by Csongor Fekete | Sep 22, 2025 | AI, Business, Machine Learning
Imagine a world where your health risks are forecasted as accurately as tomorrow’s weather. That’s the premise explored in the recent BBC article, “AI can forecast your future health – just like the weather.” Researchers are developing AI systems capable of modeling human health in a dynamic, predictive way—by treating the body like a constantly shifting system, similar to weather patterns.
Key takeaways from the article highlight how machine learning models are being trained on real-time health data such as heart rate, temperature, and activity levels. These algorithms, like those being pioneered at King’s College London, can forecast health events, disease risk, or mental health states days or even weeks in advance. This marks a shift from static, one-off diagnostics to holistic, data-driven dynamic modeling.
From a business perspective, this concept has powerful implications far beyond healthcare. In the martech and customer experience domains, organizations can adopt similar Machine Learning models to forecast customer behavior, retention risks, or satisfaction shifts in real time. For customer-centric companies, creating custom AI models that interpret behavioral data—clickstreams, support tickets, survey results—can deliver predictive insights that drive timely, personalized interventions.
A practical use-case could involve a martech company deploying a real-time predictive model to gauge churn risk across its CRM. Much like the health forecasts in the article, the system continuously evaluates customer signals to anticipate when engagement is declining or needs are evolving. An AI agency like HolistiCrm could configure this solution holistically, integrating marketing, service, and behavioral data streams for high performance.
The result is enhanced satisfaction, better-targeted marketing, and optimized resource allocation—a clear business value that mirrors the transformative potential of AI in healthcare.
Read the original article here: https://news.google.com/rss/articles/CBMiWkFVX3lxTE4xNUF3LTJETlNHMjhreUtNUGlnYl83LWJUOWFndFlDY1NyZUpqYkVITDl2eXlHWE9jQnR1Vi00RG9YMEdIX0tGTVVMTC1ERl9yVHp5U2ZqbWY5d9IBX0FVX3lxTE51ZHdVSDE1emlKNFJXTEdQbG9lLVdpdllvQnlMUDQ0SEU5OFdGTnZNWjUtYXE4d1YyN21zdmtPRnc4Q0dTOExsSHh5eDJiMldZZ0YwZkFQVEppdzE4amNN?oc=5 (original article)
by Csongor Fekete | Sep 22, 2025 | AI, Business, Machine Learning
China’s AI sector is reaching new frontiers with the release of DeepSeek, a large language model developed with transparency and scientific rigor. As detailed in the South China Morning Post, DeepSeek's engineers have disclosed the architectural design and training data, showcasing a growing emphasis on openness and reproducibility in advanced machine learning projects.
Key learnings from the article include:
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Transparent Design: DeepSeek’s developers published white papers that reveal key design choices, promoting academic integrity and enabling further AI research and development.
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Scale and Performance: DeepSeek was trained on a vast multilingual dataset, signaling China's attempt to create a globally competitive model with generalist capabilities.
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Localization Power: Its training includes extensive Chinese language content, making it better suited for tailored regional use-cases compared to Western models.
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Innovation in Training Efficiency: The team introduced optimizations for faster model training and inference, ensuring better performance at lower compute costs.
From a business perspective, this breakthrough opens new opportunities for martech and customer experience strategies. By deploying custom AI models like DeepSeek, companies can localize their machine learning models to better reflect language, behavior, and purchasing habits specific to their customer base.
For instance, a B2C retail brand entering the Chinese market can integrate a localized GPT-like model to enhance chatbot conversations, translate product descriptions with cultural nuance, and personalize marketing campaigns in real time. This directly impacts customer satisfaction and conversion rates, reducing churn while increasing engagement.
HolistiCrm, as an AI consultancy, helps companies evaluate and deploy language-specific or multilingual Machine Learning models to maximize marketing and customer performance. The development of models like DeepSeek underlines the growing potential of holistic AI solutions optimized for regional and sector-specific needs—offering a clear edge in today’s competitive digital economy.
Original article
by Csongor Fekete | Sep 21, 2025 | AI, Business, Machine Learning
A breakthrough in predictive health has emerged with a new AI model capable of identifying susceptibility to over 1,000 diseases, as reported by the Financial Times. This powerful Machine Learning model, trained on genomic, lifestyle, and clinical data, marks a holistic shift in the role of AI from diagnostics to proactive healthcare. By assessing disease risk at scale, it opens avenues for early intervention and personalized care strategies.
The model, developed by researchers in collaboration with Google DeepMind and academic institutions, utilizes multimodal data and custom AI architectures to accurately predict conditions such as diabetes, cancer, and cardiovascular diseases. While ethical issues around data use and privacy must be addressed, the implications for transforming preventive medicine are profound.
From a business perspective, this development illustrates how healthcare providers, martech firms, or AI consultancies could deliver high-impact solutions through custom AI models tailored to individual client needs. For example, a health insurance platform integrating this model could dynamically adjust premiums based on risk profiles, improving customer satisfaction through transparent and personalized offerings. Similarly, pharmaceutical companies can optimize clinical trial recruitment, enhancing performance and reducing time to market.
The lesson for AI agencies and AI experts is clear: combining multimodal data sources and domain-specific knowledge into Machine Learning solutions creates tangible business value. HolistiCrm’s approach to marketing and martech can leverage similar strategies—using predictive AI to anticipate customer behavior, personalize outreach, and ultimately drive engagement.
Read the original article: https://news.google.com/rss/articles/CBMicEFVX3lxTFBscm51TTVKeXlZemVZek84UGloVVAxLXJqckozbmxRTzFaSXowWXhBNXQtRWs0TF8weFlZTnZnOTd4TjlWWG13MVFDSlFPSGlLb0U4RjcwSXY0RWlKVDlkUUVUbDAxZGtITGhNTzNkY0E?oc=5.
by Csongor Fekete | Sep 21, 2025 | AI, Business, Machine Learning
Youngstown State University (YSU) has received a $746,000 research grant from the U.S. Department of Defense, placing it at the forefront of AI research. The initiative, driven by YSU’s Center for Excellence in Manufacturing, focuses on developing a robust and secure AI framework specifically for defense applications. The funding supports research into Machine Learning model development, automated testing, and rigorous evaluation—all aimed at boosting reliability in high-stakes environments.
Key takeaways from the announcement include:
- YSU’s investment in AI research is aligned with national priorities in defense and security.
- Emphasis is placed on the transparency and repeatability of Machine Learning models.
- The project includes student and faculty collaboration, creating a holistic approach to AI capability development.
From a business standpoint, this use-case demonstrates how custom AI models built under stringent performance requirements—such as military applications—can be adapted for other sectors like marketing, martech, and customer engagement. For example, businesses aiming to maximize customer satisfaction can benefit from AI systems designed for reliability and efficiency, especially in critical decision-making processes.
A Holistic AI consultancy or AI agency can draw inspiration from such research to reinforce its methodology for building trustable and high-performance AI systems. By collaborating with academia or investing in similar research, companies can remain on the cutting edge of AI innovation while delivering measurable business value.
Original article: https://news.google.com/rss/articles/CBMikwFBVV95cUxPR2xBQWJNVm5XbmkwOUl4WVNTZ3BrT29XNGwzYWYzazNDdDBjYy1oYUxRcE50bkhjRnNoNjhqR2FYWm12WC1iek1ibGVvZS1Lby1Vamp1RVh6Z2pGUzFtb2loNElVdjZGX0xaeVZkZ0VkMUl6YU1EWnNDSWEyLXYxeEFtV2Fwcmpka0VNcUxqU1MxUjQ?oc=5
by Csongor Fekete | Sep 20, 2025 | AI, Business, Machine Learning
Strava's latest partnership with Oakley introduces a cutting-edge integration with the Meta Vanguard Performance AI Glasses, marking a significant step in the evolution of wearable martech. These AI-powered glasses enable real-time performance feedback and display live biometric and training data from Strava during workouts, bridging the gap between digital insights and physical performance in a hands-free, immersive experience.
Key highlights from the announcement:
- Seamless synchronization between Oakley's Meta glasses and Strava accounts provides athletes immediate access to their fitness metrics.
- AI-driven performance enhancement tools adapt dynamically to the user's physical data, elevating personalization and user satisfaction.
- The glasses’ voice-activated interface allows effortless interaction without interrupting activity flow.
For brands and AI agencies like HolistiCrm, this presents a compelling use case for integrating custom AI models into wearables and fitness platforms. By applying machine learning models to real-time biometric data, businesses can create holistic fitness profiles that enhance user performance, improve retention, and differentiate product offerings in the saturated health tech market.
AI consultancies can add business value by helping fitness and wellness companies build tailored algorithms that optimize training plans, prevent injuries, and increase customer satisfaction. This use of AI in marketing and performance optimization is a powerful example of how martech evolves into truly smart ecosystems.
The collaboration between Strava and Oakley also serves as a leading-edge benchmark in AI-powered customer experience—where hardware, software, and data science converge to create value beyond basic tracking.
original article.
by Csongor Fekete | Sep 20, 2025 | AI, Business, Machine Learning
Reducing latency in large language model (LLM) inference is a critical performance challenge, especially for real-time applications in marketing automation, customer experience platforms, and martech solutions. The latest article from NVIDIA Developer showcases how NVIDIA’s Run:ai Model Streamer drastically reduces cold start latency in LLM inference by implementing a streaming model loading architecture.
Typically, LLMs are large and require several seconds to load into memory before delivering the first response — a problem known as cold start latency. This introduces bottlenecks for custom AI models in production environments aimed at delivering immediate responses in customer-facing use cases. The Run:ai Model Streamer bypasses the need for full model loading during every inference request, instead allowing inference to begin as the model is streamed into memory. The result is up to 90% reduction in cold start latency and more efficient GPU utilization.
The implications for enterprises, especially those building customized Machine Learning models for CRM platforms, are significant. In martech and customer engagement domains, delivering instantaneous results—recommendations, segmentation insights, chatbot responses—translates directly into higher customer satisfaction, deeper engagement, and ultimately more conversions.
A use-case within a Holistic martech ecosystem could involve integrating this technology with dynamic customer profiling tools. By ensuring AI models load instantly and respond seamlessly, brands can maintain high engagement through personalized user journeys without latency disruptions. Implementing streaming inference optimizations with the help of an AI consultancy or AI agency enables businesses to unlock faster go-to-market AI solutions, increase model performance efficiency, and optimize cloud resource expenditure.
This innovation shows how advanced infrastructure can empower smarter, more responsive ML models—paving the way for real-time, data-driven marketing.
Read the original article: https://news.google.com/rss/articles/CBMiswFBVV95cUxPUVFITlZ2N1JGelJlQ2htQ2RneXdRRnZUM0RTYnFlNFBCR2JtZjE4ZjFpdTRxWm5sYV9oME4yZ0RxLXVHQTQ0eUQyLTlxVW9lbTBpaG5GQlVkaUJUckVaVzVwOWNtUDR5Snp0UDVzbnNMQmltemp3VG4tcG93b1V2eTFObFpHdm81NzlGSm91T0htYkpGRk1OUy1yRkxLcEJBUjJFS0ZqTUlRUWx2N2NwVzRVUQ?oc=5
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