Policy analysis, methodology and industry observation around real enterprise problems.
Reports about farmers losing crops due to 'AI' often blame AI as a whole, ignoring which specific model or tool was used. This article argues that vague attribution is unscientific and harms trust in AI, urging media and practitioners to be precise and apply human oversight.
Most AI learning videos online are designed for clicks, not real value. From a practitioner’s perspective, this article exposes typical money-grabbing tactics (fear-mongering, instant-genius promises, fake demos), offers three clear warning signs, and advises industry professionals to learn AI through small-scale, hands-on projects rather than fragmented videos.
As AI reshapes industries, which majors face automation risk? This article offers an industry-reconstruction perspective on career planning, identifying high-risk patterns, valued directions, and actionable strategies to stay relevant.
With rapid aging, the silver economy is a key direction for urban industrial upgrading. How can AI be applied in family memory digitization, emotional companionship, and health monitoring? This article explores real opportunities and practical paths using the 'small incision, deep reconstruction, continuous upgrade' methodology, assisting local governments, elderly care institutions, and industrial platforms in finding actionable entry points.
This article provides a practical evaluation framework for traditional enterprises to determine which business processes are worth prioritizing for AI reconstruction, avoiding blind trends or resource waste. Key dimensions include: whether the problem is real and urgent, whether data and rules are available, whether investment is controllable, and whether results are measurable. It aligns with the 'small entry, deep reconstruction, continuous upgrade' methodology.
AI for mobile scenarios enables frontline personnel to perform tasks such as identification, prompting, recording, reporting, and decision support, bringing AI capabilities closer to the actual field environment.
Oversight of back-of-house operations in restaurant chains can enhance the visibility of food safety management through AI recognition, temperature monitoring, anomaly alerts, and process logging.
Manufacturing companies can use industrial vision and data analysis capabilities to identify, warn and trace defects, batches, equipment and process anomalies.
The future is not about more systems, but using AI to activate existing assets, optimize process, cut cost and control risk.
