by Csongor Fekete | Jun 12, 2025 | AI, Business, Machine Learning
The United Kingdom is making a bold statement in the AI landscape: it aims to become an AI maker, not just an AI taker. The recent collaboration with NVIDIA to establish a national AI Research Resource underscores this ambition. This initiative brings state-of-the-art infrastructure to UK innovators, empowering them to train and deploy custom AI models at scale.
Key takeaways from the article highlight the massive investment in NVIDIA’s advanced computing technology, including the latest H100 Tensor Core GPUs. These technologies will power everything from foundational machine learning model development to high-performance applications across industries. Importantly, the infrastructure is not limited to academic institutions—it’s designed to facilitate public and private sector innovation alike.
For companies operating in martech and customer experience, this signals an opportunity to leverage holistic AI consultancy services to enhance performance and customer satisfaction. Businesses can now affordably train domain-specific models tailored to their marketing needs—something generic AI tools often fall short of delivering. By developing in-house or partnered custom AI models, organizations can deepen their AI maturity and respond faster to customer insights.
HolistiCrm, for instance, can support businesses aiming to integrate this next-gen AI capacity into actionable solutions. A use-case may involve creating an adaptive Machine Learning model that personalizes marketing messages in real-time, based on customer behavior and sentiment. This can dramatically increase engagement, loyalty, and conversion rates, delivering tangible business value.
The UK’s AI infrastructure push, powered by NVIDIA, sets a high bar for performance-focused innovation and marks a global shift towards sovereign capabilities in AI development.
Read the original article: https://news.google.com/rss/articles/CBMiVkFVX3lxTE9nTU1XaWdBRjBRaE9VcXEwWFpOQi1ieHRkWnFYUlRVTEFycFFUUXRtYzM5WUFUM0tPVXh1VjZzMlNPenpRQklYQ2wtRm5sZEtEWlZGR0hB?oc=5
by Csongor Fekete | Jun 12, 2025 | AI, Business, Machine Learning
Recent research from Apple Machine Learning team, titled “The Illusion of Thinking,” presents a rigorous analysis of reasoning models in AI through the lens of problem complexity. The paper highlights a critical finding: even state-of-the-art models often deliver promising outputs but struggle with deeper reasoning when task complexity escalates. These models can give a perception of understanding (referred to as the "illusion of thinking"), even when actual reasoning capabilities may be minimal or inconsistent.
Key takeaways from the study include:
- Reasoning tasks are not uniform—models may perform well on simple problems but demonstrate significant drop-offs as complexity increases.
- The performance of AI models does not always correlate with actual reasoning—largely due to reliance on pattern recognition over logic-based deduction.
- Creating standardized benchmarks stratified by difficulty is essential for evaluating true reasoning performance in future research.
For marketers and martech leaders working with an AI consultancy or AI agency, these insights serve as a vital guardrail when deploying custom AI models. It underscores the importance of aligning Machine Learning model capabilities with problem complexity—particularly in predictive marketing, customer segmentation, and automated decision-making.
A powerful use-case lies in enhancing customer satisfaction scoring. Marketing teams often use AI to predict customer churn or sentiment. Applying holistic thinking and properly validating the model’s reasoning performance ensures the predictions are not only accurate on the surface but also grounded in logical causality. This directly impacts business value by avoiding misclassified customer intents, deploying better-targeted campaigns, and ultimately improving retention.
As marketing becomes increasingly AI-driven, the harmony between problem complexity and model design—not just raw performance metrics—will separate effective strategies from superficial ones.
Original article: https://news.google.com/rss/articles/CBMicEFVX3lxTE42SXFaQkRiT1YwQ2lVeXFRazFKcER4ZmhlRFBqYkU5ZzhRY09VR01UeHFGclFEc0ZjOWI4bkp3andkX1hIODVjS29NSHpZMlRZWkpUcG84NGNQdEpqQUw5OEZKVlZ3Wl91aUxpRDdFME4?oc=5
by Csongor Fekete | Jun 11, 2025 | AI, Business, Machine Learning
A recent breakthrough highlighted by the National Science Foundation showcases how a custom Machine Learning model can predict the onset of neurodegenerative diseases years before symptoms emerge. This predictive AI model leverages large clinical and molecular datasets to identify early biomarkers, optimizing both diagnosis and treatment approaches. The model’s performance in forecasting diseases like ALS and frontotemporal dementia offers a transformative window for intervention, potentially improving outcomes and lowering healthcare costs.
The key learning here is the power of predictive analytics in high-stakes domains. Translating this use-case into the martech or CRM landscape opens up substantial business value. For instance, a Holistic CRM platform enhanced with AI can harness historical customer data to forecast behavior, churn, or satisfaction dips long before they surface. Similar to how early biomarker detection enables preemptive healthcare strategies, custom AI models can proactively adjust customer engagement flows, marketing personalization, and sales interventions—boosting customer lifetime value and operational efficiency.
This exemplifies how an AI agency or AI consultancy can drive innovation by applying high-precision predictive modeling in new contexts. Just as foreseeing a disease alters clinical strategies, anticipating a customer's lifecycle opens the door to higher loyalty, retention, and marketing performance—key success metrics in today’s competitive digital economy.
Read the original article: https://news.google.com/rss/articles/CBMiigFBVV95cUxQYlFuZ1J0X2RHNVNUVURwSEMwdEJNZzBCMmRYeFJRbjRmRE0tNzc3QVhxTlc0M090QnNLQjRIN1BXd0JLT3pabFZaUDVYRXRnc2RpbWdqMWR4Wk9ZTEh6bnRtTFBsRFBUdTlsaGF1bno4STZ2UVJGQXFYNlB4X2hSdXRVQmhIXy1WRmc?oc=5
by Csongor Fekete | Jun 11, 2025 | AI, Business, Machine Learning
Artificial Intelligence continues to transform the boundaries of what’s possible in both science and business. A recent article in The Economist explores how a custom AI model was used to estimate the age of the Dead Sea Scrolls, demonstrating the potential of machine learning in unlocking historical mysteries. Researchers harnessed Machine Learning to analyze the chemical composition of salt crystallization on parchment paper, identifying aging patterns that would be impossible—or incredibly time-consuming—for humans to detect.
Key takeaways from the article include:
- The adoption of a Machine Learning model allowed researchers to work with fragmented, fragile data at scale.
- Custom AI models proved capable of generating high-precision outcomes in non-traditional domains.
- The project demonstrated how AI can complement, rather than replace, human expertise by offering actionable insights in complex, nuanced areas.
From a business perspective, this use-case carries profound implications for martech and AI consultancy. In marketing, a similar approach can analyze unstructured datasets—such as customer interaction logs or behavioral cues—to predict customer churn, segment audiences, or fine-tune campaigns for higher performance. Just as the AI system decoded subtle chemical signatures in ancient texts, a holistic Machine Learning strategy could extract latent behavioral signals to drive personalization and customer satisfaction.
AI experts and agencies deploying these methods can help businesses build domain-specific, custom AI models that enhance decision-making and turn raw data into tangible business value. AI is not just about automation—it's about augmentation, improving how teams interpret complex information and bring strategic clarity to pressing problems.
original article
by Csongor Fekete | Jun 10, 2025 | AI, Business, Machine Learning
As the deployment of AI agents accelerates across industries—from customer service to marketing automation and sales ops—the need for reliable evaluation frameworks becomes mission-critical. IBM Research’s latest article, "The Future of AI Agent Evaluation," dives deep into how current evaluation methods fall short in capturing the dynamic capabilities of modern AI agents and proposes a more holistic approach grounded in real-world adaptability, context-awareness, and task generalization.
Key takeaways from IBM Research's findings include:
- Traditional benchmarks are too rigid, often missing the nuance of how AI agents perform in complex, evolving environments.
- Future evaluation models must incorporate metrics beyond simple accuracy—such as contextual reasoning, adaptability, and interactive decision-making.
- Simulation-based testing and continuous learning environments are essential to evaluate not just if an AI agent performs, but how well it learns and evolves over time.
For businesses, particularly in martech and CRM, these insights underscore the importance of designing custom AI models that are not merely accurate, but also robust, scalable, and adaptable to real-world customer contexts. At HolistiCrm, a focus on holistic Machine Learning model evaluation creates transparent, high-performance systems that enhance customer satisfaction and return on investment.
A compelling use-case aligned with this research would be the deployment of adaptive AI agents in customer support systems. By embedding agents that continuously learn from interactions and get evaluated against real-world behaviors—not just static datasets—organizations can reduce resolution time, elevate service quality, and increase long-term customer loyalty. A holistic AI consultancy approach ensures these AI agents are continuously refined for business relevance and performance optimization.
AI evaluation frameworks are no longer just academic exercises—they are strategic levers that determine the commercial success of intelligent systems.
Read the original article: https://news.google.com/rss/articles/CBMiXkFVX3lxTFBFcjNqcWt3cG1aMWRocFZMR0FLNVZSMHZIVTdlUExlSmZnM1cxTWFfbHpmWld5VE5ZZHBkdzlHU3NDUFNzUlF3U19BTDdTMEZmQmdEOUZDNVZBc1ozdHc?oc=5 (original article)
by Csongor Fekete | Jun 10, 2025 | AI, Business, Machine Learning
AI is transforming the landscape of market research, delivering speed, scale, and cost-efficiency unlike ever before. According to Andreessen Horowitz's article, “Faster, Smarter, Cheaper: AI Is Reinventing Market Research,” businesses are now using generative AI and custom Machine Learning models to gain rapid consumer insights at a fraction of traditional research costs.
Key takeaways include:
- Traditional market research is slow, expensive, and often limited to small sample sizes.
- Generative AI enables companies to simulate consumer responses and generate qualitative insights in hours, not weeks.
- AI-powered market research tools harness massive datasets, reducing bias and increasing accuracy.
- Custom AI models allow companies to tailor output specific to their brand voice or target audience – addressing one-size-fits-all limitations of legacy tools.
- Startups are leading the charge in building holistic martech stacks that integrate AI into every stage of research and marketing execution.
For business leaders, the practical value here is clear. Integrating AI into customer understanding workflows means not only enhanced performance in marketing campaigns but also smarter product decisions informed by dynamic, real-time feedback loops. AI consultancy services like those at HolistiCrm can help organizations build proprietary models that reflect their unique customer base, brand positioning, and go-to-market strategy.
A concrete use-case lies in leveraging a custom AI model to conduct ongoing audience sentiment analysis. Instead of quarterly surveys, a marketing team can tap into scalable point-in-time insights to continuously optimize messaging, increase customer satisfaction, and improve conversion across channels — all while drastically reducing research spend and turnaround time.
The future of martech is personalized, fast, and data-rich. Holistic integration of Machine Learning models isn’t just a tech upgrade — it’s a strategic imperative.
Original article: https://news.google.com/rss/articles/CBMiTEFVX3lxTE0wTTJQMXZFNjlZaVlXMlBlUmJDZnNHNEpVVW8tMjJDZS1HZ1JIdmFEMXJmSUEwWkVPTEN1c0QzaFdpYlVmdHI3TWJSdkU?oc=5
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