Artificial intelligence Archives | Page 25 of 673

Singapore and Google strengthen collaboration on AI innovation and digital governance

Google and Singapore’s Ministry of Digital Development and Information have announced an expanded National AI Partnership designed to accelerate the deployment of frontier AI technologies across the country’s economy and public sector.

The initiative builds on earlier collaboration between Google and Singapore’s digital authorities and aims to support healthcare innovation, scientific research, workforce development, enterprise transformation, and AI governance. Officials said the partnership aligns with Singapore’s National AI Strategy and broader ambitions to position the country as a global AI hub.

A major focus of the collaboration involves healthcare and life sciences. Google DeepMind is exploring AI co-clinician systems with Singapore’s public healthcare sector, examining how AI agents could support doctors and patients throughout medical treatment and decision-making processes.

Google DeepMind will also collaborate with the National Research Foundation to train researchers on agentic AI systems designed to accelerate scientific discovery. Additional partnerships with the Agency for Science, Technology and Research will focus on AI-enabled research and secure cloud-based scientific analysis tools.

The agreement also expands AI deployment in education. Google and Singapore’s Ministry of Education plan to strengthen educator training programmes and integrate AI-powered teaching support tools across schools. Officials said the partnership aims to improve digital learning capabilities while supporting broader AI workforce readiness initiatives.

Singapore and Google additionally announced plans to collaborate on AI safety, governance, and cybersecurity frameworks. A joint initiative involving Cyber Security Agency of Singapore and other agencies is examining how AI agents interact with real-world digital systems and how governance rules should evolve around autonomous AI technologies.

Officials described the partnership as part of a wider effort to deploy frontier AI responsibly while supporting economic growth, public services, and digital transformation.

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MIT researchers explore AI-driven approaches to drug discovery

AI is increasingly being used in drug discovery to analyse large chemical datasets and identify potential therapeutic compounds. Researchers estimate that the number of potentially useful small-molecule compounds is too large for experimental testing alone, increasing reliance on computational screening methods.

Researchers at MIT are developing machine learning models designed to predict molecular behaviour and chemical reaction pathways. The research focuses on identifying promising drug candidates and improving how chemical reactions can be simulated and understood using data-driven methods.

The research incorporates chemical principles such as reaction mechanisms and physical constraints into AI models. The group has developed models including ShEPhERD, which predicts molecular interactions with proteins, and FlowER, which models chemical reaction outcomes.

Research in the group also extends to automated experimentation, structure analysis and experimental design, aiming to build more efficient workflows for drug discovery. According to the researchers, the broader aim is to improve the realism and accuracy of computational predictions in chemistry.

Why does it matter?

AI-driven chemistry significantly reduces the time and cost required to identify viable drug candidates by narrowing down vast chemical search spaces that would otherwise be impossible to evaluate experimentally.

Embedding chemical principles into machine learning models also improves reliability, making computational predictions more useful for real-world pharmaceutical development and potentially accelerating the delivery of new treatments.

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Google explores AI-assisted scientific discovery through Gemini for Science

Google has introduced Gemini for Science, a collection of AI tools and experiments designed to support scientific research workflows. The initiative combines capabilities from systems including Co-Scientist, AlphaEvolve, Empirical Research Assistance, and NotebookLM.

According to Google, the AI-based tools are intended to support tasks such as hypothesis generation, literature analysis, and computational research.

Google said three experimental tools will initially be released through Google Labs, focusing on hypothesis generation, computational discovery and literature analysis. The company also announced Science Skills for Google Antigravity, integrating multiple life sciences databases and research tools.

Google said the programme is being developed in collaboration with more than 100 research institutions and scientific organisations. The company also highlighted research partnerships and conference collaborations linked to AI-supported scientific research.

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Study examines local warming effects linked to data centre expansion

New research suggests that expanding data centre infrastructure may contribute to localised warming effects similar to urban heat islands.

The study, published in the Journal of Engineering for Sustainable Buildings and Cities, examined several data centres in the Phoenix metropolitan area and found measurable increases in surrounding air temperatures. Researchers reported temperature increases ranging from approximately 1.5 to 4 degrees Fahrenheit within areas located downwind from facilities.

Data centres generate waste heat through cooling systems used to support high-performance computing operations.

According to the researchers, large data centre campuses can generate concentrated thermal output associated with high energy consumption.

The findings come as global demand for AI, cloud computing, and digital services continues to drive the construction of new facilities across the US and other regions. Northern Virginia, Phoenix, and several European locations have become major hubs for hyperscale infrastructure development.

The researchers said the observed effects differ from traditional urban heat islands because of continuous cooling activity and continuous energy consumption. The study noted that clusters of facilities may produce cumulative effects that require further investigation.

The researchers discussed potential implications for energy demand, infrastructure planning, and surrounding communities. The study said elevated local temperatures could influence cooling demand and related environmental conditions.

Furthermore, scientists stressed that additional peer-reviewed research remains necessary to determine the long-term climatic significance of large-scale data centre expansion.

Why does it matter?

The findings reflect growing scrutiny surrounding the environmental footprint of AI infrastructure. Data centres already face criticism over electricity consumption, water usage, and grid pressure. The possibility that concentrated AI infrastructure may also influence local temperatures introduces another dimension to debates surrounding sustainable digital expansion.

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Anthropic challenges US government restrictions on AI technology use

The US AI company Anthropic attempted to challenge a decision by the US Department of Defense to ban the use of its technology in government institutions. The company had been given 30 days to file an appeal after the Pentagon classified its products as a ‘supply chain risk’.

However, instead of following the proper procedures and sending the appeal to the designated official address, the head of Anthropic’s legal team emailed it directly to two DoW officials.

Reports cited by multiple outlets suggested disagreements between Anthropic and US defence authorities over potential military applications of AI systems and related safety restrictions. The reports referred to discussions about military and intelligence-related AI applications. After Pentagon asked Anthropic to bend its rules and company refused, Anthropic was latter classified as a ‘supply chain risk’.

Reports described the designation as an unusual step involving a major US AI company. According to reports, federal agencies were instructed to suspend use of Anthropic technologies following the classification decision.

Anthropic CEO Dario Amodei has said the company previously cooperated with the US military in areas such as intelligence analysis, modelling and simulation, operational planning, and cyber operations. However, he has also argued that the government’s action was not legally justified and indicated that the company has no choice but to challenge it in court.

Amodei went on to say that Anthropic does not believe private companies should be involved in operational military decision-making, particularly when systems could enable fully autonomous weapons or mass surveillance.

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CNIL reports record complaints and data breaches

The French data protection authority CNIL reported a record year in 2025 for complaints, fines and data breach notifications, while preparing for new responsibilities under the EU AI Act.

CNIL received 20,150 complaints in 2025, up 10% from 2024. The complaints covered issues linked to work, commerce, real estate, social networks and data breaches, with around 1,900 complaints directly concerning breaches.

The authority also received 6,167 data breach notifications, an increase of 9.5% from 2024. Hacking accounted for one in two reported incidents, while cybersecurity failures represented one-third of investigations and nearly 30% of sanctions.

In total, CNIL carried out 323 investigations and issued 259 corrective measures, including 83 sanctions worth nearly €487 million. Two major sanctions accounted for a large share of the total, while the simplified procedure introduced in 2022 allowed faster action in less complex cases.

Cybersecurity will become an even bigger enforcement focus in 2026, with CNIL planning to devote 50% of its controls and enforcement actions to data security. Checks will focus on organisations affected by breaches, those subject to complaints and sectors processing large volumes of sensitive or highly personal data.

The report also highlights CNIL’s role in supporting professionals and public authorities. In 2025, it processed 539 health authorisation applications, handled 1,351 professional advice requests, delivered 90 opinions on draft laws or regulatory texts and launched seven public consultations.

On AI, CNIL is already designated to monitor prohibited uses under the EU AI Act and is expected to become the market surveillance authority for certain high-risk AI systems, including in biometrics, migration, law enforcement, employment and education.

The authority also published AI resources for designers and developers, developed a traceability tool for open-source AI models and joined the PANAME project with ANSSI, Inria and PEReN to test whether AI models process personal data.

Why does it matter?

CNIL’s annual report shows how data protection enforcement is increasingly shaped by cybersecurity and AI. Record breach notifications and complaints point to growing pressure on organisations to secure personal data, while CNIL’s future AI Act responsibilities place the authority at the centre of France’s oversight of prohibited and high-risk AI systems.

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EU publishes draft guidance on high-risk AI classification under AI Act

The European Commission has opened a public feedback period on draft guidance related to high-risk AI systems under the EU AI Act.

The draft guidance is intended to help providers and deployers determine whether AI systems fall within the Act’s high-risk category. The document includes examples illustrating how classification criteria may apply in different situations.

Under the AI Act, high-risk classification applies to specified AI use cases that may affect health, safety, or fundamental rights. According to the Commission, the guidance is intended to support consistent interpretation of the rules before compliance obligations take effect.

The document has been made available through the AI Act Single Information Platform. Stakeholders, including businesses, public authorities, researchers, civil society organisations, and citizens, may submit comments until 23 June 2026. The Commission said additional guidance related to high-risk AI obligations is expected at a later stage.

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Google and UNICEF launch AI-focused education partnership

Google and UNICEF have launched a global partnership focused on AI-supported education initiatives and digital learning infrastructure.

The initiative, funded through Google.org, will initially focus on Brazil, India, Pakistan, and Kenya. According to the organisations, the programme will address areas including literacy, numeracy, teacher support, and digital access.

Google said the partnership aims to combine AI tools with UNICEF’s education programmes to support localised digital learning systems. The initiative includes teacher training, educational technology deployment, and AI-supported learning tools.

Several Google AI tools, including Gemini, NotebookLM, and ReadAlong, will support the initiative. UNICEF said the programme is intended to support digital skills, AI literacy, and the integration of AI tools into classrooms.

The organisations also highlighted goals related to digital inclusion and education access in regions facing infrastructure limitations.

UNICEF said annual reports will assess programme implementation and scalability.

Why does it matter?

Governments, international organisations, and technology companies are increasingly positioning AI as a major component of future education systems. Partnerships involving AI-driven learning tools may significantly influence digital literacy, educational access, workforce preparation, and long-term economic development.

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Google launches Gemini 3.5 with advanced agentic AI capabilities

Google has announced Gemini 3.5, a new family of AI models designed to combine frontier-level reasoning with stronger agentic capabilities across consumer, developer and enterprise products.

The company is launching the series with Gemini 3.5 Flash, which it describes as its strongest agentic and coding model so far. Google said the model is built for complex, long-horizon tasks, including multi-step workflows, coding, document analysis and enterprise automation.

According to Google, Gemini 3.5 Flash outperforms Gemini 3.1 Pro on several coding and agentic benchmarks, including Terminal-Bench 2.1, GDPval-AA and MCP Atlas, while also improving multimodal reasoning. The company also claimed the model is significantly faster than other frontier models when measured by output tokens per second.

Google said the model can support agentic workflows through its Antigravity development platform, including the use of collaborative subagents for coding, financial document preparation, data analysis and other complex enterprise tasks. It also highlighted richer multimodal capabilities, including interactive web interfaces, visual reasoning and generative UI experiences.

Gemini 3.5 Flash is available through the Gemini app, AI Mode in Google Search, the Gemini API in Google AI Studio, Android Studio, Google Antigravity and Gemini Enterprise products. Google also said Gemini 3.5 Pro is being used internally and is expected to roll out next month.

The announcement also introduced Gemini Spark, a personal AI agent powered by Gemini 3.5 Flash. Google said Spark is designed to run continuously, helping users navigate digital tasks and take action under their direction. It is starting with trusted testers, with a beta planned for Google AI Ultra subscribers in the US.

Alongside performance improvements, Google said Gemini 3.5 was developed under its Frontier Safety Framework. The company said it strengthened safeguards related to cybersecurity and chemical, biological, radiological and nuclear risks, while adding safety training and interpretability tools intended to reduce harmful outputs and mistaken refusals.

The launch reflects a wider industry shift from conversational AI assistants towards systems that can plan, coordinate and execute tasks across digital environments, with major AI companies increasingly competing on agentic workflows, coding performance, multimodal interaction and enterprise integration.

Why does it matter?

Gemini 3.5 shows how the AI race is moving beyond chatbot performance towards systems that can act across software, workflows and enterprise environments. Faster agentic models could help automate coding, analysis and business operations, but they also raise governance questions around supervision, safety, accountability and how much autonomy users and organisations should give to AI agents.

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OpenAI expands verification tools as AI slop blurs digital trust

OpenAI has announced new measures to strengthen the provenance and verification of AI-generated content as synthetic media becomes more widespread across digital platforms.

The company said it is expanding support for Content Credentials and compliance with the Coalition for Content Provenance and Authenticity (C2PA) standard. The standard uses metadata and cryptographic signatures to help ensure that information about a piece of media travels securely with the content, including details on where it came from and how it may have been created or edited.

OpenAI also plans to integrate Google DeepMind’s SynthID watermarking into images generated through ChatGPT, Codex and the OpenAI API. The company said SynthID will add an invisible watermarking layer that complements C2PA metadata, particularly when metadata is removed, lost, or altered during file conversions, resizing, screenshots, or other transformations.

The company said it is adopting a multi-layered provenance approach that combines metadata, watermarking and public verification tools rather than relying on a single detection method. According to OpenAI, C2PA can provide richer contextual information, while SynthID can help preserve a signal when metadata does not survive.

The move also connects to wider concerns about AI slop, as synthetic media and low-quality AI-generated content become harder to distinguish from authentic images. Provenance tools cannot solve the problem alone, but they can provide clearer signals about how digital media was created or modified.

OpenAI also previewed a public verification tool that will allow users to check whether ChatGPT, Codex or the OpenAI API generated an uploaded image. The tool will look for provenance signals, including Content Credentials and SynthID watermarks. Still, OpenAI said it will not make a definitive judgement when no signal is detected, because provenance signals can sometimes be removed.

At launch, the verification tool is limited to OpenAI-generated content. The company said it aims to support wider cross-platform verification efforts in the coming months and eventually expand support to more types of online content.

Why does it matter?

AI-generated content is becoming harder to distinguish from authentic media, fuelling concerns around AI slop, deepfakes and manipulated information. Provenance systems such as Content Credentials, watermarking and verification tools can help people understand where media came from and whether it was generated or modified by AI. However, OpenAI’s approach also shows the limits of technical detection: metadata can be stripped, watermarks may not survive every transformation, and no single method can provide complete certainty.

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