by Csongor Fekete | May 6, 2025 | AI, Business, Machine Learning
The White House has initiated a public call for comments to update the National Artificial Intelligence Research and Development Strategic Plan, reflecting a growing recognition of the transformative role of AI across both public and private sectors. The goal is to ensure that the U.S. maintains global leadership in AI by incorporating emerging priorities such as trustworthy AI, equity, and human-centric design.
Key areas for feedback include enhancing coordination of AI R&D among federal agencies, investing in ethical and safe AI practices, and reinforcing national AI infrastructures. The revised plan will also look to address concerns around bias, explainability, and real-world implementation, aligning innovation with societal values.
For businesses, this evolving policy landscape offers a timely opportunity to align strategic goals with national AI objectives. In industries like martech and CRM, the adoption of custom AI models can directly respond to priorities outlined in the draft plan. Leveraging Holistic AI solutions—focusing on data transparency, interpretability, and human-centered design—can enhance both marketing performance and customer satisfaction.
A relevant use-case would be an AI-powered customer segmentation platform developed by an AI agency for a retail CRM. Using a machine learning model aligned with ethical AI guidelines, this system would personalize messaging while eliminating bias, increasing engagement by 35% and reducing churn by 20%. These outcomes not only improve KPIs, but also conform with the national push for responsible AI, demonstrating how businesses can unlock value while staying ahead of regulatory changes.
The inclusion of public voices in AI policymaking signals a future where alignment between government standards and private innovation is key. Organizations working with AI consultancy partners can turn this alignment into a competitive advantage.
Read the original article: https://news.google.com/rss/articles/CBMingFBVV95cUxNUDlfNno1VlF1ZEp4OFZEVzNKNkRDangtRGs4SUtmMl9JbVFyczUyazFpT2FxLW4tMGpTZDBta1pLbENkODNWMW1iR2hqTlVlN0FSajVWZUdkY2V6S1E3aW1WUTJXTTA3UUROLVBWRDJ1VHIzakdFcnkweWg0aWk5YVlldnVLYmpFa3dJSVNLUkdNbWVwckRleG5vQkVIUQ?oc=5
by Csongor Fekete | May 5, 2025 | AI, Business, Machine Learning
A recent study by Pew Research Center highlights deep public skepticism about AI's role in journalism. According to the report, a significant majority of Americans anticipate that Artificial Intelligence will have a negative impact on both the quality of news and the working conditions for journalists. Only 15% believe AI will improve news quality, while nearly three-quarters expect a decline. Moreover, almost 80% anticipate that AI will intensify job losses in media sectors.
This public concern reflects deeper tensions in martech and content-centric industries where automation promises efficiency but risks authenticity. As AI models increasingly automate content creation and dissemination, the need for custom AI models that align with ethical standards and editorial integrity becomes critical.
From a business value perspective, media companies can leverage custom Machine Learning models developed through AI consultancies like HolistiCrm to enhance performance without sacrificing trust. By implementing AI tools that support journalists—such as intelligent research assistants, audience targeting algorithms, and real-time fact-checking systems—organizations can boost productivity while maintaining credibility and customer satisfaction.
For instance, integrating holistic AI-driven content recommendation engines can help publishers tailor experiences to individual readers, increasing engagement and marketing ROI. With a balanced approach, guided by AI experts, businesses can turn widespread skepticism into a pathway for innovation and renewed trust in digital journalism.
Original article: https://news.google.com/rss/articles/CBMiwgFBVV95cUxOcFFEVkgteFJiMVlwUXlweUNvWWVvSFVIU1BwMUl1REpPZVJIbGd0dXZLLUJWZS0yNmlyQ2hJdWxiNXJuS2ExRUprQVhFQjFObkRTbmowWm5Ydy16TE9Cc1FLWDVWS3pjVHFGb0IzZzJMR1NGZ1dxTXNTUmRwNmd6UU5TSXdKZGRKQXBzRkEyRTlYUWdPRVREcVIteE9VMXFRa05YWW9ITldTMXZna215ZmlFOVhvbjBuY19kMzJYV0E4QQ?oc=5
by Csongor Fekete | May 5, 2025 | AI, Business, Machine Learning
Cisco is advancing its cybersecurity strategy to meet the mounting challenges of the AI era, with a major update to its Security Cloud platform. This new approach emphasizes holistic, AI-native architecture that simplifies operations across complex tech environments. Key innovations include Cisco’s AI Assistant for Security, and expansions into Extended Detection and Response (XDR), Secure Access Service Edge (SASE), and multicloud defense—all designed to boost performance and resilience.
These updates reflect a broader trend in martech and enterprise IT where AI is embedded not only into product features but also into the strategic core, with AI experts prioritizing automatic threat detection and real-time response. By integrating custom AI models, Cisco aims to combat evolving cyber threats at scale, offering customers proactive protection with reduced manual workload and higher satisfaction.
From a business value perspective, a similar use-case for HolistiCrm could focus on leveraging custom Machine Learning models to secure customer data across martech platforms. AI consultancy services can help build automated anomaly detection systems that notify marketing teams of suspicious activity, ensuring compliance and preserving brand trust. As marketing data pipelines become more complex, holistic AI-driven security solutions aren’t just IT concerns—they’re essential business enablers.
A secure martech stack opens possibilities for companies to innovate faster, increase marketing performance, and maintain long-term customer satisfaction. AI agencies with expertise in security modeling gain a competitive edge by empowering clients with trusted, scalable systems that turn risk into resilience.
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by Csongor Fekete | May 4, 2025 | AI, Business, Machine Learning
As global competition intensifies in the AI space, the recent article "Visualizing U.S. vs. Chinese AI Model Performance" by Visual Capitalist highlights a key battleground: benchmark dominance. The analysis compares language and vision AI models developed in the U.S. and China, showcasing how each region excels across different dimensions of Machine Learning performance.
The U.S. continues to lead in the development of top-performing language models, such as OpenAI’s GPT-4, Anthropic's Claude, and Google Gemini, consistently ranking high in metrics like MMLU and HellaSwag. These benchmarks test reasoning, commonsense understanding, and world knowledge—attributes critical in personalized marketing and enterprise software.
China, while trailing in language, is rapidly advancing in multimodal and vision AI domains, demonstrating competitive results with models like MiniCPM and InternLM-XComposer2. These Chinese models show strength in AI's ability to interpret and generate text and images simultaneously, a core feature in modern martech innovation and customer interaction tools.
Key learnings include:
- U.S. AI models emphasize linguistic sophistication, giving them an edge in platforms requiring deep contextual understanding in natural language processing.
- Chinese developers are pushing hard on multimodal performance, which ties closely with immersive, customer-facing AI experiences.
- Across the board, benchmark performance is becoming an indicator of real-world AI application readiness in martech, marketing, customer satisfaction, and automation.
A relevant HolistiCrm use-case could be building a custom AI model for a global e-commerce client using U.S.-based LLMs for customer query understanding, sentiment analysis, and hyper-personalized marketing content. By integrating visual AI inspired by top-performing Chinese models, product recommendation systems could be enriched with enhanced product tagging and visual search. The result: increased customer satisfaction, improved conversion rates, and measurable performance gains in campaign ROI.
For businesses, combining language and vision AI rooted in benchmark-proven platforms ensures a holistic, future-ready AI strategy that reflects global innovation trends in real time.
Read the original article here: Visualizing U.S. vs. Chinese AI Model Performance – original article.
by Csongor Fekete | May 4, 2025 | AI, Business, Machine Learning
The U.S. Defense Advanced Research Projects Agency (DARPA) is launching a new AI-powered initiative aimed at revolutionizing mathematical research. This ambitious program, dubbed the "AI-assisted Accelerated Mathematics (AAAM)," is designed to enhance human-led mathematical discovery through the development and application of custom large language models.
The core idea is to create Machine Learning models that can contribute meaningfully to solving complex, fundamental mathematical problems. Rather than merely assist with automation, the models will be trained on repositories of mathematical knowledge and refined through human expert feedback—delivering a hybrid, collaborative form of reasoning. Key to the program is boosting mathematical productivity and enabling innovative solutions to long-standing unresolved problems.
For martech and customer-focused businesses, this represents a significant inspiration for how AI consultancy and AI agencies like HolistiCrm could rethink problem-solving. In the context of customer satisfaction, predictive modeling and pattern recognition draw on similar principles to solve business challenges: identifying trends, optimizing performance metrics, and personalizing outreach in real time.
A potential use-case aligned with this AI approach is in building custom AI models for marketing optimization. By training a language or predictive model on a business’s unique customer data, an AI expert can extract patterns and generate insights that radically improve customer segmentation, message personalization, and campaign performance. Much like AAAM’s vision for AI in mathematics, this approach doesn't replace human marketers but augments them, accelerating innovation and enabling a more holistic martech strategy.
Such a model shifts the traditional paradigm, where businesses move from descriptive analytics to prescriptive action, backed by a machine that "reasons" through complex data landscapes in collaboration with its human operators.
original article: DARPA to 'radically' rev up mathematics research. Yes, with AI
by Csongor Fekete | May 3, 2025 | AI, Business, Machine Learning
The pace at which AI is redefining the Human Resources function is no longer subtle—it’s transformational. In Josh Bersin's recent article, “The End of HR As We Know It? AI Is Starting To Change Everything,” the evolving role of HR through the lens of AI disruption is expertly unpacked. The article emphasizes that traditional HR, rooted in compliance and process management, must urgently evolve toward what Bersin dubs "Systemic HR"—a people-first, capability-building approach where AI is deeply embedded in every layer.
Key takeaways include the rapid growth of Generative AI in HR tech stacks, where automation now enhances recruitment, onboarding, skills intelligence, and performance management. This shift is not merely about efficiency; it’s about empowering teams to focus on high-impact, human-centric work while machine learning models handle repetitive tasks.
Companies ready to embrace custom AI models and a holistic mindset can unlock new levels of employee satisfaction and operational performance. One compelling use-case is in the deployment of AI-driven skills intelligence platforms. Imagine a martech company using an AI agency like HolistiCrm to build a custom Machine Learning model that dynamically maps employee skills, career goals, and learning pathways. This allows personalized development plans at scale, improves talent retention, and drives long-term value creation.
Ultimately, HR is no longer an administrative function—it is becoming an intelligent, experience-driven business enabler. AI consultancies that align martech strategy with human-centric AI can lead clients into this new era.
Read the original article by Josh Bersin: The End of HR As We Know It? AI Is Starting To Change Everything.
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