Select Page
AI Can Give You an NPC That Remembers. It Could Also Get Your Favorite Artist Fired

AI Can Give You an NPC That Remembers. It Could Also Get Your Favorite Artist Fired

AI’s presence in the gaming industry has evolved from a mere novelty to an indispensable force. With every algorithmic breakthrough, new possibilities and challenges arise for gamers and developers alike.

In March 2023, a Reddit user shared a story of how AI was being used where she worked. “I lost everything that made me love my job through Midjourney overnight,” the author wrote. The post got a lot of attention, and its author agreed to talk to WIRED on condition of anonymity, out of fear of being identified by her employer.

“I was able to get a huge dopamine rush from nailing a pose or getting a shape right. From having this ‘light bulb moment’ when I suddenly understood a form, even though I had drawn it hundreds of times before,” says Sarah (not her real name), a 3D artist who works in a small video game company.

Sarah’s routine changed drastically with version 5 of Midjourney, an AI tool that creates images from text prompts. Midjourney has also been widely criticized for violating copyright of visual artists and stealing their work in order to train its image generation engine, criticism that’s led to a massive copyright lawsuit.

When Sarah started working in the gaming industry, she says, there was high demand for 3D environmental and character assets, all of which designers built by hand. She says she spent 70 percent of her time in a 3D motion capture suit and 20 percent in conceptual work; the remaining time went into postprocessing. Now the workflow involves no 3D capture work at all.

Her company, she explains, found a way to get good and controllable results using Midjourney with images taken from the internet fed to it, blending existing images, or simply typing a video game name for a style reference into the prompt. “Afterwards, most outputs only need some Photoshopping, fixing errors, and voilà: The character that took us several weeks before now takes hours—with the downside of only having a 2D image of it,” says Sarah. “It’s efficiency in its final form. The artist is left as a clean-up commando, picking up the trash after a vernissage they once designed the art for,” she adds.

“Not only in video games, but in the entire entertainment industry, there is extensive research on how to cut development costs with AI,” says Diogo Cortiz, a cognitive scientist and professor at the Pontifícia Universidade de São Paulo. Cortiz worries about employment opportunities and fair compensation, and he says that labor rights and regulation in the tech industry may not match the gold rush that’s been indicative of AI adoption. “We cannot outsource everything to machines. If we let them take over creative tasks, not only are jobs less fulfilling, but our cultural output is weakened. It can’t be all about automation and downsizing,” he says, adding that video games reflect and shape society’s values.

Cortiz says that gaming companies must, either as an industry or individually, collaboratively discuss AI, its usage, where it should be applied, and how far it can go. “The committees need to have diversity in terms of gender, age, class, and ethnicity, to discuss and create a more inclusive AI,” he says. “They need to make their AI principles available to everyone.” He adds that gamers should have access to how companies use AI so there can be greater transparency, trust, and more developed digital literacy on the topic.

In practice, that means companies should disclose the AI tools employed in games and make their AI committee available to write public reports and answer questions from all stakeholders involved in a video game—developers, players, and investors.

Labor-Saving or Labor-Crushing?

“Incorporation of AI in our workflows relies on three axes: creating more believable worlds, reducing the number of low-value tasks for our creators, and improving the player experience,” says Yves Jacquier, executive director of Ubisoft La Forge.

Jacquier describes several ways that his company is already experimenting with AI, from smoother AI-driven motion transitions in Far Cry 6, which make the game look more natural, to the bots designed to improve the new player experience in Rainbow Six Siege. There’s also Ghostwriter, an AI-powered tool that allows scriptwriters to create a character and a type of interaction they would like to generate and offers them several variations to choose from and edit.

Elon Musk Has Fired Twitter’s ‘Ethical AI’ Team

Elon Musk Has Fired Twitter’s ‘Ethical AI’ Team

As more and more problems with AI have surfaced, including biases around race, gender, and age, many tech companies have installed “ethical AI” teams ostensibly dedicated to identifying and mitigating such issues.

Twitter’s META unit was more progressive than most in publishing details of problems with the company’s AI systems, and in allowing outside researchers to probe its algorithms for new issues.

Last year, after Twitter users noticed that a photo-cropping algorithm seemed to favor white faces when choosing how to trim images, Twitter took the unusual decision to let its META unit publish details of the bias it uncovered. The group also launched one of the first ever “bias bounty” contests, which let outside researchers test the algorithm for other problems. Last October, Chowdhury’s team also published details of unintentional political bias on Twitter, showing how right-leaning news sources were, in fact, promoted more than left-leaning ones.

Many outside researchers saw the layoffs as a blow, not just for Twitter but for efforts to improve AI. “What a tragedy,” Kate Starbird, an associate professor at the University of Washington who studies online disinformation, wrote on Twitter. 

Twitter content

This content can also be viewed on the site it originates from.

“The META team was one of the only good case studies of a tech company running an AI ethics group that interacts with the public and academia with substantial credibility,” says Ali Alkhatib, director of the Center for Applied Data Ethics at the University of San Francisco.

Alkhatib says Chowdhury is incredibly well thought of within the AI ethics community and her team did genuinely valuable work holding Big Tech to account. “There aren’t many corporate ethics teams worth taking seriously,” he says. “This was one of the ones whose work I taught in classes.”

Mark Riedl, a professor studying AI at Georgia Tech, says the algorithms that Twitter and other social media giants use have a huge impact on people’s lives, and need to be studied. “Whether META had any impact inside Twitter is hard to discern from the outside, but the promise was there,” he says.

Riedl adds that letting outsiders probe Twitter’s algorithms was an important step toward more transparency and understanding of issues around AI. “They were becoming a watchdog that could help the rest of us understand how AI was affecting us,” he says. “The researchers at META had outstanding credentials with long histories of studying AI for social good.”

As for Musk’s idea of open-sourcing the Twitter algorithm, the reality would be far more complicated. There are many different algorithms that affect the way information is surfaced, and it’s challenging to understand them without the real time data they are being fed in terms of tweets, views, and likes.

The idea that there is one algorithm with explicit political leaning might oversimplify a system that can harbor more insidious biases and problems. Uncovering these is precisely the kind of work that Twitter’s META group was doing. “There aren’t many groups that rigorously study their own algorithms’ biases and errors,” says Alkhatib at the University of San Francisco. “META did that.” And now, it doesn’t.

To Fix Tech, Democracy Needs to Grow Up

To Fix Tech, Democracy Needs to Grow Up

There isn’t much we can agree on these days. But two sweeping statements that might garner broad support are “We need to fix technology” and “We need to fix democracy.”

There is growing recognition that rapid technology development is producing society-scale risks: state and private surveillance, widespread labor automation, ascending monopoly and oligopoly power, stagnant productivity growth, algorithmic discrimination, and the catastrophic risks posed by advances in fields like AI and biotechnology. Less often discussed, but in my view no less important, is the loss of potential advances that lack short-term or market-legible benefits. These include vaccine development for emerging diseases and open source platforms for basic digital affordances like identity and communication.

At the same time, as democracies falter in the face of complex global challenges, citizens (and increasingly, elected leaders) around the world are losing trust in democratic processes and are being swayed by autocratic alternatives. Nation-state democracies are, to varying degrees, beset by gridlock and hyper-partisanship, little accountability to the popular will, inefficiency, flagging state capacity, inability to keep up with emerging technologies, and corporate capture. While smaller-scale democratic experiments are growing, locally and globally, they remain far too fractured to handle consequential governance decisions at scale.

This puts us in a bind. Clearly, we could be doing a better job directing the development of technology towards collective human flourishing—in fact, this may be one of the greatest challenges of our time. If actually existing democracy is so riddled with flaws, it doesn’t seem up to the task. This is what rings hollow in many calls to “democratize technology”: Given the litany of complaints, why subject one seemingly broken system to governance by another?

At the same time, as we deal with everything from surveillance to space travel, we desperately need ways to collectively negotiate complex value trade-offs with global consequences, and ways to share in their benefits. This definitely seems like a job for democracy, albeit a much better iteration. So how can we radically update democracy so that we can successfully navigate toward long-term, shared positive outcomes?

The Case for Collective Intelligence

To answer these questions, we must realize that our current forms of democracy are only early and highly imperfect manifestations of collective intelligence—coordination systems that incorporate and process decentralized, agentic, and meaningful decisionmaking across individuals and communities to produce best-case decisions for the collective.

Collective intelligence, or CI, is not the purview of humans alone. Networks of trees, enabled by mycelia, can exhibit intelligent characteristics, sharing nutrients and sending out distress signals about drought or insect attacks. Bees and ants manifest swarm intelligence through complex processes of selection, deliberation, and consensus, using the vocabulary of physical movement and pheromones. In fact, humans are not even the only animals that vote. African wild dogs, when deciding whether to move locations, will engage in a bout of sneezing to determine whether quorum has been reached, with the tipping point determined by context—for example, lower-ranked individuals require a minimum of 10 sneezes to achieve what a higher-ranked individual could get with only three. Buffaloes, baboons, and meerkats also make decisions via quorum, with flexible “rules” based on behavior and negotiation. 

But humans, unlike meerkats or ants, don’t have to rely on the pathways to CI that our biology has hard-coded into us, or wait until the slow, invisible hand of evolution tweaks our processes. We can do better on purpose, recognizing that progress and participation don’t have to trade off. (This is the thesis on which my organization, the Collective Intelligence Project, is predicated.)

Our stepwise innovations in CI systems—such as representative, nation-state democracy, capitalist and noncapitalist markets, and bureaucratic technocracy—have already shaped the modern world. And yet, we can do much better. These existing manifestations of collective intelligence are only crude versions of the structures we could build to make better collective decisions over collective resources.

China’s Jidu Robo-1 Looks Like It’s From the Future. Maybe It Is

China’s Jidu Robo-1 Looks Like It’s From the Future. Maybe It Is

The collaboration with Geely could give Jidu a big boost when it comes to the notoriously tricky business of making cars at high volume and with high reliability, says Tu Le, managing director of Sino Auto Insights, an analyst firm focused on China’s automotive sector. He adds that China’s auto industry is electrifying at a faster pace than either Europe or the US because of government policies, a less entrenched gasoline-powered industry, and because such a large population allows new technologies to catch on more quickly.

JiDU Robo1 car exterior pedestrian sensor light under front headlight

Courtesy of Baidu

JiDU Robo1 car interior display

Courtesy of Baidu

The Robo-1 shows how big, innovative, and fast-moving China’s auto industry is, says Mingyu Guan, a partner at consulting firm McKinsey & Company, who focuses on the sector. Guan says that most of China’s big internet companies are developing automotive technology, in one way or another, and consumers expect an app-like experience in their vehicles. “China is like a leading beacon for the industry,” Guan says.

Baidu’s leap into automaking with Jidu is also a sign of China’s tech industry evolution. Over the past couple of years, large internet, social media, and popular app companies have faced increased regulatory scrutiny and pressure, with strict new rules around data privacy and algorithmic transparency, for instance.

The Chinese government also has signaled an intent to more tightly regulate the internet while also encouraging the development of technologies with long-term economic importance. Baidu and other firms are apparently keen to reinvent themselves by focusing on “deep tech” viewed as more valuable by the state, including technologies for electric vehicles and autonomous driving. Baidu’s most recent quarterly results, issued in May, also show that revenue from Baidu AI Cloud increased 45 percent year over year in the first quarter of 2022, while online marketing revenue shrank by 4 percent. Net losses for the period were $133 million.

Baidu has made significant investments, and received government encouragement, for autonomous driving. In November 2017, the Chinese government named Baidu one of a handful of AI “national champions” and gave the company responsibility for building an autonomous driving platform that could be used across the industry. The government’s backing also gave Baidu a leg up in working with existing automotive companies. In March the company published over 3,700 patent applications related to the technology in China. And this April, Apollo Go, Baidu’s autonomous taxi service, which operates in 10 cities in China already, received the country’s first permit for testing autonomous vehicles without a driver behind the wheel in Beijing.

Apollo also integrates with a smart-city platform that Baidu sells, and which has been adopted by 41 cities in China. This platform promises to help local authorities predict and manage congestion, road safety, and pollution using AI. Baidu CEO Robin Li touted the potential for autonomous driving to reduce road accidents, congestion, and carbon emissions in China at Baidu’s annual developer conference held in December 2021.

Jidu will no doubt be encouraged by the wider progress that China’s auto industry has made, driven in large part by the rise of electric vehicles. Chinese sales of electric vehicles jumped 169 percent in 2021 compared to a year earlier, according to data from the China Passenger Car Association, an industry organization. For 2021, electric cars accounted for 14.8 percent of Chinese car sales, compared to 4.1 percent in the US. Chinese car firms are also now exporting a growing number of EVs to Europe.

Automation Isn’t the Biggest Threat to US Factory Jobs

Automation Isn’t the Biggest Threat to US Factory Jobs

The number of American workers who quit their jobs during the pandemic—over a fifth of the workforce—may constitute one of the largest American labor movements in recent history. Workers demanded higher pay and better conditions, spurred by rising inflation and the pandemic realization that employers expected them to risk their lives for low wages, mediocre benefits, and few protections from abusive customers—often while corporate stock prices soared. At the same time, automation has become cheaper and smarter than ever. Robot adoption hit record highs in 2021. This wasn’t a surprise, given prior trends in robotics, but it was likely accelerated by pandemic-related worker shortages and Covid-19 safety requirements. Will robots automate away the jobs of entitled millennials who “don’t want to work,” or could this technology actually improve workers’ jobs and help firms attract more enthusiastic employees?

The answer depends on more than what’s technologically feasible, including what actually happens when a factory installs a new robot or a cashier aisle is replaced by a self-checkout booth—and what future possibilities await displaced workers and their children. So far, we know the gains from automation have proved notoriously unequal. A key component of 20th-century productivity growth came from replacing workers with technology, and economist Carl Benedikt Frey notes that American productivity grew by 400 percent from 1930 to 2000, while average leisure time only increased by 3 percent. (Since 1979, American labor productivity, or dollars created per worker, has increased eight times faster than workers’ hourly compensation.) During this period, technological luxuries became necessities and new types of jobs flourished—while the workers’ unions that used to ensure livable wages dissolved and less-educated workers fell further behind those with high school and college degrees. But the trend has differed across industrialized countries: From 1995 to 2013, America experienced a 1.3 percent gap between productivity growth and median wage growth, but in Germany the gap was only 0.2 percent.

Technology adoption will continue to increase, whether America can equitably distribute the technological benefits or not. So the question becomes, how much control do we actually have over automation? How much of this control is dependent on national or regional policies, and how much power might individual firms and workers have within their own workplaces? Is it inevitable that robots and artificial intelligence will take all of our jobs, and over what time frame? While some scholars believe that our fates are predetermined by the technologies themselves, emerging evidence indicates that we may have considerable influence over how such machines are employed within our factories and offices—if we can only figure out how to wield this power.

While 8 percent of German manufacturing workers left their jobs (voluntarily or involuntarily) between 1993 and 2009, 34 percent of US manufacturing workers left their jobs over the same period. Thanks to workplace bargaining and sectoral wage-setting, German manufacturing workers have better financial incentives to stay at their jobs; The Conference Board reports that the average German manufacturing worker earned $43.18 (plus $8.88 in benefits) per hour in 2016, while the average American manufacturing worker earned $39.03 with only $3.66 in benefits. Overall, Germans across the economy with a “medium-skill” high school or vocational certificate earned $24.31 per hour in 2016, while Americans with comparable education averaged $14.55 per hour. Two case studies illustrate the differences between American and German approaches to manufacturing workers and automation, from policies to supply chains to worker training systems.

In a town on the outskirts of the Black Forest in Baden-Württemberg, Germany, complete with winding cobblestone streets and peaked red rooftops, there’s a 220-person factory that’s spent decades as a global leader in safety-critical fabricated metal equipment for sites such as highway tunnels, airports, and nuclear reactors. It’s a wide, unassuming warehouse next to a few acres of golden mustard flowers. When I visited with my colleagues from the MIT Interactive Robotics Group and the Fraunhofer Institute for Manufacturing Engineering and Automation’s Future Work Lab (part of the diverse German government-supported Fraunhofer network for industrial research and development), the senior factory manager informed us that his workers’ attitudes, like the 14th-century church downtown, hadn’t changed much in his 25-year tenure at the factory. Teenagers still entered the firm as apprentices in metal fabrication through Germany’s dual work-study vocational system, and wages are high enough that most young people expected to stay at the factory and move up the ranks until retirement, earning a respectable living along the way. Smaller German manufacturers can also get government subsidies to help send their workers back to school to learn new skills that often equate to higher wages. This manager had worked closely with a nearby technical university to develop advanced welding certifications, and he was proud to rely on his “welding family” of local firms, technology integrators, welding trade associations, and educational institutions for support with new technology and training.

Our research team also visited a 30-person factory in urban Ohio that makes fabricated metal products for the automotive industry, not far from the empty warehouses and shuttered office buildings of downtown. This factory owner, a grandson of the firm’s founder, complained about losing his unskilled, minimum-wage technicians to any nearby job willing to offer a better salary. “We’re like a training company for big companies,” he said. He had given up on finding workers with the relevant training and resigned himself to finding unskilled workers who could hopefully be trained on the job. Around 65 percent of his firm’s business used to go to one automotive supplier, which outsourced its metal fabrication to China in 2009, forcing the Ohio firm to shrink down to a third of its prior workforce.

While the Baden-Württemberg factory commanded market share by selling specialized final products at premium prices, the Ohio factory made commodity components to sell to intermediaries, who then sold to powerful automotive firms. So the Ohio firm had to compete with low-wage, bulk producers in China, while the highly specialized German firm had few foreign or domestic competitors forcing it to shrink its skilled workforce or lower wages.

Welding robots have replaced some of the workers’ tasks in the two factories, but both are still actively hiring new people. The German firm’s first robot, purchased in 2018, was a new “collaborative” welding arm (with a friendly user interface) designed to be operated by workers with welding expertise, rather than professional robot programmers who don’t know the intricacies of welding. Training welders to operate the robot isn’t a problem in Baden-Württemberg, where everyone who arrives as a new welder has a vocational degree representing at least two years of education and hands-on apprenticeship in welding, metal fabrication, and 3D modeling. Several of the firm’s welders had already learned to operate the robot, assisted by prior trainings. And although the German firm manager was pleased to save labor costs, his main reason for the robot acquisition was to improve workers’ health and safety and minimize boring, repetitive welding sequences—so he could continue to attract skilled young workers who would stick around. Another German factory we visited had recently acquired a robot to tend a machine during the night shift so fewer workers would have to work overtime or come in at night.