資料科學與機器學習(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.

毫無疑問，機器學習演算法通過為我們的日常生活增加新維度來形成一個更加無縫的世界。 社群還鼓勵更全面和協調地採用機器學習技術，包括在教育，政府服務和社群福利等重要領域中利用機器學習，以改善社會生活質量。

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.

通過這些舉措，我們在客戶服務行業中看到了機器學習的積極影響，客戶服務行業正在逐步轉向使用機器學習來管理其不斷增長的需求。 現在，通過檢視零售，銀行，醫療保健和政府服務中使用的聊天機器人，開發了移動應用程式以維持無縫的政府服務交付。

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.

自動駕駛汽車，機器人技術，人工智慧(AI)，物聯網(IoT)和3D列印等眾多轉型技術是第四次工業革命的新時代。 人工智慧是通過機器學習和深度學習推動第四次工業革命的最重要技術之一。 機器學習具有巨大的潛力，並在包括零售，安全，醫療保健和運輸等企業中使用案例。

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.

精通機器學習的企業必須專注於兩件事-業務案例收益和用例收益。 他們必須採取敏捷和分階段的戰略來開始建立動力。

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強調，與2017年相比，2018年在營銷組織中採用機器學習技術的比例增加了44％。預計客戶服務團隊對該技術和演算法的使用量將比2017年增加143％接下來的18個月。

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，實施機器學習的最大挑戰是冗餘或失業的威脅。 實際上，應該開發機器學習以適應勞動力市場中的不同目的，而不是使其變得多餘。 機器學習可能支援勞動力的效率，因為勞動密集型和單調的任務可以由機器完成，而人類則專注於更高級別的定性任務。

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.

世界經濟論壇(WEF)報告稱，儘管到2022年機器學習和演算法將取代7500萬個工作崗位，但它們將在同一時期再創造1.33億個新角色。

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.

機器學習技術和演算法以多種方式影響著我們的日常生活。 安全是機器學習技術的最大受益者之一。 例如，在中東，正在執行機器學習以進行影象處理，面部識別和預測分析。 這項技術正在推動其通過聊天機器人在零售領域提供更多個性化體驗的可能性。

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.

在醫療保健領域，機器學習通過說服綜合演算法和軟體支援醫生診斷患者和疾病，幫助行業從傳統方法轉變。 在能源和研發方面，機器學習使您可以進行精確的鑽探，儲層管理以及駕駛安全和生產。 機器學習也有望改變全球的運輸行業。 在阿聯酋，首批自動計程車已經開始試驗，並計劃在解決了安全性和交通可行性之後，將其引入公路。

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.

總之，機器學習廣泛地代表著巨大的收入增長機會。 例如，在阿聯酋，政府披露了一項戰略，通過利用跨政府服務和私營部門的機器學習技術，將該國定位為人工智慧的樞紐。 它還旨在招募和培訓人員以從事不久將僱用機器學習工程師的行業。 對於希望將來增加機器學習功能的企業來說，這是一個巨大的機會。

關於作者 (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是一名研究人員，負責收集，組織和分析意見和資料以解決問題，探索問題和預測趨勢。

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.

他幾乎在機器學習和深度學習的每個領域工作。 他正在多個領域進行實驗和研究，包括計算機視覺，自然語言處理和強化學習。