence. The engineering is useful for flying airplanes, diagnosing diseases, and writing articles like this one. But the results are for the most part irrelevant to understanding human thinking, reasoning, processing information, deciding, perceiving, etc., because the results are all observer relative and not the real thing.

The weird marriage of behaviorism – any system that behaves as if it had a mind really does have a mind – and dualism – the mind is not an ordinary part of the physical, biological world like digestion – has led to the confusions that I have been attempting to expose.

Recordamos que Searle es un destacado filósofo de la corriente analítica que lleva cultivando desde hace tiempo la filosofía de la mente (entre otras cosas) y que desde hace tiempo ha adoptado una actitud crítica frente a algunas posiciones de los investigadores que trabajan en Inteligencia Artificial. Ha vivido ya varios veranos e inviernos dentro del campo de la IA. Es por lo tanto un “perro viejo” (con todos los respetos  y sin ánimo de ofender) en estas lides.

Floridi ha contestado a las críticas y Searle ha replicado (su replica se puede leer éste mismo enlace).

Extracto.

Among other achievements, Turing made valuable contributions to the theory of computation. We are all in his debt. It is a discredit to his memory to attribute to him the exaggerated and implausible views advanced by Floridi.

Relacionado: Is the brain a good model for machine intelligence ?. 2012. Es un debate en el que participan Rodney Brook, profesor emérito del MIT y experto en robótica y Demis Hassanis, neurocientífico,  uno de los diseñadores de Deep Brain, aplicación que se vendió a Google por 400 millones de usd, Dennis Bray, Neurocientífico  y Amnon Shashua, ingeniero computacional.

6. Imperialismo  computacional. Un blog que analiza críticamente las nuevas tecnologías: Utopia or Dystopia.

7. Fronteras en el campo del análisis masivo de datos , también llamado Big Data. (Frontiers in massive  data analysis).

Se trata de un informe sobre las metodologías e industria del  Big Data elaborado por un equipo para el National Research Council of the National Academies de EEUU (The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance). El informe se puede consultar en html en el enlace.

Sinceramente no tenía muy claro a que se refería el concepto de Big Data. Tras hojear muy en diagonal algunas partes de éste informe ya lo tengo mucho más claro. También tengo más claro que es  una tecnología  que hay que utilizar con mucho cuidado:  fuentes heterogéneas de datos cuya integración puede ser problemática, muestras sesgadas, acumulación de errores durante la  fase de cocina (análisis de datos),  malas interpretaciones de los resultados de estos análisis etc….

Algunos de los proyectos científicos que podrían hacer  uso de estas tecnologías y sobre los que hemos hablado recientemente son los análisis genéticos de estructura poblacional, los estudios sobre sexualidad humana o los proyectos de neurociencias de tipo conectoma.  También las aplicaciones industriales y empresariales pueden ser muy interesantes siempre y cuando las metodologías estén validadas.

Extracto 1.

The current report is the result of a study that addressed the following charge:

  • Assess the current state of data analysis for mining of massive sets and streams of data,
  • Identify gaps in current practice and theory, and
  • Propose a research agenda to fill those gaps.

Thus, this report examines the frontiers of research that is enabling the analysis of massive data. The major research areas covered are as follows:

  • Data representation, including characterizations of the raw data and transformations that are often applied to data, particularly transformations that attempt to reduce the representational complexity of the data;
  • Computational complexity issues and how the understanding of such issues supports characterization of the computational resources needed and of trade-offs among resources;
  • Statistical model-building in the massive data setting, including data cleansing and validation;
  • Sampling, both as part of the data-gathering process but also as a key methodology for data reduction; and
  • Methods for including humans in the data-analysis loop through means such as crowdsourcing, where humans are used as a source of training data for learning algorithms, and visualization, which not only helps humans understand the output of an analysis but also provides human input into model revision.

The research and development necessary for the analysis of massive data goes well beyond the province of a single discipline, and one of the main conclusions of this report is the need for a thoroughgoing interdisciplinarity in approaching problems of massive data.

–Computer scientists involved in building big-data systems must develop a deeper awareness of inferential issues,

statisticians must concern themselves with scalability, algorithmic issues, and real-time decision-making.

Mathematicians also have important roles to play, because areas such as applied linear algebra and optimization theory (already contributing to large-scale data analysis) are likely to continue to grow in importance.

–Also, as just mentioned, the role of human judgment in massive data analysis is essential, and contributions are needed from social scientists and psychologists as well as experts in visualization.

–Finally, domain scientists and users of technology have an essential role to play in the design of any system for data analysis, and particularly so in the realm of massive data, because of the explosion of design decisions and possible directions that analyses can follow.

Extracto 2.

A number of challenges in both data management and data analysis require new approaches to support the big data era. These challenges span generation of the data, preparation for analysis, and policy-related challenges in its sharing and use, including the following:

  • Dealing with highly distributed data sources,
  • Tracking data provenance, from data generation through data preparation,
  • Validating data,
  • Coping with sampling biases and heterogeneity,
  • Working with different data formats and structures,
  • Developing algorithms that exploit parallel and distributed architectures,
  • Ensuring data integrity,
  • Ensuring data security,
  • Enabling data discovery and integration,
  • Enabling data sharing,
  • Developing methods for visualizing massive data,
  • Developing scalable and incremental algorithms, and
  • Coping with the need for real-time analysis and decision-making.

It is natural to be optimistic about the prospects. Several decades of research and development in databases and search engines have yielded a wealth of relevant experience in the design of scalable data-centric technology. In particular, these fields have fueled the advent of cloud computing and other parallel and distributed platforms that seem well suited to massive data analysis. Moreover, innovations in the fields of machine learning, data mining, statistics, and the theory of algorithms have yielded  data-analysis methods that can be applied to ever-larger data sets.

However, such optimism must be tempered by an understanding of the major difficulties that arise in attempting to achieve the envisioned goals. In part, these difficulties are those familiar from implementations of large-scale databases—finding and mitigating bottlenecks, achieving simplicity and generality of the programming interface, propagating metadata, designing a system that is robust to hardware failure, and exploiting parallel and distributed hardware—all at an unprecedented scale. But the challenges for massive data go beyond the storage, indexing, and querying that have been the province of classical database systems (and classical search engines) and, instead, hinge on the ambitious goal of inference. Inference is the problem of turning data into knowledge, where knowledge often is expressed in terms of entities that are not present in the data per se but are present in models that one uses to interpret the data. Statistical rigor is necessary to justify the inferential leap from data to knowledge, and many difficulties arise in attempting to bring statistical principles to bear on massive data. Overlooking this foundation may yield results that are not useful at best, or harmful at worst. In any discussion of massive data and inference, it is essential to be aware that it is quite possible to turn data into something resembling knowledge when actually it is not. Moreover, it can be quite difficult to know that this has happened.

Indeed, many issues impinge on the quality of inference. A major one is that of “sampling bias.”

Another major issue is “provenance.” Many systems involve layers of inference, where “data” are not the original observations but are the products of an inferential procedure of some kind. This often occurs, for example, when there are missing entries in the original data. In a large system involving interconnected inferences, it can be difficult to avoid circularity, which can introduce additional biases and can amplify noise.

Finally, there is the major issue of controlling error rates when many hypotheses are being considered. Indeed, massive data sets generally involve growth not merely in the number of individuals represented (the “rows” of the database) but also in the number of descriptors of those individuals (the “columns” of the database). Moreover, we are often interested in the predictive ability associated with combinations of the descriptors; this can lead to exponential growth in the number of hypotheses considered, with severe consequences for error rates.

Relacionado: entre los autores de este informe está Michael Jordan, que ha sido entrevistado recientemente por IEEE Espectrum y ha realizado declaraciones críticas en relación a las exageraciones que aparecen en los medios con respecto a la tecnología Big Data. También critica los chips neuromiméticos, sobre los que hemos hablado recientemente.

Extractos.

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

Spectrum: Another point you’ve made regarding the failure of neural realism is that there is nothing very neural about neural networks.

Michael Jordan: There are no spikes in deep-learning systems. There are no dendrites. And they have bidirectional signals that the brain doesn’t have.

We don’t know how neurons learn. Is it actually just a small change in the synaptic weight that’s responsible for learning? That’s what these artificial neural networks are doing. In the brain, we have precious little idea how learning is actually taking place.

La parte correspondiente a Big Data es también muy recomendable…

Extracto.

Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?

Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.

 8. Historia^2 del software.

Muy interesante.  No es exactamente una historia del software sino más bien una reflexión sobre como se debería de contar esa historia. No confundir software con lenguaje de programación. Esa es otra historia. El autor es uno de los destacados historiadores de este producto.  Bibliografía completa y dos interesantes tablas al final.

Visto en el Blog CC.

9. Se busca: historiador de ciencias y tecnologías computacionales.

Es un enlace a un artículo que recoge la polémica entre el historiador del artículo del punto anterior con Knuth, que además de científico computacional también es historiador de las ciencias computacionales.

De  su lectura se desprende que la historia de esta disciplina está todavía muy poco madura. Historiadores amateurs (sin ánimo de ofender, me refiero a los propios protagonistas de la disciplina una vez se retiran, que saben mucho de ella pero poco sobre como escribir historia), poca teoría, excesiva documentación que no se sabe como tratar, etc…

Esto es  normal: la historia se escribe después de la acción y cuando ésta ha causado un cierto impacto. Como hemos visto  en la entrada anterior de la serie Imperialismo Computacional,  ahora mismo es cuando las Ciencias y Tecnologías Computacionales están causando verdadero impacto.

Extractos.

Work in the history of computing has been seen by most in the humanities as dull and provincial, excessively technical and devoid of big historical ideas. Whereas fields such as environmental history have produced widely recognized classics that convince non-specialists of the scholarly potential, historians of computing are still inching toward broad acceptance of their relevance. 

… 

Thus the kind of historical work Knuth would like to read would have to be written by computer scientists themselves. Some disciplines support careers spent teaching history to their students and writing history for their practitioners. Knuth himself holds up the history of mathematics as an example of what the history of computing should be.

It is possible to earn a Ph.D. within some mathematics departments by writing a historical thesis (euphemistically referred to as an “expository” approach). Such departments have also been known to hire, tenure, and promote scholars whose research is primarily historical. Likewise medical schools, law schools, and a few business schools have hired and trained historians. A friend involved in a history of medicine program recently told me that its Ph.D. students are helped to shape their work and market themselves differently depending on whether they are seeking jobs in medical schools or in history programs. In other words, some medical schools and mathematics departments have created a demand for scholars working on the history of their disciplines and in response a supply of such scholars has arisen.

As Knuth himself noted toward the end of his talk, computer science does not offer such possibilities. As far as I am aware no computer science department in the U.S. has ever hired as a faculty member someone who wrote a Ph.D. on a historical topic within computer science, still less someone with a Ph.D. in history. I am also not aware of anyone in the U.S. having been tenured or promoted within a computer science department on the basis of work on the history of computer science.

I share Knuth’s regret that the technical history of computer science is greatly understudied. The main cause is that computer scientists have lost interest in preserving the intellectual heritage of their own discipline. It is not, as Knuth implies, that Campbell-Kelly is representative of a broader trend of individual researchers deciding to stop writing one kind of history and to devote a fixed pool of talent to writing another kind instead. There is no zero sum game here. More work by professionally trained historians on social, institutional, and cultural aspects of computing does not have to mean less work by computer scientists themselves. They cannot count on history departments to do this for them, and I hope Knuth’s lament motivates a few to follow his lead in this area.

The history of computer science retains an important place within the diverse and growing field of the history of computing. Work of the particular kind preferred by Knuth will flourish only if his colleagues in computer science are willing to produce, reward, or commission it. I nevertheless hope he will continue to find much value in the work of historians and that we will rarely give him cause to reach for his handkerchief.

P.s. visto  también en el blog CC, que se hace eco de la polémica en una entrada.

10. El lapo azul. Genomas completos (resolución x30) a 2000 usd.

Extracto.

In terms of the technical details of the underlying raw data, the sequencing produces:

  • 2 x 150 bp paired-end reads
  • approximately 30x average depth of coverage

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