China has made its self-developed quantum computer operating system, Origin Pilot, available for public download, marking a significant step toward expanding access to quantum computing technology. Officials expect the move to lower barriers to development and accelerate the growth of the national quantum ecosystem.
Developed by Hefei-based Origin Quantum Computing Technology, the system was first introduced in 2021 and has undergone several upgrades. The platform now supports multiple technological approaches, including superconducting, ion-trap, and neutral-atom quantum processors.
Origin Pilot manages key computing functions, including resource scheduling and coordination between software and hardware systems. Features including parallel task processing and automatic qubit calibration aim to improve the efficiency and stability of quantum operations.
Opening unified programming interfaces allows research institutions, universities and developers worldwide to connect to Chinese quantum chips and conduct programming through independent frameworks. Project leaders say users can download the system directly from the company’s official website and begin quantum development activities.
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AI should be integrated into classrooms in Luxembourg rather than avoided, according to Gilbert Busana of the University of Luxembourg. Speaking to RTL Today in Luxembourg, he said ignoring AI would be a disservice to pupils and teachers alike.
Busana argued that AI should be taught both as a standalone subject and across disciplines in Luxembourg schools. Clear guidelines are needed to define when and how pupils may use AI, alongside transparency about its role in assignments.
He stressed that developing AI literacy in Luxembourg is essential to protect critical thinking. Assessment methods may shift away from focusing solely on final outputs towards evaluating the learning process itself.
Teachers in Luxembourg are increasingly becoming coaches rather than simple transmitters of knowledge. Busana said continuous professional training and collaboration within schools in Luxembourg will be vital as AI reshapes education.
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Researchers at Kyoto University have presented an AI robot monk designed to assist with religious ceremonies and spiritual guidance. The prototype, revealed at Shoren-in temple, demonstrates how robotics and faith traditions may coexist.
Equipped with an AI system based on Buddhist scriptures, the robot answers questions about personal struggles and wider social concerns. During a demonstration, it offered reflective advice while performing gestures such as bowing and placing its palms together.
Developers combined a chatbot powered by modern language technology with movements from an existing humanoid robot built by a Chinese manufacturer. Careful programming aimed to reproduce calm behaviour associated with traditional monks.
Japan faces a gradual decline in the number of active temples and clergy, encouraging the exploration of technological support within religious life. Project leaders believe the AI monk could represent a significant shift in preserving spiritual services for future communities.
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Brewer Heineken has appointed Romain Apert as chief digital and technology officer, placing AI at the forefront of efforts to simplify operations and drive transformation. He will join the company’s executive team on 15 May.
Apert joins from Mars, where he served as chief information officer for the petcare division, bringing extensive experience in global technology leadership. He succeeds Ronald den Elzen, who leaves the company after a 31-year career.
The appointment forms part of Heineken’s strategy to use technology and data to streamline processes and strengthen efficiency across the business. AI is expected to play a central role in supporting these ambitions.
Company leadership views digital innovation as essential to future growth as the brewer continues modernising its operations worldwide. The transition marks a new phase in embedding technology deeper into Heineken’s organisational strategy.
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The Central Bank of the UAE has partnered with Abu Dhabi-based AI company Core42 to develop a sovereign financial cloud infrastructure in the UAE. The system is designed to ensure data sovereignty and strengthen protection against cyber threats.
According to the Central Bank of the UAE, the platform will operate on a centralised, highly secure and isolated infrastructure. It aims to support continuous financial services while boosting operational agility across the UAE.
The infrastructure will be powered by AI and provide automation and real-time data analysis for licensed institutions in the UAE. It will also enable unified management of multi-cloud services within a single regulatory framework.
Core42, established by G42 in 2023, said finance must remain sovereign as it relies on digital infrastructure. The Central Bank of the UAE described the project as a key pillar of its financial infrastructure transformation programme.
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Multimodal sensing allows physical AI systems to combine inputs such as vision, audio, lidar and touch to build situational awareness in real time. The approach enables machines to operate autonomously in complex physical environments.
The architecture typically includes input modules for individual sensors, a fusion module to combine relevant data, and an output module to generate actions. Applications range from robotics and autonomous vehicles to spatial AI systems navigating dynamic 3D spaces.
Fusion techniques vary by use case, from Bayesian networks for uncertainty management to Kalman filters for navigation and neural networks for robotic manipulation. The aim is to leverage complementary sensor strengths while maintaining reliability.
Implementation presents technical challenges including environmental noise filtering, calibration across time and space, and balancing redundant versus complementary sensing. Engineers must also manage tradeoffs in processing power, controllers and system design.
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UiPath has unveiled new agentic AI solutions for healthcare providers and payers. The tools focus on medical record summarisation, claim denial prevention, and prior authorisation, connecting data to speed workflows and improve efficiency.
Healthcare organisations face labour shortages and fragmented systems, making revenue cycle management challenging. Providers produce large volumes of clinical documentation that must be quickly turned into actionable insights for accurate reimbursement.
The platform converts records into concise, citation-backed summaries, automates claim review and appeals, and streamlines eligibility checks. AI predicts risks, reduces errors, and accelerates clinical and administrative processes for providers and payers alike.
UiPath partners with innovators such as Genzeon to embed domain expertise. The solution addresses rising costs, complex regulations, and labour challenges, helping teams make data-driven decisions and improve patient outcomes.
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Low solubility and poor bioavailability remain major hurdles in small-molecule drug development, often preventing promising candidates from reaching clinical trials. Traditional trial-and-error methods are time-consuming and depend heavily on the limited availability of active pharmaceutical ingredients (APIs).
AI and machine learning now provide predictive models that anticipate solubility, permeability and systemic exposure. These tools let scientists prioritise high-impact experiments while conserving valuable material.
Digital platforms combine predictive algorithms with stability testing to guide excipient and technology selection. AI can simulate molecular interactions and dose scenarios, helping teams identify risks early and refine first-in-human doses safely.
End-to-end AI/ML workflows integrate data, modelling and manufacturing insights. However, this accelerates development timelines, lowers the risk of late-stage reformulations and connects early formulation choices directly to clinical and manufacturing outcomes.
While AI enhances efficiency and precision, it does not replace human expertise. It amplifies formulation scientists’ work, freeing them to focus on innovative design, problem-solving and delivering high-quality therapies to patients more rapidly.
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Large language models are designed to mimic human conversation, but treating them like people can mislead users. Politeness, flattery, or threats do not consistently improve the accuracy of AI responses.
Experts recommend focusing on how questions are structured rather than on word choice. Asking for multiple options, giving examples, and conducting step-by-step interviews can make AI outputs more relevant and useful.
Role-playing may be effective for creative or exploratory tasks, but it can reduce reliability when precise answers are required. AI models are constantly updated, making old prompting tricks largely ineffective.
Maintaining neutrality in prompts prevents biased responses, and while politeness may not improve AI performance, it can make interactions more comfortable. Developing careful prompt strategies is more effective than relying on manners alone.
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In December 2025, the Macquarie Dictionary, Merriam-Webster, and the American Dialect Society named ‘slop’ as the Word of the Year, reflecting a widespread reaction to AI-generated content online, often referred to as ‘AI slop.’ By choosing ‘slop’, typically associated with unappetising animal feed, they captured unease about the digital clutter created by AI tools.
As LLMs and AI tools became accessible to more people, many saw them as opportunities for profit through the creation of artificial content for marketing or entertainment, or through the manipulation of social media algorithms. However, despite video and image generation advances, there is a growing gap between perceived quality and actual detection: many overestimate how easily AI content evades notice, fueling scepticism about its online value.
As generative AI systems expand, the debate goes beyond digital clutter to deeper concerns about trust, market incentives, and regulatory resilience. How will societies manage the social, economic, and governance impacts of an information ecosystem increasingly shaped by automated abundance? In simplified terms, is AI slop more than a simple digital nuisance, or do we needlessly worry about a transient vogue that will eventually fade away?
The social aspect of AI slop’s influence
The most visible effects of AI slop emerge on large social media platforms such as YouTube, TikTok, and Instagram. Users frequently encounter AI-generated images and videos that appropriate celebrity likenesses without consent, depict fabricated events, or present sensational and misleading scenarios. Comment sections often become informal verification spaces, where some users identify visual inconsistencies and warn others, while many remain uncertain about the content’s authenticity.
However, no platform has suffered the AI slop effect as much as Facebook, and once you take a glance at its demographics, the pieces start to come together. According to multiple studies, Facebook’s user base is mostly populated by adults aged 25-34, but users over the age of 55 make up nearly 24 percent of all users. While seniors do not constitute the majority (yet), younger generations have been steadily migrating to social platforms such as TikTok, Instagram, and X, leaving the most popular platform to the whims of the older generation.
Due to factors such as cognitive decline, positivity bias, or digital (il)literacy, older social media users are more likely to fall for scams and fraud. Such conditions make Facebook an ideal place for spreading low-quality AI slop and false information. Scammers use AI tools to create fake images and videos about made-up crises to raise money for causes that are not real.
The lack of regulation on Meta’s side is the most glaring sore spot, evidenced by the company pushing back against the EU’s Digital Services Act (DSA) and Digital Markets Act (DMA), viewing them as ‘overreaching‘ and stifling innovation. The math is simple: content generates engagement, resulting in more revenue for Facebook and other platforms owned by Meta. Whether that content is authentic and high-quality or low-effort AI slop, the numbers don’t care.
The economics behind AI slop
At its core, AI content is not just a social media phenomenon, but an economic one as well. GenAI tools drastically reduce the cost and time required to produce all types of content, and when production approaches zero marginal cost, the incentive to churn out AI slop seems too good to ignore. Even minimal engagement can generate positive returns through advertising, affiliate marketing, or platform monetisation schemes.
AI content production goes beyond exploiting social media algorithms and monetisation policies. SEO can now be automated at scale, thus generating thousands of keyword-optimised articles within hours. Affiliate link farming allows creators to monetise their products or product recommendations with minimal editorial input.
On video platforms like TikTok and YouTube, synthetic voice-overs and AI-generated visuals are on full display, banking on trending topics and using AI-generated thumbnails to garner more views on a whim. Thanks to AI tools, content creators can post relevant AI-generated content in minutes, enabling them to jump on the hottest topics and drive clicks faster than with any other authentic content creation method.
To add salt to the wound, YouTube content creators share the sentiment that they are victims of the platform’s double standards in enforcing its strict community guidelines. Even the largest YouTube Channels are often flagged for a plethora of breaches, including copyright claims and depictions of dangerous or illegal activities, and harmful speech, to name a few. On the other hand, AI slop videos seem to fly under YouTube’s radar, leading to more resentment towards AI-generated content.
Businesses that rely on generative AI tools to market their services online are also finding AI to be the way to go, as most users are still not too keen on distinguishing authentic content, nor do they give much importance to those aspects. Instead of paying voice-over artists and illustrators, it is way cheaper to simply create a desired post in under a few minutes, adding fuel to an already raging fire. Some might call it AI slop, but again, the numbers are what truly matter.
The regulatory challenge of AI slop
AI slop is not only a social and economic issue, but also a regulatory one. The problem is not a single AI-generated post that promotes harmful behaviour or misleading information, but the sheer scale of synthetic content entering digital platforms. When large volumes of low-value or deceptive material circulate on the web, they can distort information ecosystems and make moderation a tough challenge. Such a predicament shifts the focus from individual violations to broader systemic effects.
In the EU, the DSA requires very large online platforms to assess and mitigate the systemic risks linked to their services. While the DSA does not specifically target AI slop, its provisions on transparency, content recommendation algorithms, and risk mitigation could apply if AI content significantly affects public discourse or enables fraud. The challenge lies in defining when content volume prevails over quality control, becoming a systemic issue rather than isolated misuse.
Debates around labelling AI slop and transparency also play a large role. Policymakers and platforms have explored ways to flag AI-generated content throughout disclosures or watermarking. For example, OpenAI’s Sora generates videos with a faint Sora watermark, although it is hardly visible to an uninitiated user. Nevertheless, labelling alone may not address deeper concerns if recommendation systems continue to prioritise engagement above all else, with the issue not only being whether users know the content is AI-generated, but how such content is ranked, amplified, and monetised.
More broadly, AI slop highlights the limits of traditional content moderation. As generative tools make production faster and cheaper, enforcement systems may struggle to keep pace. Regulation, therefore, faces a structural question: can existing digital governance frameworks preserve information quality in an environment where automated content production continues to grow?
Building resilience in the era of AI slop
Humans are considered the most adaptable species on Earth, and for good reason. While AI slop has exposed weaknesses in platform design, monetisation models, and moderation systems, it may also serve as a catalyst for adaptation. Unless regulatory bodies unite under one banner and agree to ban AI content for good, it is safe to say that synthetic content is here to stay. However, sooner or later, systemic regulations will evolve to address this new AI craze and mitigate its negative effects.
The AI slop bubble is bound to burst at some point, as online users will come to favour meticulously crafted content – whether authentic or artificial over low-quality content. Consequently, incentives may also evolve along with content saturation, leading to a greater focus on quality rather than quantity. Advertisers and brands often prioritise credibility and brand safety, which could encourage platforms to refine their ranking systems to reward originality, reliability, and verified creators.
Transparency requirements, systemic risk assessments, and discussions around provenance disclosure mechanisms imply that governance is responding to the realities of generative AI. Instead of marking the deterioration of digital spaces, AI slop may represent a transitional phase in which platforms, policymakers, and users are challenged to adjust their expectations and norms accordingly.
Finally, the long-term outcome will depend entirely on whether innovation, market incentives, and governance structures can converge around information quality and resilience. In that sense, AI slop may ultimately function less as a permanent state of affairs and more as a stress test to separate the wheat from the chaff. In the upcoming struggle between user experience and generative AI tools, the former will have the final say, which is an encouraging thought.
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