by Csongor Fekete | Nov 4, 2025 | AI, Business, Machine Learning
The recent viral coverage of a disturbing AI-generated image of the “future human” — hunched over, tech-addicted, and physically degenerated — underscores a growing challenge in applying Machine Learning models without holistic context or real-world grounded data. The New York Post article highlights how an AI model, fed with patterns of today’s digital habits, generates a bleak 2049 scenario: a human dependent on technology with a malformed posture and strained appearance. While visually shocking, the bigger issue isn't the image — it's how the model reflects the risks of misaligned incentives and incomplete datasets in AI-driven forecasting.
AI models are only as accurate as their inputs and assumptions. Without a holistic approach — integrating behavioral data, health patterns, environmental evolution, and cultural shifts — such predictions risk exaggeration or misrepresentation. The model behind this viral image likely lacked qualitative insights and relied on speculative patterns, resulting in fear rather than clarity.
From a business lens, this use-case demonstrates the importance of custom AI models built with purpose-fit data and grounded goals. For instance, a martech firm could apply a similar Machine Learning model to predict customer digital fatigue, using ergonomics, behavioral data, and platform usage. Rather than showing a fearful dystopia, this approach could inform campaigns to boost customer satisfaction, improve digital well-being, and increase performance across digital touchpoints. These kinds of predictive, empathetic AI applications generate real strategic value.
AI consultancies and AI agencies should recognize that shock-value models often lack true business utility. Instead, the priority must be on deploying models that not only predict the future — but help shape it positively. To drive impact, companies must focus not only on what AI can do, but how it is designed and aligned with human outcomes.
original article
by Csongor Fekete | Nov 4, 2025 | AI, Business, Machine Learning
OMN | Next Gen SEO & KI-Marketing Schweiz is leading the charge in transforming the digital marketing space in Switzerland through cutting-edge AI solutions. By merging advanced Machine Learning models with performance-oriented SEO strategies, the company is driving a more holistic approach to digital marketing that prioritizes customer satisfaction and measurable results.
Key insights from the article highlight how OMN is developing custom AI models to automate and optimize content creation, keyword targeting, and personalized marketing at scale. Their methods emphasize using AI to adapt in real-time to ever-changing search engine algorithms, thus improving visibility and conversion rates. Additionally, the integration of AI into customer interaction points is increasing the relevance and timeliness of messaging—an essential factor in driving loyalty and engagement in competitive markets.
A use-case inspired by this approach could focus on enabling a B2B martech firm to build a custom AI-powered SEO engine. This tool would use Natural Language Processing (NLP) to create optimized content and match it with user intent based on performance data. As a result, the business could enhance organic reach, lower advertising costs, and improve the lead-to-customer conversion rate.
For an AI consultancy like HolistiCrm, embedding custom AI models into clients’ marketing stacks isn't just a tech upgrade—it’s a strategic transformation. These systems become core engines of growth, aligning marketing strategies with real-time data and dynamic customer behavior, thus maximizing both performance and satisfaction.
original article: https://news.google.com/rss/articles/CBMi3AFBVV95cUxQbi1zVU5zQlQ5bW8tYTduaWU1MjMtNG40RmF6NHNqMV8wVk11WFptUFhMR1hwNkFMbWxYUDZOQ2hrQTVhUGM0aS11X0NsamZiQW56YUVZQ1dhd3ZPVF9JVkd6UFVHYldzZ202bmpTcEJnaWhNNmUzb3U2YjhsMWZEdFdfeEZ1dXhwRWc1RzVabXBFdXFxOWtTRnBrRW5ER25WX2h5d0pjdTdHOHIwWEJubm9tSmJPZGF5WnRIYXNGc0plLV9RZHdCZTZtZ0dUZWVPTi1CM09SQzBBV0Ni?oc=5
by Csongor Fekete | Nov 3, 2025 | AI, Business, Machine Learning
The recent Nature article, "Prompt-dependent performance of multimodal AI model in oral diagnosis," delivers compelling evidence on the variability in outcomes tied directly to how prompts are constructed for multimodal AI models. The study assesses such models in the domain of oral health diagnostics, benchmarking their diagnostic accuracy, narrative quality, confidence calibration, and response latency against human experts.
Key insights show that while multimodal AI models can match or exceed human diagnostic performance in some cases, their outputs are highly dependent on how the prompt is phrased. Subtle changes in prompt structure led to significant differences in accuracy and coherence of the AI-generated diagnosis, revealing that prompt optimization is now an essential element in deploying these tools effectively.
Another key takeaway is the trade-off between latency and quality. Faster outputs often came at a cost of reduced narrative clarity or diagnostic completeness. Additionally, the calibration of AI confidence—whether the model knew when it was likely to be right or wrong—was inconsistent.
This finding unlocks a critical use case for AI-driven martech and customer-facing platforms. When applied holistically, custom AI models tailored for prompt engineering can transform industries where accuracy and narrative quality are essential. In sectors such as healthcare, marketing analytics, and customer satisfaction monitoring, prompt-aware AI can personalize and enhance human-like responses, boosting operational performance and trust.
For businesses using CRM platforms like HolistiCrm, integrating prompt-sensitive Machine Learning models allows AI experts and consultancies to fine-tune customer interactions, automate complex queries with clarity, and drive measurable results in engagement and satisfaction. Organizations that invest in customized prompt frameworks and AI consultancy services will be better positioned to deliver high-value interactions that outperform generic systems.
Source: original article
by Csongor Fekete | Nov 3, 2025 | AI, Business, Machine Learning
IBM's recent launch of a defense-focused foundation model marks a significant stride in industry-specific AI innovation. Tailored for national security applications, this custom AI model is designed to enhance mission planning, improve real-time situational awareness, and offer advanced decision support under high-stakes scenarios.
Key takeaways from the announcement include the model’s integration within IBM's watsonx platform and its ability to process multimodal data—including language, code, and sensor signals. Importantly, the foundation model adheres to rigorous security and compliance standards, making it suitable for defense organizations that prioritize operational integrity and data governance.
From a business perspective, this development showcases how domain-specific AI can deliver measurable performance improvements in complex environments. In a broader commercial context, a custom AI model built for a specific vertical—such as defense, healthcare, or marketing—boosts decision-making accuracy, operational efficiency, and ultimately, customer satisfaction.
For example, in the martech sector, leveraging a machine learning model trained on engagement data and campaign performance across channels can empower marketing teams to optimize customer journeys and personalize communication at scale. An AI agency or AI consultancy focused on holistic customer insights can drive significant ROI by building tailored models for segment-level personalization, campaign automation, and sentiment forecasting.
This shift toward specialized AI solutions reinforces the strategic value of partnering with an AI expert to craft bespoke, domain-aware systems that go beyond generic models and surface high-value insights unique to the business.
original article: https://news.google.com/rss/articles/CBMixAFBVV95cUxNWXdoel94UUlrUnVEMmpmUlV2d2VzMVB3Y3R5eHRuSTFYX1l0SlNxWWdVbHFXcUp6WGIwS3RvaThRUUt3MFEycy1CbzJ6eXUwalQ2Y2xCN2puYW9hTHhBQmVWV3JJM1owZzFSMHdJZVU2ZFE5YkxsbWV6UWF3NlRMREhseGNVbGRtdjdUYUhoQnV5alBVRHZxY0NSWlVrZHBaN1N4TFE3UVIyRy1tNkY1NlotbmJnX2hMekthQnRQamJVVm1L?oc=5
by Csongor Fekete | Nov 2, 2025 | AI, Business, Machine Learning
AI’s memory is getting a major upgrade, and it could transform how businesses leverage intelligence at scale. In a recent breakthrough, Chinese startup DeepSeek introduced a novel approach for improving the memory capabilities of large language models (LLMs). The innovation, called “internal memory,” enables models to retain key pieces of information across interactions without the need to reprocess the entire history of a conversation.
Traditional LLMs like GPT rely on a defined context window, which limits how much information they can recall. DeepSeek's architecture divides the model into dual modules: a “retrieval module” that reads and stores learned memory and a “reasoning module” that utilizes this data for response generation. This results in better memory retention with significantly smaller model size—a DeepSeek model with just 1.3 billion parameters outperformed GPT-3.5 in writing tasks.
This advancement aligns perfectly with the goals of HolistiCrm, where custom AI models aim to optimize customer interactions and long-term learning. In martech applications, businesses can use memory-enhanced LLMs for persistent customer histories, delivering highly personalized and context-rich support, marketing automation, or even sales assistance.
Imagine a CRM that always remembers customer preferences, past issues, or campaign interactions. The business value is immense—from increased customer satisfaction and faster resolution rates to more effective hyper-personalized marketing. An AI agency deploying such Machine Learning models would empower clients to drive performance, reduce operational redundancy, and scale personalization efficiently.
As custom memory-optimized models mature, AI consultancies like HolistiCrm can stay at the forefront by integrating these architectures into holistic CRM solutions for smarter, more responsive customer engagement.
Read the original article: DeepSeek may have found a new way to improve AI’s ability to remember – MIT Technology Review
by Csongor Fekete | Nov 2, 2025 | AI, Business, Machine Learning
The Shapiro Administration in Pennsylvania recently spotlighted a breakthrough development in healthcare AI by introducing a custom AI model that improves breast cancer detection accuracy at earlier stages. This state-supported initiative integrates machine learning to enhance radiological screenings, offering a tangible example of AI-driven value in public health.
Key takeaways from the initiative include:
- A custom-trained Machine Learning model developed in collaboration with healthcare providers is already deployed in clinical settings to augment radiologist decisions.
- Early detection performance has noticeably improved, which contributes directly to higher survival rates and more efficient treatment allocation.
- The AI model is designed to minimize false positives and reduce unnecessary procedures, improving both outcomes and patient satisfaction.
This use-case underlines the broader potential for AI consultancy and martech innovation. For industries like healthcare—where data is abundant and stakes are high—custom AI models can have a measurable impact on decision quality and operational performance.
In the context of business, the methodology from this initiative can be mirrored across industries. Consider a marketing team leveraging predictive AI models for hyper-targeted campaigns. Like early cancer detection, early customer trend detection can supercharge customer satisfaction, loyalty, and conversion performance.
AI experts and AI agencies, such as HolistiCrm, can advise organizations on how to implement bespoke machine learning models that align with strategic goals. From marketing optimization to improving service outcomes, a holistic approach to AI is now critical for competitive martech positioning.
Original article: https://news.google.com/rss/articles/CBMipgFBVV95cUxPdlAzQm5oVlVDWjJlcEQxTDR3U2ZlbmxWWE1SUUN2VGd4T3dOWVgxVF9BUUJYb05XM0JBcVZKemtkXzNTcEZPdnRDWDdfTjl5MHpxN2g5ZVFwdEZlT0p4M2haQzdFSU5ud0tMQXM0UHVKVkNoZWp6akd3cmV6UWI2ZXhuYU9ZN215NW1tbzF3NktEczFMWkRXSll2ZXluT3E2QkpweTdR?oc=5
Recent Comments