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By James Dawes, Macalester College 

After 13-year-old Willis Gibson became the first human to beat the original Nintendo version of Tetris, he dedicated his special win to his father, who passed away in December 2023.

The Oklahoma teen beat the game by defeating level after level until he reached the “kill screen” – that is, the moment when the Tetris artificial intelligence taps out in exhaustion, stopping play because its designers never wrote the code to advance further. Before Gibson, the only other player to overcome the game’s AI was another AI.

For any parent who has despaired over their children sinking countless hours into video games, Gibson’s victory over the cruel geometry of Tetris stands as a bracing corrective.

Despite the stereotypes, most gamers are anything but lazy. And they’re anything but mindless.

The world’s top players can sometimes serve as reminders of the best in us, with memorable achievements that range from the heroic to the inscrutably weird.

The perfect run

“Speedrunning” is a popular gaming subculture in which players meticulously optimize routes and exploit glitches to complete, in a matter of minutes, games that normally take hours, from the tightly constrained, run-and-gun action game Cuphead to the sprawling role-playing epic Baldur’s Gate 3.

In top-level competition, speedrunners strive to match the time of what’s referred to as a “TAS,” or “tool-assisted speed run.” To figure out the TAS time, players use game emulators to choreograph a theoretically perfect playthrough, advancing the game one frame at a time to determine the fastest possible time.

Success requires punishing precision, flawless execution and years of training.

The major speedrunning milestones are, like Olympic races, marked by mere fractions of a second. The urge to speedrun likely sprouts from an innate human longing for perfection – and a uniquely 21st century compulsion to best the robots.

A Twitch streamer who goes by the username Niftski is currently the human who has come closest to achieving this androidlike perfection. His 4-minute, 54.631-second world-record speedrun of Super Mario Bros. – achieved in September 2023 – is just 0.35 seconds shy of a flawless TAS.

Watching Niftski’s now-famous run is a dissonant experience. Goofy, retro, 8-bit Mario jumps imperturbably over goombas and koopa troopas with the iconic, cheerful “boink” sound of his hop.

Meanwhile, Niftski pants as his anxiety builds, his heart rate – tracked on screen during the livestream – peaking at 188 beats per minute.

When Mario bounces over the final big turtle at the finish line – “boink” – Niftski erupts into screams of shock and repeated cries of “Oh my God!”

He hyperventilates, struggles for oxygen and finally sobs from exhaustion and joy.

The largest world and its longest pig ride

This list couldn’t be complete without an achievement from Minecraft, the revolutionary video game that has become the second-best-selling title in history, with over 300 million copies sold – second only to Tetris’ 520 million units.

Minecraft populates the video game libraries of grade-schoolers and has been used as an educational tool in university classrooms. Even the British Museum has held an exhibition devoted to the game.

Minecraft is known as a sandbox game, which means that gamers can create and explore their own virtual worlds, limited only by their imagination and a few simple tools and resources – like buckets and sand, or, in the case of Minecraft, pickaxes and stone.

So what can you do in the Minecraft playground?

Well, you can ride on a pig. The Guinness Book of World Records marks the farthest distance at 414 miles. Or you can collect sunflowers. The world record for that is 89 in one minute. Or you can dig a tunnel – but you’ll need to make it 100,001 blocks long to edge out the current record.

My personal favorite is a collective, ongoing effort: a sprawling, global collaboration to recreate the world on a 1:1 scale using Minecraft blocks, with blocks counting as one cubic meter.

At their best, sandbox games like Minecraft can bring people closer to the joyful and healthily pointless play of childhood – a restorative escape from the anxious, utility-driven planning that dominates so much of adulthood.

The galaxy’s greatest collaboration

The Halo 3 gaming community participated in a bloodier version of the collective effort of Minecraft players.

The game, which pits humans against an alien alliance known as the Covenant, was released in 2007 to much fanfare.

Whether they were playing the single-player campaign mode or the online multiplayer mode, gamers around the world started seeing themselves as imaginary participants in a global cause to save humanity – in what came to be known as the “Great War.”

They organized round-the-clock campaign shifts, while sharing strategies in nearly 6,000 Halo wiki articles and 21 million online discussion posts.

Halo developer Bungie started tracking total alien deaths by all players, with the 10 billion milestone reached in April 2009.

Game designer Jane McGonigal recalls with awe the community effort that went into that Great War, citing it as a transcendent example of the fundamental human desire to work together and to become a part of something bigger than the self.

Bungie maintained a collective history of the Great War in the form of “personal service records” that memorialized each player’s contributions – medals, battle statistics, campaign maps and more.

The archive beggars comprehension: According to Bungie, its servers handled 1.4 petabytes of data requests by players in one nine-month stretch. McGonigal notes, by way of comparison, that everything ever written by humans in all of recorded history amounts to 50 petabytes of data.

Gamification versus gameful design

If you’re mystified by the behavior of these gamers, you’re not alone.

Over the past decade, researchers across a range of fields have marveled at the dedication of gamers like Gibson and Niftski, who commit themselves without complaint to what some might see as punishing, pointless and physically grueling labor.

How could this level of dedication be applied to more “productive” endeavors, they wondered, like education, taxes or exercise?

From this research, an industry centered on the “gamification” of work, life and learning emerged. It giddily promised to change people’s behaviors through the use of extrinsic motivators borrowed from the gaming community: badges, achievements, community scorekeeping.

The concept caught fire, spreading everywhere from early childhood education to the fast-food industry.

Many game designers have reacted to this trend like Robert Oppenheimer at the close of the eponymous movie – aghast that their beautiful work was used, for instance, to pressure Disneyland Resort laborers to load laundry and press linens at anxiously hectic speeds.

Arguing that the gamification trend misses entirely the magic of gaming, game designers have instead started promoting the concept of “gameful design.” Where gamification focuses on useful outcomes, gameful design focuses on fulfilling experiences.

Gameful design prioritizes intrinsic motivation over extrinsic incentives. It embraces design elements that promote social connection, creativity, a sense of autonomy – and, ultimately, the sheer joy of mastery.

When I think of Niftski’s meltdown after his record speedrun – and Gibson’s, who also began hyperventilating in shock and almost passed out – I think of my own children.

I wish for them such moments of ecstatic, prideful accomplishment in a world that sometimes seems starved of joy.

About the Author:

James Dawes, Professor of English, Macalester College

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Anjana Susarla, Michigan State University; Casey Fiesler, University of Colorado Boulder, and Kentaro Toyama, University of Michigan 

2023 was an inflection point in the evolution of artificial intelligence and its role in society. The year saw the emergence of generative AI, which moved the technology from the shadows to center stage in the public imagination. It also saw boardroom drama in an AI startup dominate the news cycle for several days. And it saw the Biden administration issue an executive order and the European Union pass a law aimed at regulating AI, moves perhaps best described as attempting to bridle a horse that’s already galloping along.

We’ve assembled a panel of AI scholars to look ahead to 2024 and describe the issues AI developers, regulators and everyday people are likely to face, and to give their hopes and recommendations.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

2023 was the year of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though I think that anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so – in ways that often do more harm than good.

However, as the year went on, there was a recognition that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. I don’t think we should be revamping education to put AI at the center of everything, but if students don’t learn about how AI works, they won’t understand its limitations – and therefore how it is useful and appropriate to use and how it’s not. This isn’t just true for students. The more people understand how AI works, the more empowered they are to use it and to critique it.

So my prediction, or perhaps my hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative artificial intelligence is that, in contrast to ELIZA’s very basic pattern matching and substitution methodology, it is much more difficult to find language “sufficiently plain” to make the AI magic crumble away.

I think it’s possible to make this happen. I hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. I hope that media outlets help cut through the hype. I hope that everyone reflects on their own uses of this technology and its consequences. And I hope that tech companies listen to informed critiques in considering what choices continue to shape the future.

Kentaro Toyama, Professor of Community Information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years we will have a machine with the general intelligence of an average human being.” With the singularity, the moment artificial intelligence matches and begins to exceed human intelligence – not quite here yet – it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024? First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory and global supremacy. Expect more powerful AI, in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning – what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will it require a fundamentally different approach, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so I expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire – comically, tragically or both. Deepfakes, AI-generated images and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI – like Elon Musk and Sam Altman – can’t seem to stop themselves from building ever more powerful AI. I expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, what I most hope for 2024 – though it seems slow in coming – is stronger AI regulation, at national and international levels.

Anjana Susarla, Professor of Information Systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multi-modal, meaning the data used to train them comes not only from textual sources such as Wikipedia and Reddit, but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multi-modal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed on a variety of hardware and in a variety of applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open source LLMs could usher in a world of autonomous AI agents – a world that society is not necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. My chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as pose new types of algorithmic harms.

The deluge of synthetic content produced by generative AI could unleash a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy and serendipity provided by search engines, social media platforms and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosure of AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies like the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it is clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes and society.

About the Authors:

Anjana Susarla, Professor of Information Systems, Michigan State University; Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder, and Kentaro Toyama, Professor of Community Information, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.