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Artificial intelligence and journalism | Opinion

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2 months ago

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AI applications continue to rapidly expand into all areas of life. They are transforming processes and workflows in the domains they permeate, while also creating new opportunities. However, alongside these contributions, AI also brings various risks, ranging from compromising data security to leaving individuals vulnerable, reinforcing biases, deepening inequalities and generating misinformation. These risks vary in scale and nature depending on the specific characteristics of the field in which AI is applied.

Journalism is one of the fields most profoundly affected by AI, and it is deeply felt across a wide spectrum, including data analysis, content creation, content personalization and editorial processes. It has become an especially valuable ally in investigative journalism. Moreover, AI now contributes to every stage of the news cycle, including the gathering, reporting, storytelling and distribution of news. In areas where digitalization is extensive, AI acts as a transformative force. Given that journalism is one such field, many researchers argue that AI is not merely a tool in journalism but a transformative power that is reshaping the profession itself.

The widespread adoption of machine learning has opened new horizons, particularly for investigative journalism. It has enabled the easy analysis of big data based on the specific details of a given topic, as well as the identification of underlying patterns within the data. This in-depth contribution has significantly facilitated and enhanced the quality of investigative journalism and news production, especially in complex fields such as elections, health, education, finance and monetary markets, and sports. Thanks to AI, information with news value and complex narratives, previously difficult to detect due to structural complexity, can now be uncovered and presented to the public. As a result, news production capacity has increased significantly with AI technologies. For news agencies in particular, this increased capacity provides a major advantage in terms of both public influence and economic gain.

On the other hand, it has also become possible to conduct in-depth public opinion analysis through social media and other digital platforms. In this way, reader and viewer responses to news content can be evaluated more comprehensively. Additionally, analyzing user preferences on news platforms and recommending new content accordingly has become a common practice, helping to extend the time users spend on these platforms.

One of the most significant contributions of AI is its ability to enable personalized content production. AI, which is widely used to generate personalized educational content in the field of education, has similarly started to be extensively applied in journalism for collecting, evaluating and distributing personalized content tailored to individual users. In short, AI technologies are making increasingly essential contributions to enhancing productivity and efficiency in journalism. The expectation is that the time saved through this increase in productivity will be used to improve the overall quality of journalism.

Research findings on the impact of AI on employee productivity indicate that increases in efficiency and output are particularly significant among low- and medium-skilled workers. In other words, AI technologies help compensate for skill gaps in these employee groups. When used in journalism in this way – complementing rather than replacing humans – AI can enhance productivity without causing major negative effects on employment. At the same time, it can create additional time that journalists can devote to improving the quality of their reporting.

However, there is a clear risk that journalism positions involving routine tasks, such as writing standard news reports and performing data analysis, may be fully taken over by AI. On the other hand, as noted above, the integration of AI technologies into journalism as a transformative force requires workers in the field to rapidly acquire new skills to remain relevant in a changing industry. Therefore, improving AI literacy and skills among journalism professionals is of critical importance. Without investment in the development of these capabilities, many journalists may face the risk of losing their current positions.

On the other hand, the greatest risk associated with personalized news content is the reduction in content diversity and the reinforcement of informational comfort zones by directing users toward echo chamber-like content. As a result, individuals are increasingly exposed to information that supports their existing beliefs and attitudes, while their access to differing opinions and news becomes limited. This makes it more difficult for people to encounter diverse content, and the interpretation of events begins to vary significantly depending on the boundaries of each echo chamber. One of the greatest risks facing modern societies is the clustering of the public into distinct groups and their confinement within echo chambers. As AI further enhances the personalization of news content, it is likely to intensify the formation of these echo chambers. This poses a serious threat to the overall health and cohesion of modern societies.

Although AI is highly capable of analyzing big data and detecting patterns, the lack of transparency in how these analyses are conducted due to the “black box” nature of many AI systems raises serious concerns, particularly in news content production and investigative journalism. The opaque nature of AI-generated analysis and content can result in the production of news that lacks transparency and accountability. Since AI itself cannot be held responsible for the content it produces, an important question arises: Can journalists who use AI in this way be held accountable for non-transparent content and analysis? This issue is also actively debated in the academic world.

For example, as generative AI tools began to be used in the production of scientific articles and even appeared as co-authors in some cases, editorial teams of academic journals faced intense debate over whether AI could be recognized as an author. Prestigious journals such as Science have taken a firm stance, stating not only that AI cannot be listed as an author, but also that AI-generated content, such as text or graphic,s should not be used in academic articles at all. However, more flexible policies have gradually emerged. According to these, AI can never be considered a co-author, but if it contributes to the quality of a scientific article, its role in the production process must be clearly disclosed within the article. At the heart of all these debates and efforts to find solutions lies the fundamental issue that AI cannot bear responsibility for its contributions and cannot be held accountable for its actions. A similar precaution must be implemented in the field of journalism as well.

Another major concern regarding the widespread use of AI in journalism is the risk of perpetuating biases. Since AI technologies make predictions, optimizations, and generate content based on real-world data, the training data effectively serves as a form of memory. This “memory” can contain biased judgments and linguistic patterns related to religion, race, gender and other characteristics of different social groups – biases that can be directly reproduced in new content. As a result, AI-generated journalistic content may replicate these same biases, leading to the proliferation of biased news. Furthermore, when such biased content circulates within echo chambers and is repeatedly interpreted through the lens of partial perspectives, it increases the risk of deepening social inequalities. The same dynamic is present in culturally embedded content generation through AI. As we discussed in a previous article titled “The Powerful Wave of Orientalism Driven by Artificial Intelligence,” AI applications continue to produce content that preserves orientalist tones. These systems attempt to maintain control over the right to represent “the East” from a detached, often Western and white-centric perspective, disconnected from the reality of the cultures they depict.

In addition, with the advancement of artificial intelligence technologies, the production of highly realistic yet false video content (deepfakes) has become increasingly widespread. The ease with which such manipulative and misleading content can be created not only heightens social unrest but also poses threats to individual safety. In this context, another risk is the potential of AI to generate false content, which has negative implications for journalism. As is well known, generative AI sometimes produces information that appears coherent within the text but is factually incorrect, a phenomenon referred to as “hallucination” or “confabulation.” Relying entirely on AI for news content production increases not only the risk of biased reporting but also the risk of misinformation. Therefore, editorial oversight is critically important in eliminating such risks. To ensure this, editorial teams must possess a strong level of AI literacy, and this literacy must be continuously updated.

In summary, AI applications have a transformative and therefore far-reaching impact on the field of journalism. The opportunities it provides have already significantly reshaped processes and workflows in this domain and have led to notable economic gains. However, it is also clear that this transformation brings numerous risks, ranging from negative effects on employment in journalism to the production of biased and false content. As in other fields, the most human-centered approach in journalism is to use AI technologies in a way that complements human effort rather than replaces it. Otherwise, while the economic benefits of AI may concentrate in the hands of a narrow group, the risks it poses will affect broader segments of society. Moreover, the risks associated with AI have made editorial oversight more critical than ever before. In this context, increasing AI literacy and supporting the development of related skills will enhance the potential to benefit from these technologies in a balanced and responsible way.

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AI Research

Artificial Intelligence Of Things (AIoT) Guide

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51 minutes ago

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September 4, 2025

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For organizations exploring connected technologies, the conversation around the Internet of Things (IoT) has shifted. They are looking to make devices smarter, responsive and capable of operating with greater insight.

The Artificial Intelligence of Things (AIoT) combines the sensor-driven, networked structure of IoT with the decision-making power of artificial intelligence (AI) at the edge to collect and act on data. This generates insights to help businesses improve efficiency and reliability and streamlines user experience.

Explore what AIoT is, its benefits and use cases below.

What is AIoT?

AIoT is the convergence of AI with IoT. The technology brings intelligence to devices that connect and transmit data, transforming them into systems that interpret information, learn from patterns and support decision-making.

The key components of AIoT systems include:

  • Sensors and actuators: Sensors interact with the surroundings by collecting and measuring data, while actuators translate instructions from the AI system into actions.
  • Connectivity: Connectivity solutions enable communication between devices.
  • Edge computing: Devices equipped with embedded AI chips or processors handle data processing to improve response times.
  • AI models: AI algorithms analyze patterns, detect anomalies, forecast trends and support automated decision-making. These models are trained on historical or real-time data and deployed at the edge or in the cloud.
  • Cloud infrastructure: The cloud stores large volumes of data and supports remote device management and software updates.

The difference between IoT and AIoT

IoT systems collect data from sensors, transfer it through networks and store it in the cloud for later analysis. These systems may need oversight to make decisions based on the data collected. This creates delays and increases dependence on bandwidth.

AIoT embeds intelligence closer to where data is generated. It reduces latency and the bandwidth required to move data. By analyzing and interpreting data at the edge, it streamlines decision-making, allowing businesses to react faster to changing conditions.

AIoT architecture

Explore how AIoT’s architecture allows it to collect, analyze and share information.

Edge computing AIoT

Edge-based computing has the following layers:

  • Collection terminal layer: This includes sensors, cameras and mobility devices that gather information from surrounding environments.
  • Connectivity layer: This layer consists of hardware and software that transmits data to nearby edge devices.
  • Edge layer: Edge AI processors handle real-time analytics, pattern recognition and insight generation.

Cloud-based AIoT

Cloud-based AIoT layers include:

  • Device layer: This layer includes beacons, sensors, embedded devices and tags that collect and transmit data.
  • Connectivity layer: The connectivity layer comprises software and hardware that facilitate secure and reliable data transfer to the cloud.
  • Cloud layer: AI workloads are run here for analysis and insights.
  • User communication layer: The user communication layer interfaces with dashboards, apps or web portals for the end user.

Benefits of integrating AI in IoT devices

Implementing a new technology should drive measurable outcomes in performance, cost and improve user experience. Here are the benefits of connecting devices to AIoT.

1. Boosts operational efficiency

AIoT increases efficiency by enabling systems to respond to conditions in real time. When decisions are made at the edge, devices can automatically adjust behaviors.

This means:

  • Heating, ventilation, and air conditioning (HVAC) systems regulate airflow based on occupancy patterns.
  • Supply chains reroute based on traffic or inventory signals.
  • Machinery modulates speed or power use in response to production demand.
  • The result is smarter resource use, better coordination and fewer interventions.

2. Enhances safety

AIoT improves safety by enabling instant decision-making, which helps prevent accidents.

For example:

  • Edge-enabled cameras detect unauthorized access or unusual motion patterns and trigger alarms immediately.
  • Equipment shuts down or enters safe mode if abnormal vibration or heat is detected.
  • Smart infrastructure manages traffic flows or alerts authorities to emergencies before a hazard escalates.

3. Reduces human error

By automating data interpretation and decision-making, AIoT reduces the room for human error in routine or high-risk tasks. In sectors where conditions change rapidly, AI-enabled devices adjust irrigation or voltage levels automatically.

In administrative settings, AIoT monitor systems and flag inconsistencies or risks that might be overlooked. It supports teams by handling the repetitive, high-precision tasks where mistakes may slip through.

4. Improves decision-making

With access to real-time data, historical trends and contextual cues, AIoT recommends or takes action based on potential outcomes. For example, in manufacturing, AIoT identifies production bottlenecks and suggests changes to scheduling. In smart cities, traffic data combined with weather and event schedules drive predictive rerouting. This capability augments human judgment to streamline the gathering and analysis of insights.

5. Streamlines predictive maintenance

By monitoring equipment health and comparing it to past performance data, AI models detect subtle signs of wear, imbalance or malfunction before failure occurs. This helps prevent costly downtime. Additionally, technicians are alerted to service needs when they are required, rather than relying on fixed schedules or post-failure repair.

6. Provides scalability

As more devices are added, the system becomes more intelligent. New sensors or devices feed data into shared models, enriching the entire system. Firmware and AI models are updated remotely, and because intelligence resides at the edge, the system doesn’t become dependent on a single hub. This flexibility allows businesses to scale while maintaining performance and control.

7. Maximizes data value

AIoT systems extract and turn data into meaningful, actionable outputs. This reduces data overload while uncovering patterns some analytics tools might miss. For example, temperature fluctuations combined with vibration readings in a cold storage unit might signal compressor strain. AIoT flags this before damage occurs. It also enables real-time dashboards for operations teams, providing visibility into system health and performance metrics.

8. Helps manage energy

The system enables low energy use by allowing devices to self-optimize based on context. For example, lighting systems in smart buildings dim or shut off based on occupancy. Industrial machines shift loads to off-peak hours, while electric vehicles and grid systems balance loads in response to demand and supply.

This level of precision reduces energy bills and supports green initiatives, helping business demonstrate their environmental responsibility.

9. Supports personalization

At the consumer and user experience level, AIoT allows for tailored and intuitive engagements. Devices learn user preferences, anticipate needs, and adjust accordingly. For instance, thermostats learn daily routines and adjust temperatures as needed. Digital signage in retail adapts based on customer flow and demographic signals, providing an exceptional user experience. Even medical devices can personalize treatment or therapy based on individual patient concerns.

AIoT brings contextual awareness in every interaction, making devices feel more aligned with consumer needs.

Considerations when implementing AIoT

AIoT has several benefits. However, businesses should keep the following considerations in mind when deploying the technology:

Data privacy and security: AIoT devices collect and process vast amounts of data, some of which may be sensitive, personal or proprietary. This makes privacy and security a priority in industries adhering to strict privacy regulations. Businesses should map out where and how data is being used, and align with security policies, regulatory requirements and industry standards.

Interoperability: Ensuring your systems are interoperable across vendors, protocols and applications can be complex in legacy arrangements. Open standards and flexible connectivity options can reduce integration friction. Businesses can partner with providers who factor in interoperability. They can offer customizable solutions that can plug into existing systems instead of requiring complete overhauls.

System complexity: Building a cohesive and balanced system requires specialized expertise. Businesses can work alongside partners who specialize in edge computing, embedded systems and connectivity integration to reduce friction in design and deployment. With the right support, complexity becomes manageable.

AIoT applications and examples

As AIoT matures, intelligent edge devices may help various sectors meet their needs. Below are the use cases of the system in different industries.

Manufacturing

Embedded AI processors in industrial equipment monitor vibration, pressure and temperature to detect early signs of mechanical failure. This makes it possible to perform maintenance before problems interrupt production. Computer vision systems powered by AI also support inspection on assembly lines, automatically flagging defects or inconsistencies in products.

These tools detect variations invisible to the naked eye, raising quality control standards while reducing manual inspection workloads.

Facilities also use smart sensors to handle energy consumption. By tracking usage patterns, models suggest optimizations or adjust systems during peak hours, helping factories control costs.

Smart home technology

In residential settings, AIoT enables homes to respond to owners’ behavior. For example, far-field voice recognition allows devices to understand commands even in noisy conditions or from across the room.

Occupancy sensors and AI-driven climate control systems work in tandem to adjust temperatures and lighting based on who’s home, what rooms are in use and time of day preferences. These features reduce energy consumption while enhancing comfort.

Biometric authentication methods like fingerprint or presence-based access help control smart locks or safes. These systems learn user routines and flag irregular access attempts or unexpected activity.

Smart cities and public infrastructure

One common use case in smart cities and public infrastructure is traffic optimization. By combining data from cameras and road sensors with AI, traffic lights adjust based on vehicle and pedestrian flow, easing congestion and improving safety.

Sensors across cities monitor air quality, noise pollution and water infrastructure. When they exceed thresholds, relevant departments use these insights to create quality standards, assess law compliance and notify the public about environmental conditions.

Retail

In retail, AIoT is driving a shift toward more responsive, tailored in-store experiences while giving businesses visibility into their operations. Smart cameras and shelf sensors monitor customer movement, identify high-traffic zones and optimize product placement. For example, interactive kiosks, enhanced by touch interfaces, adjust content based on user engagement, providing dynamic recommendations or support. These systems personalize the experience and reduce the need for additional staff.

Smart shelves equipped with weight sensors and computer vision detect when items are low or misplaced, automatically triggering restocking processes or alerting staff. This reduces out-of-stock scenarios and makes inventory manageable.

Health care

Wearable devices equipped with AI monitor vital signs and detect patterns that might indicate a problem before symptoms worsen. These insights are then shared with health care providers, enabling early intervention.

In hospitals, edge computing AI devices monitor patients in intensive care units. These systems process data to flag anomalies, supporting faster response times in intervention.

Diagnostic imaging is another area where AIoT is gaining ground. Devices embedded with AI analyze scans for signs of disease, accelerating diagnosis and supporting radiologists by flagging subtle indicators they might miss.

Agriculture

In agriculture, AIoT systems help farmers operate precisely and sustainably. Smart soil sensors monitor moisture, nutrient content and pH levels, sending this data to processors that determine optimal watering or fertilization schedules. This reduces the overuse of resources and helps crops grow under optimal conditions.

Computer vision applications also scan plants for signs of disease or pest infestation so that farmers use targeted interventions, preventing widespread crop damage.

Weather stations powered by AI models analyze local setting patterns and historical trends to recommend planting schedules or harvesting times. This leads to better crop yield, less waste and efficient processes.

Telecommunications

Telecom providers operate vast and decentralized infrastructure, making AIoT valuable for network visibility and uptime. AI-enabled edge monitoring systems track performance indicators to help provide reliable service to end users. These systems help automate routine diagnostics and preemptively schedule maintenance.

Additionally, smart energy systems manage consumption based on load and demand, supporting sustainability and lowering operational costs.

Energy

In energy and utilities, smart grids collect data and feed it into AI models that analyze consumption patterns, detect anomalies and make recommendations for efficiency improvements.

Distributed energy resources like solar panels or wind turbines use edge-based AI to optimize performance. For instance, processors might adjust the tilt angles of solar panels in response to changing light conditions.

Transportation and logistics

In autonomous or semi-autonomous vehicles, edge AI systems handle decision-making, detect obstacles, adjust routes and improve driver safety. These systems process visual and sensor data to avoid delays associated with sending everything to the cloud.

In warehousing operations, AI-driven systems manage inventory and adjust to supply demands, automate stock replenishment and optimize floor layouts. This boosts throughput without requiring constant human oversight.

Implement IoT AI solutions

Synaptics offers a comprehensive portfolio of advanced human interface solutions designed to help businesses implement AIoT. Our sensing, processing and connectivity systems help you create devices that respond in real time.

With robust connectivity options across wireless standards and protocols, we ensure your devices stay responsive and reliable wherever they’re deployed. Our solutions are flexible and scalable, and are optimized for low-power, high-performance settings.



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Research has shown that people who do not use AI technology more than those who are well aware of ar..

Published

3 hours ago

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September 4, 2025

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According to a study by the University of Southern California in the United States, “The less AI you understand, the more magical AI you feel.”

AI-generated image of a college student who is disappointed with low grades [Production = Gemini]

Research has shown that people who do not use AI technology more than those who are well aware of artificial intelligence (AI) technology. In addition, there are studies showing that excessive use of Generative AI tools such as ChatGPT is linked to lower academic achievement, and it is analyzed that dependence on AI should be vigilant.

According to a study by researchers at the University of Southern California in the United States and the University of Bocconi in Italy on the 4th, the lower the understanding of AI, the more often they accept it as magic and use it.

The researchers evaluated 234 university undergraduate students with their understanding of AI (literacy), then gave them writing tasks on a specific topic and investigated whether to use Generative AI tools.

As a result, students with lower scores in AI understanding showed a stronger tendency to use AI for tasks. The researchers analyzed, “People with low understanding of AI perceive AI like magic,” and “They are likely to be in awe when AI performs tasks that were thought to be a unique attribute of humans.”

Conversely, people with a high understanding of AI know that AI works based on computer algorithms, not magic, so they don’t rely too much on it.

In March this year, a study found that sincere students use fewer Generative AI tools, and that AI dependence can lead to a decrease in self-efficacy and academic achievement. The researchers investigated and analyzed the frequency of AI learning and self-efficacy of learning after the end of the semester in 326 undergraduate students.

Sundas Azim, a professor at SZABIST University in Pakistan who conducted the study, said, “In the case of tasks conducted by students relying on Generative AI, AI produced similar responses, resulting in less classroom participation or discussion activities.” As a result, students with more AI use tended to have relatively lower average GPA.

It is analyzed that services such as ChatGPT can be effective when they need immediate help in their studies, but can have a negative impact on long-term learning and achievement.



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Alberta Follows Up Its Artificial Intelligence Data Centre Strategy with a Levy Framework

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3 hours ago

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September 4, 2025

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Alberta is introducing a levy framework for data centres powering artificial intelligence technologies, the Province recently announced.

Effective by the end of 2026, a 2% levy on computer hardware will apply to grid-connected data centres of 75 megawatts or greater, according to a statement from Alberta.

The levy will be fully offset against provincial corporate income taxes, the government says. Once a data centre becomes profitable and pays corporate income tax in Alberta, the levy will not result in any additional tax burden.

Data centres of 75MW or greater will be recognized as designated industrial properties, with property values assessed by the province. Land and buildings associated with data centres will be subject to municipal taxation.

The framework was forged through a six-week consultation with industry stakeholders, according to Nate Glubish, Minister of Technology and Innovation.

“Alberta’s government has a duty to ensure Albertans receive a fair deal from data centre investments,” the provincial minister remarked. “This approach strikes a balance that we believe is fair to industry and Albertans, while protecting Alberta’s competitive advantage.”

Glubish added that the Alberta government is also exploring other options. This includes a payment in lieu of taxes program that would allow companies to make predictable annual payments instead of fluctuating levy amounts, as well as a deferral program to ease cash-flow pressures during construction and early years of operation.

“After working closely with industry, we’re introducing a fair, predictable levy that ensures data centres pay their share for the infrastructure and services that support them,” commented Nate Horner, Minister of Finance.

“This approach provides stability for businesses while generating new revenue to support Alberta’s future,” he posits.

The decision builds on the Alberta Artificial Intelligence Data Centre Strategy, introduced in 2024.

The strategy aims to capture a larger share of the global AI data centre market, which is expected to exceed $820 billion by 2030 as Alberta becomes a data centre powerhouse within Canada.

However, the Province’s tactics have not gone uncriticized.



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