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AspenTech官方Twitter发布新的消息：
With remote collaboration, operational agility and autonomous production becoming ever more critical to business continuity – the importance of #AI is top-of-mind for many executives.
@adi_pendyala dives into #ArtificialIntelligence in @DisruptiveAsean https://bit.ly/2S7Iqvy
下面内容来自 https://bit.ly/2S7Iqvy
Leveraging Artificial Intelligence to Accelerate Digital Transformation Strategy amidst the Current Economic Conditions
在当前经济形势下利用人工智能加快数字化转型战略
By Adi Pendyala, Senior Director at Aspen Technology
作者：Aspen Technology高级总监Adi Pendyala
As the world’s economies grapple with the current fallout, every organization, whether small or large, public or private, is finding new ways to operate effectively and meet the needs of their customers as lockdowns, international border closures and quarantine measures disrupt the economic value chain from manufacturing to logistics.
According to The World Bank’s Global Economic Prospects Report released in June 2020, COVID-19 led to steep recessions across many countries. The baseline forecast in the report envisioned a 5.2 percent contraction in global GDP in 2020—the deepest global recession in decades.
Artificial intelligence (AI) plays an important role in providing the tools to augment decision making, generate faster and more insightful analytics and enable real-time supply chain management. Machine learning technology enables computers to mimic human intelligence and ingest large volumes of data to quickly identify patterns and insights.
Artificial intelligence (AI) is a well-recognised and used buzzword. However, it means different things in different situations. Whilst most people think of AI as a technology in its own right, it is actually more of a general term used to refer to a number of different technologies that enable systems to act intelligently.
When it comes to business applications, AI can support intelligent functionality by helping the system sense, understand, perform and learn. By using machine learning or deep learning to train a system, the system can assess how to act in each situation by analysing data, rather than relying on prescriptive, hard-coded actions. The resulting agility and responsiveness mean that quality, accuracy and overall performance are drastically improved – and this is what makes the system truly intelligent.
In the current climate and with uncertain times ahead, several enterprises are looking at how they can rapidly adapt and accelerate their digital transformation strategy. With remote collaboration, operational agility and autonomous production becoming ever more critical to their business continuity – the importance of AI is on top-of-mind of many executives.
Importance of Machine Learning
What sets AI apart from other automation technologies is its ability to learn and adapt. In an industrial environment, AI systems can have a significant impact on business performance by largely reducing manual labour, quickly identifying patterns in large amounts of data and analysing and extracting features from both structured and unstructured datasets. Most importantly, it can learn from these tasks and improve over time.
Machine learning can be executed in a number of ways: supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses pre-organised training data and feedback from humans to learn the relationship of given inputs to a given output. This method is useful if the input data and predicted behaviour type is already classified, but the algorithm needs to be applied to multiple different datasets.
Unsupervised learning does not require any pre-defined labels in the data – no output variables need to be pre-identified, and the algorithm can analyse input data to find patterns and make classifications.
Finally, reinforcement learning allows the system to learn to perform a task by trial and error. In essence, this method is based on rewards and punishments, with the overall aim of maximising rewards and minimising punishments in the feedback received for its actions. This approach is particularly useful when there are limited training data to use, or when it is difficult to identify the desired outcome and this is the only real way to interact with and learn from the data.
Why, What and How of Enterprise AI
In the fast-paced digital world, organisations are turning to AI to revolutionise more than just their technology. Instead they are looking to redefine business processes as a whole. From pioneering innovation to everyday customer service, AI is transforming the business landscape, and defining this paradigm shift is the key to understanding enterprise AI. The “Constellation of AI,” a paradigm introduced in the book ‘Human + Machine: Reimagining Work in the Age of AI’ by Paul R. Daugherty and H. James Wilson, is one such framework that exists to try and explain the application of AI on an enterprise level.
Using this framework, enterprise AI can be viewed across three levels. The first level identifies the ‘why’ and the ‘what’ – the business applications that use data to provide greater value to its stakeholders. The second level identifies the suite of AI capabilities that can be leveraged to power the business application. And the third level looks at the ‘how’ – which machine learning methods can deliver the pre-identified AI capability.
The framework also enables the complexities of AI-based business applications to be simplified and fully assessed to allow enterprises to build an all-inclusive AI program, analyse and define the business value for each AI initiative, and determine the basic requirements that would drive a successful AI program and justify investment.
Future of AI Adoption
While there is clear business value in adopting enterprise AI, asset-intensive, process-based industries are significantly behind other sectors when it comes to implementation.
This is largely due to the need for new skills and a lack of quality data. According to market research firm Gartner, 56% of enterprise leaders feel they need updated skills to accomplish AI-enabled tasks, and 34% said that poor data quality is a key concern. 42% of Gartner respondents also said they do not fully understand the benefits of AI or the implied return on investment (ROI) due to the challenge of quantifying the benefits of AI.
However, by 2024, ROI will be measured by quantifying AI investments and linking them to specific KPIs – giving the future of enterprise AI a clear direction of travel in terms of measurement and real-world statistics. And by establishing a common understanding of AI’s enterprise value and setting out clear guidance for business application, organisations can capitalise on the simple Constellation of AI framework to implement successful AI projects, now and in the future.
以下为Google机器自动翻译：
随着世界经济努力应对当前的问题，每个企业，无论大小，公营或私营，都在寻找新的方式来有效运营并满足客户需求，因为封锁，国际边界​​关闭和检疫措施破坏了经济价值链从制造到物流。
根据世界银行2020年6月发布的《全球经济前景报告》，COVID-19导致许多国家的经济严重衰退。该报告的基准预测预计，到2020年全球GDP将收缩5.2％，这是数十年来最严重的全球衰退。
人工智能（AI）在提供工具以增强决策，生成更快和更深入的分析以及实现实时供应链管理方面发挥着重要作用。机器学习技术使计算机能够模仿人类的智能并吸收大量数据，从而快速识别出模式和见解。
人工智能（AI）是一个公认的常用词汇。但是，在不同情况下它意味着不同的事物。虽然大多数人将AI本身视为一种技术，但实际上它实际上更是一个通用术语，用于指代使系统能够智能运行的多种不同技术。
在业务应用程序方面，人工智能可以通过帮助系统感知，理解，执行和学习来支持智能功能。通过使用机器学习或深度学习来训练系统，系统可以通过分析数据来评估如何在每种情况下采取行动，而不是依赖于规定性的硬编码动作。由此产生的敏捷性和响应能力意味着质量，准确性和整体性能得到了极大的提高–这就是使该系统真正智能化的原因。
在当前的气候和不确定的时代中，几家企业正在研究如何快速适应并加速其数字化转型战略。随着远程协作，业务敏捷性和自主生产对其业务连续性的重要性变得越来越重要– AI的重要性已成为许多高管的首要考虑因素。
机器学习的重要性
AI与其他自动化技术的不同之处在于其学习和适应能力。在工业环境中，人工智能系统可以通过大大减少体力劳动，快速识别大量数据中的模式以及从结构化和非结构化数据集中分析和提取特征来对业务绩效产生重大影响。最重要的是，它可以从这些任务中学习并随着时间的推移而改进。
机器学习可以通过多种方式执行：监督学习，无监督学习和强化学习。监督学习使用预先组织的训练数据和来自人类的反馈来学习给定输入与给定输出的关系。如果输入数据和预测的行为类型已经分类，则该方法很有用，但是该算法需要应用于多个不同的数据集。
无监督学习不需要在数据中添加任何预定义标签-无需预先识别输出变量，并且该算法可以分析输入数据以查找模式并进行分类。
最后，强化学习允许系统通过反复试验来学习执行任务。本质上，此方法基于奖励和惩罚，其总体目标是在收到的针对其操作的反馈中最大化奖励和最小化惩罚。当使用的培训数据有限或难以确定所需的结果并且这是与数据进行交互和从中学习的唯一真实方法时，此方法特别有用。
企业AI的原因，方式和方式
在快节奏的数字世界中，组织正在转向AI进行革命，而不仅仅是其技术。相反，他们希望重新定义整个业务流程。从开创性创新到日常客户服务，人工智能正在改变业务格局，定义这种范式转变是理解企业人工智能的关键。在“星座AI的，”书中介绍的范例“人+机器：Reimagining工作在AI时代”由保罗·R·多尔蒂和H.詹姆斯·威尔逊，就是这样的一个框架，它的存在是为了尝试和解释中的应用企业级的AI。
使用此框架，可以从三个层次上查看企业AI。第一层标识“为什么”和“什么” –使用数据为利益相关者提供更大价值的业务应用程序。第二层确定可以利用的AI功能套件来为业务应用程序提供动力。第三级研究“方法”-哪种机器学习方法可以提供预先确定的AI功能。
该框架还使基于AI的业务应用程序的复杂性得以简化和充分评估，从而使企业能够建立一个包罗万象的AI程序，分析和定义每个AI计划的业务价值，并确定将推动AI计划发展的基本要求。成功的AI计划并证明投资合理性。
人工智能采用的未来
尽管采用企业AI具有明显的商业价值，但在实施方面，资产密集型，基于流程的行业却远远落后于其他行业。
这主要是由于对新技能的需求以及缺乏质量数据。根据市场研究公司Gartner的说法，56％的企业领导者认为他们需要更新的技能才能完成AI任务，34％的企业领导者认为数据质量差是关键问题。42％的Gartner受访者还表示，由于量化AI收益的挑战，他们不完全了解AI的收益或隐含的投资回报率（ROI）。
但是，到2024年，将通过量化AI投资并将其与特定的KPI关联来衡量ROI，从而为企业AI的未来提供一个明确的衡量和实际统计方向。通过建立对AI企业价值的共识并制定明确的业务应用指南，组织可以利用简单的AI星座框架来实现现在和将来成功的AI项目。
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