Una charla dada durante el evento sobre Movilidad Sostenible del Foro del Futuro Próximo en 2017.
Singularidad Tecnológica
Evento sobre la Singularidad Tecnológica organizado por la Sección de Pensamiento Marginal del Ateneo de Madrid en 2021.
PRESENTA: Brígida de Fez Algarra
INTRODUCE: José Luis Cordeiro
MODERA: Lola Marcos
PONENTES:
Antonio Miguel Carmona, PhD (Economía) Profesor de Economía, Oficial del Ejército del Aire (RV) y Político
Gabriel Vázquez Torres, Ingeniero Informático, MSc (c) Experto en Data Science e Inteligencia Artificial
Ricardo Sanz, PhD (Ingeniería) Profesor e Investigador de la Universidad Politécnica de Madrid
Lógica y Terminators
La inteligencia artificial es una tecnología muy relevante y de gran impacto potencial en la industria y la sociedad. En esta charla se comentaron algunos de los principales temas que sirvieron de base para un debate con los futuros ingenieros industriales de UPM ETSII. Una conferencia pronunciada dentro del ciclo de conferencias Hazte Industrial.
La versión PDF de las diapositivas de la charla se puede descargar desde aquí.
FFP 2017 Tecnología e Inmovilidad
This is a speech given at the FFP 2017. FFP2017 was a conference organised by the Foro del Futuro Próxmo that took place on September 28-29 de septiembre de 2017 in the Escuela de Ingenieros Industriales de la Universidad Politécnica de Madrid.
FFP2017 analized the impact of mobility technologies in the mobility of people.
A 3 minute version can be seen here
It has always been models
There is a relatively recent boom on model-based X. Model-based development, model-based design, model-based systems engineering, …
In all the domains of engineering, it looks like we have just discovered the use of models to support our work. But this is, obviously, false. It has always been models. All around. All the time.
When an engineer-to-be starts his studies, the first he learns is physics and mathematics: i.e. how to model reality and the language to build the model. In recent parlance we would say that math is just a metamodel of reality. A model of the models that we use to capture the knowledge we have about an extant system or the ideas we have about a system to be engineered.
The distinction between knowledge and ideas may seem relevant but it is not so much. They’re all about mental content; that may or may not be related or co-related to some reality out there. Both knowledge and ideas are models of realities-that-are or realities-to-be that are of relevance to us or our stakeholders.
It has always been models. In our minds and in the collaborative processes that we use to engineer our systems. Model-based X is not new. It is just good, old-fashioned engineering.
It has always been models
There is a relatively recent boom on model-based X. Model-based development, model-based design, model-based systems engineering, model-based minds, …
In all the domains of engineering, it looks like we have just discovered the use of models to support our work. But this is, obviously, false. It has always been models. All around. All the time.
When an engineer-to-be starts his studies, the first he learns is physics and mathematics: i.e. how to model reality and the language to build the model. In recent parlance we would say that math is just a metamodel of reality. A model of the models that we use to capture the knowledge we have about an extant system or the ideas we have about a system to be engineered.
The distinction between knowledge and ideas may seem relevant but it is not so much. They’re all about mental content; that may or may not be related or co-related to some reality out there. Both knowledge and ideas are models of realities-that-are or realities-to-be that are of relevance to us or our stakeholders.
It has always been models. In our minds and in the collaborative processes that we use to engineer our systems. Model-based X is not new. It is just good, old-fashioned engineering.
The Self Beyond Humans
I will give a talk titled The Self Beyond Humans at Reykjavik University on May 16, 2013. The talk addresses the issue of the construction of the self from the perspective of machine consciousness.
Many current research trends point toward a technology of robot selfhood. The pursuit of selves for machines is motivated from a desire to equip robots with sophisticated human-like competences. Self and self-awareness constitute one of the cornerstones of consciousness, a whimsically peculiar aspect of our humanhood. While humans are the best “ground truth” we have in this respect, the best example to inspect and imitate, anthropomorphism is a procrustean path that shall be followed with care. Many attempts to create artificial selves are based on a shallow replication of biological behavioral traits; a true engineering technology of robot selves, however, must be based on a rigorous theory of consciousness, beyond humans.
A scientific, general theory of consciousness should be much more than just some “scientific progress towards understanding how consciousness can emerge form the activity of neurons and their interactions”. While the human brain is our best source of information about consciousness, the construction of a universal, general theory of consciousness is hampered by the almost absolute and excessive focus on the human brain, human cognition, and human neurophysiology. Human brains should not be the only systems we consider in work; a general theory should address at least the many other systems of interest: other kinds of animals, machines, and even social groups. In this talk I will address the emergence of a theoretical framework for Self Beyond Humans. This theoretical framework shall eventually lead to technological assets for robot selfhood to enable them to properly operate in ecological, medical, technical and economic terms in a variety of circumstances. A positive theory of self shall be centered on system functional architecture, sidetracking philosophical discussions on the nature of ‘content and self’ and leveraging the value of concrete topologies and measurements.
Future robots will have selves that may be enormously alien to humans; but, in a very precise sense, they will be quite similar to ours but with a deeper, purer essence, devoid of all that noise produced by biological evolution.
Sensores Inteligentes y el Futuro de las Máquinas
La incorporación de inteligencia artificial a los sensores de las máquinas permite el desarrollo de aplicaciones sofisticadas de monitorización y control que llevarán, eventualmente, a la construcción de máquinas auto-conscientes.
El primer nivel ataca el problema del monitorizar el cambio
La integración de tecnología de sensores inteligentes y de medición avanzada en las máquinas y sistemas mecánicos, permite implementar aplicaciones de monitorización de condición de máquinas. Los sistemas de mantenimiento basados en la condición permiten disminuir las interrupciones no programadas y optimizar el rendimiento de la máquina, reduciendo costes de mantenimiento y reparación. Se puede utilizar también la tecnología de medición y sensores para aumentar la seguridad de la maquinaria gracias a la disponibilidad de la información sobre el estado del sistema en cualquier momento durante la operación. La disponibilidad de diferentes infraestructuras técnicas permite desplegar los sistemas de monitorización en la propia máquina, por medio de sistemas empotrados, o remotamente, por medio de sistemas distribuidos.
El segundo nivel ataca el problema de anticipar y controlar el cambio
La explotación local de la información de condición y la disponibilidad de modelos operativos de la máquina, permite hacer una integración más transparente de la información del sensor con el controlador, habilitando mecanismos de anticipación y control del cambio. Esto permite desarrollar sistemas de protección o de auto-curación o máquinas. La construcción de sistemas adaptativos permite adaptar la operación de la maquina a las características cambiantes de los componentes mecánicos que la constituyen. La maquina se hace auto-consciente de su propio cuerpo. Esto permite además la mejora de los modelos de la propia máquina, teniendo en cuenta el desgaste y la variación observada, permitiendo implementar controladores adaptativos de mejores prestaciones.
El tercer nivel ataca el problema del aprovechar el cambio
Por último, mediante la integración de sensores avanzados con inteligencia artificial se pueden construir máquinas que aprovechan la dinámica propia y de la realidad, para buscar condiciones optimas de explotación. La máquinas con controles inteligentes dinámicos se adaptan a la evolución de los parámetros ambientales y del proceso de fabricación para acercarse a resultados cercanos a los óptimos. La incorporación de modelos flexibles de conocimiento de los procesos en curso -en los que participa la máquina- permite un comportamiento adaptable y flexible. La máquina puede detectar alteraciones en sí misma, en las materias prima con las que opera, o en las tareas a las que tiene que hacer frente dentro del proceso productivo.
Architectures of Mind
The investigation about what are the best architectures for mind construction has too many intervening threads and interferences. Sometimes heterogeneous people of different domains get together to try to clarify some of the issues concerning mind architecture. In many of these gatherings, people from science and technology try to devise strategies for building computational models or system archures to create artificial minds.
One of these efforts was the EU Funded ICEA Project. The IST 027819 ICEA Integrating Cognition, Emotion and Autonomy was a four-year project, funded by IST Cognitive Systems Unit. The Project was focused on brain-inspired cognitive architectures, robotics and embodied cognition, bringing together cognitive scientists, neuroscientists, psychologists, computational modelers, roboticists and control engineers. The primary aim of the project is to develop a novel cognitive systems architecture integrating cognitive, emotional and bioregulatory (self-maintenance) processes, based on the architecture and physiology of the mammalian brain.
The works but extremely interesting and the teams involved were mostly very active and dedicated to the work. However, the results obtained -basically more elaborated scientific ideas- didn’t have a translation in a concrete architectural implementation of a system that could prove the insights. Different systems wer implemented -real, simulated- but the overall picture was a bit lost.
We need stronger tema integration and specially more convergent objectives to get these types of activities produce the expected and potential results. This is not an easy taks however, as the goal of a cognitive psychologist of an industrial control engineer seem so far a part that convergence sounds more lie a myth than a real, strategical possibility.
All this said, something in fully basic: technology departs from science. Mind engineering must depart from mind since and hence, convergence is necessary. This will only happen if we broaden the tagets and pursue a General Theory of Mind not trapped in the details of rat brains or humanoid robot computer-laden hearths.
Mentes y metáforas
Recientemente planteé a mis alumnos la realización de un ejercicio complementario a la docencia en una asignatura de programación de computadores dentro del ámbito de la ingeniería de control.
El propósito del ejercicio era leer un artículo clásico del ámbito de la inteligencia artificial y realizar un breve comentario personal sobre él (Turing – Computing Machinery and Intelligence). Yo esperaba que hubiera alguien que lo leyera con interés y que escribiera un ensayo personal entre un mar de ensayos con comentarios rutinarios sobre el articulo.
Mi sorpresa ha sido lo contrario: los ensayos rutinarios son los menos; estando rodeados de un mar de opiniones, impresiones, juicios, esperanzas y miedos que me han hecho disfrutar de ver tantas mentes jóvenes, tan vivas y tan capaces de pensar por sí mismas. Ha sido sorprendente descubrir, en esta generación acusada de pasividad y desapego- chispas de alegría intelectual.
Mónica, una de mis alumnas, dice,
“Me gustaría ver como un computador puede sorprendernos. No me refiero con ello a los posibles fallos físicos o de software. Hablo de ser capaz de utilizar una metáfora o decirnos que le gustaría poseer la Luna. Y un ordenador no hará eso nunca a no ser que el programador quiera. Un niño querrá cogerla sólo con mirarla. Le parecerá bonita de manera innata, y querrá poseerla.” — Mónica Romero
En estos tiempos de crisis de la ciencia y la tecnología, me han devuelto una cierta esperanza. Estoy seguro de que alguno de ellos construirá El robot que poseyó la Luna. Un gran proyecto y un gran título para una novela de Lem o de Heinlein.