by Csongor Fekete | Jul 13, 2025 | AI, Business, Machine Learning
In a recent study highlighted by Yale Insights, five leading AI models were tested on their ability to fact-check public statements—using several of Donald Trump’s claims as the benchmark. The research exposed significant gaps in performance, accuracy, and consistency. While the models often offered factual corrections, they frequently failed to identify misinformation comprehensively or confidently, with results varying across platforms and depending heavily on prompt phrasing.
Key learnings include:
- AI models show promise in real-time content verification but currently lack consistent precision.
- Sensitivity to prompt construction influences the validity of responses, raising questions about model reliability.
- Transparency of data sources and confidence scores are crucial for trust in AI-generated outputs.
- There is noticeable variance between general-purpose models and those fine-tuned for specific tasks.
The implications for martech and business strategy are significant. For brands handling large-scale content—blogs, social media, or customer communications—implementing a custom AI model trained specifically to detect misinformation can build trust and protect brand reputation. This is particularly relevant in political marketing, healthcare, and finance, where false or misleading claims have serious consequences.
At HolistiCrm, integrating such targeted Machine Learning models into marketing workflows helps validate claims before distribution, thereby increasing customer satisfaction and compliance. AI consultancy services become essential here—not just for deploying tools, but for refining them to align with business goals, datasets, and tone.
A real-world use-case: a political campaign or advocacy group using a custom fact-verification tool to validate public messaging in real time. This ensures holistic communication, reduces reputational risks, and enhances messaging performance. With a reliable internal content validation system, businesses can scale outreach while maintaining accuracy and integrity.
Read more in the original article.
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by Csongor Fekete | Jul 12, 2025 | AI, Business, Machine Learning
Mayo Clinic researchers have developed a custom AI model capable of detecting surgical site infections (SSIs) through patient-submitted photos. This Machine Learning model, trained on a dataset of over 1,000 images, achieved 91% accuracy and outperformed both patients and non-infectious disease clinicians in identifying infections. The model was designed to help patients and care teams identify early signs of infection and intervene quickly, reducing complications and the burden on healthcare systems.
This use of AI highlights the power of combining holistic health data ecosystems with visual recognition models to drive better care outcomes. Notably, the system maintains high performance across diverse lighting and image quality conditions—an essential factor in real-world patient photo submissions.
The application of custom AI models in healthcare illustrates how AI consultancies and martech platforms can build decision-support models that scale human expertise. For AI experts and AI agencies, similar approaches can be extended to other image-based diagnostics in areas like dermatology, wound care, and chronic condition monitoring. Beyond healthcare, industries seeking to elevate customer satisfaction through proactive service interventions can draw parallels from Mayo Clinic’s results.
For CRM and martech providers like HolistiCrm, the key takeaway lies in empowering customer journeys with accessible, automated insights. Whether it's visual detection of product use issues or personalized service optimization, integrating Machine Learning models that analyze real-time visual data can unlock business value while increasing customer satisfaction and loyalty at scale.
original article: https://news.google.com/rss/articles/CBMi5gFBVV95cUxNTWtzdUxrcFJQYUM0VkJURThxX2xRdTREZzZFSFdYMFRGWmJWRnFYWDBPb19ZYkw5d1RSY1hUREFycUNIa01OUmpibDAyeXRlZk05QTcxV285bU5CVnNadlk3cTdpWTMwbWRlbkJwSzR1and0NDFoY3VESzE3WHA4VDAwSlpPOVNwMWtMTVdJNjFVelpkRjVleExoZnlqaTFHaVB5VFJwSlZnX3VFU2FKUGpEQU0ycTdPT2k0elFtU2Nud2xBeFlQdk1PMWN2TndNTnBQUFhnY2ZaYWMzeldjRGNCeVpRUQ?oc=5
by Csongor Fekete | Jul 12, 2025 | AI, Business, Machine Learning
A recent study explored the unexpected ability of ChatGPT to perform spacecraft piloting tasks with remarkable precision, a function typically reserved for highly-specialized machine learning systems in aerospace. In test simulations, the large language model responded effectively to complex commands, adjusting spacecraft trajectory and orientation autonomously. While not originally designed for this use-case, early indications suggest that generalist AI models like ChatGPT can adapt to highly technical environments through carefully structured prompts and scenario-based planning.
The core learning from the article is that natural language models can serve as interfaces for complex control systems, even in mission-critical applications like aerospace navigation, when paired with the right data and environments. This insight opens up a range of martech and commercial opportunities far beyond space travel.
In sectors like marketing or customer service, this innovation signals how custom AI models can be deployed in business-critical environments outside of traditional use-cases. For instance, a Holistic AI agency approach could leverage similar prompt-based reasoning engines to optimize marketing performance—automating campaign orchestration, interpreting analytics on the fly, or even adjusting real-time bidding strategies by modeling human-like decision making under pressure.
A specific martech use-case involves deploying a Machine Learning model via a natural language interface to dynamically coordinate multi-channel promotional activities based on customer engagement signals. Much like spacecraft navigation, campaign steering involves reacting rapidly to changing variables—click-through rates, conversion windows, and customer satisfaction metrics. Embedding a custom AI model trained on brand-specific performance data allows marketing teams to focus on strategy, trusting the AI for tactical execution.
This application can dramatically increase business value by reducing decision lag, decreasing operational costs, and improving customer satisfaction. As the article illustrates, the potential extends far beyond its original realm, making the case for AI consultancies like HolistiCrm to push innovation into high-performance marketing systems.
Read the original article: https://news.google.com/rss/articles/CBMixgFBVV95cUxOWUlERmJ6V2JVaG9MOWZlY1BwUTcwRXp5eHg0VG1yYUlxOWFNMTdNOHJxWUhrekdDMVM0QXF3Mmd4NlRmYUdiRFdPaU5UZ1ZYQjJ6aWxJOTZEbTZiUUhGQXdVVUhkbEJwVTNpVldjMXZuSFZXYUtteDdhUUhLR3VwOEVDYnF3OU9nT0V5a0tuQUxKOHdTZHlmZXFURXhuQVZZZWE3cm00akhGbVQ5UVhoWWdBY0JmNjNMN1dFSUVuSk9LM3lQLWc?oc=5
by Csongor Fekete | Jul 11, 2025 | AI, Business, Machine Learning
AI Innovation at UNC-Chapel Hill: Charting the Future of Custom AI Models
AI research at the University of North Carolina at Chapel Hill is pushing the boundaries of how Machine Learning and AI can solve real-world problems. The university’s AI initiative is not only investing in technological advancements but also fostering interdisciplinary collaboration, marking a holistic approach to AI development.
Key highlights of the article include:
- Building custom AI models through interdisciplinary teams combining medicine, public health, law, and engineering.
- Prioritizing ethical AI design and development rooted in fairness, privacy, and transparency.
- Developing AI use-cases with social impact, especially in healthcare, policy-making, and community engagement.
- Significant investment in AI infrastructure and talent to boost research performance and innovation.
This focus on responsible, tailored AI model creation opens critical opportunities for businesses as well. For instance, marketing departments can benefit from ethically trained predictive models that enhance customer segmentation and campaign effectiveness. A healthcare provider can use a custom model for patient triage prediction, improving customer satisfaction, resource efficiency, and compliance.
A similar use-case in a martech context can involve organizations partnering with an AI consultancy or AI agency like HolistiCrm to develop vertical-specific Machine Learning models. These models—trained on internal customer data—can boost marketing automation, personalize content delivery, and improve sales outcomes while maintaining ethical AI practices.
Investing in AI research that aligns with performance and societal value can give businesses a substantial edge, both in technology adoption and customer trust.
Read the original article: https://news.google.com/rss/articles/CBMiX0FVX3lxTE00ZHNCQzc0d3JxTF9JZlZIRVpoVDY2dG5DWEtXUWh6SG5YUVk1X244MXRpeG9jSXB2M1h6RXNob0FLQjJncWJCT0V0YWlTUDdrc2RWemkxY1JQZkxvMENJ?oc=5.
by Csongor Fekete | Jul 11, 2025 | AI, Business, Machine Learning
As AI continues to reshape industries, we are witnessing a new wave of career shifts and strategic pivots. In the recent Business Insider article titled “I Turned Down a Graduate Program Offer As AI Destroys My Industry,” the author shares a personal reflection on how generative AI has rapidly disrupted the writing and journalism space, influencing their decision to decline a traditional career path in academia and journalism.
Key takeaways from the article include:
- The impact of generative AI models like ChatGPT on jobs once considered uniquely human and creative.
- A growing realization that industries such as content writing and media are being redefined by automation, reshaping conventional career trajectories.
- The importance of upskilling, adaptability, and embracing AI tools rather than resisting their adoption.
This shift presents a critical learning moment for businesses, especially in marketing and martech domains. Holistic marketing strategies that integrate AI are not just a competitive advantage—they're imperative for survival. Businesses can harness custom AI models, developed with the guidance of an AI expert or AI consultancy, to augment or replace manual content creation, sentiment analysis, campaign optimization, and lead segmentation.
A relevant use-case: A digital marketing agency facing rising content production costs and tightening margins integrates a custom Machine Learning model to generate personalized marketing copy at scale. By doing so, the agency reduces labor costs, improves campaign speed, and boosts customer satisfaction through highly targeted messaging—ultimately increasing overall performance.
As the landscape evolves, AI implementation isn’t about replacing people; it’s about reimagining processes that better align with future market demands. An AI agency with a holistic approach can help organizations navigate this transformation strategically and sustainably.
original article: https://news.google.com/rss/articles/CBMikgFBVV95cUxQeWVBTkJwYmJCOEdCb0wwcVpPNnpvLWQ2bWZSREY2M01kcXlhXzFRQWdMSDhnaXRjYVpvMFFIT2RJeGV0TTJJREJ4Mld1NWJYUFpOajUxcGpiWlJ4eF9EMG5JQ0FBclpVVzNxbzI3M0NKckxOclF3Ul9BQVIxQmJRdlNwbGdpMm95QzJjakwwLWxtZw?oc=5
by Csongor Fekete | Jul 10, 2025 | AI, Business, Machine Learning
Scientists from the National Oceanic and Atmospheric Administration (NOAA) and Google are collaborating to enhance the accuracy and speed of hurricane forecasting using advanced AI technology. This partnership focuses on leveraging Google's machine learning capabilities to improve the NOAA’s Global Forecast System (GFS), which plays a crucial role in predicting weather events critical to public safety and economic resilience.
The initiative integrates custom AI models trained on decades of atmospheric and satellite data. These models are showing promising results, already outperforming traditional weather prediction systems for short-term forecasts. The project targets improving the fidelity of intensity and trajectory forecasts of hurricanes—especially vital as climate change increases the frequency and severity of extreme weather.
Key learnings from this collaboration include:
- Custom AI models can significantly outperform legacy systems when trained with domain-specific datasets.
- Speed gains from AI reduce the forecasting cycle time, enhancing decision-making for emergency responses.
- Cross-sector collaboration amplifies innovation by combining scientific data with cutting-edge martech solutions.
For businesses, especially those in logistics, insurance, and utilities, similar AI use-cases can provide resilient infrastructure against climate risks. For example, a holistic Machine Learning model developed by an AI consultancy could help forecast disruptions in supply chains due to severe weather events, minimizing operational downtime and improving customer satisfaction.
In the broader context, this is a clear signal that AI agencies and AI experts should be integrating environmental modeling into their solution portfolios. Delivering predictive performance through tailored AI not only addresses high-impact global challenges but also opens new avenues for business continuity planning and long-term value creation.
Original article: https://news.google.com/rss/articles/CBMikgFBVV95cUxOemhLQ2Y0ZWpqeUVQaC1OUVRILXVSS3l1WW9rQXZzY2pTSXgyUUlheVNSc0pST25lSEdVSGxqUllzcnkzZ05GUVY4UVhUNjViVDA2MEFGcEduNy1CSy1kTXlvbW12WFhHMExUTlFBUXlTNlE2eTd3enBFQ002MjdsejlNenc3MUpKZWgyWmpvVVZJQQ?oc=5
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