Creating AI-driven journalism using neurosemantic methods involves integrating artificial intelligence (AI) technologies into the process of content creation, analysis, and distribution. This allows for the automation of routine tasks, improves journalists’ efficiency, and enables the creation of personalized content for audiences. Below are the key steps and approaches to integrating AI into journalism:
1. Data Collection and Processing
AI can significantly simplify the process of gathering information by analyzing large volumes of data, including text, visual, and audio materials:
• Utilizing natural language processing (NLP) algorithms to analyze texts, identify key topics, and determine the tone of the text.
• Using tools for automatic transcription of interviews (e.g., Whisper) or analyzing large datasets, as seen in the “Panama Papers” investigation.
2. Content Generation
AI can create texts based on structured data:
• Automatically generating news about weather, finance, or sports.
• Producing headlines, subheadings, and summaries for articles using models like ChatGPT.
• Creating visual content through generative models such as Midjourney or Stable Diffusion.
3. Fact-Checking and Combating Misinformation
Neural networks assist journalists in verifying the accuracy of information:
• Tools like Factfox can analyze data for fake news and biases.
• Using algorithms for cross-checking information from multiple sources.
4. Content Personalization
AI can adapt content to suit the interests of specific audiences:
• Recommendation systems suggest articles to readers based on their preferences.
• Generating brief news summaries to attract younger audiences, similar to how NRK uses GPT models.
5. Ethical Considerations and Control
Successful AI implementation requires addressing ethical concerns:
• Developing editorial policies on AI usage to prevent bias and unethical applications.
• Editors reviewing AI-generated content to ensure quality and accuracy.
6. Implementing Neurosemantic Methods
Neurosemantics enables AI models to better understand text meaning:
• Using large language models (LLM) like GPT to analyze complex relationships in data and generate coherent texts.
• Applying algorithms to detect hidden patterns in data and create new storylines.
7. AI Tool Integration in Editorial Work
For successful AI-driven journalism, it is crucial to train staff to work with new technologies:
• Conducting training workshops on AI tools (e.g., “Yandex Neural Networks for Media Work” workshop).
• Developing customized AI models for specific editorial tasks.
The use of neurosemantic methods in journalism not only speeds up content creation but also enhances its quality through deep data analysis and content personalization. However, collaboration between journalists and AI remains key—technology handles routine tasks, while humans retain creative and analytical roles.
Neurosemantics and AI
Neurosemantics is an approach to data processing where information is structured in the form of semantic maps and knowledge graphs. By leveraging neural networks and artificial intelligence methods, these systems can not only store data but also understand its meaningful connections. This allows AI to generate contextually relevant messages that can influence perception and beliefs.
Methods of Influencing Cognitive Processes
AI can employ several methods for neurosemantic persuasion:
• Natural Language Processing (NLP): AI analyzes textual data to tailor messages to an individual’s language, communication style, and preferences.
• Personalization: AI systems can offer personalized recommendations or arguments based on behavioral analysis.
• Reasoning Modeling: Logical and hybrid approaches enable AI to create persuasive arguments using data from semantic networks and expert systems.
Application in Neurosemantic Programming
Neurosemantic programming (NSP) is a modern method of influencing human cognition using AI. It involves:
• Developing models of human behavior.
• Influencing decision-making through tailored stimuli.
• Utilizing cognitive maps to predict human responses to specific triggers.
Examples of Application
1. Medicine
• Diabetes Diagnosis Through Breath Analysis: A neurosemantic knowledge base helped correlate acetone levels in breath with blood sugar levels, enabling a painless diagnostic method.
• Neurosemantic Brain Maps: fMRI research has led to the creation of neurosemantic maps linking text semantics to specific brain activity regions, with potential applications in neurosurgery and speech rehabilitation.
2. Marketing
• Neurosemantic Marketing: Personalizes user interactions by considering their mood, context, and time of day.
• ZMET (Zaltman Metaphor Elicitation Technique): Used by leading brands (Coca-Cola, Toyota) to uncover subconscious consumer needs and develop effective strategies.
3. Education
• Adaptive Learning Technologies: Neurosemantics analyzes texts and develops educational materials tailored to students’ cognitive abilities, enhancing learning outcomes.
4. Technology
• Innovative Knowledge Bases: Neurosemantic databases enable the discovery of associative links between various disciplines, aiding technological innovation and interdisciplinary research.
• Human-Robot Interaction: AI utilizes neurosemantic models to improve speech comprehension by analyzing eye movements and communication context.
5. Social Technologies
• Big Data Analysis: Neurosemantics is actively used for sentiment analysis in social media, allowing companies to develop more precise communication strategies and predict user behavior.
These examples highlight the broad applications of neurosemantics in solving complex problems that require a deep understanding of meaning and cognitive processes.
Among the research centers mentioned, the following are actively integrating AI into their work for future AI-diplomacy or AI-journalism:
1. DiploFoundation (AI and Diplomacy)
- DiploFoundation is at the forefront of exploring AI's role in diplomacy. Their research focuses on three key areas:
- AI as a tool for diplomatic practice: Supporting decision-making, analyzing trends, drafting speeches, and aiding negotiations.
- AI as a topic for diplomatic negotiations: Addressing global norms, ethical considerations, and safety challenges in AI governance.
- AI shaping the diplomatic environment: Understanding how AI influences the geopolitical context in which diplomacy occurs.
- They have partnered with institutions to map AI's challenges and opportunities in diplomacy, including its human rights implications and ethical dimensions[4].
2. Israel's Digital Diplomacy Division
- Israel is leveraging generative AI for digital diplomacy. For example, its Foreign Ministry uses AI tools to create multilingual digital avatars for communication. This enhances outreach by allowing diplomats to deliver messages in multiple languages without requiring fluency.
- Such applications demonstrate how AI can augment traditional diplomatic practices by enabling broader and more personalized engagement.
3. International Research Centre on Artificial Intelligence (IRCAI), Slovenia
- IRCAI focuses on advancing AI research under UNESCO's auspices. While its primary mission includes addressing societal challenges like climate change and health, it also emphasizes ethical AI governance and multi-stakeholder collaboration, which are critical for shaping future frameworks in AI-diplomacy.
4. Nigerian Media (AI in Journalism)
- A study on Nigerian media outlets like LTV and TVC highlights the growing use of AI in journalism. Applications include data analysis, automation of news gathering, and content creation. However, ethical concerns such as bias and transparency remain challenges[12].
- This reflects a broader trend of integrating AI into journalistic workflows to enhance efficiency while addressing accountability issues.
5. SOAS Centre for International Studies and Diplomacy (CISD)
- While primarily focused on global diplomacy education, CISD integrates cutting-edge technologies into its programs. This includes exploring how digital tools like AI can transform international relations and policy-making.
6. Loughborough University London (IDIA)
- The Institute for Diplomacy and International Affairs examines the impact of digital technologies, including AI, on diplomacy. Their interdisciplinary approach addresses trust in negotiations and other critical areas where AI can play a role.
These centers demonstrate significant potential for advancing both AI-diplomacy and AI-journalism through research, education, and practical applications.
7. Department of Government Efficiency (DOGE): Elon Musk leads this newly formed entity under Trump’s administration. While not a traditional research center, it functions as a platform for Musk to influence U.S. governance and foreign policy, often aligning these with his business interests. For example, Musk has used this role to advocate for deregulation and restructuring of federal agencies, which has implications for international business relations. One of the methods – AI influencing.
Bogoljub Karic
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