by Csongor Fekete | Nov 14, 2025 | AI, Business, Machine Learning
Baidu has announced the release of ERNIE 4.0, a powerful open-source multimodal AI model that it boldly states outperforms industry front-runners like OpenAI's GPT-5 and Google DeepMind’s Gemini. This announcement positions Baidu at the forefront of AI innovation, especially in the multimodal domain where integration of text, image, and audio data drives cutting-edge applications.
ERNIE 4.0 demonstrates strong benchmarks in reasoning, logical understanding, and multilingual translation. Notably, Baidu claims ERNIE-ViLG 4.0, the image generation component, produces results superior to Midjourney, opening opportunities in creative content automation—particularly valuable for marketing and martech teams seeking scalable, high-quality visual outputs.
For businesses, the fact that ERNIE is open-source offers substantial potential. This allows AI consultancy firms or an AI agency like HolistiCrm to develop custom AI models tailored to specific industry challenges. For example, a holistic customer experience platform can integrate ERNIE-based multimodal models to automate content recommendations, personalize marketing campaigns, or enable voice/image-based search—all of which boost customer satisfaction and marketing performance.
Deploying a Machine Learning model like ERNIE in a CRM context offers value beyond just technological novelty—it streamlines customer interactions, accelerates campaign development, and supports deep analytics using AI expert tools trained on multimodal data. This evolution in martech showcases how open-source AI not only lowers entry barriers but accelerates AI maturity across enterprises.
original article: https://news.google.com/rss/articles/CBMiowFBVV95cUxQNEhGOS1jMWVNVFExOUx5a25TWTRRelJONWRTbVl1RGttWHdIU1Jrd1E3WlRMWlRpTjllT3ZnLVRzU2FVUWlfNmV3Z3JlQVRsQjF4VU02a09TekxPci1Rb0xHdzZFTjFRczdvZ1FxcFdfS1p4YzduMmlXV1FmcDB0Q0dhLTF0eW9qc0pMLXc0RmxneFhod3hVZVJGU0JhVE5tVHdz?oc=5
by Csongor Fekete | Nov 14, 2025 | AI, Business, Machine Learning
The Kaiser Permanente Division of Research has developed the largest-ever AI model designed to interpret echocardiograms, achieving a groundbreaking advancement in medical imaging analysis. Trained on more than 1 million echocardiograms from over 800,000 patients, this custom AI model has exceeded the diagnostic performance of board-certified cardiologists across 23 key heart measurements.
Key insights from the research highlight that the model not only improves diagnostic consistency but also enables faster and more accurate heart disease detection. Additionally, it performed well across subpopulations, supporting equity in healthcare diagnostics. The collaborative effort involved Stanford University, Cedars-Sinai Medical Center, and UCSF, emphasizing a unified approach to data scalability and model precision.
For a martech and innovation-focused AI agency like HolistiCrm, this medical use-case underscores the power of vertical-specific Machine Learning models. When translated into the marketing or customer experience domain, similar scalable and custom AI models can analyze vast repositories of behavioral customer data to detect patterns, forecast trends, and personalize interactions at scale. This results in improved customer satisfaction, streamlined operations, and increased marketing performance.
Much like cardiologists now leveraging augmented intelligence for decision support, marketing and CX teams can harness bespoke AI expertise through holistic AI consultancy—turning raw interaction data into strategic value. The success of this echocardiogram model reiterates the importance of training AI with domain-specific data sets to achieve real-world impact.
Original article: https://news.google.com/rss/articles/CBMiiwFBVV95cUxPQWI2bGFUc1ljWmhJX0ZoS0tfSUg2MlRsTTlGSXVtSmo5RzNvT3lrZlVpa0xUQ1dtNjllWWtSUU9aZk1QQ21lQXBnWDZkcFBqTjE5cGwyWERrWk1wRXgxVGk3NlJJTDBjMzVpb21oYnBTNW9VUUFkaTgwLXEtd1ViVTctcHJGeUp3MlZj?oc=5
by Csongor Fekete | Nov 13, 2025 | AI, Business, Machine Learning
Meta's recent unveiling of its Generative Ads Model (GEM) marks a significant shift in martech, bringing a central Machine Learning "brain" to unify ad recommendation systems across its multiple platforms. GEM is designed as a general-purpose model capable of understanding text, images, and structured data to create holistic performance improvements in ad generation and recommendation. By consolidating formerly fragmented pipelines, GEM aims to streamline Meta's AI stack, boost experimentation speed, and deliver more personalized, relevant ads to users through reinforcement and supervised learning techniques.
Key takeaways from Meta's engineering push include the transition to a modular architecture that facilitates custom AI models, efficient scaling, and enhanced customer satisfaction. Training a robust multi-modal model like GEM required vast datasets, model distillation, and groundwork in self-supervised learning, all of which highlight the need for deep AI expertise and infrastructure.
For businesses looking to adopt such innovations, use cases around dynamic creative optimization are compelling. For instance, a brand using GEM-like generative AI capabilities via an AI consultancy or AI agency can automatically generate, test, and deploy ad variations based on user behavior and engagement signals. This greatly improves marketing performance while reducing cost per conversion—becoming a cornerstone of modern data-driven campaigns. Holistic CRM systems driven by such models can empower brands to elevate customer journeys with less manual input and more intelligence.
In today’s attention economy, businesses that invest in advanced AI strategies—built with help from expert AI consultants—can gain a significant competitive edge in personalization and scale.
Source: original article
by Csongor Fekete | Nov 13, 2025 | AI, Business, Machine Learning
Amanda Kahlow, founder of 6sense, has launched a bold new venture named 1mind, backed by $30 million in funding. The startup aims to revolutionize sales by replacing traditional human roles with custom AI agents, pushing the boundaries of performance and scalability in martech.
The core idea behind 1mind is to use "AI minds" as highly personalized sales reps capable of handling multiple buyer interactions simultaneously. These custom AI models are designed to align deeply with a company’s tone, values, and customer journey while leveraging massive datasets to optimize outcomes. Kahlow envisions transforming the way brands communicate—more intelligent, more scalable, and more emotionally aware.
A standout use-case emerging from this innovation is in demand generation. Imagine deploying AI agents that can autonomously qualify leads, nurture them with contextual messaging, and tailor offers based on predictive behavior modeling. This kind of AI-supported funnel management can dramatically increase ROI and customer satisfaction while reducing time-to-sale.
For businesses dedicated to holistic AI implementation, especially in CRM and martech, the approach showcased by 1mind provides a viable blueprint. With the right AI partner or AI consultancy, companies can build Machine Learning models uniquely designed to amplify sales performance and humanize automation—reshaping customer relationships in the process.
Original article: https://news.google.com/rss/articles/CBMiygFBVV95cUxNR1h4Tjk5QVN2ME5XUGpkZndHcndjNDVSdzlqSTB4dHoybEgwWk5uRkROaFVTZXZPVnNUZGJMSkEwNnlHSTMxSFJoVUkwZ29GUTV5d0haWkVzWUx6ZTdMbUM4cXRFTWF3XzZaYUh4RHlNcUJfOUVKNS1sTm02bGpqX2dLZGpsSGNRSm9SOGxGbVo0aUVndm5DWGtsT28wcGw4eUR4ODdNTFp5ek9fblpoMjd2M24zRnVFQTA4YlNvTHEyYl92amlEVWl3?oc=5
by Csongor Fekete | Nov 12, 2025 | AI, Business, Machine Learning
A new entrant in the generative AI space is shaking the landscape with bold claims: a Chinese AI model named "01.AI Yi-34B" asserts it can outperform both GPT-5 and Anthropic’s Claude Sonnet 4.5. Even more notable, it’s open-source and freely available. According to benchmark results on Hugging Face’s leaderboard, the model ranks third globally for open large language models (LLMs) and first among models with fewer than 70 billion parameters, making it a compelling alternative for developers and enterprises alike.
The creator, venture capitalist and former Google China leader Kai-Fu Lee, highlights that 01.AI Yi-34B achieves top-tier performance with only 34 billion parameters, suggesting efficient architecture and data curation. It stands out not just for cost-effectiveness but also for multilingual capabilities, including English and Chinese, with plans for expanding its linguistic diversity. Optimized versions for laptops and smartphones are reportedly on the horizon, indicating a shift toward broader accessibility and AI democratization.
The implications for businesses are significant. Custom AI models that rival big-budget alternatives create opportunities for brands to develop tailored Machine Learning models at reduced cost without sacrificing performance. For martech and marketing, this means faster go-to-market strategies with advanced customer segmentation, personalized recommendations, and real-time analytics grounded in high-quality language comprehension.
A relevant use-case in CRM could involve deploying a lightweight, multilingual model like Yi-34B to improve customer satisfaction through smarter, more context-aware virtual assistants or intelligent ticket routing. HolistiCrm’s AI consultancy could leverage such models to develop bespoke AI features that align with each client’s unique data and workflow needs—delivering holistic performance across touchpoints.
As the competitive landscape evolves, partnering with an AI agency that enables adoption of open-access, high-performing LLMs will be crucial to future-proof strategies and elevate customer experiences.
Original article
by Csongor Fekete | Nov 12, 2025 | AI, Business, Machine Learning
Opendoor’s Q3 results paint a telling picture of the challenges and opportunities businesses face when integrating advanced technology into core operations. While the company's revenue of $980 million outpaced Wall Street expectations, its EPS fell short, resulting in a third-quarter net loss of $106 million. The discrepancy stems largely from the company’s transition to a new AI-powered pricing model.
This shift toward custom AI models is driven by a desire to improve unit economics and overall performance. Opendoor is adapting its Machine Learning model to be more "holistic," integrating both real-time market data and behavioral insights to price homes more effectively. Although this AI transformation has introduced short-term volatility, it signals a longer-term strategy focused on optimization and scalability.
From a martech perspective, this case study highlights how tailored AI solutions can enhance customer satisfaction by creating more accurate, responsive pricing systems. In real estate, a more dynamic pricing engine can improve conversion rates and reduce holding costs—both key to profitability.
For AI consultancies and agencies, this reflects a valuable use-case: combining domain-specific expertise with custom Machine Learning modeling to unlock efficiencies in sectors undergoing digital transformation. Companies adopting AI thoughtfully—while aligning it with business goals—are more likely to see sustainable growth.
As organizations in verticals like property, finance, or marketing begin similar transitions, the importance of engaging an AI expert or AI consultancy becomes clear. Building, testing, and maintaining such systems requires both technical expertise and a deep understanding of business objectives.
Original article: https://news.google.com/rss/articles/CBMihwFBVV95cUxQVE1mdXZvQWhFaFEwdjRNZHo3YjVvbXRHUHpQUHJicjdTN3dCb2RHT182ajA1TFp5M295Ti1BUkJ5dkU2M21FMFNtVXJIQUJBRExlNHo2ZjZfWTI4ajc1SlNDR1A4TXAzM040Z1BBMmtZWWlpR3B6M25DYUV3aHVuLTZmVXlkYmc?oc=5
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