The new book explores the uneasy relationship between Chinese internet users and a government that is always watching. Let's play a game of two truths and a lie. Of the three following statements, which one would you guess is made up? China was once home to the world's largest gay dating app with more users than Grindr, and it later went public on Nasdaq. The app's founder was a Chinese police officer who didn't come out at work until after he had been running an online forum for gay men for a decade.

Sam Altman is the king of generative artificial intelligence. But is he the person we should trust to guide our explorations into AI? This week, we do a deep dive on Sam Altman, from his Midwest roots to his early startup days, his time in venture capital, and his rise and fall and rise again at OpenAI. You can follow Michael Calore on Mastodon at @snackfight, Lauren Goode on Threads and @laurengoode, and Zoë Schiffer on Threads @reporterzoe. Write to us at uncannyvalley@wired.com.

Over the last ten years, the realm of Artificial Intelligence (AI) has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI's widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift towards a new paradigm, suggesting a future where AI's capabilities may redefine the boundaries of SE, potentially even more than human input. In this paper, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards.

IBM is one of the oldest technology companies in the world, with a raft of innovations to its credit, including mainframe computing, computer-programming languages, and AI-powered tools. But ask an ordinary person under the age of 40 what exactly IBM does (or did), and the responses will be vague at best. "Something to do with computers, right?" was the best the Gen Zers I queried could come up with. If a Millennial knows anything about IBM, it's Watson, the company's prototype AI system that prevailed on Jeopardy in 2011. Check out more from this issue and find your next story to read. In the chronicles of garage entrepreneurship, however, IBM retains a legendary place--as a flat-footed behemoth.

Data sets are not neutral, but represent particular social and political norms, which can specifically affect marginalized groups. In recent years, many popular data sets have been identified by the machine-learning community as having an alarming number of potential legal and ethical problems--representational harms, effects of bias, privacy infringement, and unclear or dubious downstream use. This has led several data sets to be taken down or heavily redacted. In practice, however, they continue to be available and widely used, either in their original form, such as via online torrents, or in derivative form, as subsets or modifications of the original data set or models pretrained on the deprecated data set. Moving forward, a fundamental change in data set culture is necessary.

In the early two-thousands, Merlin Mann, a Web designer and avowed Macintosh enthusiast, was working as a freelance project manager for software companies. He had held similar roles for years, so he knew the ins and outs of the job; he was surprised, therefore, to find that he was overwhelmed--not by the intellectual aspects of his work but by the many small administrative tasks, such as scheduling conference calls, that bubbled up from a turbulent stream of e-mail messages. "I was in this batting cage, deluged with information," he told me recently. Why was I having such a hard time?" In the nineteen-nineties, the spread of e-mail had transformed knowledge work. With nearly all friction removed from professional communication, anyone could bother anyone else at any time. Many e-mails brought obligations: to answer a question, look into a lead, arrange a meeting, or provide feedback. Work lives that had once been sequential--two or three blocks of work, broken up by meetings and phone ...

Let's get this out of the way: I am sleeping with a robot. I hold it in my arms each night and feel its chest rise and fall against mine. Without arms to hold me back, it is forever my little spoon. Without a voice to bid me sweet dreams, it simply sits there, purring against me. The robot with which I sleep is called the Somnox.

By the first few months of 1982, it had become more common to see electronics stores, toy stores, and discount variety stops selling 2600 games. This was before Electronics Boutique, Software Etc., and later, GameStop . Mostly you bought games at stores that sold other electronic products, like Sears or Consumer Distributors. Toys'R' Us was a big seller of 2600 games. To buy one, you had to get a piece of paper from the Atari aisle, bring it to the cashier, pay for it, and then wait at a pickup window behind the cash register lanes. Everyone had a favorite store in their childhood; here's a story about one of mine. A popular "destination" in south Brooklyn is Kings Plaza, a giant (for Brooklyn) two-story indoor mall with about 100 stores. My mother and grandmother were avid shoppers there. To get to the mall from our house, it was about a 10-minute car service ride. So once a week or thereabouts, we'd all go. The best part for me was when we went inside via its Avenue U entrance instead of on the Flatbush Avenue side.

Recruiters are using it on their platforms to target the best candidates. Banks have embraced it as a means to identify patterns of behavior that might indicate fraud. Power companies are deploying it to drive energy efficiency. Marketers see it as a way to optimise their campaigns. But if you're company that is selling A.I.- driven solutions.

"[[[When in the course of human events it becomes necessary for one people [to dissolve the political bands [which have connected them with another]] and [to assume among the powers of the earth, the separate and equal station [to which the laws of Nature and of Nature's God entitle them]]], a decent respect to the opinions of mankind requires [that they should declare the causes [which impel them to the separation]]]." But how did it ever make its way into the world? At 71 words, it is composed of eight separate clauses, each anchored by its own verb, nested within one another in various arrangements. The main clause (a decent respect to the opinions of mankind requires …) hangs suspended above a 50-word subordinate clause that must first be unfurled. To some linguists, Noam Chomsky among them, sentences like these illustrate an essential property of human language. These scientists have argued that recursion, a technique that allows chunks of language such as sentences to be embedded inside each other (with no hard limit on the number of nestings) is a universal human ability, perhaps even the one uniquely human ability that supports language. It's what allows us to create--literally--an infinite variety of novel sentences out of a limited inventory of words.