DEEP MIND

IT IS THE STRUCTURE and the TYPE OF COMPUTER

Taken from Science.    Shimon Ullman.

In an updated paper about the current possibilities of creating a functional artificial brain similar to the human brain, the weaknesses and strengths of such efforts are exposed in Science. The analysis begins with the question: ¿Can  machines think?, self-answered by Alan Turing, in 1950, who alluded  to: a) A mathematical negative formulated by Kurt Gödel and Church, Kleene, Boater and Turing, referred to digital computers, which despite having an infinite capacity, such machines in front of certain things or questions would not respond or would do it wrongly. Penrose suggested (1980,1994), that certain human brain molecular structures could adopt a state of quantum superposition and entanglement (more so, if electrons transiting through human neural circuits do so in ionic forms), paving the way for the future use of artificial quantum computers in order to equal human brain performance. b) Returning to Turing, he closed his point of view   postulating by that time (1950), that for machines to start thinking like a human brain the important thing was to imitate the complex biological neural computational systems, having as a guide the human neural circuits, imitation that to the present time  has made possible the creation of neural circuits systems imitating the cerebral cortex (deep network), constructed with successive layers of elements similar to neurons connected by artificial synapses, producing speech recognitions, complex games, translation of texts, computer vision, classification and segmentation of objects, capture of images, where someone  try to produce a short verbal description of an image, answer visual questions and of human communication about the content of an image, or non-visual tasks: analyze humor and sarcasm, comprehension and intuitive aspects of social things, serve as assistants persons, in medical diagnosis, automatic car handling. Despite this, there are problems to be solved: I) To improve the adjustment of learning through synapses to produce desired output patterns, conditioned by training at the inputs. II) Achieve a learning with deep artificial neural networks that go beyond simple memorization producing logical outputs, not necessarily programmed during the learning process. III) In this perspective, highlights the incorporation into AI (artificial intelligence), associated to deep neural artificial networks of the called: learning reinforcement: LR (mapping of situations or actions to maximize the signal of reward or reinforcement, not to take certain actions, but to discover through trial, error and reward the best option in order to modify the behavior). LR models combined with AI algorithms, are currently applied to video games, Go and Chess, reaching in this last level of world champions with only 4 hours of training. IV) However, the most notable differences between the biological circuit and artificial neural networks systems are those based on structure: biological neurons are complex and diverse in morphology, physiology and neurochemistry. The entrances to excitatory pyramidal neurons are distributed over very complex dendritic branches. Cortical inhibitory neurons exhibit different functions, none of these heterogeneities being included in artificial neural networks.  Biological cerebral cortical circuits are more complex than models of artificial neural networks, including lateral connectivity between neurons, as well as local connections, more extensive connections and connections up and down in the hierarchical cortical regions. V) It is expected that artificial neural networks will promote a real human understanding, in order to address broad aspects of cognition and general artificial intelligence (IGA). Meanwhile, these techniques continue to be perfected under the guidance of neuroscience. VI) There are other functional differences between biological and artificial systems: A) AI's current artificial models rest heavily on the empirical side using simple and uniform artificial neural network structures employing large sets of training data for learning. Biological systems carry out tasks with limited training, learning about pre-existing network structures already encoded in circuits before learning, with which insects, fish and pigeons, perform complex navigation tasks using part of an elaborate set of innate mechanisms with sophisticated computational capabilities. B) Therefore, the development of complex cognitive and perceptual activities in children with little training, in the first months of their lives is possible, recognizing they complex instruments such as their hands, following people with their gazes  and distinguish visually if the characteristics of certain animals are dangerous or not, while developing an incipient understanding of physical or social interactions, through unsupervised learning, given the presence of innate cognitive systems generated by evolution, which facilitated the acquisition of significant concepts and skills. Recent models of visual learning in childhood, show that significant and complex concepts are not innate or learned by the child, but are proto-concepts that provide signals of internal teaching guiding the learning system along pathways that lead to a progressive acquisition and organization of complex concepts with little or no explicit training. Sometimes, a particular pattern of moving images provides an internal signal for the recognition of their hands that helps them to manipulate objects guiding the learning system in the direction of their gaze. Innate structures implemented in cortical regions with specified connectivity warn initially of specific input errors. Perhaps in the future, these pre-existing structures could be coupled to artificial neural models to simulate human learning. Imagine computational learning methods starting from proto-concepts with structures inserted in humans or robots that learn to quickly become familiar with unknown environments in an efficient and flexible way, very different from the current learning procedures. Summing up: I) Following Shimon Ullman, we believe that each machine or robot of the future that must possess a human-like brain should it be  virginally inserted a basic code -not to obey commands- but to complete what it should be (or do), before each new situation or environment, using analogies, logic or emerging thinking solutions. II) Today's hyper-super-computers have no future for this purpose because: a') They use artificial neuron-artificial neuron transmissions, using electrons that circulate through metallic means. a'') Human neurons send messages to other neurons using ions that are more adapted to quantum models (entanglements and others), allowing almost simultaneous transmissions in all possible planes, including feedback-type ones. Although current computers transmit information in several planes, they lack artificial-totalizing organizer neurons, which to be functional should be spherical or pyramidal. b') Although the human brain allows the circulation of 20% of total human body blood at every moment, it does not get very hot, because the membranes of its neurons are covered by a fat content resistor that allows them to capture electrons from the environment and adapt them to ions that allow the transmission of the message to millions of other neurons . b'') You have to copy this resistor model and include it in a quantum computer. c') The problem of reduced space of the artificial brain is solved in quantum computers using artificial neurons with spherical or pyramidal shapes capable (or almost) of dealing with thousands or millions of other artificial neurons.

MORPHOGENESIS

From quantamagazine

MORPHOGENESIS: RANDOM or  NEED?

In 1952, Alan Mathison Turing designed a mathematical model of biological pattern formation (The chemical basis of morphogenesis, 1952, https://doi.org/10.1098/rstb.1952.0012), which would allow developing varieties of rays, spots and scales that would emerge from the interaction of two chemical agents, capable of explaining myriads of biological patterns: mammalian hair, bird feathers, etc. Turing hypothesized that these chemical substances (morphogens), which would react and diffuse together with each other through tissues, could explain the morphogenesis. Although such a system could initially be homogeneous it could later develop different structural patterns due to the instability of the homogeneous equilibrium induced by random disturbances. Turing discussed in his paper a possible mechanism by which the genes of a zygote could determine the anatomical structure of the resulting organism. Now Gareth Fraser (University of Florida), proposes that denticles of the skin of sharks are established in real life by mechanisms similar to that proposed by Turing. Fraser suggests that developing embryos of several   vertebrates establish such patterns in their outer layers since the first vertebrates appeared. The Turing model: reaction-diffusion mechanism, requires an activator and an inhibitor, when disseminating between tissues. The activator initiates the process, forming a spot and promoting its production, while the inhibitor would stop the 2 actions, spreading faster, preventing the formation of activation pockets. Depending on when and where they were released, and the activation pockets would manage to form spaced points and stripes. According to Turing, an initial row of points would stimulate the production of rows of points parallel to the sides of the embryo until it is covered. Rory Cooper assures that from very old the sharks had denticles (little scales), that covered its body as an armor, serving to protection, to diminish the friction or to lodge bacteria, useful in communication procedures. Fraser's colleagues constructed a model of mathematical interactions between the activator and the inhibitor, making the frequencies of diffusion, production and degradation of the 2 morphogens to produce patterns similar to shark skin. When Cooper used in situ hybridization, a technique of luminous expression of genes in tissues noticed that these were light up in both sharks and chickens, expressing highly conservative mechanisms. In Biology, if the expression of a gene is reduced and the pattern disappears, it is concluded that the suppressed gene plays an important role in the production of the pattern. Cooper used a chemical to inhibit the activator of the chicken feathers, impregnating it along the proto-denticles of the shark's embryo. On the other hand, the chemicals to inhibit the expression of the activation of the genes of a bird had identical effect in a shark, forming a dead flat area devoid of denticles, being able to correct also the production, degradation and diffusion frequencies of the activator and inhibitor. The quite conservative nature favors the biological diversity based on a strict set of principles reused again and again, evolutionarily.

MORFOGÉNESIS: ¿AZAR O NECESIDAD?

En 1952, Alan Mathison Turing diseño un modelo matemático de formación de patrones biológicos  (The chemical basis of morphogenesis, 1952, https://doi.org/10.1098/rstb.1952.0012), que permitirían desarrollar variedades de rayas, manchas y escamas que emergerían de la interacción de dos agentes químicos, capaces de explicar miríadas de patrones biológicos: pelo de mamíferos, plumas de aves, etc. Turing hipotetizo que estas sustancias químicas (morfogenos), que reaccionarían y difundirían en juntos entre si a través de los tejidos, podrían explicar la morfogénesis. Aunque tal sistema podría ser inicialmente homogéneo podría desarrollar más tarde patrones estructurales diferentes debido a la inestabilidad del equilibrio homogéneo inducido por disturbios al azar. Turing discutió en su artículo un posible mecanismo por el cual los genes de un zigote podían determinar la estructura anatómica del organismo resultante.  Ahora Gareth Fraser (U Florida), propone que los dentículos de la piel de los tiburones son establecidos en la vida real por mecanismos semejantes al propuesto por Turing.  Fraser, sugiere que embriones en desarrollo de diversos vertebrados establecen tales patrones en sus capas externas desde que aparecieron los primeros vertebrados.  El modelo de Turing: mecanismo de reaccion-difusion, requiere de un activador y un inhibidor, al diseminarse entre los tejidos. El activador inicia el proceso, formando una mancha y promoviendo su producción, en tanto que el   inhibidor detendría las 2 acciones, diseminándose más rápido, impidiendo la formación de bolsillos de activación. Dependiendo de cuando y donde fuesen liberados, los bolsillos de activación se las arreglarían para formar puntos y rayas espaciadas. Según Turing, una fila de puntos inicial estimularía la producción de filas de puntos paralelas a los lados del embrión hasta cubrirla. Rory Cooper, asegura que desde muy antiguo   los tiburones tenían dentículos (escamas), que cubrían su cuerpo a modo de una armadura, sirviendo como protección, para disminuir la fricción o, para albergar bacterias, útiles en la comunicación. Los colegas de Fraser construyeron un modelo de interacciones matemáticas entre el activador y el inhibidor, logrando que las frecuencias de difusión, producción y degradación de los 2 morfogenos   produjeran s patrones semejantes a la piel del tiburón.  Cuando Cooper empleo hibridación in situ, una técnica de expresión luminosa de genes en tejidos noto que estos se encendían tanto en tiburones como en pollos, expresando mecanismos altamente conservadores.  En Biología, si se reduce la expresión de un gene y el patrón desaparece, se concluye que el gene suprimido juega un rol importante en la producción del patrón. Cooper empleo un químico para inhibir al activador de las plumas del pollo, impregnándolo a lo largo de los proto-dentículos   del embrión del tiburón. De otro lado, los químicos para inhibir la expresión de la activación de los genes de un ave tenían efecto idéntico en un tiburón, formando una zona plana muerta desprovista de dentículos, lográndose corregir además la producción, degradación y frecuencias de difusión del activador e inhibidor.  La naturaleza bastante conservadora, favorece la diversidad biológica basándose en un set estricto de principios reusados una y otra vez, evolutivamente.

THE   ENIGMATIC   CODE

Now that it is released worldwide the movie The Imitation game, which recounts one of many creative contributions of  the mathematician, logical and British philosopher Alan Turing, we will analyze 2  of their contributions : I) The decryption operation of Enigma machine, created in 1919, by the German Arthurt Sherbius, to keep secret international business communications. During World War II, an improved Enigma began sending encrypted messages to German units at war. Enigma, considered unbreakable, became the most powerful weapon of the German war command. Formatted as typewriter, Enigma worked like this: a) Following on with electric current, normal messages were sent by pressing keyboard letters. Then, electric current   mobilized  5 mechanical rotors, containing letters and numbers, with the mission to encrypt the message. b) Mixtures of letters and numbers of these rotors was about 187 million, of millions of combinations. As the order of the 5 rotors was changed daily, Enigma encrypted  and deciphered messages. c) In a secret place Turing,  analyzed the weaknesses of some captured messages  (Enigma, encrypted  few letters) and human (Germans sent daily messages, with the same format : wind speed, temperature and atmospheric pressure). The capture of an Enigma machine in a German submarine, facilitated  Turing to create   the machine The Bombe (embryo of modern computers). To this end Turing,  analyzed some intercepted messages identifying in them : pairs of repeated letters in different places, in the same message. As Enigma was reversible, Turing  concluded that such pairs of letters be determined by the order of  rotors. So, it was important to determine the starting position of the rotor containing the original unencrypted message. To this aim, was necessary to perform a vast number of possible combinations of rotors to find matches between starting positions and letter pairs. Perform these operations simultaneously, took to build the Bombe, shortening the war. II) Turing, also employed  mathematics  for foreseeing  initial stages of morphogenesis of an animal embryo, concluding that after fertilization, certain, initially completely homogenous chemicals (morphogens) -following math patterns-  develop shortly after different morphological structures,  given the instability of the homogeneous equilibrium due to random disturbances, highlighting ring forms and those  related  to gastrulation,  starting point for building all kinds of forms (organisms).

EL   CÓDIGO  ENIGMATICO

Ahora que se estrena  a  nivel mundial  la película  The  Imitation game,  que  relata una de las tantas contribuciones  creativas del matemático, lógico y filosofo británico Alan Turing, analizaremos  2 de sus contribuciones : I) El desciframiento del funcionamiento de la maquina Enigma, creada  en 1919,  por el  alemán  Arthurt Sherbius,  para mantener en secreto  comunicaciones de negocios internacionales. Durante la segunda guerra mundial, un Enigma mejorado, empezó a enviar mensajes encriptados  a las unidades alemanas en guerra. Enigma,  considerada  como indescifrable, llego a ser la más poderosa arma del  comando  de guerra  alemán. Con formato de máquina de  escribir, Enigma  funcionaba así: a) Tras encenderse  con corriente eléctrica,   se enviaban mensajes normales presionando las letras del teclado. A continuación la corriente eléctrica movilizaba  5 rotores   mecánicos, conteniendo  letras y números, con la misión de encriptar  el mensaje. b) Las mezclas  de letras  y números de estos  rotores   era  alrededor de  187 millones, de millones de combinaciones. Mientras el  orden de los 5 rotores,  era cambiado día a día,  Enigma encriptaba y  descifraba mensajes. c) En un lugar secreto Turing, analizo las debilidades de algunos mensajes capturados (Enigma, encriptaba  pocas letras) y  humanas (los alemanes enviaban a diario, con el mismo formato  mensajes de: velocidad del viento, temperatura y presión atmosférica). La  captura de una maquina Enigma en un submarino alemán, facilito   a  Turing  crear la maquina The Bombe (embrión de las modernas computadoras). A este fin  Turing, analizo algunos mensajes interceptados identificando pares de letras repetidas en  diferentes lugares, en el mismo mensaje. Como Enigma  era reversible,  dedujo que al ser tales pares de letras determinados por el  orden de los rotores, lo importante era determinar la posición de inicio de los rotores conteniendo   el mensaje inicial no encriptado. Era necesario realizar un  vasto número de  posibles combinaciones de rotores para  encontrar coincidencias entre  posiciones de inicio y pares de letras. Llevar a cabo estas operaciones simultáneamente llevo a construir la Bombe,  acortando la guerra. II) Alan Turing también empleo matemáticas para  visionar  las fases iniciales de la morfogénesis de   un embrión  animal,  concluyendo que tras  la fertilización, ciertas sustancias químicas (morfogenos, proteínas), totalmente  homogéneas inicialmente,  desarrollan poco después  -siguiendo patrones matemáticos- estructuras morfológicas diferentes, dada la inestabilidad del equilibrio homogéneo por disturbios al azar,   destacando las  formas en anillo y las relacionadas con  la  gastrulación, punto inicial para la construcción de  toda clase de formas (organismos).