20 Million Additional Americans Will Have Broadband by 2020
It is hoped that 20 million additional Americans will have broadband by 2020. ConnectALL is a scheme meant to help those seeking employment or hoping for training and are building up their skills.
It is hoped that 20 million additional Americans will have broadband by 2020. ConnectALL is a scheme meant to help those seeking employment or hoping for training and are building up their skills. The President is the force behind the scheme and does not consider the internet to be a luxury only suitable for those with money. He believes that all students should be able to use it, and poorer children should not have to use free school Wi-Fi after the school day has ended just so that they can complete their homework.
The scheme is a follow on from the projects starting in the 1980s that allowed lower income families to have phones and continuing 20 years later with cell phones. The Chairman of the Federal Communications Commission Yom Wheeler stated that there were up to 30% pf residents of the United States who were not connected to the internet in their homes, and the figures showed that money was the problem, and the lower income families were the ones who did not have access. A household with less than $25,000 is much less likely to be connected than a family earning over $150,000 – 49% in the lower income bracket, 95% in the higher bracket.READ ALSO: Making A Breathing and Living SuperComputers
Libraries and museums are also going to see their digital access improved and to ensure this reaches the right areas; community organizations will be involved. Private companies will work with them to deliver access that is affordable.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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