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The acceleration and convergence of exponential technologies like AI are transforming and reinventing every part of our lives, taking humanity to a nearly unimaginable world. However, we cannot stress enough the need to use this technology ethically and responsibly in the best interest of the society.
AI has traversed a rocky path since the foundations of its research were laid in a 1956 white paper co-authored by Claude Shannon of Bell Labs<\/a>. It ranges from the dark days in the 1990s when AI could not connect humans and societies to the present day where technology is quickly converging and making the once unthinkable a reality.
It is often observed when we paint a picture of an advanced AI, we might imagine machines that ‘learn’, similar to the ways a toddler might learn. This makes us wonder whether AI can ‘think’ or ‘come to conclusions’ in way that humans do. Israeli Nobel Prize winner Daniel Kahneman’s popular book ‘Thinking, Fast and Slow’ breaks down human thinking into two distinct paths.
System 1 is the part of the brain that operates intuitively and suddenly, often unconsciously. This automatic response is more prone to error. System 2, meanwhile, is the deliberate, conscious process that requires more effort. It is used for complex tasks like learning a new skill and tends to be far more reliable.
The future of AI is in building system 2 thinking. It recognizes errors and biases and takes action to improve. This kind of thinking will allow us to address more complex issues to learn, to improve, and then to learn some more.
For instance, Indian chess grandmaster Viswanathan Anand learned the game of chess so well that his moves became instinctual. He mastered the complexity of the game to such an extent that his system 1 thinking became as efficient as system 2. Still, as good as he is and as much as he knows about chess, a machine-like DeepMind<\/a>’s Alpha Go Zero, which taught itself how to play chess, could easily beat him. While this calls for a highly responsible approach in designing the AI algorithms, it also demonstrates the cognitive power of AI.
Such cognitive power can be useful in solving the complex problems of today’s world which require complex thinking but simple solutions. We see how COVID-19 has dramatically made us realize the need to remove the digital divide more than ever by ‘connecting the unconnected.’ The pandemic has seen an increase in network capacity and optimization of coverage using AI, ML and automation<\/a> to keep the lifeline of networks running. Therefore, we need more system 2 thinking – a system that recognizes and strives to eliminate the economic bias against those that are unconnected and underserved.
Even as artificial intelligence<\/a> and machine learning<\/a> are making a big impact on everyday life and while their technical advancements have been incredible, the social aspect has yet to catch up. In a country like India, where 1.3 billion are striving to overcome the digital divide, we need to be more decisive in using AI for bigger challenges like healthcare, transportation, agriculture, education and more.
We also need a new type of partnership with machines and a more conscious thought process when building the technologies of tomorrow, bearing in mind the impact it can have on society. The flip side of such a cognitive power is when applied to manipulate human minds, for example, by spreading disinformation through social media, it can affect our mental health and have grave consequences for the society. Netflix’s popular documentary ‘The Social Dilemma’ details the dangerous impact social media has on our mental health through the spread of disinformation. Never have a handful of tech designers had such control over the way billions of us think, act and live our lives, which makes it an imperative for us to put a lot of thought into analysing the impact and after-effects of a technology.
Today, Nokia Bell Labs<\/a> is focussed on bringing innovation, engagement and service to humanity. We are developing methods for coordinating the interactions of Internet of Things (IoT) to human connectivity around the world. Nokia<\/a>’s award-winning app 6th Sense, which is based on the idea of data fusion and augmented intelligence, can help predict threatening situations by providing fused data and enabling a rapid response without compromising on the data privacy of individuals. This is a clear example of system 2 thinking.
AeroFarms and Nokia Bell Labs Plant Analytics is another example of AI\/ML sensors and drones that use augmented intelligence for optimised farming. The idea is not only to increase crop yield but also to build environmentally responsible farms.
AeroFarms runs the world’s largest indoor vertical farm and yields up to 390x more produce than a traditional field– all without sunlight, soil, or pesticides, all with 95% less water consumption.
We, at Nokia, are ready to serve India by bringing people, technologies and processes together for innovation and problem solving. We take pride in working with the most ambitious organizations and brightest individuals in India, to help them innovate and grow their businesses bigger, better and faster. We further look forward to facilitating the co-creation of innovative, tailored cognitive use cases and solutions by working alongside communication service providers, enterprises and partners.
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加速度和收敛指数技术人工智能改变和重塑我们生活的每一部分,人类几乎无法想象的世界。然而,我们不能强调需要使用这种技术道德和负责任的社会的最大利益。
AI以来穿过岩石路径的研究奠定了基础在1956年白皮书由克劳德·香农贝尔实验室。范围从黑暗的天在1990年代当AI不能连接人类和社会至今技术迅速收敛,让曾经不可想象成为现实。
经常观察到当我们画一幅画,一个先进的人工智能,我们想象的机器“学习”,类似于孩子可能学习方法。这让我们怀疑人工智能能否“认为”或“得出结论”的方式,人类做的。以色列诺贝尔奖得主丹尼尔•卡尼曼(Daniel Kahneman)广受欢迎的书想,快和慢的将人类思维分解成两个截然不同的路径。
系统1是大脑的一部分,操作直观,突然,常常在不知不觉中。这个自动反应更容易发生错误。同时,系统2是深思熟虑的,有意识的过程,需要更多的努力。用于复杂的任务就像学习一门新技能,往往是更可靠。
人工智能的未来在建筑系统2的思考。承认错误和偏见和采取行动来改善。这种想法将使我们能够解决更复杂的问题学习,改进,然后去学习更多。
例如,印度的国际象棋大师Viswanathan Anand学国际象棋的游戏都很好,他的举动成为本能。他掌握了游戏的复杂性,他的系统1思维成为系统2一样有效。不过,尽可能多的和他一样好,他知道下棋,一个机器DeepMind的α去零,教自己如何下棋,可以轻易地打败他。虽然这要求一个高度负责的方法设计的智能算法,它还演示了人工智能的认知能力。
这样的认知能力可以是有用的在解决当今世界的复杂问题需要复杂的思维但简单的解决方案。我们看到COVID-19极大地让我们意识到需要删除的数字鸿沟比以往任何时候都更无关的连接。流感大流行已经覆盖网络容量的增加和优化使用AI,毫升自动化保持网络运行的生命线。因此,我们需要更多系统2思考——一个系统,识别并努力消除经济歧视那些无关的和缺医少药。
即使人工智能和机器学习正在对日常生活产生巨大影响,而他们的技术进步已经令人难以置信的,社会方面尚未赶上来。在像印度这样的国家,13亿年正努力克服数字鸿沟,我们需要更果断使用人工智能等更大的挑战医疗、交通、农业、教育和更多。
我们还需要一种新型的伙伴关系机器和一个更有意识的思维过程在构建未来的技术时,要记住它对社会的影响。这样一个认知能力的另一面是当应用到操纵人类的思想,例如,通过社会媒体传播虚假信息,它会影响我们的心理健康和社会的严重后果。Netflix的受欢迎的纪录片的社会困境的细节的危险影响社交媒体对我们的心理健康通过虚假信息的传播。从来没有少数技术设计师有这样控制数十亿人的思维方式,行为和生活,这也使得它成为一个我们必须花很多心思分析技术的影响和后果。
今天,诺基亚贝尔实验室致力于将创新、参与和服务人类。我们正在开发的方法协调互动的物联网(物联网)人类连接世界各地。诺基亚的获奖应用第六感,这是基于数据融合的概念和增强智力,可以帮助预测威胁情况下通过提供融合数据,使数据隐私的前提下快速反应的人。这是一个明显的例子系统2的思考。
AeroFarms和诺基亚贝尔实验室植物分析是另一个例子的AI /毫升传感器和无人机,对优化农业使用增强情报。这个想法是不仅增加作物产量,而且建造对环境负责的农场。
AeroFarms运营着全球最大的室内垂直农场,收益率高达390 x比传统生产领域——没有阳光,土壤,或杀虫剂,用水量减少95%。
在诺基亚,我们已经准备好为印度将人,技术和流程创新和解决问题。我们在处理最雄心勃勃的组织感到自豪、最聪明的人在印度,帮助他们创新和成长企业更大,更好,更快。我们期待着进一步促进创新的共同创造,用例的认知和解决方案通过与通信服务提供商合作,企业和合作伙伴。
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