近年来，使用人工智能来增强预测, 规划, 和企业决策的系统不断增加,，但在许多情况下, 它们缺乏有关组织本身, 的详细, 具体信息, 限制了这些工具的实用性。
Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools.
“从某种意义上说,用少量资源,你必须做很多繁重的工作,”他说。作为研究人员, “，我的兴趣在于开发能够以尽可能有效的方式从大规模数据中提取信息的方法。”
“In a sense, with a small amount of resource, you have to do a lot of heavy lifting,” he says. As a researcher, “my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible.”
Andrew (1956) 和 Erna Viterbi 教授自 2005 年以来一直在麻省理工学院任教。
The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005.
2019,，他还与他人共同创立了一家名为 Ikigai Labs 的衍生公司。 Ikigai 基于 Shah的 实验室, 的多年研究，构建了表格, 时间序列数据的基础模型，该模型已获得麻省理工学院的专利并授权给该公司。该模型可以连续、大规模地从不同来源,的企业数据中,获取输入，以便通过根据实际结果测试其预测来不断学习。
In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in Shah的 lab, which was patented and licensed by MIT to the company. This model can take input from enterprise data from varied sources, continuously and at scale, so that it learns as it goes along by testing its predictions against real outcomes.
Shah 解释说，该系统是图形模型类型的扩展，例如 GPS 设备使用, 将从卫星接收到的稀疏数据转换为地球的 表面, 上位置的精确模型，或者通过数字手表中的通信系统以节能方式进行高速通信。
Shah explains that the system is an extension of the kind of graphical models that are used, for example, by GPS devices to convert a sparse amount of data received from satellites into an accurate model of a position on the Earth的 surface, or by communication system like that in a digital watch that communicates at high speed in an energy-efficient manner.
“M我的兴趣是:，如何为通用,表格数据?”设计这样的图形模型，他说。
“My interest was: How does one design such graphical models for generic, tabular data?” he says.
虽然大多数 AI 模型都是使用文本和图像进行教学的,，但该系统采用表格数据作为输入 — 结构化数据，例如电子表格中使用的熟悉的行和列格式。然后它提供了更大规模的实时规划,。
While most AI models have been taught using text and images, this system takes tabular data as its input — structured data such as the familiar kind of row-and-column format used in spreadsheets. And then it provides the kind of real-time planning, on a vastly larger scale.
Ikigai 的想法是为消费品制造商和制药公司等大型企业, 提供预测和决策技术。
The idea for Ikigai was to provide forecasting and decision-making technology for large businesses, such as consumer goods manufacturers and pharmaceutical companies.
Shah 举例说明了消费电子公司如何使用该系统。
Shah gives the example of how a consumer electronics company might use this system.
“让的说你’正在制作耳机和各种不同的东西。您生产的每种产品都有许多来自世界不同地区的小部件。一旦设备售出,，就需要支持和维护。你必须想出产品的新版本, 你必须推销它们, 你必须给它们定价 … 所以你通常会问的问题是: 如果我要在下个季度或明年出售这些产品, 在不同的地方会售出多少, 如果我改变价格, 或如果我引入促销，需求会发生什么?”
“Let的 say you’re making headphones and all sorts of different things. And each of the products that you manufacture has lots of small pieces that come from different parts of the world. And once the device is sold, it needs to be supported and maintained. And you have to come up with new versions of the product, you have to market them, you have to price them … So the questions you would typically ask would be: If I were to sell these next quarter or next year, how many will be sold in different places, and what would happen to demand if I change the price, or if I introduce promotion?”
他补充说，所有这些流程都是相互依赖的,，并且在流程的每个阶段都必须做出随着时间的推移而产生影响的决策。 “在某种程度上,”他说,“数字化这些流程并能够进行预测和不断优化最终可以带来更好的业务运营。”
He adds that all of these processes are interdependent, and at every stage of the processes decisions have to be made that have implications over time. “At some level,” he says, “digitizing these processes and being able to do predictions and constantly optimize is what leads to ultimately better business operations.”
Ikigai was recently acquired by the international firm Celonis, where Shah is now chief scientist in addition to his roles at MIT.最终,，他希望他为 Ikigai 开发的模型能够帮助 Celonis 提供能够与公司 自己的数据和业务流程集成的工具，以便提供真实世界的分析，帮助做出预测, 计划, 和决策。
Ikigai was recently acquired by the international firm Celonis, where Shah is now chief scientist in addition to his roles at MIT. Ultimately, he hopes the model he developed for Ikigai will help Celonis deliver tools that can integrate with a company的 own data and business processes in order to provide real-world analyses that can help make forecasts, plans, and decisions.
Shah 补充道，Celonis 专门为全球超过 1,400 的大型公司提供数字化和自动化运营服务。现在这些系统已完全数字化,，它们为 Ikigai的 软件提供了一个平台，以采取下一步, 从这些数字化系统中读取数据，以便提供详细的模型以允许模拟不同的选项, 预测最佳策略, 并预测给定决策集的结果。
Shah adds that Celonis has specialized in digitizing and automating operations for more than 1,400 large companies around the world. Now that these systems are fully digitized, they provide a platform for Ikigai的 software to take the next step, reading the data from these digitized systems in order to provide detailed models to allow simulation of different options, predict optimum strategies, and forecast the results of a given set of decisions.
“一旦这些流程的数字层存在并且该信息层存在,” Shah 说, “现在, 位于其之上, 我们可以将 Ikigai 堆栈用于比其他方式更大规模的决策制定。”
“Once the digital layer of these processes exists and this information layer exists,” Shah says, “now, on top of it, we can put the Ikigai stack to enable decision-making at a much larger scale than otherwise.”
虽然许多公司正在研究 AI,“ 的各个方面，但我们非常关注世界其他地方没有关注的部分领域,”，即结构化或时域数据领域。他说，, 从这些数据开始，它提供了一个非常经济高效的人工智能版本。
While so many companies are working on various aspects of AI, “we are very much focused on part of the domain that the rest of the world is not paying attention to,” which is the area of structured or time-domain data. By starting from such data, he says, it provides a very cost-effective version of AI.
“A 更窄的焦点伴随着更锐利的技术,” 他说, “但是它 足够广泛，以至于 非常有价值。”
“A narrower focus comes with sharper technology,” he says, “but it的 broad enough that it的 very valuable.”
Shah 补充道, “最近流行语 在现代 AI 流行媒体中变得相关，这是一个 ‘ 世界模型。 从某种意义上说, 这是试图构建企业流程世界模型, 可以这么说。”
Shah adds, “The recent buzzword that的 become pertinent in the modern AI popular press is a ‘world model. In a sense, this is trying to build the enterprise process world model, so to speak.”