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Modern Technology Has Increased Our Material Wealth But Not Our Happiness

  • melisaknorr11
  • Nov 21, 2023

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Modern Technology Has Increased Our Material Wealth But Not Our Happiness

Modern Technology Has Increased Our Material Wealth But Not Our Happiness

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Modern Technology Has Increased Our Material Wealth But Not Our Happiness

We use cookies and other data for reasons such as keeping the Sites reliable and secure, personalizing content and advertising, providing social media features, and analyzing how our Sites are used. You are sitting in an armchair by the fire on a cold winter night. Maybe you have a cup of tea in your hand, maybe something stronger. You open a magazine to an article you’ve been meaning to read. The headline tells a story about a promising — but also potentially dangerous — new technology going mainstream, and after reading just a few sentences, you’re hooked. The author argues that a revolution in machine intelligence is coming, and that we as a society need to get better at anticipating its consequences. But then the strangest thing happens: you realize that the author has left out the last word of the first, apparently on purpose.

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There is no sense of inner searching in your mind. The word “paragraph” appears. It may seem second nature, this fill-in-the-blank exercise, but doing it makes you think about the layers of knowledge that lie in the back of your mind. Mastery of English spelling and syntactic patterns is required. You need to understand not only the dictionary definitions of the words, but also the ways they relate to each other. You’d have to be familiar enough with the high standards of journal publishing to assume that the missing word isn’t just a typo, and that editors generally hate omitting keywords in published pieces unless the author is trying to be clever—perhaps trying to use a word Missing to make the point

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Before you can pursue this idea any further, you’re back to the article, where you realize the author has taken you to a construction complex in rural Iowa. Inside one of the buildings is a marvel of modern technology: 285,000 CPU cores housed in a giant supercomputer powered by solar arrays and cooled by industrial fans. Machines never sleep: every second of every day, they go through countless calculations using advanced techniques in machine intelligence with names like “stochastic gradient descent” and “convolutional neural networks.” The entire system is believed to be one of the most powerful supercomputers on the planet.

And you might ask, what does this computing dynamo do with all these huge resources? It basically plays the same type of game over and over billions of times per second. And the game called:

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The supercomputing complex in Iowa runs a program created by OpenAI, an organization founded in late 2015 by a handful of Silicon Valley celebrities, including Elon Musk. Greg Brockman, who until recently was Stripe’s CTO of electronic payments. and Sam Altman, then-president of startup incubator Y Combinator. For the first few years, as the trust built its programming brain, OpenAI’s technical achievements were largely overshadowed by the star power of its founders. But that changed in the summer of 2020, when OpenAI began offering limited access to a new program called Generative Pre-Trained Transformer 3, colloquially called GPT-3. Although the platform was initially only available to a small number of developers, examples of GPT-3’s uncanny ability with language — and at least the illusion of cognition — began to spread across the web and through social media. Siri and Alexa had popularized the experience of conversing with machines, but this was next-level, approaching the psychotic akin to sci-fi creations like HAL 9000 from “2001”: a computer program that could answer complex, open-ended questions. give a mixed answer. Sentences.

As a discipline, A.I. It is currently divided between a number of different approaches that target different types of problems. Some systems are optimized for problems related to movement in physical space, such as self-driving cars or robotics. Others categorize photos for you by identifying familiar faces or pets or vacation activities. Some forms of A.I. — like AlphaFold, a project from Alphabet (formerly Google) subsidiary DeepMind — is starting to tackle complex scientific problems, such as predicting the structure of proteins, which are central to drug design and discovery. Many of these experiments take a basic approach called “deep learning,” in which a neural network vaguely modeled after the structure of the human brain learns to recognize patterns or solve problems through cycles of trial and error. It is repeated endlessly and strengthens the nerve. Communication and undermining others through a process called education. The “depth” of deep learning refers to the multiple layers of artificial neurons in the neural network, layers corresponding to higher and higher levels of abstraction: for example, in a vision-based model, a layer of neurons might recognize vertical lines. That then feeds into a layer that detects the edges of physical structures, which then reports back to a layer that detects houses instead of apartment buildings.

GPT-3 belongs to a category of deep learning known as a large language model, a complex neural network trained on a gigantic dataset of text: in the case of GPT-3, about 700 gigabytes of data from Each part is extracted from the web. , including Wikipedia, along with a large collection of text from digital books. GPT-3 is the most famous and most widely available large language model, but Google, Meta (formerly Facebook), and DeepMind have all developed their own L.L.M.s in recent years. Advances in computing power—and new mathematical techniques—have enabled GPT-3’s legacy LLMs to take in much larger datasets than their predecessors and use deeper layers of artificial neurons to train them.

Modern Technology Has Increased Our Material Wealth But Not Our Happiness

If you’ve ever used an app—like Gmail—that includes an autocomplete feature that gently tells you the word “present” after you type the sentence “Unfortunately,” you’ve probably already interacted with a large language model. I can not….” But auto-completion is only the most basic expression of what software like GPT-3 can do. It turns out that with enough training data and deep enough neural networks, large language models can show significant skill if you ask them not only to fill in a missing word, but to continue writing entire paragraphs in the style of basic grammar. For example, when I gave GPT-3 the command:

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In 1958, Frank Rosenblatt introduced the Perceptron, an early version of a neural network that could only detect the location of a square on a printed card. Fifty years later, neural networks can. . .

The software painstakingly completed this thought and then continued by mentioning the historical context of the original text:

. . . Face recognition and classification, finding patterns in data and simulating the human brain. While the neural network matured in academia, it also found its way into the tech industry. In the late 1980s and early 90s, neural networks were used in speech recognition and character recognition applications. Growth in computing power and the ability to collect larger data sets allowed other neural network applications to emerge.

Since the release of GPT-3, the Internet has been full of examples of the software’s eerie possibilities with the language –

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