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Why artificial intelligence artists can be seen as ‘builders’, ‘breakers’—or both at once – The Art Newspaper

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How do artists build in broken times? Is artificial intelligence (AI) unlocking a better world—curing diseases and transforming education—or unleashing our destruction? When hype and fear drown out nuance and discussion, perhaps in art we can find a quiet moment for reflection—even resistance.

After all, artists have long guided society through uncertainty—think Dada amid the First World War or Jikken Kōbō in Japan following the Second World War. They do not offer solutions so much as new responses: ways of expressing curiosity, imagining alternatives or holding room for ambiguity. As the critic Hal Foster recently described, two tendencies have historically emerged when art confronts crisis: one rooted in Constructivism, aiming to create new order; the other more chaotic, echoing Dada, amplifying disorder.

These historical impulses connect to the present day, mapping onto AI art. In this context, artists could be seen as builders and breakers. Builders imagine AI as a medium for collaboration and new aesthetics—even hope. Breakers critique, negate and disrupt. But leading makers and curators in the field see this as no simple dichotomy. Both offer strategies for reckoning with a world in flux.

Builders see possibilities

What motivates builders is not simply using the newest AI tool—or even fashioning their own from scratch. It is aligning multidisciplinary tools with concepts to produce works that were previously impossible—while urging us to imagine what else may soon be possible. Builders leverage AI to embrace the artistry of system creation, novel aesthetics and human-machine collaboration.

Take Sougwen Chung, the Chinese Canadian artist and researcher into human-machine collaboration. “I view technology not just as a tool but as a collaborator,” Chung says. Their work explores shared agency—even identity—between human and machine, code and gesture. In Mutations of Presence (2021), Chung collaborated with D.O.U.G._4, a custom-built robotic system driven by biofeedback: specifically, electroencephalogram signals captured during meditation and real-time body tracking. The resulting pieces reveal both performance and painting, a hybrid body co-authoring with machine memory. An elegant web of painterly gestures—some made via robotic arm, others by Chung’s hand—traces a kind of recursive duet.

I see combining AI and robotics with traditional creativity as a way to think more deeply about what is human and what is machine

Sougwen Chung, artist and researcher

The work demonstrates how Chung’s novel physical creations become interconnected with new conceptual frameworks—reframing authorship as a distributed, relational process with machines—inviting new forms of aesthetic exploration. It also reasserts a long-held, often feminist belief—dating back to Donna Haraway’s A Cyborg Manifesto (1985)—that the distinction between human and machine is illusory. As Chung puts it, “I see combining AI and robotics with traditional creativity as a way to think more deeply about what is human and what is machine.”

Chung’s intimacy with these systems goes further still: “I’ve started to see them as us in another form.” That is because they are trained as extensions to Chung’s very self. “I draw with decades of my own movement data or create proprioceptive mappings triggered by alpha [brain] waves. These systems don’t possess agency in a mystical sense but they reflect back our own: our choices, biases, knowledge.” This builder tendency aligns with earlier avant-gardes that saw technology as a path toward reordering the world, including the Bauhaus and aspects of the 1960s Experiments in Art and Technology movement. Builders are not naïve. They are aware of AI’s risks. But they believe that the minimum response is to participate in the conversation.

“My artistic practice is also driven by hope and an exploration of the promises and possibilities inherent in working with technology,” Chung says. Their vision affirms a cautious optimism through direct engagement with these tools.

Breakers see warning signs

Where builders see AI’s possibility, breakers see warning signs. Breakers are sceptics, critics, saboteurs. They distrust the power structures underpinning AI and its predilection for promoting systemic biases. They highlight how corporate AI models can be trained on scraped datasets—often without consent—while profits remain centralised. They expose how AI systems exacerbate ecological challenges only to promulgate aesthetic homogenisation.

In her work This is the Future, Hito Steyerl uses neural networks to imagine medicinal plants evolved to heal algorithmic addiction and burnout Photo: Mario Gallucci; courtesy of the artist; Andrew Kreps Gallery, New York and Esther Schipper, Berlin

They are also label resistant: “Breaking and building have become indistinguishable,” the German artist, thinker and archetypal breaker Hito Steyerl says. “The paradigm of creative destruction merges both in order to implement tech in the wild, without testing, thus externalising cost and damage to societies while privatising profit.”

Breakers do not emphasise AI’s aesthetic potential; they interrogate its extractive foundations, social asymmetries and the harms it makes visible. Breakers take a far bleaker view of AI’s impact on art than builders: “Art used to be good at testing, planning, playing, assessing, mediating, sandboxing. That element has been axed—or automated—within current corporate breakbuilding,” Steyerl says.

But in Steyerl’s own work, such as This is the Future (2019), the meticulous co-ordination, criticality and sceptical spirit are evident. The artist uses neural networks to imagine medicinal plants evolved to heal algorithmic addiction and burnout. The work shows how machine learning’s inner workings, prediction, can be weaponised, satirising techno-optimism while exposing AI’s entanglement with ecological and psychological ruin.

Christiane Paul, the long-time digital art curator at the Whitney Museum of American Art in New York, underscores these issues: “In terms of ethics and bias, every artist I know working in this field is deeply concerned. You need to keep that in mind and engage with it on the level of criticality—what you would call the breakers, highlighting how ethics filter in.” An extreme breaker might reject AI entirely. But Paul suggests that artists working with AI are essential precisely because they inhabit that edge where culture and ethics are encouraged: “Art in this field, using these tools, making them, building on and with them, is deeply needed.”

Breakers remind us that celebrating new tools without understanding their costs is a form of denial. Sometimes, to truly see a system, you have to dismantle it. That clarity brings insight—but contradictions as well.

Neither utopian nor dystopian

Is it really as simple as a builder-breaker duality? “My whole life, I’ve been very suspicious of dichotomies,” Paul says. Exploring the space between seeming contradictions can even be fertile creative ground. “A steering question for my work,” Chung says, “is ‘how do we hold fear and hope in our minds at the same time?’”

Steyerl, like a true breaker, rejects the contradiction to begin with: “Breaking is a cost-cutting element of building, taking out mediation; there is no more distinction between both.” Neither position suggests retreat. Instead, they ask us to face the paradox directly. Builder and breaker are not identities; they are strategies. The distinction is porous, performative. Most artists move fluidly between them or hold on to both at the same time.

Chung continues: “My art doesn’t strictly sit within either a utopian or dystopian camp. Instead, I actively navigate and explore the complex space between potential fears and hopes concerning technology and human-machine interaction.”

Michelle Kuo, the chief curator at large at the Museum of Modern Art in New York, says: “When artists intervene in existing technologies or systems, or take action in changing the outcome of technological development, they are not only building something—they are implicitly challenging the status quo.” Kuo links “builders” with “challenging the status quo”, reinforcing the roles’ fluidity. “It is this combination of challenge and experimentation that characterises some of the most exciting work at the intersection of art and AI today,” Kuo says. For her, the AI work that can achieve both breaking and building—challenge and experimentation—truly confronts our moment, neither retreating from technology nor surrendering to it.

Artists who speak out

So, what does this all mean for the viewer living through a future that arrived faster than we feel equipped to handle?

Artists take a tool and make it do something it’s not supposed to do. They don’t reject technology wholesale

Michelle Kuo, chief curator at large, Museum of Modern Art

It means active engagement with AI—even to break it. Kuo says: “Especially when the pace of change—of AI in particular—is even more accelerated than in previous eras, it is all the more crucial that artists and others outside the tech sector learn, test, speak up and act out.” Further, we might take cues from the artists engaging with AI themselves. Kuo describes what they do: “Artists take a tool and make it do something it’s not supposed to do. They don’t reject technology wholesale. They embrace it—and then make it strange.”

The best artists urge viewers to keep an open mind, slow down, appreciate nuance, accept ambiguity and recognise that we are a crucial part of the final outcome; they break, then build.

• Peter Bauman is editor-in-chief of the digital generative art institution Le Random



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New Empirical Research Report on AI-Driven Drug Repurposing

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AI in health care could save lives and money − but change won’t happen overnight

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Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.



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Report shows China outpacing the US and EU in AI research

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Governments now face the reality that falling behind in AI capability could have serious geopolitical consequences, warns a new research report.

AI is increasingly viewed as a strategic asset rather than a technological development, and new research suggests China is now leading the global AI race.

A report titled ‘DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI’, authored by Daniel Hook, CEO of Digital Science, highlights how China’s AI research output has grown to surpass that of the US, the EU and the UK combined.

According to data from Dimensions, a primary global research database, China now accounts for over 40% of worldwide citation attention in AI-related studies. Instead of focusing solely on academic output, the report points to China’s dominance in AI-related patents.

In some indicators, China is outpacing the US tenfold in patent filings and company-affiliated research, signalling its capacity to convert academic work into tangible innovation.

Hook’s analysis covers AI research trends from 2000 to 2024, showing global AI publication volumes rising from just under 10,000 papers in 2000 to 60,000 in 2024.

However, China’s influence has steadily expanded since 2018, while the EU and the US have seen relative declines. The UK has largely maintained its position.

Clarivate, another analytics firm, reported similar findings, noting nearly 900,000 AI research papers produced in China in 2024, triple the figure from 2015.

Hook notes that governments increasingly view AI alongside energy or military power as a matter of national security. Instead of treating AI as a neutral technology, there is growing awareness that a lack of AI capability could have serious economic, political and social consequences.

The report suggests that understanding AI’s geopolitical implications has become essential for national policy.

Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!



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