by Csongor Fekete | May 14, 2025 | AI, Business, Machine Learning
Mount Sinai has developed a powerful AI model that significantly improves the prediction of delirium in hospitalized patients. This condition, a serious and often preventable complication, is notoriously difficult to diagnose early. By leveraging a Machine Learning model trained on electronic health records, the hospital system achieved increased accuracy in identifying high-risk patients — even before symptoms surface.
Key takeaways from the article include:
- The AI model processed structured clinical data from over 100,000 patient encounters, offering real-time, high-performance risk scoring.
- The system integrates directly into the electronic health record workflow, allowing clinicians to take preventative action.
- Early results show measurable improvements in patient outcomes, including reduced ICU stays and improved recovery rates.
- Interpretable AI techniques ensure clinical usability, aligning predictive power with human decision-making.
This use-case provides a blueprint for how custom AI models can deliver tangible business value beyond healthcare. In sectors like marketing and martech, integrating predictive Machine Learning models can drive customer satisfaction by anticipating user behavior, improving targeting accuracy, and optimizing campaign timing through holistic analysis.
AI consultancy and AI agency experts can extract similar value across verticals: for example, reducing churn in subscription services, improving resource allocation in logistics, or personalizing user experience in retail.
For businesses aiming to transform operations with AI, this case highlights the benefits of embedding tailored ML solutions into existing workflows—boosting performance while improving outcomes.
Original article: https://news.google.com/rss/articles/CBMi3AFBVV95cUxOZGxRQmVCcjc1aUstQUYwZnJyXzFqcWNGYTkza0ZqRUdOM2FhRGhOQjVaZG9Cc0F0eWdKdFE2TlNqSGR3ZWNPaUNfNkFROTRpQVgydUt6VkpXRE8tWXJjOVp0SUNuLTFIcXBzSjhuRzZEYVpLTkNSVXhXMEpXUThQY1ZfUkZJUGdoN2VydXpOUXNRdnpfRzdmb244NThjdGVydlUtWXA4ZVNXVEhDWHR0OHVieWFWUGltUEt5MlRWN2VaMWVZZkxDbGRxMU96T2dwdlRMZmZVTUg0QVFo?oc=5
by Csongor Fekete | May 14, 2025 | AI, Business, Machine Learning
Enterprise AI is entering a new phase. ServiceNow and Nvidia have collaborated to unveil a new reasoning large language model (LLM) designed to power more capable enterprise AI agents. This partnership signals a transformative shift in how executives and teams can use AI for business processes and customer interactions.
The model, called a "domain-specific reasoning LLM," aims to resolve complex requests and adapt its behavior to business-specific needs. Unlike generic LLMs, this custom AI model combines Nvidia's full-stack AI platform with ServiceNow’s workflow automation expertise. The integration enhances enterprise performance by improving contextual understanding and decision-making within customer service, human resources, and IT use-cases.
From the perspective of an AI consultancy or AI agency, this advancement underscores the necessity of adopting holistic AI strategies tailored to specific domains. Off-the-shelf models may offer general capabilities, but domain-specific custom AI models provide granular insight, leading to better customer satisfaction and operational efficiency.
A potential use-case for this model is in marketing and martech. For instance, using a reasoning LLM trained on historical customer behavior, brand tone, and multichannel data can automate personalized campaign creation. This not only saves time but also enhances conversion through relevant messaging, driving measurable business value.
Any business looking to maintain competitive advantage with AI should consider partnerships that bring together platform expertise and ML proficiency, reflecting a holistic approach to digital transformation.
Original article: https://news.google.com/rss/articles/CBMitwFBVV95cUxPU1VVRFA1Z1VQbkx1QkdrdUNTUTBVTE9KYlplUVlqTHpsc1BtcDNJNzU1akhoNXdldkVfMDdCREc3cm5wcjRUeTk5SG1zclJuOVFnNWhydkNIQkVVRXIzaXp3V3l4Z0c4Wm9wU0lCaTBlZ0xKc19VakpXc09XLWhQbXp0Nkp6dm5kcGF4YW13RVlzdElQT19RMjREVlBZUGhuNENXM2tvUmx1YUh1V0Iwem9CT1p1X0k?oc=5
by Csongor Fekete | May 13, 2025 | AI, Business, Machine Learning
MIT researchers have unveiled a cutting-edge hybrid AI model capable of generating smooth, high-quality videos in seconds, pushing the boundaries of what's possible in automated visual content production. The model combines the strengths of two architectures: diffusion models for clarity and detail, and transformer-based architectures for temporal consistency. This synergy allows for a leap in performance when crafting complex, coherent video content from text or image prompts.
This technical innovation holds real promise for businesses leveraging martech and AI-driven content creation. A key takeaway is how hybrid Machine Learning models can drastically reduce production time while increasing video quality — a crucial advancement for sectors like marketing, e-commerce, and customer engagement.
For instance, imagine a retail business using custom AI models to generate localized and personalized promotional videos in real-time for different customer segments. This enhances relevance, boosts engagement, and drives satisfaction. With high-performance AI, campaigns become responsive and scalable, unlocking significant competitive advantage.
AI consultancies and agencies, such as HolistiCrm, can explore integrating hybrid generative models into their custom martech stacks. These solutions provide clients with transformative tools to elevate brand storytelling, reduce creative overhead, and increase ROI.
As AI experts continue to enhance the speed and realism of generative video, enterprises have a growing opportunity to align their content strategies with such capabilities — gaining holistic, AI-powered agility in a demanding digital landscape.
Read the original article: https://news.google.com/rss/articles/CBMiogFBVV95cUxOWF9Wbk00aDJSZkQ4UWNmMWxWZzhGN2VUa1ZBNjNxNHBrWGRNVVdIVUdxemo2RFcwSHJraUgxSjlrS3VDRmlQS3VYbk1ab3dmbHpreURrMk1CRWtUMzhZVS0zZlEwcndISXBMaURhRW12cms3VTd3YzdYT3VRdGhBQmtxRDJsTlYzQ0hOWGZOUjIyMkQxVm1PR1otS2J3MmE1bWc?oc=5
by Csongor Fekete | May 13, 2025 | AI, Business, Machine Learning
As the AI industry continues to push the boundaries of model complexity and capabilities, an emerging problem is becoming impossible to ignore: hallucinations. In a recent article titled “The AI Industry Has a Huge Problem: the Smarter Its AI Gets, the More It's Hallucinating” (original article), the paradox of progress in AI is laid bare.
Key takeaways from the article include:
- Advanced AI models are increasingly generating inaccurate or fabricated information—referred to as hallucinations.
- As these models become larger and more sophisticated, their outputs may seem more confident, but that does not necessarily mean more accurate.
- The fundamental issue lies not just in training data quality, but also in the underlying architecture and objectives of the models themselves.
- Researchers and AI companies are facing growing pressure to deploy accurate and transparent systems, especially in high-stakes industries like healthcare, finance, and marketing.
For businesses looking to gain competitive advantage through AI, these insights are critical. Relying solely on general-purpose models or off-the-shelf tools can lead to diminished trust, customer dissatisfaction, and brand risk when model outputs are inaccurate.
A business use-case that addresses this challenge is the development of holistic, custom AI models tailored to a company’s specific domain. For example, in martech applications, a personalized recommender system that leverages domain-specific Machine Learning models can outperform general models by focusing only on relevant content, thus eliminating hallucination risks.
Deploying custom solutions through an expert AI consultancy like HolistiCrm can ensure higher performance, greater transparency, and a measurable uplift in marketing effectiveness and customer satisfaction.
Read the original article here: original article
by Csongor Fekete | May 12, 2025 | AI, Business, Machine Learning
Nvidia has released its fully open source transcription AI model, Parakeet-TDT-0.6B-V2, on Hugging Face, signaling a strong push toward democratizing speech-to-text capabilities. This transformer-based Machine Learning model is scaled at 600 million parameters and showcases high-performance transcription, particularly in English. Designed for flexibility, it supports a wide range of real-time audio applications and is optimized for automatic speech recognition (ASR) tasks.
The key takeaway for martech and AI-driven customer engagement is that custom AI models like Parakeet-TDT-0.6B-V2 can significantly enhance performance across voice-based channels. By tailoring speech recognition systems to specific customer profiles, dialects, or industries, businesses improve both accessibility and customer satisfaction.
A direct business use-case could be the integration of a speech transcription model into customer service systems. With a tailored Machine Learning model from an AI consultancy like HolistiCrm, businesses can automate call summaries, sentiment analysis, and CRM updates. This reduces manual workload, boosts agent productivity, and provides marketing teams with structured, real-time customer insights. The result is a more holistic approach to customer interactions, grounded in data and refined by artificial intelligence.
For industries focused on high-volume voice communications—such as healthcare, finance, and retail—this development reinforces the value of deploying AI for voice analytics as part of a broader martech strategy.
original article: https://news.google.com/rss/articles/CBMivgFBVV95cUxPWlJCZ1I0TW1WelpHdFpVOGs4eVp2TlhpX2VXZXJiZnJkNjhickQ5b0xRU2tZSmdrM3lReUV2N1NEdUxFbUU0MmtILVU3a0g4WGt6ZndMRTltdFBpR1FaX1VTSWg4OGpnNjhCdUN6NG5Ub2NhZE9fR1RDUTNUT1BXcE1ZYTdVTnFMVlZ4MmVDSXZnbWxTNFBMc0JvRGlVaktwYTVIZXlQWFVHX0FwWTF5SUFreUtha1ZaNjJqYzRR?oc=5
by Csongor Fekete | May 12, 2025 | AI, Business, Machine Learning
Anthropic has launched its ambitious AI for Science program, aiming to collaborate with leading research institutions and long-term partners to apply frontier AI systems to scientific discovery. The initiative’s primary goal is to develop large language models (LLMs) that can assist in advancing research in complex scientific domains such as molecular biology, chemistry, and physics.
Key learnings from the announcement include Anthropic’s focus on safe, interpretable, and steerable AI systems, and the belief that these capabilities are critical not only for safety alignment but also for real-world scientific utility. By co-developing tools with domain experts, Anthropic seeks to build scalable and trustworthy AI assistants for scientific reasoning and experimentation.
For businesses in the martech or CRM sectors, this development offers a glimpse into the strategic value of custom AI models and domain-specific Machine Learning models. Adopting a similar collaborative R&D model, HolistiCrm could co-create AI-powered marketing analytics solutions designed for specific industries, ensuring both high performance and customer satisfaction. For instance, a HolistiCrm use-case might involve developing a custom model that identifies patterns in campaign performance across highly regulated markets (such as biotech or pharma), offering clients actionable insights while complying with industry standards.
This opens significant opportunities for any AI consultancy or AI agency that aims to bridge technical AI innovation with real-world value creation across specialized industries.
original article: https://news.google.com/rss/articles/CBMiY0FVX3lxTE00ZDdFMFBwQWdCY3JUQWNZUlZOODFlbmxMNzcwWjZCRzdCcDF5eE02UjlvLThJOVowUU5iZG5oSW1FOTl0VFVzMlU5dkpLenAyOTJmNUZxMVRvYThyX3FBc0Fzbw?oc=5
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