Neural Networks Inspirational Quotes

Describing good relatedness to someone, no matter how precisely or how often, does not inscribe it into the neural networks that inspire love. Self-help books are like car repair manuals: you can read them all day, but doing so doesn't fix a thing. Working on a car means rolling up your sleeves and getting under the hood, and you have to be willing to get dirt on your hands and grease beneath your fingernails. Overhauling emotional knowledge is no spectator sport; it demands the messy experience of yanking and tinkering that comes from a limbic bond. If someone's relationship today bear a troubled imprint, they do so because an influential relationship left its mark on a child's mind. When a limbic connection has established a neural pattern, it takes a limbic connection to revise it.

A neural network, also known as an artificial neural network, is a type of machine learning algorithm that is inspired by the biological brain.

most students reported a state of total involvement in what was being taught, he would rate the moment “inspired.” The inspired moments of learning shared the same active ingredients: a potent combination of full attention, enthusiastic interest, and positive emotional intensity. The joy in learning comes during these moments. Such joyous moments, says University of Southern California neuroscientist Antonio Damasio, signify “optimal physiological coordination and smooth running of the operations of life.” Damasio, one of the world’s leading neuroscientists, has long been a pioneer in linking findings in brain science to human experience. Damasio argues that more than merely letting us survive the daily grind, joyous states allow us to flourish, to live well, and to feel well-being. Such upbeat states, he notes, allow a “greater ease in the capacity to act,” a greater harmony in our functioning that enhances our power and freedom in whatever we do. The field of cognitive science, Damasio notes, in studying the neural networks that run mental operations, finds similar conditions and dubs them “maximal harmonious states.

Inspired by the tangled webs of neurons in our brains, deep learning constructs software layers of artificial neural networks with input and output layers. Data is fed into the input layer of the network, and a result emerges from the output layer of the network. In between the input and output layers may be up to thousands of other layers, hence the name “deep” learning.

LOCAL SELF AS HOST FOR NONLOCAL SELF When you drop back into your daily life after meditation, you’re changed. You’ve communed with nonlocal mind for an hour, experiencing the highest possible cadence of who you are. That High Self version of you rearranges neurons in your head to create a physical structure to anchor it. You now have a brain that accommodates both the local self and the nonlocal self. My experience has been that the longer you spend in Bliss Brain, whether in or out of meditation, the greater the volume of neural tissue available to anchor that transcendent self in physical experience. Once a critical mass of neurons has wired together, a tipping point occurs. You begin to flash spontaneously into Bliss Brain throughout your day. When you’re idle for a while, like being stuck in traffic or standing in line at the grocery store, the most natural activity seems to be to go into Bliss Brain for a few moments. This reminds you, in the middle of everyday life, that the nonlocal component of your Self exists. It also brings all the enhanced creativity, productivity, and problem-solving ability of Bliss Brain to bear on your daily tasks. You become a happy, creative, and effective person. These enhanced capabilities render you much more able to cope with the challenges of life. They don’t confer exceptional luck. When everyone’s house burns down, yours does too. When the economy nosedives, it takes you with it. But because you possess resilience, and a daily experience of your nonlocal self, you take it in stride. Even when external things vanish, you still have the neural network that Bliss Brain created. No one can take that away from you. DEEPENING PRACTICES Here are practices you can do this week to integrate the information in this chapter into your life: Posttraumatic Growth Exercise 1: In your journal, write down the names of the most resilient people you’ve known personally. They can be alive or dead. They’re people who’ve gone through tragedy and come out intact. Make an appointment to spend time with at least two of the living ones in the coming month. Listen to their stories and allow inspiration to fill you. Neural Reconsolidation Exercise: This week, after a particularly deep meditation, savor the experience. Set a timer and lie down for 15 to 30 minutes. Visualize your synapses wiring together as you deliberately fire them by remembering the deliciousness of the meditation. Choices Exercise: Make 10 photocopies of illustration 7.4, the two doors. Next, analyze in what areas of your environment you often make negative choices. Maybe it’s in online meetings with an annoying colleague at work. Maybe it’s the food choices you make when you walk to the fridge. Maybe it’s the movies you watch on your TV. Tape a copy of the two doors illustration to those objects, such as the monitor, fridge, or TV. This will help you remember, when you’re under stress, that you have a choice.

Isaac Asimov’s short story “The Fun They Had” describes a school of the future that uses advanced technology to revolutionize the educational experience, enhancing individualized learning and providing students with personalized instruction and robot teachers. Such science fiction has gone on to inspire very real innovation. In a 1984 Newsweek interview, Apple’s co-founder Steve Jobs predicted computers were going to be a bicycle for our minds, extending our capabilities, knowledge, and creativity, much the way a ten-speed amplifies our physical abilities. For decades, we have been fascinated by the idea that we can use computers to help educate people. What connects these science fiction narratives is that they all imagined computers might eventually emulate what we view as intelligence. Real-life researchers have been working for more than sixty years to make this AI vision a reality. In 1962, the checkers master Robert Nealey played the game against an IBM 7094 computer, and the computer beat him. A few years prior, in 1957, the psychologist Frank Rosenblatt created Perceptron, the first artificial neural network, a computer simulation of a collection of neurons and synapses trained to perform certain tasks. In the decades following such innovations in early AI, we had the computation power to tackle systems only as complex as the brain of an earthworm or insect. We also had limited techniques and data to train these networks. The technology has come a long way in the ensuing decades, driving some of the most common products and apps today, from the recommendation engines on movie streaming services to voice-controlled personal assistants such as Siri and Alexa. AI has gotten so good at mimicking human behavior that oftentimes we cannot distinguish between human and machine responses. Meanwhile, not only has the computation power developed enough to tackle systems approaching the complexity of the human brain, but there have been significant breakthroughs in structuring and training these neural networks.