by Csongor Fekete | Apr 27, 2025 | AI, Business, Machine Learning
🔵 Blog Post:
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How Understanding User Values in AI Interactions Drives Business Value
Anthropic’s recent article, “Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions,” highlights an important and growing dimension in AI – the role of user values in shaping language model behavior. As businesses increasingly integrate AI into customer touchpoints, recognizing and respecting these values has become vital for technology adoption, customer satisfaction, and performance.
Key Learnings from Anthropic’s Research:
- By analyzing thousands of real-world interactions, Anthropic discovered that customers often seek not just information but also alignment with their personal values when engaging with AI.
- Different users prioritize diverse values – such as transparency, empathy, creativity, or professionalism – depending on the context of the interaction.
- Understanding value-centric preferences helps tune language models to be more effective, trustable, and impactful, ultimately enhancing the overall user experience.
How This Insight Can Create Business Value:
For companies using AI in marketing, customer service, or martech solutions, adapting Machine Learning models to account for customer values can dramatically improve customer engagement and loyalty. A use-case example: by incorporating value discovery into a custom AI model for a CRM platform, companies can tailor communications that are not only personalized by behavior but also emotionally and culturally aligned with individual customer expectations.
This drive toward a more holistic customer experience, backed by insights from AI experts and AI consultancy firms like HolistiCrm, brings measurable benefits. Businesses embracing AI agency services for custom model development can achieve sharper personalization, faster customer resolution times, and ultimately, higher satisfaction rates.
By deploying value-aware AI, companies move closer to true customer centricity — not just predicting needs but resonating with their beliefs. Future-focused organizations that leverage holistic AI strategies position themselves to lead in both digital innovation and human connection.
To explore the original research, see the full article:
👉🏻 Original Article
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by Csongor Fekete | Apr 27, 2025 | AI, Business, Machine Learning
Revolutionizing AI: Google DeepMind's Genie 2 Unveiled
Google DeepMind has recently showcased a groundbreaking development in artificial intelligence—Genie 2, a “world-building” AI model capable of generating dynamic virtual environments which can even be used to train robots. As highlighted in the CBS News article, this innovation not only represents a leap in AI capabilities but also opens new avenues for practical applications across industries.
Key Takeaways:
- Genie 2 is an evolved Machine Learning model that transforms static video frames into interactive, navigable 3D worlds.
- This approach allows robots and AI agents to practice and learn in diverse and complex environments without physical-world trials, significantly accelerating training processes.
- Genie 2’s technology could reshape sectors like robotics, gaming, simulation-based training, and real-world navigation.
- DeepMind emphasized that Genie 2 operates holistically, processing minimal input data to generate rich, complex environments with a high degree of realism.
Business Value Creation through Related Use-Case
A custom AI model like Genie 2 can vastly enhance marketing and martech strategies. Brands can create deeply personalized, immersive virtual experiences for customers, fostering higher engagement and satisfaction. By utilizing a holistic Machine Learning model, companies can simulate customer journeys in virtual environments, test new products or services, and optimize customer experience strategies before real-world deployment.
An AI consultancy or AI agency, such as HolistiCrm, can leverage this technology to build predictive models for customer behavior, optimize marketing campaigns, and increase overall performance. Implementation of such smart solutions enables businesses to make data-driven decisions faster, reduce operational costs, and achieve a higher return on investment. AI experts can tailor these solutions to fit unique business needs, staying ahead in competitive markets.
Ultimately, embracing innovations like Genie 2 through experienced AI consultancies is pivotal for companies looking to stay at the forefront of customer satisfaction, performance optimization, and business growth.
📖 Original article
by Csongor Fekete | Apr 26, 2025 | AI, Business, Machine Learning
Blogpost:
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Why Performance Transparency Matters in Custom AI Models
A recent article by TechCrunch highlights that OpenAI’s latest o3 model reportedly underperformed compared to earlier claims made by the company. Initial communications suggested a leading performance on the MMLU benchmark—a widely-respected measure for Machine Learning model capabilities. However, further scrutiny revealed that the o3 model scored lower than implied, raising concerns about transparency in AI performance reporting (original article).
Key Points and Learnings:
- OpenAI implied a significantly higher performance level for the o3 model than what independent evaluations later confirmed.
- Transparency and precise communication about Machine Learning model capabilities are critical to maintaining customer trust.
- Benchmarks like MMLU are essential, but real-world performance often varies based on use cases and deployment environments.
Holistic AI Planning Builds Trust and Value
In a business context, launching products or marketing campaigns based on overstated AI capabilities can harm customer satisfaction and brand reputation. HolistiCrm advocates for a holistic approach, building custom AI models that are rigorously validated under real-world conditions. Such a practice not only ensures optimal model performance but also creates genuine business value.
One relevant use-case could be a martech company developing a personalized marketing recommendation engine. By using a validated, custom Machine Learning model from a trusted AI consultancy or AI agency, the company can deliver highly relevant customer experiences that drive engagement and satisfaction. Misrepresenting model performance, on the other hand, could erode trust, causing irreparable damage to brand loyalty.
In marketing and martech sectors where personalization and trust are critical, partnering with an AI expert focused on transparent, holistic model development is not just a technical decision—it's a strategic business advantage.
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Reference: original article.
by Csongor Fekete | Apr 26, 2025 | AI, Business, Machine Learning
🔹 Blog Post: Reflections on Meta’s Use of Copyrighted Books for AI Training
Recent developments in the AI world continue to raise important ethical and business questions. According to a report from Futurism, Meta has defended its use of copyrighted books for training its Machine Learning models, claiming those works possessed "no economic value" (original article).
🔹 Key Points from the Article:
- Meta is facing scrutiny over its approach to data sourcing, particularly concerning copyrighted works.
- The company argues that using these books does not harm their economic value, suggesting they are no longer commercially significant.
- This matter feeds a growing debate on intellectual property rights in training datasets for large language models.
🔹 Learnings for AI Experts and AI Consultancy Services:
This situation highlights a crucial need for businesses to prioritize ethical, permission-based data sourcing. Building custom AI models should involve datasets curated with full transparency to foster trust, mitigate legal risks, and support sustainable customer satisfaction.
For AI-focused organizations, a holistic approach to data ethics and Machine Learning model development is critical. In martech applications, ensuring data integrity directly impacts overall performance, marketing outcomes, and brand perception.
🔹 Business Value Use-Case:
Implementing a custom AI model trained on ethically sourced, permissioned content can become a strong differentiator for businesses. For example, a martech company partnering with an AI agency like HolistiCrm could leverage such a model to refine customer segmentation, personalize marketing campaigns, and boost satisfaction metrics. Ensuring data respect demonstrates a company’s commitment to responsible innovation — enhancing brand loyalty and reducing reputational risks.
🔹 Final Thoughts:
Ethics and performance are increasingly intertwined in the AI landscape. Companies working with an AI consultancy should embrace transparent, holistic practices when training Machine Learning models to ensure long-term success in competitive markets.
Reference to full article: original article
by Csongor Fekete | Apr 25, 2025 | AI, Business, Machine Learning
🧠 Are Reasoning AI Models Too Confident?
OpenAI’s latest reasoning-focused AI models may demonstrate stronger logic processing — but there's a catch. As detailed in TechCrunch’s recent article, these models also exhibit a growing tendency to "hallucinate": in AI terms, that means confidently providing inaccurate or entirely false information.
Key highlights from the article:
- OpenAI’s new models, designed to improve reasoning, often overestimate their correctness.
- While performance in logic tasks has improved, so have hallucination rates — a significant trade-off in quality and reliability.
- Researchers note that traditional fine-tuning methods may inadvertently increase this hallucination tendency.
- Reducing hallucinations requires more robust feedback fine-tuning and real-world testing beyond benchmarks.
📈 Business Learnings & Use Case Potential
For companies aiming to integrate custom AI models into marketing or martech tools, this article underscores a critical lesson: performance gains in one area (e.g., reasoning) can unintentionally reduce reliability in another (e.g., factual accuracy). This is where a holistic approach to AI development becomes essential.
Take, for instance, a customer service chatbot powered by a Machine Learning model trained to interpret complex queries and provide product recommendations. If that chatbot hallucinates — giving incorrect specs or exaggerated claims — customer satisfaction, trust, and ultimately brand credibility suffer.
To build business value, an AI agency or AI consultancy should prioritize:
- Custom AI model training using proprietary data with domain-specific knowledge
- Ongoing feedback loops from real users to fine-tune responses
- Balancing reasoning performance with truthfulness in customer-facing interactions
HolistiCrm helps companies realize these goals by guiding the development of AI systems that amplify customer satisfaction without sacrificing performance.
In marketing and customer engagement, reliable AI is not just smart — it’s strategic.
Read the original article: OpenAI’s new reasoning AI models hallucinate more, TechCrunch.
by Csongor Fekete | Apr 25, 2025 | AI, Business, Machine Learning
Title: Microsoft's 1-Bit AI Model and the Future of Cost-Effective AI Performance
Microsoft has introduced a groundbreaking "1-bit" AI model capable of matching the performance of sophisticated large language models like BERT while running exclusively on CPUs. According to Ars Technica, this innovation reduces the model’s computational resource requirements drastically, marking a potential turning point in how holistic AI solutions can be developed and deployed across industries.
Key Highlights from the Article:
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The model, called BitNet, uses only 1-bit weights for computation, reducing memory usage and enabling it to operate efficiently on standard CPU hardware.
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BitNet achieved over 90% of BERT’s performance with only 1.3 billion parameters, demonstrating high levels of efficiency and accuracy on tasks like language understanding and classification.
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Unlike traditional deep learning models requiring GPUs or TPUs, BitNet minimizes reliance on high-end hardware, thus enabling wider accessibility and deployment.
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The implications of this breakthrough extend into both cost and energy efficiency, reducing operational barriers for AI adoption in smaller businesses or low-resource environments.
Business Value & Use Case Opportunity
At HolistiCrm, a key objective is designing custom AI models that balance efficiency and performance in customer-centric applications. Microsoft’s BitNet points to a future where high-performance machine learning models can be implemented on lower-cost hardware, opening up substantial ROI for marketing and customer engagement use-cases in martech environments.
Imagine a CRM platform embedding a lightweight Natural Language Processing (NLP) engine similar to BitNet. This engine could process customer inquiries, classify customer sentiments, or generate dynamic marketing content—all on conventional CPUs. This eliminates the need for costly cloud GPU infrastructure and drastically improves operational agility and customer satisfaction.
Use-case example:
A mid-sized eCommerce retailer could integrate a BitNet-inspired model into their marketing automation workflows. By deploying a CPU-based AI model, the business can analyze customer feedback in real-time, personalize email content, and trigger loyalty offers, increasing conversion rates without incurring high hardware costs—an approach that aligns with HolistiCrm’s holistic philosophy of scalable AI deployment.
Learnings:
- Custom lightweight models present a scalable, cost-effective path to AI adoption, especially for teams seeking faster ROI.
- Energy-efficient AI modeling aligns with modern sustainable business practices and compliance goals.
- Innovating with CPU-optimized models can address latency issues in edge deployments, such as mobile marketing or IoT-based customer touchpoints.
This innovation should serve as inspiration for AI agencies and consultancies to rethink model architecture, focusing not only on raw performance, but also on accessibility, environmental impact, and long-term scalability.
Read the original article ➤ Microsoft’s “1‑bit” AI model runs on a CPU only, while matching larger systems – Ars Technica.
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