Urgent.News

What's breaking now, across thousands of outlets.

AI

Editorial: Ghana Cannot Afford To Miss The AI Revolution

The World Bank’s October 2026 Africa Economic Update, titled Building AI Readiness, should be a wake-up call for Ghana. The report says artificial intelligence (AI) adoption in Sub-Saharan Africa is growing rapidly, but remains at an early and uneven stage. It further points out that AI activity is concentrated in a few countries notably Kenya, […] The post Editorial: Ghana Cannot Afford To Miss…

The October 2026 World Bank Africa Economic Update, "Building AI Readiness," should serve as a wake-up call for Ghana, according to an editorial in The Ghanaian Chronicle. The report reveals that artificial intelligence (AI) adoption in Sub-Saharan Africa is expanding swiftly, yet remains nascent and uneven. Notably, Kenya, Nigeria, and South Africa dominate AI activity in the region, accounting for a substantial portion of venture capital investment, research output, and advanced applications.

For Ghana, this matter transcends technology; it is an economic development issue. Nations that cultivate the ability to create and utilize AI will enjoy productivity, innovation, research, investment, and job creation advantages. Conversely, those falling behind risk becoming mere consumers of foreign technologies, paying for solutions to issues they could eventually solve independently.

The Chronicle raises a crucial question: What role does Ghana aspire to play when the AI economy reaches maturity? The Chronicle applauds Ghana's National Artificial Intelligence Strategy and initiatives like the One Million Coders Programme, as well as the Ministry of Education's plan to integrate AI into the educational curriculum.

However, the greater challenge often lies in executing policies effectively. The World Bank's warning about connectivity, electricity, skills, data, and computing infrastructure gaps should not be taken lightly. Ghana's digital divide, while mobile phone usage and internet access have surged, remains a significant issue, with connectivity not equally accessible or affordable across all communities.

To fully reap the benefits of AI, Ghana must link its policy to an all-encompassing infrastructure strategy, ensuring reliable broadband, affordable internet, modern data centers, cloud infrastructure, and access to high-performance computing. Additionally, Ghana needs to tackle data issues, digitize and standardize non-sensitive government data, and make relevant datasets available to researchers and innovators while safeguarding personal information.

The education system plays a pivotal role in this equation, but Ghana should broaden its AI focus beyond coding to include mathematics, physics, computing, statistics, data science, and scientific reasoning. Furthermore, Ghana should start preparing its youth for emerging technologies like quantum science and quantum information technology, which hold promise in healthcare, mineral exploration, cybersecurity, telecommunications, and environmental monitoring.

Ghana must not merely inquire how to utilize foreign technologies; it must contemplate how to create technologies of its own. The AI revolution is in full swing, and the quantum revolution is on the horizon. Ghana cannot afford to arrive late to either.

Written by urgent.news from The Chronicle Ghana's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at thechronicle.com.gh →

More in AI

DFlash-2: Benchmarking Z-Lab's Successor to DFlash for Accuracy and Throughput Gains

A while back, we covered DFlash, a draft-token prediction technique that uses a diffusion model. At the time, we tested it on Gemma-4-12b-it-QAT, and the native Assistant model came out ahead — DFlash…

  • DFlash-2 improves upon original DFlash design with Lightweight Path Selector
  • Local Convolution layer limits token information exchange to immediate neighbors
  • DFlash-2 models available for Qwen3.8-27B-DFlash2 and Muse-Glimmer-30B-DFlash2

LLMs Pass the Data-Science Quiz, Then Give Different Advice: A Kaggle Benchmark of 36 Measured Judgment Calls

This is a submission for the Kaggle Benchmarking Challenge . What I Benchmarked I spend a lot of time in Kaggle tabular competitions, and the decisions that cost me the most were never about model…

  • LLMs achieved 94-100% accuracy in recognizing measured answers
  • Open-ended performance varied, with 56-81% correct recommendations
  • Four topics showed lower accuracy, including T02, T03, T11, and T07

More from Friday 9 October →