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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.

By George Prior

Amazon’s $4bn investment into a ChatGPT rival reinforces why almost all investors should have some artificial intelligence (AI) exposure in their investment mix, says the CEO of one of the world’s largest independent financial advisory, asset management and fintech organizations.

The comments from Nigel Green of deVere Group comes as e-commerce giant Amazon said on Monday it will invest $4 billion in Anthropic and take a minority ownership position.  Anthropic was founded by former OpenAI (the company behind ChatGPT) executives, and recently debuted its new AI chatbot named Claude 2.

He says: “This move highlights how the big tech titan is stepping up its rivalry with other giants Microsoft, Google and Nvidia in the AI space.

“The AI Race is on, with the big tech firms racing to lead in the development, deployment, and utilisation of artificial intelligence technologies.

“AI is going to reshape whole industries and fuel innovation – and this makes it crucial for investors to pay attention and why almost all investors need exposure to AI investments in their portfolios.”

While it seems that the AI hype is everywhere now, we are still very early in the AI era.  Investors, says the deVere CEO, should act now to have the ‘early advantage’.

“Getting in early allows investors to establish a competitive advantage over latecomers. They can secure favourable entry points and lower purchase prices, maximizing their potential profits.

“This tech has the potential to disrupt existing industries or create entirely new ones. Early investors are likely to benefit from the exponential growth that often accompanies the adoption of such technologies. As these innovations gain traction, their valuations could skyrocket, resulting in significant returns on investment,” he notes.

While AI is The Big Story currently, investors should, as always, remain diversified across asset classes, sectors and regions in order to maximise returns per unit of risk (volatility) incurred.

Diversification remains investors’ best tool for long-term financial success. As a strategy it has been proven to reduce risk, smooth-out volatility, exploit differing market conditions, maximise long-term returns and protect against unforeseen external events.

Of the latest Amazon investment, Nigel Green concludes: “AI is not just another technology trend; it is a game-changer. Investors need to pay attention and include it as part of their mix.”

About:

deVere Group is one of the world’s largest independent advisors of specialist global financial solutions to international, local mass affluent, and high-net-worth clients.  It has a network of offices across the world, over 80,000 clients and $12bn under advisement.

By Bradley Wade Bishop, University of Tennessee 

Every time a scientist runs an experiment, or a social scientist does a survey, or a humanities scholar analyzes a text, they generate data. Science runs on data – without it, we wouldn’t have the James Webb Space Telescope’s stunning images, disease-preventing vaccines or an evolutionary tree that traces the lineages of all life.

This scholarship generates an unimaginable amount of data – so how do researchers keep track of it? And how do they make sure that it’s accessible for use by both humans and machines?

To improve and advance science, scientists need to be able to reproduce others’ data or combine data from multiple sources to learn something new.

Any kind of sharing requires management. If your neighbor needs to borrow a tool or an ingredient, you have to know whether you have it and where you keep it. Research data might be on a graduate student’s laptop, buried in a professor’s USB collection or saved more permanently within an online data repository.

I’m an information scientist who studies other scientists. More precisely, I study how scientists think about research data and the ways that they interact with their own data and data from others. I also teach students how to manage their own or others’ data in ways that advance knowledge.

Research data management

Research data management is an area of scholarship that focuses on data discovery and reuse. As a field, it encompasses research data services, resources and cyberinfrastructure. For example, one type of infrastructure, the data repository, gives researchers a place to deposit their data for long-term storage so that others can find it. In short, research data management encompasses the data’s life cycle from cradle to grave to reincarnation in the next study.

Proper research data management also allows scientists to use the data already out there rather than recollecting data that already exists, which saves time and resources.

With increasing science politicization, many national and international science organizations have upped their standards for accountability and transparency. Federal agencies and other major research funders like the National Institutes of Health now prioritize research data management and require researchers to have a data management plan before they can receive any funds.

Scientists and data managers can work together to redesign the systems scientists use to make data discovery and preservation easier. In particular, integrating AI can make this data more accessible and reusable.

Artificially intelligent data management

Many of these new standards for research data management also stem from an increased use of AI, including machine learning, across data-driven fields. AI makes it highly desirable for any data to be machine-actionable – that is, usable by machines without human intervention. Now, scholars can consider machines not only as tools but also as potential autonomous data reusers and collaborators.

The key to machine-actionable data is metadata. Metadata are the descriptions scientists set for their data and may include elements such as creator, date, coverage and subject. Minimal metadata is minimally useful, but correct and complete standardized metadata makes data more useful for both people and machines.

It takes a cadre of research data managers and librarians to make machine-actionable data a reality. These information professionals work to facilitate communication between scientists and systems by ensuring the quality, completeness and consistency of shared data.

The FAIR data principles, created by a group of researchers called FORCE11 in 2016 and used across the world, provide guidance on how to enable data reuse by machines and humans. FAIR data is findable, accessible, interoperable and reusable – meaning it has robust and complete metadata.

In the past, I’ve studied how scientists discover and reuse data. I found that scientists tend to use mental shortcuts when they’re looking for data – for example, they may go back to familiar and trusted sources or search for certain key terms they’ve used before. Ideally, my team could build this decision-making process of experts and remove as many biases as possible to improve AI. The automation of these mental shortcuts should reduce the time-consuming chore of locating the right data.

Data management plans

But there’s still one piece of research data management that AI can’t take over. Data management plans describe the what, where, when, why and who of managing research data. Scientists fill them out, and they outline the roles and activities for managing research data during and long after research ends. They answer questions like, “Who is responsible for long-term preservation,” “Where will the data live,” “How do I keep my data secure,” and “Who pays for all of that?”

Grant proposals for nearly all funding agencies across countries now require data management plans. These plans signal to scientists that their data is valuable and important enough to the community to share. Also, the plans help funding agencies keep tabs on the research and investigate any potential misconduct. But most importantly, they help scientists make sure their data stays accessible for many years.

Making all research data as FAIR and open as possible will improve the scientific process. And having access to more data opens up the possibility for more informed discussions on how to promote economic development, improve the stewardship of natural resources, enhance public health, and how to responsibly and ethically develop technologies that will improve lives. All intelligence, artificial or otherwise, will benefit from better organization, access and use of research data.

About the Author:

Bradley Wade Bishop, Professor of Information Sciences, University of Tennessee

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

By Anne Toomey McKenna, University of Richmond 

Numerous government agencies, including the FBI, Department of Defense, National Security Agency, Treasury Department, Defense Intelligence Agency, Navy and Coast Guard, have purchased vast amounts of U.S. citizens’ personal information from commercial data brokers. The revelation was published in a partially declassified, internal Office of the Director of National Intelligence report released on June 9, 2023.

The report shows the breathtaking scale and invasive nature of the consumer data market and how that market directly enables wholesale surveillance of people. The data includes not only where you’ve been and who you’re connected to, but the nature of your beliefs and predictions about what you might do in the future. The report underscores the grave risks the purchase of this data poses, and urges the intelligence community to adopt internal guidelines to address these problems.

As a privacy, electronic surveillance and technology law attorney, researcher and law professor, I have spent years researching, writing and advising about the legal issues the report highlights.

These issues are increasingly urgent. Today’s commercially available information, coupled with the now-ubiquitous decision-making artificial intelligence and generative AI like ChatGPT, significantly increases the threat to privacy and civil liberties by giving the government access to sensitive personal information beyond even what it could collect through court-authorized surveillance.

What is commercially available information?

The drafters of the report take the position that commercially available information is a subset of publicly available information. The distinction between the two is significant from a legal perspective. Publicly available information is information that is already in the public domain. You could find it by doing a little online searching.

Commercially available information is different. It is personal information collected from a dizzying array of sources by commercial data brokers that aggregate and analyze it, then make it available for purchase by others, including governments. Some of that information is private, confidential or otherwise legally protected.

The sources and types of data for commercially available information are mind-bogglingly vast. They include public records and other publicly available information. But far more information comes from the nearly ubiquitous internet-connected devices in people’s lives, like cellphones, smart home systems, cars and fitness trackers. These all harness data from sophisticated, embedded sensors, cameras and microphones. Sources also include data from apps, online activity, texts and emails, and even health care provider websites.

Types of data include location, gender and sexual orientation, religious and political views and affiliations, weight and blood pressure, speech patterns, emotional states, behavioral information about myriad activities, shopping patterns and family and friends.

This data provides companies and governments a window into the “Internet of Behaviors,” a combination of data collection and analysis aimed at understanding and predicting people’s behavior. It pulls together a wide range of data, including location and activities, and uses scientific and technological approaches, including psychology and machine learning, to analyze that data. The Internet of Behaviors provides a map of what each person has done, is doing and is expected to do, and provides a means to influence a person’s behavior.

Better, cheaper and unrestricted

The rich depths of commercially available information, analyzed with powerful AI, provide unprecedented power, intelligence and investigative insights. The information is a cost-effective way to surveil virtually everyone, plus it provides far more sophisticated data than traditional electronic surveillance tools or methods like wiretapping and location tracking.

Government use of electronic surveillance tools is extensively regulated by federal and state laws. The U.S. Supreme Court has ruled that the Constitution’s Fourth Amendment, which prohibits unreasonable searches and seizures, requires a warrant for a wide range of digital searches. These include wiretapping or intercepting a person’s calls, texts or emails; using GPS or cellular location information to track a person; or searching a person’s cellphone.

Complying with these laws takes time and money, plus electronic surveillance law restricts what, when and how data can be collected. Commercially available information is cheaper to obtain, provides far richer data and analysis, and is subject to little oversight or restriction compared to when the same data is collected directly by the government.

The threats

Technology and the burgeoning volume of commercially available information allow various forms of the information to be combined and analyzed in new ways to understand all aspects of your life, including preferences and desires.

The Office of the Director of National Intelligence report warns that the increasing volume and widespread availability of commercially available information poses “significant threats to privacy and civil liberties.” It increases the power of the government to surveil its citizens outside the bounds of law, and it opens the door to the government using that data in potentially unlawful ways. This could include using location data obtained via commercially available information rather than a warrant to investigate and prosecute someone for abortion.

The report also captures both how widespread government purchases of commercially available information are and how haphazard government practices around the use of the information are. The purchases are so pervasive and agencies’ practices so poorly documented that the Office of the Director of National Intelligence cannot even fully determine how much and what types of information agencies are purchasing, and what the various agencies are doing with the data.

Is it legal?

The question of whether it’s legal for government agencies to purchase commercially available information is complicated by the array of sources and complex mix of data it contains.

There is no legal prohibition on the government collecting information already disclosed to the public or otherwise publicly available. But the nonpublic information listed in the declassified report includes data that U.S. law typically protects. The nonpublic information’s mix of private, sensitive, confidential or otherwise lawfully protected data makes collection a legal gray area.

Despite decades of increasingly sophisticated and invasive commercial data aggregation, Congress has not passed a federal data privacy law. The lack of federal regulation around data creates a loophole for government agencies to evade electronic surveillance law. It also allows agencies to amass enormous databases that AI systems learn from and use in often unrestricted ways. The resulting erosion of privacy has been a concern for more than a decade.

Throttling the data pipeline

The Office of the Director of National Intelligence report acknowledges the stunning loophole that commercially available information provides for government surveillance: “The government would never have been permitted to compel billions of people to carry location tracking devices on their persons at all times, to log and track most of their social interactions, or to keep flawless records of all their reading habits. Yet smartphones, connected cars, web tracking technologies, the Internet of Things, and other innovations have had this effect without government participation.”

However, it isn’t entirely correct to say “without government participation.” The legislative branch could have prevented this situation by enacting data privacy laws, more tightly regulating commercial data practices, and providing oversight in AI development. Congress could yet address the problem. Representative Ted Lieu has introduced the a bipartisan proposal for a National AI Commission, and Senator Chuck Schumer has proposed an AI regulation framework.

Effective data privacy laws would keep your personal information safer from government agencies and corporations, and responsible AI regulation would block them from manipulating you.

About the Author:

Anne Toomey McKenna, Visiting Professor of Law, University of Richmond

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

By Robert Mahari, Massachusetts Institute of Technology (MIT); Jessica Fjeld, Harvard Law School, and Ziv Epstein, Massachusetts Institute of Technology (MIT) 

In 2022, an AI-generated work of art won the Colorado State Fair’s art competition. The artist, Jason Allen, had used Midjourney – a generative AI system trained on art scraped from the internet – to create the piece. The process was far from fully automated: Allen went through some 900 iterations over 80 hours to create and refine his submission.

Yet his use of AI to win the art competition triggered a heated backlash online, with one Twitter user claiming, “We’re watching the death of artistry unfold right before our eyes.”

As generative AI art tools like Midjourney and Stable Diffusion have been thrust into the limelight, so too have questions about ownership and authorship.

These tools’ generative ability is the result of training them with scores of prior artworks, from which the AI learns how to create artistic outputs.

Should the artists whose art was scraped to train the models be compensated? Who owns the images that AI systems produce? Is the process of fine-tuning prompts for generative AI a form of authentic creative expression?

On one hand, technophiles rave over work like Allen’s. But on the other, many working artists consider the use of their art to train AI to be exploitative.

We’re part of a team of 14 experts across disciplines that just published a paper on generative AI in Science magazine. In it, we explore how advances in AI will affect creative work, aesthetics and the media. One of the key questions that emerged has to do with U.S. copyright laws, and whether they can adequately deal with the unique challenges of generative AI.

Copyright laws were created to promote the arts and creative thinking. But the rise of generative AI has complicated existing notions of authorship.

Still from ‘All watched over by machines of loving grace’ by Memo Akten, 2021. Created using custom AI software.
Memo Akten, CC BY-SA

Photography serves as a helpful lens

Generative AI might seem unprecedented, but history can act as a guide.

Take the emergence of photography in the 1800s. Before its invention, artists could only try to portray the world through drawing, painting or sculpture. Suddenly, reality could be captured in a flash using a camera and chemicals. As with generative AI, many argued that photography lacked artistic merit. In 1884, the U.S. Supreme Court weighed in on the issue and found that cameras served as tools that an artist could use to give an idea visible form; the “masterminds” behind the cameras, the court ruled, should own the photographs they create.

From then on, photography evolved into its own art form and even sparked new abstract artistic movements.

AI can’t own outputs

Unlike inanimate cameras, AI possesses capabilities – like the ability to convert basic instructions into impressive artistic works – that make it prone to anthropomorphization. Even the term “artificial intelligence” encourages people to think that these systems have humanlike intent or even self-awareness.

This led some people to wonder whether AI systems can be “owners.” But the U.S. Copyright Office has stated unequivocally that only humans can hold copyrights.

So who can claim ownership of images produced by AI? Is it the artists whose images were used to train the systems? The users who type in prompts to create images? Or the people who build the AI systems?

Infringement or fair use?

While artists draw obliquely from past works that have educated and inspired them in order to create, generative AI relies on training data to produce outputs.

This training data consists of prior artworks, many of which are protected by copyright law and which have been collected without artists’ knowledge or consent. Using art in this way might violate copyright law even before the AI generates a new work.

For Jason Allen to create his award-winning art, Midjourney was trained on 100 million prior works.

Was that a form of infringement? Or was it a new form of “fair use,” a legal doctrine that permits the unlicensed use of protected works if they’re sufficiently transformed into something new?

While AI systems do not contain literal copies of the training data, they do sometimes manage to recreate works from the training data, complicating this legal analysis.

Will contemporary copyright law favor end users and companies over the artists whose content is in the training data?

To mitigate this concern, some scholars propose new regulations to protect and compensate artists whose work is used for training. These proposals include a right for artists to opt out of their data’s being used for generative AI or a way to automatically compensate artists when their work is used to train an AI.

Muddled ownership

Training data, however, is only part of the process. Frequently, artists who use generative AI tools go through many rounds of revision to refine their prompts, which suggests a degree of originality.

Answering the question of who should own the outputs requires looking into the contributions of all those involved in the generative AI supply chain.

The legal analysis is easier when an output is different from works in the training data. In this case, whoever prompted the AI to produce the output appears to be the default owner.

However, copyright law requires meaningful creative input – a standard satisfied by clicking the shutter button on a camera. It remains unclear how courts will decide what this means for the use of generative AI. Is composing and refining a prompt enough?

Matters are more complicated when outputs resemble works in the training data. If the resemblance is based only on general style or content, it is unlikely to violate copyright, because style is not copyrightable.

The illustrator Hollie Mengert encountered this issue firsthand when her unique style was mimicked by generative AI engines in a way that did not capture what, in her eyes, made her work unique. Meanwhile, the singer Grimes embraced the tech, “open-sourcing” her voice and encouraging fans to create songs in her style using generative AI.

If an output contains major elements from a work in the training data, it might infringe on that work’s copyright. Recently, the Supreme Court ruled that Andy Warhol’s drawing of a photograph was not permitted by fair use. That means that using AI to just change the style of a work – say, from a photo to an illustration – is not enough to claim ownership over the modified output.

While copyright law tends to favor an all-or-nothing approach, scholars at Harvard Law School have proposed new models of joint ownership that allow artists to gain some rights in outputs that resemble their works.

In many ways, generative AI is yet another creative tool that allows a new group of people access to image-making, just like cameras, paintbrushes or Adobe Photoshop. But a key difference is this new set of tools relies explicitly on training data, and therefore creative contributions cannot easily be traced back to a single artist.

The ways in which existing laws are interpreted or reformed – and whether generative AI is appropriately treated as the tool it is – will have real consequences for the future of creative expression.

About the Author:

Robert Mahari, JD-PhD Student, Massachusetts Institute of Technology (MIT); Jessica Fjeld, Lecturer on Law, Harvard Law School, and Ziv Epstein, PhD Student in Media Arts and Sciences, Massachusetts Institute of Technology (MIT)

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

By Kate Saenko, Boston University 

Generative AI is the hot new technology behind chatbots and image generators. But how hot is it making the planet?

As an AI researcher, I often worry about the energy costs of building artificial intelligence models. The more powerful the AI, the more energy it takes. What does the emergence of increasingly more powerful generative AI models mean for society’s future carbon footprint?

“Generative” refers to the ability of an AI algorithm to produce complex data. The alternative is “discriminative” AI, which chooses between a fixed number of options and produces just a single number. An example of a discriminative output is choosing whether to approve a loan application.

Generative AI can create much more complex outputs, such as a sentence, a paragraph, an image or even a short video. It has long been used in applications like smart speakers to generate audio responses, or in autocomplete to suggest a search query. However, it only recently gained the ability to generate humanlike language and realistic photos.

AI chatbots and image generators run on thousands of computers housed in data centers like this Google facility in Oregon.
Tony Webster/Wikimedia, CC BY-SA

Using more power than ever

The exact energy cost of a single AI model is difficult to estimate, and includes the energy used to manufacture the computing equipment, create the model and use the model in production. In 2019, researchers found that creating a generative AI model called BERT with 110 million parameters consumed the energy of a round-trip transcontinental flight for one person. The number of parameters refers to the size of the model, with larger models generally being more skilled. Researchers estimated that creating the much larger GPT-3, which has 175 billion parameters, consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent, the equivalent of 123 gasoline-powered passenger vehicles driven for one year. And that’s just for getting the model ready to launch, before any consumers start using it.

Size is not the only predictor of carbon emissions. The open-access BLOOM model, developed by the BigScience project in France, is similar in size to GPT-3 but has a much lower carbon footprint, consuming 433 MWh of electricity in generating 30 tons of CO2eq. A study by Google found that for the same size, using a more efficient model architecture and processor and a greener data center can reduce the carbon footprint by 100 to 1,000 times.

Larger models do use more energy during their deployment. There is limited data on the carbon footprint of a single generative AI query, but some industry figures estimate it to be four to five times higher than that of a search engine query. As chatbots and image generators become more popular, and as Google and Microsoft incorporate AI language models into their search engines, the number of queries they receive each day could grow exponentially.

AI bots for search

A few years ago, not many people outside of research labs were using models like BERT or GPT. That changed on Nov. 30, 2022, when OpenAI released ChatGPT. According to the latest available data, ChatGPT had over 1.5 billion visits in March 2023. Microsoft incorporated ChatGPT into its search engine, Bing, and made it available to everyone on May 4, 2023. If chatbots become as popular as search engines, the energy costs of deploying the AIs could really add up. But AI assistants have many more uses than just search, such as writing documents, solving math problems and creating marketing campaigns.

Another problem is that AI models need to be continually updated. For example, ChatGPT was only trained on data from up to 2021, so it does not know about anything that happened since then. The carbon footprint of creating ChatGPT isn’t public information, but it is likely much higher than that of GPT-3. If it had to be recreated on a regular basis to update its knowledge, the energy costs would grow even larger.

One upside is that asking a chatbot can be a more direct way to get information than using a search engine. Instead of getting a page full of links, you get a direct answer as you would from a human, assuming issues of accuracy are mitigated. Getting to the information quicker could potentially offset the increased energy use compared to a search engine.

Ways forward

The future is hard to predict, but large generative AI models are here to stay, and people will probably increasingly turn to them for information. For example, if a student needs help solving a math problem now, they ask a tutor or a friend, or consult a textbook. In the future, they will probably ask a chatbot. The same goes for other expert knowledge such as legal advice or medical expertise.

While a single large AI model is not going to ruin the environment, if a thousand companies develop slightly different AI bots for different purposes, each used by millions of customers, the energy use could become an issue. More research is needed to make generative AI more efficient. The good news is that AI can run on renewable energy. By bringing the computation to where green energy is more abundant, or scheduling computation for times of day when renewable energy is more available, emissions can be reduced by a factor of 30 to 40, compared to using a grid dominated by fossil fuels.

Finally, societal pressure may be helpful to encourage companies and research labs to publish the carbon footprints of their AI models, as some already do. In the future, perhaps consumers could even use this information to choose a “greener” chatbot.

About the Author:

Kate Saenko, Associate Professor of Computer Science, Boston University

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

By Chris Impey, University of Arizona 

The famous first image of a black hole just got two times sharper. A research team used artificial intelligence to dramatically improve upon its first image from 2019, which now shows the black hole at the center of the M87 galaxy as darker and bigger than the first image depicted.

I’m an astronomer who studies and has written about cosmology, black holes and exoplanets. Astronomers have been using AI for decades. In fact, in 1990, astronomers from the University of Arizona, where I am a professor, were among the first to use a type of AI called a neural network to study the shapes of galaxies.

Since then, AI has spread into every field of astronomy. As the technology has become more powerful, AI algorithms have begun helping astronomers tame massive data sets and discover new knowledge about the universe.

Better telescopes, more data

As long as astronomy has been a science, it has involved trying to make sense of the multitude of objects in the night sky. That was relatively simple when the only tools were the naked eye or a simple telescope, and all that could be seen were a few thousand stars and a handful of planets.

A hundred years ago, Edwin Hubble used newly built telescopes to show that the universe is filled with not just stars and clouds of gas, but countless galaxies. As telescopes have continued to improve, the sheer number of celestial objects humans can see and the amount of data astronomers need to sort through have both grown exponentially, too.

For example, the soon-to-be-completed Vera Rubin Observatory in Chile will make images so large that it would take 1,500 high-definition TV screens to view each one in its entirety. Over 10 years it is expected to generate 0.5 exabytes of data – about 50,000 times the amount of information held in all of the books contained within the Library of Congress.

There are 20 telescopes with mirrors larger than 20 feet (6 meters) in diameter. AI algorithms are the only way astronomers could ever hope to work through all of the data available to them today. There are a number of ways AI is proving useful in processing this data.

Picking out patterns

Astronomy often involves looking for needles in a haystack. About 99% of the pixels in an astronomical image contain background radiation, light from other sources or the blackness of space – only 1% have the subtle shapes of faint galaxies.

AI algorithms – in particular, neural networks that use many interconnected nodes and are able to learn to recognize patterns – are perfectly suited for picking out the patterns of galaxies. Astronomers began using neural networks to classify galaxies in the early 2010s. Now the algorithms are so effective that they can classify galaxies with an accuracy of 98%.

This story has been repeated in other areas of astronomy. Astronomers working on SETI, the Search for Extraterrestrial Intelligence, use radio telescopes to look for signals from distant civilizations. Early on, radio astronomers scanned charts by eye to look for anomalies that couldn’t be explained. More recently, researchers harnessed 150,000 personal computers and 1.8 million citizen scientists to look for artificial radio signals. Now, researchers are using AI to sift through reams of data much more quickly and thoroughly than people can. This has allowed SETI efforts to cover more ground while also greatly reducing the number of false positive signals.

Another example is the search for exoplanets. Astronomers discovered most of the 5,300 known exoplanets by measuring a dip in the amount of light coming from a star when a planet passes in front of it. AI tools can now pick out the signs of an exoplanet with 96% accuracy.

Making new discoveries

AI has proved itself to be excellent at identifying known objects – like galaxies or exoplanets – that astronomers tell it to look for. But it is also quite powerful at finding objects or phenomena that are theorized but have not yet been discovered in the real world.

Teams have used this approach to detect new exoplanets, learn about the ancestral stars that led to the formation and growth of the Milky Way, and predict the signatures of new types of gravitational waves.

To do this, astronomers first use AI to convert theoretical models into observational signatures – including realistic levels of noise. They then use machine learning to sharpen the ability of AI to detect the predicted phenomena.

Finally, radio astronomers have also been using AI algorithms to sift through signals that don’t correspond to known phenomena. Recently a team from South Africa found a unique object that may be a remnant of the explosive merging of two supermassive black holes. If this proves to be true, the data will allow a new test of general relativity – Albert Einstein’s description of space-time.

Making predictions and plugging holes

As in many areas of life recently, generative AI and large language models like ChatGPT are also making waves in the astronomy world.

The team that created the first image of a black hole in 2019 used a generative AI to produce its new image. To do so, it first taught an AI how to recognize black holes by feeding it simulations of many kinds of black holes. Then, the team used the AI model it had built to fill in gaps in the massive amount of data collected by the radio telescopes on the black hole M87.

Using this simulated data, the team was able to create a new image that is two times sharper than the original and is fully consistent with the predictions of general relativity.

Astronomers are also turning to AI to help tame the complexity of modern research. A team from the Harvard-Smithsonian Center for Astrophysics created a language model called astroBERT to read and organize 15 million scientific papers on astronomy. Another team, based at NASA, has even proposed using AI to prioritize astronomy projects, a process that astronomers engage in every 10 years.

As AI has progressed, it has become an essential tool for astronomers. As telescopes get better, as data sets get larger and as AIs continue to improve, it is likely that this technology will play a central role in future discoveries about the universe.

About the Author:

Chris Impey, University Distinguished Professor of Astronomy, University of Arizona

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