Original article

MIT News: https://news.mit.edu/index.php/2026/helping-ai-models-meet-real-world-0714

Introduction

Professor Devavrat Shah’s work focuses on AI systems that use tabular and time-series enterprise data to make continuous predictions and decisions with limited computational resources. Good IELTS practice for business, AI, and systems vocabulary.

Vocabulary

tabular data — data organized in rows and columns; proliferate — increase rapidly; forecasting — predicting future outcomes; computational resources — processing, memory, and related capacity; at scale — at large practical scale; spinoff company — a company created from an institution; foundation model — a reusable model adaptable to multiple tasks; sparse — containing relatively little information; real-time planning — planning using continuously updated information; interdependent — mutually dependent; digitize — convert processes or information into digital form; optimize — make as effective as possible; cost-effective — producing good results relative to cost.

Reading comprehension

1. Why are many existing AI tools of limited usefulness to businesses?

Reference answer
Generic AI tools often lack detailed knowledge of a specific company’s own processes and data.

2. How is Shah’s system different from models trained mainly on text and images?

Reference answer
It primarily consumes structured tabular and time-series data and is designed for large-scale real-time planning and forecasting.

3. How does the model improve its predictions over time?

Reference answer
It continuously compares predictions with real outcomes and updates what it learns from the enterprise data stream.

4. Why are business processes described as interdependent?

Reference answer
Decisions about production, pricing, marketing, supply, maintenance, and future products affect one another over time.