数据科学与机器学习(DATA SCIENCE AND MACHINE LEARNING)

Undoubtedly, a machine learning algorithm is forming a more seamless world by adding a new dimension to our daily lives. The community also motivated a more comprehensive and coordinated adoption of machine learning technologies, including utilizing machine learning in essential areas such as education, government services, and community wellbeing, to improve the quality of social life.

毫无疑问，机器学习算法通过为我们的日常生活增加新维度来形成一个更加无缝的世界。 社区还鼓励更全面和协调地采用机器学习技术，包括在教育，政府服务和社区福利等重要领域中利用机器学习，以改善社会生活质量。

We see a positive influence of machine learning as a result of these initiatives in the customer service industry, which is progressively turning to machine learning in managing its growing needs. Mobile applications are now developed to sustain seamless government service delivery by seeing chatbots used across retail, banking, healthcare and government services.

通过这些举措，我们在客户服务行业中看到了机器学习的积极影响，客户服务行业正在逐步转向使用机器学习来管理其不断增长的需求。 现在，通过查看零售，银行，医疗保健和政府服务中使用的聊天机器人，开发了移动应用程序以维持无缝的政府服务交付。

Numerous transformational technologies such as an autonomous vehicle, robotics, artificial intelligence (AI), the Internet of Things (IoT) and 3D printing, is the new era of the fourth industrial revolution. AI is one of the most paramount technologies driving the fourth industrial revolution through machine learning and deep learning. Machine learning has enormous potential and uses cases across businesses, including retail, security, healthcare, and transportation.

自动驾驶汽车，机器人技术，人工智能(AI)，物联网(IoT)和3D打印等众多转型技术是第四次工业革命的新时代。 人工智能是通过机器学习和深度学习推动第四次工业革命的最重要技术之一。 机器学习具有巨大的潜力，并在包括零售，安全，医疗保健和运输等企业中使用案例。

Businesses that are profound in taking up machine learning must focus on two things — the business case benefits and the use case benefits. They must adopt an agile and phased strategy to start building momentum.

精通机器学习的企业必须专注于两件事-业务案例收益和用例收益。 他们必须采取敏捷和分阶段的战略来开始建立动力。

A research firm, Venture Scanner, emphasize that the adoption of machine learning technologies in marketing organizations increased by 44% in 2018, as compared to 2017. The use of this technology and algorithm by customer service teams is projected to increase by 143% over the next 18 months.

一家研究公司Venture Scanner强调，与2017年相比，2018年在营销组织中采用机器学习技术的比例增加了44％。预计客户服务团队对该技术和算法的使用量将比2017年增加143％接下来的18个月。

However, the most significant challenges to implementing machine learning are the perceived threat of redundancy or unemployment. In reality, machine learning should be developed to adapt to different purposes in the labour market rather than make it redundant. Machine learning might support the efficiency in the workforce, as labour intensive and monotonous tasks can be done by machines, while humans focus on higher-level qualitative tasks.

然而，实施机器学习的最大挑战是冗余或失业的威胁。 实际上，应该开发机器学习以适应劳动力市场中的不同目的，而不是使其变得多余。 机器学习可能支持劳动力的效率，因为劳动密集型和单调的任务可以由机器完成，而人类则专注于更高级别的定性任务。

The World Economic Forum (WEF) reports that despite machine learning and algorithms substituting 75 million jobs by 2022, they will create another 133 million new roles by the same period.

世界经济论坛(WEF)报告称，尽管到2022年机器学习和算法将取代7500万个工作岗位，但它们将在同一时期再创造1.33亿个新角色。

Machine learning technologies and algorithms are influencing our everyday lives in numerous ways. One of the largest beneficiaries of machine learning technology is security. For instance, in the Middle East, machine learning is being executed for image processing, facial recognition, and predictive analytics. This technology is driving its possibility to deliver more personalized experiences in the retail sector through chatbots.

机器学习技术和算法以多种方式影响着我们的日常生活。 安全是机器学习技术的最大受益者之一。 例如，在中东，正在执行机器学习以进行图像处理，面部识别和预测分析。 这项技术正在推动其通过聊天机器人在零售领域提供更多个性化体验的可能性。

In healthcare, machine learning is helping the industry shift from traditional methods by persuading the use of comprehensive algorithms and software to support doctors in diagnosing patients and disease. When it comes to energy and R&D, machine learning is allowing precision drilling, reservoir management and driving safety and production. Machine learning is also poised to transform the transportation sector globally. In the UAE, the first autonomous taxis have launched trials and projected to introduce them on the roads soon, once safety and traffic feasibility have been addressed.

在医疗保健领域，机器学习通过说服综合算法和软件支持医生诊断患者和疾病，帮助行业从传统方法转变。 在能源和研发方面，机器学习使您可以进行精确的钻探，储层管理以及驾驶安全和生产。 机器学习也有望改变全球的运输行业。 在阿联酋，首批自动出租车已经开始试验，并计划在解决了安全性和交通可行性之后，将其引入公路。

In conclusion, machine learning represents a tremendous revenue growth opportunity widely. For example, in the UAE, the government has disclosed a strategy to position the country as a hub for Artificial Intelligence by leveraging machine learning technology across government services and the private sector. It also targets to recruit and train people to work in industries that will employ machine learning engineer shortly. This is a massive opportunity for businesses looking to grow their machine learning capabilities in the future.

总之，机器学习广泛地代表着巨大的收入增长机会。 例如，在阿联酋，政府披露了一项战略，通过利用跨政府服务和私营部门的机器学习技术，将该国定位为人工智能的枢纽。 它还旨在招募和培训人员以从事不久将雇用机器学习工程师的行业。 对于希望将来增加机器学习功能的企业来说，这是一个巨大的机会。

关于作者 (About the Author)

Wie Kiang is a researcher who is responsible for collecting, organizing, and analyzing opinions and data to solve problems, explore issues, and predict trends.

Wie Kiang是一名研究人员，负责收集，组织和分析意见和数据以解决问题，探索问题和预测趋势。

He is working in almost every sector of Machine Learning and Deep Learning. He is carrying out experiments and investigations in a range of areas, including Computer Vision, Natural Language Processing, and Reinforcement Learning.

他几乎在机器学习和深度学习的每个领域工作。 他正在多个领域进行实验和研究，包括计算机视觉，自然语言处理和强化学习。