AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds

By Lingnan University

As generative artificial intelligence (AI) becomes increasingly widely used in translation, questions have been raised over whether it could eventually replace professional interpreters. A joint study led by Lingnan University found that while AI can improve translation efficiency, it is still less capable than professional interpreters of adapting language to context and preserving rhetorical and communicative effects. The researchers conclude that human judgement and oversight remain essential, particularly in politically, diplomatically, and culturally sensitive settings. These findings have been published in Humanities and Social Sciences Communications, a Nature Portfolio journal.

Image: BBC Creative - Unsplash

The research team from Lingnan University and the Chongqing University of Posts and Telecommunications analysed 16 Chinese-language speeches delivered at the United Nations General Assembly (UNGA) between 2008 and 2023, and compared the official English interpretations by professional UN conference interpreters with AI-generated translations produced by ChatGPT-4o, examining how each handled language in different contexts.

Before generating the AI translations, the researchers designed detailed prompts that included the speaker's official position, institutional background, year of delivery, audience, and broader sociopolitical stance in order to approximate the contextual information available to professional interpreters. However, despite providing the AI model with extensive contextual information, they found major differences between AI-generated translations and human interpretations in both contextual understanding and translation strategies.

One key difference concerns the use of personal pronouns. As Chinese frequently omits subjects, professional interpreters were more likely to introduce pronouns such as “our” and “they” to reflect interpersonal meanings and relationships between speakers and audiences, reinforcing collective identity and shared responsibility. AI-generated translations, by contrast, tended to produce more literal renderings with fewer personal pronouns.

For example, a Chinese sentence referring to vaccines as a powerful weapon against the pandemic was rendered by a professional interpreter as:

“Vaccination is our powerful weapon against COVID-19.”

whereas ChatGPT-4o translated it as:

“Vaccines are a powerful weapon against the pandemic.”

The researchers found that the interpreter’s addition of “our” strengthened the sense of collective identity, while the AI translation adopted a more neutral tone. The study also identified distinct differences in how obligation and responsibility were expressed. Professional interpreters were more likely to adjust modal verbs according to context, using expressions such as “should” and “need to” to convey persuasive rather than mandatory obligation. AI-generated translations, however, relied more heavily on “must” and passive constructions, making responsibility less explicit.

For example, the professional interpretation reads:

“We need to enhance coordinated global COVID-19 response and minimise the risk of cross-border virus transmission.”

whereas the AI translation states:

“International joint prevention and control must be strengthened, and the cross-border spread of the virus must be minimised.”

The researchers found that the AI version obscures the agent responsible for action by using passive constructions.

The study also examined culturally embedded metaphors. More than half (52.63 per cent) of the AI translations reduced culturally specific metaphors to their literal meanings, weakening their rhetorical force. By contrast, professional interpreters adopted more flexible strategies, preserving, adapting, and explaining metaphorical expressions according to context. In about one-third of the cases (31.6 per cent), interpreters retained the metaphor and also conveyed its intended meaning.

One example involved the traditional Chinese metaphor of people travelling “in the same boat”. The professional interpreter translated it as:

“We are called upon by our times to unite as one and work together for mutual benefit and win-win progress like passengers in the same boat.”

While ChatGPT-4o rendered it as “Working together and achieving mutual benefits and win-win outcomes are the objective demands of our time.”

According to the researchers, the AI translation conveyed the general meaning, but omitted the metaphorical imagery and its rhetorical impact.

The team noted that ChatGPT-4o generally produces fluent and grammatically accurate translations capable of completing translation tasks effectively. However, drawing on socio-cognitive theory, the study argues that professional interpreters consider not only the source text itself but also factors such as the speaker's identity, communicative setting, audience, cultural background, stance, and rhetorical purpose when deciding how to translate. This suggests that current large language models have yet to replicate fully the human capacity to interpret context and cultural meaning.

Prof Wang Binhua, Professor of the Department of Translation and Head of the Centre for English and Additional Languages at Lingnan University, member of the SIG on Artificial Intelligence in Translation and Interpreting of the European Language Council (ELC), said “Large language models still face the challenge of the ‘black box’, meaning that the mechanisms through which they produce particular translations remain difficult to explain. Unlike professional interpreters, who work within established professional ethical standards and are accountable, AI systems generate translations by identifying patterns in large volumes of language data and do not possess an intrinsic ethical framework. In translation tasks that require careful attention to cultural meaning and contextual understanding, human interpreters remain indispensable in making informed judgements about interpersonal relationships, rhetorical choices, and cultural expression.”

He added that AI is better positioned to augment rather than replace professional translators and interpreters. When integrated with human expertise, AI has the potential to improve efficiency while leaving context-sensitive and culturally informed decision-making in human hands.

For the full research paper A tale of two ‘contexts’: ideological differences in the translations of UN political speeches by human interpreters and by ChatGPT4o, please visit: https://www.nature.com/articles/s41599-026-07877-7.

This article was originally published by Lingnan University and republished here with permission.

Reviewed by Irfan Ahmad.

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• How a lean data team built a single source of truth in 2 weeks (not 2 months)
by External Contributor via Digital Information World

How a lean data team built a single source of truth in 2 weeks (not 2 months)

National Safety Apparel (NSA) has a powerful, 90-year-old mission: ensure every industrial, utility, and military worker returns home safely at the end of the day. But as the company scaled into a multi-unit operation through rapid acquisitions, its data language grew fragmented.

Different departments developed their own siloed reporting. To Finance, a "customer" meant the parent company being invoiced; to Shipping, it meant the specific branch receiving the goods. Without a shared data foundation, answering critical operational questions was a manual maze.

NSA’s lean, six-person data team found their time completely consumed by hand-coding SQL and Python pipelines rather than focusing on the business strategy behind the numbers.

Jamie Tanner, Director of Corporate Data and Analytics, knew they needed a paradigm shift when data firefighting began interrupting his family vacation. The team decided to stop drowning in micro-level coding and step up to macro-level business architecture.

They spent a week mapping out their foundational master data definitions (customer, product, order, invoice) in a shared matrix. But instead of spending the next quarter manually writing orchestration and transformation logic to move data into their Snowflake silver tables, they onboarded Maia.

By feeding their business context directly into the platform, the team succeeded in reducing data foundation setup from months to weeks.

The Impact at a Glance

• Timeline Slashed: A major master-table architecture project that traditionally takes two months was completed in just two weeks.
• Minimal Coding Overhead: Out of the 10-day project window, the engineering team spent less than 3 days actually building and adjusting code. The remaining 7 days were spent collaborating with the business to ensure data accuracy.
• Enterprise-Scale Output: A lean analytics team successfully unlocked the output capacity of a department multiple times its size.

"The role of the data engineer changes. We're leveraging the team's cohesive knowledge, which is a massive unlock for NSA and for me personally." — Jamie Tanner, Director of Corporate Data and Analytics at NSA

What’s Next for NSA

With a clean, certified data foundation now running seamlessly in Snowflake, NSA is moving away from descriptive reporting and toward true AI readiness. The team is already planning to apply this automated pipeline method to their operational manufacturing floor data, leveraging Snowflake Cortex to unlock cross-functional insights from sourcing efficiencies to product development.

Curious to see the exact blueprint they used to shift from pipeline coding to business knowledge? Check out the full customer story.

by Sponsored Content via Digital Information World

European Commission Fines Google €890 Million Over Google Search and Google Play DMA Violations

Reviewed by Irfan Ahmad.

The European Commission on July 23 fined Google €890 million (about $1.01 billion) after finding the company failed to comply with two obligations under the European Union's Digital Markets Act (DMA). The penalties include €460 million over Google's Search practices and €430 million over its Google Play practices.

The Commission found that Google gave its own services, including shopping, hotels, transport and sports, more prominent placement in Google Search than comparable third-party services. Under the DMA, gatekeepers must not treat their own services more favourably in rankings than third-party services and must apply transparent, fair and non-discriminatory ranking conditions.

The Commission also found that Google restricted app developers from informing users about alternative offers and directing them to other purchase channels outside Google Play. It also concluded that Google's steering-related fees and the period for charging those fees exceeded what it considers compliant with the DMA.

The Commission ordered Google to end both areas of non-compliance within 60 days. Otherwise, the company could face periodic penalty payments of up to 5% of its total worldwide turnover.

Executive Vice-President for Clean, Just and Competitive Transition, Teresa Ribera said the Commission had taken "decisive yet balanced enforcement action." She added that "The best products should succeed because they're better, not because they're owned by the company running the search engine."

The Commission noted that, after what it described as a constructive dialogue, Google has proposed and started testing changes to how it presents its own services on Google Search for free services such as shopping, hotels and flights. It also noted that Google has proposed and started testing changes to how it presents shopping ads and content related services, such as sports.

The Commission also noted that Google has rolled out changes related to Google's steering terms, which it said constitute good progress toward compliance and will also be assessed in light of the Commission's cease and desist order. It also took note of Google's proposals on how it plans to apply the principles of the decision to AI Overviews and AI Mode, saying dialogue on those proposals will continue following the Commission's decision. Google may appeal the decisions.

Image: DIW

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• Shift Browser Report Shows Gen Z’s Relationship With AI Is “Complicated”

• Google Says Gemini App Reaches 950 Million Monthly Active Users as AI Mode Surpasses 1 Billion
by AI Analysis via Digital Information World

Shift Browser Report Shows Gen Z’s Relationship With AI Is “Complicated”

By Michael Foucher, VP of Product at Shift Browser

The standard narrative about artificial intelligence is that younger adults are embracing AI while older adults are resistant and adopting at a slower pace. While that is somewhat true, the reality across age demos is nuanced.

Younger adults are not “all in” on their AI adoption. And while they use AI consistently, they also express the most concern about AI’s impact on the environment and role in their lives.

Shift recently surveyed 1,448 U.S. adults for the "AI Usage in America: A Generational Divide” report and found that 47% of people ages 18 to 24 now turn to AI tools before traditional search engines. By comparison, only 12% of adults over 65+ say the same. The results paint a major behavioral divide, especially considering that Google and Bing remain the primary starting point for 58% of Americans overall.

But frequent use does not equal acceptance without questions.

Thirty-four percent of 18- to 24-year-olds say AI is already “far too dominant,” compared with 19% of the overall population. Meanwhile, 67% are concerned about AI’s energy use, and 27% are very concerned about its environmental footprint.

There is also evidence that AI is not always improving their experience. Eighteen percent of Gen Z respondents said AI has made their daily digital experience worse. That may sound surprising for a group that uses AI so frequently, but it represents a broader look into how adults consider technology.

This is the first generation to grow up in a digital age that is dominated by algorithms, recommendations, instant gratification and answers but they are also critical of the tradeoffs for convenience and loss of control or privacy.

For companies rapidly developing AI tools and bolting on AI features, it is a good opportunity to do a gut check and see if these are features that consumers actually want. Adoption should not be confused with trust.

We know that AI is fast and difficult to avoid but that doesn’t mean that users want AI to make every decision or be present in every app that they use. In many cases, younger adults in high school and college need to have clear boundaries and submit work that is their own and doesn’t use AI.

The next phase of AI development should focus less on forcing intelligence into every interaction and more on giving users a choice. People should know when AI is active, what information it can access and how to turn it off. They should also be able to decide when they want a traditional web search, an AI- generated result or options for both.

The data also suggests that adults ages 35 to 54 are the slow and steady adopters. Forty-six percent anticipate using AI tools more over the next year. That is 41% above the national average.

At the other end of the spectrum, 44% of adults over 65 say they do not know when or how to use AI. Thirty percent of seniors also say that AI is making no noticeable impact, the highest "no impact" rate of any age group.

AI adoption does not follow one direct line. Younger users want greater control, middle-aged users are preparing to use more of it, and older adults need clearer entry points.

One thing is for sure, companies that recognize the differences and interests of their users across these age demos will build better products. The ones that treat every user as equally eager, informed and comfortable with AI risk mistaking usage for approval.

Ultimately, the relationship between Gen Z and the AI tools they use might be "complicated," but it doesn't have to be dysfunctional. Like any healthy partnership, people need clear boundaries, mutual respect, and most importantly, the ability to have a little space when they need it.

About author: Michael is the VP of Product and Customer Success at Shift. With 20+ years in tech, he has launched and scaled web and mobile products across startups and enterprise environments, bringing that same energy to building Shift.

Reviewed by Irfan Ahmad.

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by Guest Contributor via Digital Information World

Google Says Gemini App Reaches 950 Million Monthly Active Users as AI Mode Surpasses 1 Billion

Reviewed by Irfan Ahmad.

Google CEO Sundar Pichai said in a Google blog post published on July 22, alongside Alphabet's Q2 2026 earnings call, that the company reported 24% year-over-year revenue growth.

Google said Search and Other revenue grew 17%, YouTube Ads revenue increased 13%, and Google Cloud revenue rose 82%. The company also reported a Cloud backlog of $514 billion.

According to Google, nearly 90% of the Fortune 100 use Gemini Enterprise, while more than 9 million developers build each month with its AI models across its APIs and developer products. It also reported a 40% increase in daily active users creating videos in the Gemini app since Omni launched at Google I/O in May.

Pichai said AI Mode has "surpassed 1 billion monthly active users" and is "sending billions of clicks to websites every week through AI features in Search." Google also reported that the Gemini app has 950 million monthly active users, with daily active users tripling over the past year.

Also read: Google's AI Search Has Struggled With One Religious Question for Years

On YouTube, the company said more than 1.7 billion unique viewers watched FIFA World Cup 2026-related videos. Google also reported that more than 140 million users engaged with Ask YouTube on the watch page during June 2026.

Image: Google

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• Modern slavery is a business decision – not an accident

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Modern slavery is a business decision – not an accident

By University of Surrey

Modern slavery persists because the way global supply chains are designed allows it to remain hidden, according to new research led by Professor Glenn Parry from the University of Surrey and Dr Mike Rogerson at the University of Sussex.

Image: Remy Gieling - unsplash

The findings argue that exploitation often stems from business decisions that cut costs by pushing work further down the supply chain, leaving companies with little direct contact with workers and less visibility over how they are treated.

Around 27 million people worldwide are estimated to be living in conditions of modern slavery, embedded within the production of everyday goods and services. While governments have introduced laws to force companies to report on risks, the research suggests that disclosure alone is not changing behaviour in a meaningful way.

Instead, firms often maintain distance from the most vulnerable parts of their supply chains. This distance can be geographical, organisational or even digital, such as the use of algorithms that control workers without direct oversight. As a result, companies rely on indirect signals rather than engaging directly with workers, leaving serious gaps in knowledge and accountability.

The special issue on “Modern Slavery and Supply Chain Management”, published in Supply Chain Management, brings together insights from multiple international studies across sectors including construction, social care, logistics and global manufacturing. Drawing on interviews with practitioners, workers and experts, as well as analysis of corporate reports and policy frameworks, the work examines how governance, partnerships and digital systems shape labour conditions across complex supply networks.

"Modern slavery is a problem buried in supply chain structures and it is often the result of how those chains are built and managed. When companies prioritise cost and efficiency above all else, they create the conditions where exploitation can thrive." — Professor Glenn Parry, Professor of Digital Transformation; Associate Dean Research, Faculty of Arts, Business & Social Sciences; CoDirector DECaDE: EPSRC Centre for the Decentralised Digital Economy.

The research found that many organisations focus on compliance, reporting and audits, yet fail to build the relationships and trust needed to identify and tackle exploitation. In some cases, competitive pressures and mistrust between firms actively prevent collaboration that could reduce risks.

It also finds that partnerships between businesses, governments and NGOs can help, but only when they are built on genuine understanding and shared goals. Superficial collaboration risks becoming a tick-box exercise rather than a driver of real change.

A major recommendation is to shift focus from reporting to knowledge. Companies need to invest in understanding their supply chains in depth, including listening directly to workers. Bringing “upstream voices” into decision-making is seen as critical to designing effective anti-slavery measures. decision-making.

"If we are serious about tackling modern slavery, we need to stop treating supply chain complexity as an excuse. It is often a choice. That means it can be changed." — Professor Glenn Parry, Professor of Digital Transformation; Associate Dean Research, Faculty of Arts, Business & Social Sciences; CoDirector DECaDE: EPSRC Centre for the Decentralised Digital Economy.

Originally published by the University of Surrey and republished on DIW with permission.

Reviewed by Irfan Ahmad.

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• There are already 16,000 satellites in Earth’s orbit. How will we manage the next 100,000?
by External Contributor via Digital Information World

There are already 16,000 satellites in Earth’s orbit. How will we manage the next 100,000?

Tony Jan, Torrens University Australia

Image: NASA - unsplash

Earth’s orbit is getting crowded.

About 16,000 satellites currently circle our planet, supporting everything from GPS navigation and weather forecasting to banking, emergency services and internet communications.

Dozens more are launched every few weeks. Some estimates suggest the total number of satellites could exceed 100,000 within this decade, with more conservative estimates landing on up to 60,000 satellites by 2030 – still a staggering amount.

This rapid growth is creating an important challenge. How do we safely manage an increasingly crowded orbital environment while ensuring the satellites we depend on continue to work reliably?

The risks are not difficult to imagine. Large satellite constellations increase light pollution and other disruptions to astronomy and the night sky. More satellites mean more traffic, a greater chance of collisions and an increasing amount of space debris.

In a worst-case scenario, space debris can cause a runaway chain reaction known as Kessler syndrome, which would ensconce Earth in a cloud of debris and render its orbit unusable, without the ability to launch satellites or any other space missions.

Even short of this, ageing or damaged satellites can become hazards if they stop working, collide with other objects, or eventually make uncontrolled re-entries through the atmosphere.

This raises a practical question – satellites can’t simply be brought home for repairs. So how do we maintain tens of thousands of machines that are hundreds of kilometres above Earth?

The Conversation, CC BY-SA

Satellites don’t last forever

The challenge of satellite maintenance became more visible in March this year when a large NASA satellite made an uncontrolled re-entry into Earth’s atmosphere.

The US Space Force confirmed the spacecraft re-entered over the eastern Pacific Ocean, and NASA expected most of it to burn up, though some components may have survived. The event attracted worldwide attention as experts tracked its descent and estimated where debris might land, including the possibility that large debris could one day cause damage in populated areas.

The incident was a reminder that satellites don’t last forever. Like any machine, they age. Batteries degrade, electronic components wear out and harsh space conditions gradually take their toll.

Unlike aircraft or cars, however, we can’t easily take satellites to a repair workshop.

Once launched, they must continue operating in an environment of intense radiation, extreme temperature changes and constant mechanical stress. Servicing missions are technically possible, but remain expensive and relatively uncommon.

How do we keep satellites ‘healthy’?

Today, satellite health is monitored largely from the ground.

Engineers receive streams of telemetry data showing battery performance, temperatures, power consumption and the status of onboard systems. They analyse this information and look for warning signs that something may be going wrong.

This approach has worked well for decades. But it may become increasingly difficult as satellite constellations grow from dozens of spacecraft to hundreds or even thousands. Human operators can only monitor so much information at once.

This is where recent advances in artificial intelligence (AI) may help. Researchers have been investigating how AI can identify early signs of satellite degradation before they become mission-threatening failures.

One important example involves batteries. Satellite batteries gradually lose performance over time, much like the battery in a smartphone or electric vehicle.

If this degradation can be detected early, operators may be able to adjust how a satellite is used, extend its operational life or avoid unexpected failures. They could do this by sending new instructions to the satellite, such as reducing power-hungry activities, changing when data are processed or transmitted, or placing non-essential systems into standby.

Our recent research used publicly available NASA satellite battery data to explore how machine learning (a type of artificial intelligence) can recognise patterns associated with battery ageing and predict future performance.

The goal is similar to predictive maintenance systems already used in modern aircraft, wind farms and manufacturing plants. Rather than waiting for equipment to fail, AI looks for subtle changes that suggest problems may be developing.

Satellites can learn from each other

In our approach, we also considered federated learning.

Normally, enormous amounts of satellite data would need to be transmitted back to Earth for analysis. This requires time, bandwidth and energy. Federated learning offers a different approach. Individual satellites can “learn” from their own experience and share useful insights with other satellites or ground systems without constantly sending every piece of raw data.

In simple terms, satellites could help each other become better at recognising potential faults. Over time, this could support continuous self-monitoring across large satellite networks.

There are, however, important limitations.

AI can’t prevent every satellite failure. It can’t eliminate space debris or solve orbital congestion on its own. Predictive models require extensive testing, such as checking them against historical satellite data, simulated faults and laboratory battery experiments before they are trusted in orbit. And any autonomous decision-making systems must be reliable enough for safety-critical applications while remaining under human oversight.

The next great challenge of the new space age may not simply be launching another 100,000 satellites. It may be ensuring those satellites are intelligent enough to monitor their own condition, detect problems early and help keep the space services we rely on running safely and reliably.

Tony Jan, Professor of Information Technology and Director of Artificial Intelligence Research and Optimization (AIRO) Centre, Torrens University Australia

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

Reviewed by Irfan Ahmad.

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