Artificial Intelligence<\/a> (AI<\/a>) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world.
As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China<\/a> and India<\/a>, which are leading the way in building national AI plans within the developing world.
Oxford Insights<\/a>, a consultancy firm that advises organisations and governments on matters relating to digital transformation, has ranked the preparedness of 160 countries across the world when it comes to using AI in public services. The US ranks first in their 2021 Government AI Readiness Index, followed by Singapore<\/a> and the UK.
Notably, the lowest-scoring regions in this index include much of the developing world, such as sub-Saharan Africa, the Carribean and Latin America<\/a>, as well as some central and south Asian countries.
The developed world has an inevitable edge in making rapid progress in the AI revolution. With greater economic capacity, these wealthier countries are naturally best positioned to make large investments in the research and development needed for creating modern AI models.
In contrast, developing countries often have more urgent priorities, such as education, sanitation, healthcare and feeding the population, which override any significant investment in digital transformation. In this climate, AI could widen the digital divide that already exists between developed and developing countries.
The hidden costs of modern AI
<\/strong>
AI is traditionally defined as \"the science and engineering of making intelligent machines\". To solve problems and perform tasks, AI models generally look at past information and learn rules for making predictions based on unique patterns in the data.
AI is a broad term, comprising two main areas - machine learning and deep learning. While machine learning tends to be suitable when learning from smaller, well-organised datasets, deep learning algorithms are more suited to complex, real-world problems - for example, predicting respiratory diseases using chest X-ray images.
Many modern AI-driven applications, from the Google<\/a> translate feature to robot-assisted surgical procedures, leverage deep neural networks. These are a special type of deep learning model loosely based on the architecture of the human brain.
Crucially, neural networks are data hungry, often requiring millions of examples to learn how to perform a new task well. This means they require a complex infrastructure of data storage and modern computing hardware, compared to simpler machine learning models. Such large-scale computing infrastructure is generally unaffordable for developing nations.
Beyond the hefty price tag, another issue that disproportionately affects developing countries is the growing toll this kind of AI takes on the environment. For example, a contemporary neural network costs upwards of US$150,000 to train, and will create around 650kg of carbon emissions during training (comparable to a trans-American flight). Training a more advanced model can lead to roughly five times the total carbon emissions generated by an average car during its entire lifetime.
Developed countries have historically been the leading contributors to rising carbon emissions, but the burden of such emissions unfortunately lands most heavily on developing nations. The global south generally suffers disproportionate environmental crises, such as extreme weather, droughts, floods and pollution, in part because of its limited capacity to invest in climate action.
Developing countries also benefit the least from the advances in AI and all the good it can bring - including building resilience against natural disasters.
Using AI for good
<\/strong>
While the developed world is making rapid technological progress, the developing world seems to be underrepresented in the AI revolution. And beyond inequitable growth, the developing world is likely bearing the brunt of the environmental consequences that modern AI models, mostly deployed in the developed world, create.
But it's not all bad news. According to a 2020 study, AI can help achieve 79 per cent of the targets within the sustainable development goals. For example, AI could be used to measure and predict the presence of contamination in water supplies, thereby improving water quality monitoring processes. This in turn could increase access to clean water in developing countries.
The benefits of AI in the global south could be vast - from improving sanitation, to helping with education, to providing better medical care. These incremental changes could have significant flow-on effects. For example, improved sanitation and health services in developing countries could help avert outbreaks of disease.
But if we want to achieve the true value of \"good AI\", equitable participation in the development and use of the technology is essential. This means the developed world needs to provide greater financial and technological support to the developing world in the AI revolution. This support will need to be more than short term, but it will create significant and lasting benefits for all.
(This article is syndicated by PTI from The Conversation)<\/em>
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赫尔大学由Joyjit Chatterjee和尼娜Dethlefs Cottingham
人工智能(人工智能如今)不仅仅是一个口号。权力面部识别在智能手机和电脑,翻译外国语言之间,系统过滤垃圾邮件和识别有毒社交媒体上的内容,甚至可以检测癌变肿瘤。这些例子,以及无数其他现有的和新兴的应用人工智能,帮助简化人们的日常生活,特别是在发达国家。
据报道2021年10月,44个国家有他们自己的国家AI的战略计划,显示他们愿意开拓进取在全球人工智能比赛。这些包括新兴经济体中国和印度牵头负责的方式构建国家AI计划在发展中国家。
牛津的见解咨询公司建议组织和政府有关数字转换,已经排名全球160个国家的准备时使用人工智能在公共服务。美国居第一位在他们一份2021年的政府AI准备指数,紧随其后新加坡和英国。
值得注意的是,这个指数最低的地区包括许多发展中国家,如撒哈拉以南非洲、加勒比和拉丁美洲,以及一些亚洲国家中部和南部。
发达国家有一个不可避免的边缘在人工智能革命取得快速进展。这些富裕国家更大的经济能力,自然是最好的定位进行大型投资创建现代人工智能模型所需的研究和开发。
相比之下,发展中国家往往有更紧迫的优先事项,如教育、卫生、医疗和喂养人口,覆盖任何重大投资数字转换。在这种气候下,人工智能可以扩大数字鸿沟,发达国家和发展中国家之间已经存在。
现代人工智能的隐性成本
人工智能是传统上被定义为“智能机器”的科学和工程。解决问题和执行任务,AI模型通常看过去的信息和学习规则根据独特的模式做出预测的数据。
人工智能是一个广泛的术语,包括两个主要方面——机器学习和深入学习。而机器学习往往是合适的时候学习小,组织严密的数据集,深入学习算法更适合于复杂,现实问题——例如,预测呼吸道疾病使用胸部x光图像。
许多现代AI-driven应用,从谷歌翻译功能,符合手术过程,利用深层神经网络。这是一种特殊类型的深学习模式松散地基于人类的大脑结构的变化。
至关重要的是,神经网络数据饿了,常常需要数以百万计的例子来学习如何执行一个新任务。这意味着他们需要一个复杂的数据存储和现代计算机硬件基础设施,而简单的机器学习模型。这样大规模的计算基础设施通常是发展中国家负担不起。
除了价格不菲,不成比例地影响发展中国家的另一个问题是越来越多的人数这种人工智能的环境。例如,当代神经网络成本150000美元以上的训练,并将创造约650公斤的二氧化碳排放量在训练(相当于美飞行)。训练一个更高级的模型会导致大约五倍产生的碳排放总量平均汽车在其整个生命周期。
发达国家在历史上不断增加的碳排放的主要贡献者,但这样的负担很大程度上不幸的是土地大多数发展中国家的排放。南半球一般有不成比例的环境危机,如极端天气、干旱、洪水和污染,部分原因是其有限的投资气候行动的能力。
进步的发展中国家也受益最少的人工智能和所有它能带来好——包括建筑对自然灾害的韧性。
使用人工智能好
而发达国家正在快速的技术进步,发展中国家似乎是弱势的人工智能革命。和其他不公平的增长,发展中国家可能是首当其冲的受到环境影响,现代人工智能模型,主要部署在发达国家,创造。
但这并不都是坏消息。乐动扑克根据2020年的一项研究中,人工智能可以帮助实现可持续发展目标中的目标的79%。例如,人工智能可以用来测量和预测污染水源的存在,从而提高水质监测过程。这反过来可能会增加在发展中国家获得干净的水。
AI在南半球的好处可能是巨大的,从改善环境卫生、帮助和教育,提供更好的医疗服务。这些增量的变化可能会产生重大的影响。例如,改善发展中国家的卫生设施和卫生服务可以帮助避免疾病的暴发。
但是如果我们想要实现“人工智能”的真正价值,公平参与技术的开发和使用是至关重要的。这意味着发达国家需要向发展中国家提供更多的资金和技术支持的人工智能革命。这种支持需要超过短期内,但它会带来重大和持久的好处。
从对话中(本文是银团PTI)
人工智能(人工智能如今)不仅仅是一个口号。权力面部识别在智能手机和电脑,翻译外国语言之间,系统过滤垃圾邮件和识别有毒社交媒体上的内容,甚至可以检测癌变肿瘤。这些例子,以及无数其他现有的和新兴的应用人工智能,帮助简化人们的日常生活,特别是在发达国家。
据报道2021年10月,44个国家有他们自己的国家AI的战略计划,显示他们愿意开拓进取在全球人工智能比赛。这些包括新兴经济体中国和印度牵头负责的方式构建国家AI计划在发展中国家。
牛津的见解咨询公司建议组织和政府有关数字转换,已经排名全球160个国家的准备时使用人工智能在公共服务。美国居第一位在他们一份2021年的政府AI准备指数,紧随其后新加坡和英国。
值得注意的是,这个指数最低的地区包括许多发展中国家,如撒哈拉以南非洲、加勒比和拉丁美洲,以及一些亚洲国家中部和南部。
发达国家有一个不可避免的边缘在人工智能革命取得快速进展。这些富裕国家更大的经济能力,自然是最好的定位进行大型投资创建现代人工智能模型所需的研究和开发。
相比之下,发展中国家往往有更紧迫的优先事项,如教育、卫生、医疗和喂养人口,覆盖任何重大投资数字转换。在这种气候下,人工智能可以扩大数字鸿沟,发达国家和发展中国家之间已经存在。
现代人工智能的隐性成本
人工智能是传统上被定义为“智能机器”的科学和工程。解决问题和执行任务,AI模型通常看过去的信息和学习规则根据独特的模式做出预测的数据。
人工智能是一个广泛的术语,包括两个主要方面——机器学习和深入学习。而机器学习往往是合适的时候学习小,组织严密的数据集,深入学习算法更适合于复杂,现实问题——例如,预测呼吸道疾病使用胸部x光图像。
许多现代AI-driven应用,从谷歌翻译功能,符合手术过程,利用深层神经网络。这是一种特殊类型的深学习模式松散地基于人类的大脑结构的变化。
至关重要的是,神经网络数据饿了,常常需要数以百万计的例子来学习如何执行一个新任务。这意味着他们需要一个复杂的数据存储和现代计算机硬件基础设施,而简单的机器学习模型。这样大规模的计算基础设施通常是发展中国家负担不起。
除了价格不菲,不成比例地影响发展中国家的另一个问题是越来越多的人数这种人工智能的环境。例如,当代神经网络成本150000美元以上的训练,并将创造约650公斤的二氧化碳排放量在训练(相当于美飞行)。训练一个更高级的模型会导致大约五倍产生的碳排放总量平均汽车在其整个生命周期。
发达国家在历史上不断增加的碳排放的主要贡献者,但这样的负担很大程度上不幸的是土地大多数发展中国家的排放。南半球一般有不成比例的环境危机,如极端天气、干旱、洪水和污染,部分原因是其有限的投资气候行动的能力。
进步的发展中国家也受益最少的人工智能和所有它能带来好——包括建筑对自然灾害的韧性。
使用人工智能好
而发达国家正在快速的技术进步,发展中国家似乎是弱势的人工智能革命。和其他不公平的增长,发展中国家可能是首当其冲的受到环境影响,现代人工智能模型,主要部署在发达国家,创造。
但这并不都是坏消息。乐动扑克根据2020年的一项研究中,人工智能可以帮助实现可持续发展目标中的目标的79%。例如,人工智能可以用来测量和预测污染水源的存在,从而提高水质监测过程。这反过来可能会增加在发展中国家获得干净的水。
AI在南半球的好处可能是巨大的,从改善环境卫生、帮助和教育,提供更好的医疗服务。这些增量的变化可能会产生重大的影响。例如,改善发展中国家的卫生设施和卫生服务可以帮助避免疾病的暴发。
但是如果我们想要实现“人工智能”的真正价值,公平参与技术的开发和使用是至关重要的。这意味着发达国家需要向发展中国家提供更多的资金和技术支持的人工智能革命。这种支持需要超过短期内,但它会带来重大和持久的好处。
从对话中(本文是银团PTI)
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