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      "body": "In the case of artificial neurons, the sum of the inputs multiplied by their associated weights determines the “nerve impulse” that the neuron receives. This value is processed inside the cell by an activation function that returns a value that is sent as the neuron’s output.\n\nJust as our brain is made up of interconnected neurons, an artificial neural network is made up of interconnected artificial neurons grouped into different levels that we call layers:\n\n» A layer is a set of neurons whose inputs come from a previous layer (or from the input data in the case of the first layer) and whose outputs are the input of a subsequent layer.» \n\nIn the following image we can see a red one with four layers:\n\n\n![1000508788.jpg](https://cdn.steemitimages.com/DQmcGA6AjrNE2k8jKHwzjxG2B1NSoKoWNFGKc9s7taPD71z/1000508788.jpg)\n\nThe neurons in the first layer receive as input the actual data that feeds the neural network. This is why the first layer is known as the input layer. The output of the last layer is the visible result of the network, so the last layer is known as the output layer. The layers between the input layer and the output layer are known as hidden layers since we do not know both the input and output values.\n\nA neural network, therefore, is always composed of an input layer, an output layer (if there is only one layer in the neural network, the input layer matches the output layer) and can contain 0 or more hidden layers. The concept of Deep Learning is born from using a large number of hidden layers in networks.\n\nHow do you train a neural network?\n\nTraining a neural network involves adjusting each of the input weights of all the neurons that make up the neural network, so that the output layer's responses fit as closely as possible to the data we know.\n\nIn the following animation, we can see a very simplified example of the training process of a network to detect, in this case, a cat in an image. The thickness of each arrow represents the weight that that input has in the neural network. As we can see, as the weights are adjusted, the error decreases.\n\nIf we want the neural network to be able to generalize and identify cats in any image, it is important to use a large number of images for training, both images that are cats (labeled as 1) and images that are not cats (labeled as 0), including as much variability as possible. With this, the network will be able to adjust its parameters to satisfy as much as possible all the images, so it will be able to accurately extract the features that identify the presence of a cat in an image.",
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      "body": "In the case of artificial neurons, the sum of the inputs multiplied by their associated weights determines the “nerve impulse” that the neuron receives. This value is processed inside the cell by an activation function that returns a value that is sent as the neuron’s output.\n\nJust as our brain is made up of interconnected neurons, an artificial neural network is made up of interconnected artificial neurons grouped into different levels that we call layers:\n\n» A layer is a set of neurons whose inputs come from a previous layer (or from the input data in the case of the first layer) and whose outputs are the input of a subsequent layer.» \n\nIn the following image we can see a red one with four layers:\n\n\n![1000508788.jpg](https://cdn.steemitimages.com/DQmcGA6AjrNE2k8jKHwzjxG2B1NSoKoWNFGKc9s7taPD71z/1000508788.jpg)\n\nThe neurons in the first layer receive as input the actual data that feeds the neural network. This is why the first layer is known as the input layer. The output of the last layer is the visible result of the network, so the last layer is known as the output layer. The layers between the input layer and the output layer are known as hidden layers since we do not know both the input and output values.\n\nA neural network, therefore, is always composed of an input layer, an output layer (if there is only one layer in the neural network, the input layer matches the output layer) and can contain 0 or more hidden layers. The concept of Deep Learning is born from using a large number of hidden layers in networks.\n\nHow do you train a neural network?\n\nTraining a neural network involves adjusting each of the input weights of all the neurons that make up the neural network, so that the output layer's responses fit as closely as possible to the data we know.\n\nIn the following animation, we can see a very simplified example of the training process of a network to detect, in this case, a cat in an image. The thickness of each arrow represents the weight that that input has in the neural network. As we can see, as the weights are adjusted, the error decreases.\n\nIf we want the neural network to be able to generalize and identify cats in any image, it is important to use a large number of images for training, both images that are cats (labeled as 1) and images that are not cats (labeled as 0), including as much variability as possible. With this, the network will be able to adjust its parameters to satisfy as much as possible all the images, so it will be able to accurately extract the features that identify the presence of a cat in an image.",
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      "body": "La adopción de nuevas disrupciones tecnológicas requiere de un proceso y un tiempo determinado. Los nuevos avances son adquiridos con cada vez más velocidad. Hace apenas unos pocos años, la Inteligencia Artificial (IA) parecía un asunto futurista que, difícilmente podría alterarnos el día a día en el corto plazo. Sin embargo, hoy es ya una realidad que aspira a revolucionar diversos aspectos de nuestra sociedad en los próximos 5 años. A continuación, se muestran 5 principales avances que se producirán en los próximos 5 años gracias a la Inteligencia Artificial:\n\n![1000508782.jpg](https://cdn.steemitimages.com/DQmNf3dun2RgZuGFd5Ax2vgRpuXv9KpBuwbxsEoeQxWVeiQ/1000508782.jpg)\n\nLa Inteligencia Artificial avanza hacia modelos de aprendizaje basados en patrones no humanos.\n\nEn el 2016, un robot diseñado para competir en el complejo juego de Go, fue capaz de vencer, por 100 partidas a cero, al campeón mundial. Lo más sorprendente de este robot, AlphaGo Zero, es que adquirió el conocimiento mediante un método conocido como Reinforement learning donde no se necesita aprender a partir de comportamiento humanos, sino que es capaz de generar conocimiento desde cero. Además, estos patrones de aprendizaje permiten a las máquinas acumular miles de años de conocimiento en cuestión de horas. Según este punto de partida, quién sabrá que nuevos AlphaGo Zero surgirán y qué serán capaces de hacer.\n\nRiesgo para los doctores en sus diagnósticos y en los tratamientos si no hacen uso del machine learning\n\nRecientemente, unos investigadores chinos y americanos han desarrollado un sistema basado en Inteligencia Artificial para la detección de enfermedades infantiles comunes, desde un simple gripe hasta una meningitis. Para ello, han compilado y procesado más de 1,3 millones de registros de casi 600.000 pacientes. El Doctor Kang Zhang, director de genética ocular de la Universidad de California ha creado su propio sistema que detecta signos de ceguera diabética basándose tanto en texto como en imágenes médicas. En este sentido, los expertos auguran un futuro próximo donde los médicos sean incapaces de realizar ningún diagnóstico sin el uso de Inteligencia Artificial pues no querrán dejar de pasar ningún tipo de información.\n\nLa tecnología cuántica facilitará el diseño de medicamentos y las pruebas de los mismos\nHasta la fecha, los químicos y farmacéuticos realizan sus diseños en base a las propiedades moleculares y deben sintetizar numerosas variables para testar sus medicamentos y predicciones. La computación cuántica puede transformar este proceso engorroso y costoso. Así, se podrán realizar numerosos diseños en paralelo que podrán ser testeados gracias a la tecnología cuántica.\n\nLos avances en Inteligencia Artificial aumentan la vulnerabilidad de los sistemas de seguridad\nLos diferentes ámbitos de nuestra sociedad cuentan con sistemas informáticos de defensa y de seguridad. Nuestros edificios, nuestros hogares, las instituciones financieras, el ejército y un largo etcétera. Sin embargo, ya contamos con numerosos ejemplos donde estos sistemas han sido quebrantados, destruyendo los objetivos. El expresidente Obama sufrió la difusión de unos vídeos falsos cuando estaba al frente de la Casa Blanca donde se le escuchaba decir frases que nunca había pronunciado. Con el auge de la Inteligencia Artificial, estos sistemas están en mayor riesgo de ser hackeados.\n\nAvances en fabricación en materia de precisión atómica.\n\nLa Inteligencia Artificial está revolucionando las relaciones de los humanos con los materiales. Entre otros usos, se está utilizando para desarrollar nuevos materiales para tecnologías “limpias” tales como paneles solares, baterías, etc. Hoy en día, crear un nuevo material requiere de entre 15 y 20 años según los expertos. Sin embargo, la tecnología de Inteligencia Artificial permitirá acortar estos tiempos en gran medida.",
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      "body": "Un día como hoy, yo estaba en mi lugar de trabajo, tenía el cargo como coordinador de grupo, me encontraba en la entrada principal de la empresa, de un momento se apersonó una mujer pequeña, morena y delgada, donde se entrevistó conmigo solicitando trabajo, desde el punto que nos vimos cara a cara, hubo una conexión entre ella y yo, fue un sentimiento muy lindo, yo la ayude a conseguir el trabajo, ella ese mismo día tuvo una entrevista con el jefe del departamento, una vez retirándose de la empresa me dijo que gracias y que venía el día siguiente a trabajar, se fue muy contenta y yo estabas más feliz, esperando el día siguiente para verla, llegó el día más especial cuando ella llegó yo fui a recibirle y darle la bienvenida a la empresa, dándole la inducción duramos toda la mañana hablando sobre el trabajo y como era todo, desde ese momento comenzó un sentimiento de amor entre ella y yo, intercambiamos número charlamos de nuestra vida personal pasaron 4 meses conociéndonos, donde por fin llegó el día más esperado fue cuando yo le dije si quería ser el amor de mi vida, ella estaba esperando que yo le preguntara día a día lo esperaba, se le notaba los enamorada que estaba, le brillaban los ojos, luego de 4 meses pasamos de ser amigos, a ser pareja, en la empresa tuvimos que renunciar por qué estaban encontrá de nuestra relación, no podíamos ser pareja trabajando juntos, nosotros hacíamos nuestro trabajo bien y en la empresa ella por su lado y yo por el mío, fue algo injusto, nos retiramos ambos el mismo día, el día siguiente salimos a buscar trabajo, ella no consiguió nada y yo si, desde ese entonces nos veíamos poco por motivo que me la pasaba trabajando y ella decidió estudiar, yo la ayuda con sus estudios, en nuestros tiempos libres nos veíamos disfrutamos juntos, hasta el día de hoy llegamos 4 años de relación, ella nunca había ido a un estadio de beisbol yo la llevé, tenía 3 años sin el a la playa yo la llevé, creo que he sido una buena persona para ella..",
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