We are in the process of transitioning to a new economy where highly complex, custom products are manufactured on demand by automated manufacturing systems. For example, 3D printers are revolutionizing production of metal parts in aerospace, automotive, and medical industries. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. In this talk, I will show that computation is an integral component of modern design and manufacturing. I will demonstrate how computational tools allow creating digital materials with precisely controlled physical properties and how these digital materials are used to automatically synthesize product designs with desired specifications. I will also show how computational tools enable real-time, closed-feedback loop in additive manufacturing systems to improve their reliability and to fabricate complex products with integrated electronics.
Justin Solomon X-Consortium Career Development Associate Professor of Electrical Engineering and Computer Science
Asuman Ozdaglar Joseph F. and Nancy P. Keithley, Professor of Electrical Engineering and Computer Science
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.”
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Formate Economy and AI-Assisted Catalyst Search Ju Li Battelle Energy Alliance Professor, MIT Department of Nuclear Science & Engineering Professor, MIT Department of Materials Science and Engineering
Carbon efficiency is one of the most pressing problems of carbon dioxide electroreduction today. While there have been studies on anion exchange membrane electrolyzers with carbon dioxide (gas) and bipolar membrane electrolyzers with bicarbonate (aqueous) feedstocks, both suffer from low carbon efficiency. In anion exchange membrane electrolyzers, this is due to carbonate anion crossover, whereas in bipolar membrane electrolyzers, the exsolution of carbon dioxide (gas) from the bicarbonate solution is the culprit. Here, we first elucidate the root cause of the low carbon efficiency of liquid bicarbonate electrolyzers with thermodynamic calculations and then achieve carbon-efficient carbon dioxide electro- reduction by adopting a near-neutral-pH cation exchange membrane, a glass fiber intermediate layer, and carbon dioxide (gas) partial pressure management. We convert highly concentrated bicarbonate solution to solid formate fuel with a yield (carbon efficiency) of greater than 96%. A device test is demonstrated at 100 mA cmÀ2 with a full-cell voltage of 3.1 V for over 200 h. ["A carbon-efficient bicarbonate electrolyzer," Cell Reports Physical Science 4 (2023) 101662]
Over the past decade, research on the development of multi-cellular engineered living systems has produced technologies and capabilities that are now positioned to facilitate a fundamental understanding of disease processes and can help to identify innovative therapeutic strategies. Globally, while many labs are engaged in the development of new and more sophisticated organ models for drug discovery and screening, there is an urgent need to disrupt the way drugs are currently developed. Our vision is to humanize drug development based on a new approach that integrates microphysiological system models of disease and enhanced model control/interrogation, with modern systems biology and systems immunology. This is the focus of Living Machines, one of five threads in the New Engineering Education Transformation (NEET) program to reimagine engineering education at MIT in which sophomores, juniors and seniors, under the guidance of faculty mentors and instructors, learn, discover, build and engineer living systems for broad applications in biotechnology and medical devices. This webinar will share the perspectives of 3 MIT faculty, their research capabilities and interests in which NEET students can participate, and that of several NEET students and what they can or hope to achieve.