data

Auto Added by WPeMatico

Coca-Cola uses AI to improve retailer ordering in Malaysia

ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, business strategy, data, Features, Inside AI, malaysia, Marketing AI, Retail, Retail & Logistics AI, retail logistics

Coca-Cola is using AI to recommend which products Malaysian retailers should order and in what quantities through its Coke Buddy platform. The Perfect Basket feature uses Coca-Cola’s Central Recommendation Engine to analyse previous orders, ordering frequency, seasonality, weather, and purchasing patterns among similar businesses. Coca-Cola said Coke Buddy currently supports about 39,000 retail outlets across […]

Coca-Cola uses AI to improve retailer ordering in Malaysia Read More »

From MIT to IBM, expediting AI and quantum deployment

ai, AI (Artificial Intelligence), Algorithms, Alumni/ae, Artificial Intelligence, Careers, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Graduate, postdoctoral, Industry, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Computing Research Lab, MIT-IBM Watson AI Lab, Quantum Computing, School of Engineering

The experience of transitioning from research based in theory to focusing on real-world application can vary significantly for different researchers. However, for two former MIT graduate students and a former postdoc, all now at IBM, working with the MIT-IBM Computing Research Lab (formerly the MIT-IBM Watson AI Lab) during their formative years enabled them to

From MIT to IBM, expediting AI and quantum deployment Read More »

Data Intelligence: Building Your Competitive Advantage in the Era of AI

ai, AI (Artificial Intelligence), AI & ML, Artificial Intelligence, Commentary, data

To keep pace with modern business, data strategy is shifting toward more autonomous real-time systems that deliver intelligence at the moment decisions are made. Driven by agentic AI, modern data teams are moving beyond simply looking at what happened. Now they’re automating complex workflows that analyze what’s happening, anticipate what might happen next, and recommend

Data Intelligence: Building Your Competitive Advantage in the Era of AI Read More »

When AI art has no author: Study finds generated images often can’t be traced to training data

ai, AI (Artificial Intelligence), Artificial Intelligence, Arts, Arts, Technology and society, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), ethics, Law, Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Technology and policy, Technology and society

When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory

When AI art has no author: Study finds generated images often can’t be traced to training data Read More »

How a medical database developed at MIT evolved into a global standard of data-sharing

ai, AI (Artificial Intelligence), Artificial Intelligence, data, Electrical engineering and computer science (EECS), Harvard-MIT Health Sciences and Technology, Health care, Health sciences and technology, history, History of MIT, History of science, Information systems, Institute for Medical Engineering and Science (IMES), Internet, Medicine, Research Laboratory of Electronics, School of Engineering

Before the advancement of scientific data storage and collaboration via the cloud, medical investigators seeking health research breakthroughs had to overcome significant obstacles to collaboration and key clinical data gathering. Data were siloed and difficult to distribute, so those looking to undertake research had no option but to gather them themselves. This not only made research

How a medical database developed at MIT evolved into a global standard of data-sharing Read More »

Cómo anticipar el burnout y construir organizaciones públicas más saludables

Advanced Analytics, ai, AI (Artificial Intelligence), AI Ethics, Analytics, analytics platform, Artificial Intelligence, Customer Experience, data, Data Analytics, digital transformation, Real-Time Analytics, Trustworthy AI, 全般

El bienestar laboral se ha consolidado como una prioridad estratégica para las organizaciones, tanto públicas como privadas. La Organización Mundial de la Salud define el burnout como un síndrome derivado del estrés crónico mal gestionado en el trabajo, caracterizado por agotamiento, distanciamiento mental de la actividad profesional y una disminución […] The post Cómo anticipar

Cómo anticipar el burnout y construir organizaciones públicas más saludables Read More »

3 Questions: Neural transparency and the future of AI design

ai, AI (Artificial Intelligence), Algorithms, Apps, Artificial Intelligence, Computer science and technology, data, ethics, Faculty, Human-computer interaction, Interview, Machine Learning, Media Lab, Mental health, Research, Safety, School of Architecture and Planning, Technology and society

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside

3 Questions: Neural transparency and the future of AI design Read More »

Helping AI models to meet the real world

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Business and management, Computer science and technology, data, Electrical engineering and computer science (EECS), Faculty, IDSS, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), MIT intellectual property, MIT Schwarzman College of Computing, Profile, Research, School of Engineering, Startups

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. Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the

Helping AI models to meet the real world Read More »

Jesse Thaler named director of the Laboratory for Nuclear Science

ai, AI (Artificial Intelligence), Artificial Intelligence, data, Department of Energy (DoE), Faculty, IDSS, Laboratory for Nuclear Science, Leadership, National Science Foundation (NSF), Physics, School of Science

Professor Jesse Thaler has been named director of the MIT Laboratory for Nuclear Science (LNS), effective Aug. 1. He succeeds Professor Bolek Wyslouch, who directed LNS for the past decade. Thaler is a theoretical particle physicist who combines techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. “In his research,

Jesse Thaler named director of the Laboratory for Nuclear Science Read More »

Q&A: What is agentic AI today, and what do we want it to be?

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Interview, Machine Learning, MIT Schwarzman College of Computing, School of Engineering

The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon. To understand the fundamentals and potential impacts of these

Q&A: What is agentic AI today, and what do we want it to be? Read More »