Cell Painting to Accelerate Drug Discovery: Finding Disease Phenotypes and Candidate Therapeutics Using Images Anne Carpenter Senior Director of the Imaging Platform, Institute Scientist, Broad Institute of Harvard and MIT
In this talk, Bryan Moser of the MIT System Design and Management at the Massachusetts Institute of Technology explores how organizations—particularly in Japan—can evolve from experience-driven decision-making to model-based transformation (MBX). Drawing on decades of research and field experience, he explains why traditional R&D and management approaches are under pressure from globalization and rapid technological change, and how interactive, computational models can strengthen portfolio decisions, improve collaboration, and accelerate innovation.
Technology is drastically reshaping the business world. And in this world marked by tight competition, organizations are required to innovate and develop at the speed of light to remain ahead – regardless of what industry they belong to, or which countries they operate in. Our Management of Technology: Roadmapping & Development online program provides an overview of the principles, methods, and management tools for technologically enabled systems and organizations.
The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the month long Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers — including MIT’s Marvin Minsky and Claude Shannon — to discuss “how to make machines use language” and “form abstractions and concepts.” A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that “within a generation ... the problem of creating ‘artificial intelligence’ will substantially be solved.”
The problem, of course, turned out to be much more difficult than AI’s pioneers had imagined. In recent years, by exploiting machine learning — in which computers learn to perform tasks from sets of training examples — artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Atari games or drive cars using vision with great success.
But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, “These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are.”
Poggio thinks that AI research needs to revive its early ambitions. “It’s time to try again,” he says. “We know much more than we did before about biological brains and how they produce intelligent behavior. We’re now at the point where we can start applying that understanding from neuroscience, cognitive science and computer science to the design of intelligent machines.”
Networks are the foundation of our digital society and economy, increasingly connecting people, devices, and systems across every sector, from healthcare and education to manufacturing and global supply chains. They will play a critical role in our AI-enabled future, powering data centers, smartphones, augmented reality experiences, connected vehicles, drones, sensors, and delivering the user experiences that define modern life.
Leaders in innovation are often the same individuals who become major players in global transformation. Our eight-week Leadership & Innovation program merges the development of leadership skills with the vision needed to address the innovation process within an organization. In this dynamic course, you will learn to lead from self-knowledge and creativity, and enhance your ability to build teams and organizations with a culture of innovation.
Jacob Witten Postdoctoral Fellow, MIT