From 7285832779ccab353fa5b3f8f78fbe4d3830733b Mon Sep 17 00:00:00 2001 From: coolneng Date: Thu, 1 Jul 2021 02:53:01 +0200 Subject: [PATCH] Add AI definition, early history and ANN --- Dissertation.org | 38 +++++++++++++++++++++++++- Dissertation.pdf | Bin 722578 -> 814343 bytes assets/bibliography.bib | 39 ++++++++++++++++++++++++++- assets/figures/artificial-neuron.png | Bin 0 -> 16306 bytes assets/figures/neural-network.png | Bin 0 -> 52812 bytes 5 files changed, 75 insertions(+), 2 deletions(-) create mode 100644 assets/figures/artificial-neuron.png create mode 100644 assets/figures/neural-network.png diff --git a/Dissertation.org b/Dissertation.org index 011425f..225fcdd 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -62,7 +62,7 @@ Aunque las tecnologías de secuenciación paralelas (NGS) han revolucionado el e Para contrarrestar este obstáculo, varias técnicas mitigatorias se han puesto en marcha. Una de las más populares es el uso de una secuencia de consenso, que es un perfil estadístico a partir de un alineamiento múltiple de secuencias. Es una forma básica de descubrimiento de patrones, en la que un alineamiento múltiple de secuencias más amplio se resume en las características que se conservan. Este tipo de análisis permite determinar la probabilidad de cada base en cada posición de una secuencia. cite:10.1093/bioinformatics/btg109 -#+CAPTION: Alineamiento múltiple de secuencias. La secuencia de consenso aparece en la parte inferior y está formada por los nucleótidos que más aparecen en cada posición. cite:Nagar2013 +#+CAPTION: Alineamiento múltiple de secuencias. La secuencia de consenso aparece en la parte inferior y está formada por los nucleótidos con más ocurrencias en cada posición. cite:Nagar2013 #+label: fig:consensus-sequence [[./assets/figures/consensus-sequence.png]] @@ -85,6 +85,42 @@ Debido a la diversidad de uniones, las moléculas de anticuerpos y TCR muestran Frente a la evidencia recaudada, diversos métodos computacionales basados en la inteligencia artificial se aplican para aliviar estos impedimentos. ** Inteligencia artificial + +La inteligencia artificial (IA) es uno de los campos más nuevos de la ciencia y la ingeniería. La investigación empezó después de la Segunda Guerra Mundial, y el término se acuñó en 1956, en la conferencia de Dartmouth College. La definición de inteligencia artificial sigue generando debate a día de hoy, por ende acotaremos la definición de inteligencia artificial al estudio de los agentes inteligentes. cite:book:771224 + +Un agente es cualquier elemento capaz de percibir su entorno mediante sensores y actuar en consecuencia en ese entorno mediante actuadores. Un agente inteligente es aquel que actúa para conseguir el mejor resultado o, cuando hay incertidumbre, el mejor resultado esperado. En términos matemáticos, formulamos que el comportamiento de un agente se describe por la función de agente que asigna a cualquier entrada una acción. cite:book:771224 + +*** Historia inicial de la inteligencia artificial + +Los comienzos de la inteligencia artificial (1956-1969) se caracterizan por un entusiasmo y optimismo generalizado. Los rápidos avances en /hardware/, junto con el desarrollo de sistemas que redefinían las posibilidades de los ordenadores excedían las expectativas de los especialistas. cite:book:771224 La atmósfera frenética, durante este periodo, se puede recapitular a partir de de la propuesta de la conferencia de Dartmouth: + +\vspace{0.5cm} + +El estudio se basa en la conjetura de que cada aspecto del aprendizaje o cualquier otro rasgo de la inteligencia puede ser, en principio, precisamente descifrado. Se intentará encontrar un método para hacer que las máquinas utilicen el lenguaje, formen abstracciones y conceptos, resuelvan problemas que ahora están reservados a los humanos, y que se mejoren a sí mismas. cite:McCarthy_Minsky_Rochester_Shannon_2006 + +\vspace{0.5cm} + +A finales de los años 1960, los investigadores se toparon con numerosos obstáculos al tratar de usar sus sistemas para resolver problemas más complejos. Debido a una carencia de resultados decisivos, la financiación en este ámbito académico fue cancelada. cite:book:771224 + +Los sistemas de conocimiento (década de 1970) aprovecharon conocimiento específico de una rama para escalar los sistemas inteligentes, intercambiando sistemas generales con bajo rendimiento por sistemas específicos al problema con mejor rendimiento. Estos sistemas novedosos se empezaron a comercializar en los años 80, creando la industria de la IA. cite:book:771224 + +** Redes neuronales artificiales + +Una red neuronal artificial es un modelo de computación bioinspirado, formado por capas de neuronas artificiales. Comenzaremos definiendo el concepto de neurona artificial, con el fin de introducir la noción de red neuronal artificial de forma clara y concisa. + +Una neurona artificial es un modelo de una neurona biológica, cada neurona recibe un conjunto de señales y, al dispararse, transmite una señal a las neuronas interconectadas. Las entradas (/inputs/) se inhiben o amplifican mediante unos pesos numéricos asociados a cada conexión. El disparo, \ie activación, se controla a través de la función de activación. La neurona recoge todas las señales entrantes y calcula una señal de entrada neta en función de los pesos respectivos. La señal de entrada neta sirve de entrada a la función de activación que calcula la señal de salida. cite:book:80129 + +#+CAPTION: Diagrama de una neurona artificial cite:book:80129 +#+NAME: fig:artificial-neuron +[[./assets/figures/artificial-neuron.png]] + +Una red neuronal artificial (ANN) es una red de capas de neuronas artificiales. Una ANN está formada por una capa de entrada, capas ocultas y una capa de salida. Las neuronas de una capa están conectadas, total o parcialmente, a las neuronas de la capa siguiente. También son posibles las conexiones de retroalimentación con las capas anteriores. La estructura típica de una ANN es la siguiente: + +#+CAPTION: Estructura de una red neuronal artificial cite:book:80129 +#+ATTR_HTML: :height 30% :width 50% +#+NAME: fig:neural-network +[[./assets/figures/neural-network.png]] + ** Hacia el Deep Learning * Estado del arte ** Bioinformática diff --git a/Dissertation.pdf b/Dissertation.pdf index a3279fc9b7a34b93cf7bba995430cd393f4d4a18..cc831dc385071807a67abffcc5aa48e4795a6c82 100644 GIT binary patch delta 127195 zcmb^XWmH^Uur-V#Aq0X1cM0z94#C~s-Q8_N2oT)eg1fsD0>RxSxVyW2`#IyFf(xo2*A6zI-42U!Fw)uYK}zj za3FN-)4o7gVvxth2LvL2CWGEd?qB_mzj}O_>Ad**{zrLU=TxhKN0{l$^0t3XnPveM z<2t4$+52zfIg#`aD0Bwzo4nN>Z}P{DkKMrQOOx*tA{MdH;NtFS6BTIz%SiAKn6BFQ zN0Qwnpn#22f6!*zOnvvEEB7{!E`+wZ_cC2@sJcYsVl#WIu^Q-n?Rt5dmze)7xmg(c zWl1tOOOFAUt3f9BpFoJbky2Md`>miqGBr#ebin3{FZtB$!waS3CiMSpm6^HBoZi0mVSIeUkY=3z30mj83M*mM zY0zeRD@Om8l?B>BXBQUxOuG2P#^|h}BG=(($f{}`78rc>`Ju%o`r%a^)ThjVkRdCj zP+;dWNi~M)bmP05h!y^YJt}2t9+n56+iqAnoS)d)~Jr6U{ 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