Microsoft announced that its Phi series of generative AI models has a new member - Phi-4. Compared to previous versions, Phi-4 shows improvements in several areas, particularly in solving mathematical problems, thanks to the enhanced quality of training data. As of Thursday evening, access to Phi-4 is relatively limited, available only on Microsoft's newly launched Azure AI Foundry development platform and restricted to research purposes under the Microsoft Research Licensing Agreement. Image source note: Image generated by AI, image licensed.

With the rapid development of artificial intelligence (AI) technologies in industrial sectors, experts indicate that high-quality data and data governance will be more important than generative technologies. By 2025, companies must focus more on scalable and flexible solutions when adopting AI, rather than solely relying on Generative AI (GenAI). Image note: The picture is generated by AI, authorized by the image service provider Midjourney, based on the analysis by Qlik, the key to fully harnessing AI's potential lies in companies' investments in high-quality, real-time data.

Recently, the 2024 State of AI Report released by Appen has garnered significant attention. Based on a survey of over 500 IT decision-makers in the U.S., the report reveals that generative AI has grown by 17% in the past year. However, alongside this growth, companies are facing considerable challenges in data management. Si Chen, Appen's Head of Strategy, mentioned in an interview that as AI models are increasingly applied to more complex and specialized domains, the demands for data have risen significantly.

Informatica's latest CDO Insights 2024 report indicates that data quality is the biggest barrier to implementing generative AI. The survey shows that 45% of companies have already implemented generative AI, while 53% plan to do so. The report highlights that data quality, data privacy, and AI ethics are major concerns for businesses when implementing generative AI. The use of data management tools also impacts generative AI projects, and attention to data quality is key to successful implementation.

ChatGPT has been publicly released for nearly a year, with 100 million users and a surge in application raising a series of issues. Its widespread use in enterprise environments emphasizes the importance of data quality, particularly data that is relevant, accurate, and timely to the company. Key aspects of addressing data quality issues include consistent business context, timely updates, and completeness of data. The concept of responsible AI is gradually taking the lead, stressing fair, inclusive, safe, transparent, accountable, and ethical intentions.