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Featured Videos

RECENT VIDEOS

10 Results | Last Page
 

01.25.2019
37 mins
ILP Video

The Promise, Limits and Future of Intelligent Autonomy in the Air and on the Ground

Nicholas Roy
Bisplinghoff Professor of Aeronautics & Astronautics
Director, Robust Robotics Group
MIT Department of Aeronautics and Astronautics
Artificial intelligence and machine learning are disrupting industries across the globe, from self-driving cars to smart home assistants to automated call centers. There are many potential benefits including improved safety and productivity and reduced environmental footprint, however, there are technological limits, and not every sector of the economy is reaping the same level of benefits. State of the art AI and robotics will be discussed, along with how these technologies are impacting a range of business sectors, such as transportation, telecommunications, construction, and media. Emerging technologies in both academic and industrial research and development labs will be highlighted, alongside a summary of current hard problems and how these technologies are likely to evolve over time.
Read More

01.25.2019
37 mins
ILP Video

From Data to Information to Action: Reasoning, Uncertainty and Resource Limitations

John Fisher
Principal Research Scientist
MIT Computer Science and Artificial Intelligence Laboratory
How many conscious decisions does an adult make in a single day? Estimates vary widely, ranging from the low hundreds to an astounding 35,000. While not all decisions bear equal importance -- "Should I study engineering or physics?" versus "Should I wear a blue shirt or a green shirt?" -- there is a cost to the decision-making process itself. Uncertainty in the outcome may be difficult to quantify. Information sources may be numerous and complex. Time is often a critical limitation. As such, individuals expend resources -- reasoning, information gathering, physical energy -- in order to make decisions.

Analogously, in many distributed sensing problems, resource limitations (e.g. time, energy, computation, etc.) constrain the process of data integration and decision-making. In this talk, I will highlight some of the appealing properties of structured probabilistic models as a representation beyond their primary (and well known) use as a framework for inference. For example, Value of information (VoI) analysis, informed by the structure of the model, facilitates analysis of the trade-off between exploiting the informational utility of a distributed set of information sources and the resources necessary to acquire them, fuse them into a model of uncertainty, and ultimately reason over the representation for decision making. A critical aspect of this process is understanding the relations between information, uncertainty and risk.

In the course of this talk, I will present a variety of real-world applications of these methods highlighting both their advantages as well as pointing to new challenges.
Read More

01.25.2019
37 mins
ILP Video

Towards Accelerated Medical Innovation

Jeffrey M. Karp
Professor of Medicine, Brigham and Women’s Hospital, Harvard Medical School
Principal Faculty, Harvard Stem Cell Institute
Affiliate faculty, Broad Institute and at the Harvard-MIT Division of Health Sciences and Technology
For companies and academics aiming to innovate in the medical space, we often don’t spend enough time thinking about the problem we are trying to solve. This goes far beyond reading the academic literature. We need to think critically and constantly pressure test our assumptions to really understand problems and turn them into opportunities for solutions. This often involves connecting biology and materials science to practical considerations for technology implementation by the clinician, as well as all the other factors, such as market pull, regulatory, reimbursement, manufacturing, and patents for example. It’s about committing to a journey that can lead to critical insights that direct us towards the most tractable solutions. And for that, we need a different set of tools to help challenge our thinking and constantly bring in fresh ideas and perspectives

One approach we have implemented to constantly bring in new ideas, is to turn to nature for inspiration. Millions and millions of years of research and development at our fingertips, and all we need to do is look outside to the amazing creatures that inhabit our planet. We like to harness lessons from nature for inspiration, from creatures such as geckos, spiders, jellyfish, porcupine quills, snails, and spiny-headed worms.

Another approach is radical simplicity — reducing a problem to its essence. This tool has been harnessed to develop a new skin care approach that is advancing towards global market adoption, therapeutic strategies to combat inflammatory bowel disease and arthritis that are advancing towards clinical studies, and a drug combination to functionally restore hearing in patients with chronic hearing loss that is currently being tested in a Phase I/II trial. Some of the technologies are rapidly advancing to the clinic and some are already on the market helping patients. We must constantly pave new paths to continual innovation that is essential in our fast-changing world.
Read More

01.25.2019
45 mins
ILP Video

MIT Startup Exchange: Introduction with Lightning Talks

Marcus Dahllöf
Program Director
MIT Startup Exchange
Manijeh Goldberg - Privo Technologies: Nano cancer treatment
A.J. MacKinnon - Affectiva: Emotion AI
Nan-Wei Gong - figur8: Medical device, movement intelligence
Alexander Shkolnikg - LiquidPiston: New combustion engine
Oz Locker - ClimaCell: Microweather data AI/ML
Daisy Zhuo - Interpretable AI: Predictive AI for life sciences/other
Adam Behrens - Cambridge Crops: Advanced materials for perishables/food
Hyunjun Park - CATALOG: New DNA storage tech
Dan Sturtevant - Silverthread: Software code health, quality assessment
Read More

01.25.2019
39 mins
ILP Video

Inverse materials design through artificial intelligence and physics-based simulations

Rafael Gomez-Bombarelli
Toyota Assistant Professor in Materials Processing
MIT Department of Materials Science and Engineering
Machine learning tools, combined with theoretical simulations can effectively accelerate design of novel materials. Data-driven approaches can access the information embedded in years of experiments, perform rapid optimization of high-dimensional experimental conditions and design parameters, increase the accuracy and speed of physics-based simulations, or design new molecules and crystals automatically. By combining cutting-edge machine learning models on experimental data with automated theoretical simulations (molecular dynamics, electronic structure) the Gomez-Bombarelli addresses the design and optimization of novel materials in multiple areas such as small molecules (organic electrolytes for flow batteries), soft materials (lithium-conducting polymers, organic light-emitting diodes) and crystalline frameworks (zeolite catalysts). Here, we will describe recent results and ongoing work in using machine learning as the connector between multiple scales of simulation and experiment and automation of computer simulations.
Read More

01.25.2019
37 mins
ILP Video

The Promise, Limits and Future of Intelligent Autonomy in the Air and on the Ground

Nicholas Roy
Bisplinghoff Professor of Aeronautics & Astronautics
Director, Robust Robotics Group
MIT Department of Aeronautics and Astronautics
Artificial intelligence and machine learning are disrupting industries across the globe, from self-driving cars to smart home assistants to automated call centers. There are many potential benefits including improved safety and productivity and reduced environmental footprint, however, there are technological limits, and not every sector of the economy is reaping the same level of benefits. State of the art AI and robotics will be discussed, along with how these technologies are impacting a range of business sectors, such as transportation, telecommunications, construction, and media. Emerging technologies in both academic and industrial research and development labs will be highlighted, alongside a summary of current hard problems and how these technologies are likely to evolve over time.
Read More

01.25.2019
37 mins
ILP Video

From Data to Information to Action: Reasoning, Uncertainty and Resource Limitations

John Fisher
Principal Research Scientist
MIT Computer Science and Artificial Intelligence Laboratory
How many conscious decisions does an adult make in a single day? Estimates vary widely, ranging from the low hundreds to an astounding 35,000. While not all decisions bear equal importance -- "Should I study engineering or physics?" versus "Should I wear a blue shirt or a green shirt?" -- there is a cost to the decision-making process itself. Uncertainty in the outcome may be difficult to quantify. Information sources may be numerous and complex. Time is often a critical limitation. As such, individuals expend resources -- reasoning, information gathering, physical energy -- in order to make decisions.

Analogously, in many distributed sensing problems, resource limitations (e.g. time, energy, computation, etc.) constrain the process of data integration and decision-making. In this talk, I will highlight some of the appealing properties of structured probabilistic models as a representation beyond their primary (and well known) use as a framework for inference. For example, Value of information (VoI) analysis, informed by the structure of the model, facilitates analysis of the trade-off between exploiting the informational utility of a distributed set of information sources and the resources necessary to acquire them, fuse them into a model of uncertainty, and ultimately reason over the representation for decision making. A critical aspect of this process is understanding the relations between information, uncertainty and risk.

In the course of this talk, I will present a variety of real-world applications of these methods highlighting both their advantages as well as pointing to new challenges.
Read More

01.25.2019
37 mins
ILP Video

Towards Accelerated Medical Innovation

Jeffrey M. Karp
Professor of Medicine, Brigham and Women’s Hospital, Harvard Medical School
Principal Faculty, Harvard Stem Cell Institute
Affiliate faculty, Broad Institute and at the Harvard-MIT Division of Health Sciences and Technology
For companies and academics aiming to innovate in the medical space, we often don’t spend enough time thinking about the problem we are trying to solve. This goes far beyond reading the academic literature. We need to think critically and constantly pressure test our assumptions to really understand problems and turn them into opportunities for solutions. This often involves connecting biology and materials science to practical considerations for technology implementation by the clinician, as well as all the other factors, such as market pull, regulatory, reimbursement, manufacturing, and patents for example. It’s about committing to a journey that can lead to critical insights that direct us towards the most tractable solutions. And for that, we need a different set of tools to help challenge our thinking and constantly bring in fresh ideas and perspectives

One approach we have implemented to constantly bring in new ideas, is to turn to nature for inspiration. Millions and millions of years of research and development at our fingertips, and all we need to do is look outside to the amazing creatures that inhabit our planet. We like to harness lessons from nature for inspiration, from creatures such as geckos, spiders, jellyfish, porcupine quills, snails, and spiny-headed worms.

Another approach is radical simplicity — reducing a problem to its essence. This tool has been harnessed to develop a new skin care approach that is advancing towards global market adoption, therapeutic strategies to combat inflammatory bowel disease and arthritis that are advancing towards clinical studies, and a drug combination to functionally restore hearing in patients with chronic hearing loss that is currently being tested in a Phase I/II trial. Some of the technologies are rapidly advancing to the clinic and some are already on the market helping patients. We must constantly pave new paths to continual innovation that is essential in our fast-changing world.
Read More

01.25.2019
45 mins
ILP Video

MIT Startup Exchange: Introduction with Lightning Talks

Marcus Dahllöf
Program Director
MIT Startup Exchange
Manijeh Goldberg - Privo Technologies: Nano cancer treatment
A.J. MacKinnon - Affectiva: Emotion AI
Nan-Wei Gong - figur8: Medical device, movement intelligence
Alexander Shkolnikg - LiquidPiston: New combustion engine
Oz Locker - ClimaCell: Microweather data AI/ML
Daisy Zhuo - Interpretable AI: Predictive AI for life sciences/other
Adam Behrens - Cambridge Crops: Advanced materials for perishables/food
Hyunjun Park - CATALOG: New DNA storage tech
Dan Sturtevant - Silverthread: Software code health, quality assessment
Read More

01.25.2019
39 mins
ILP Video

Inverse materials design through artificial intelligence and physics-based simulations

Rafael Gomez-Bombarelli
Toyota Assistant Professor in Materials Processing
MIT Department of Materials Science and Engineering
Machine learning tools, combined with theoretical simulations can effectively accelerate design of novel materials. Data-driven approaches can access the information embedded in years of experiments, perform rapid optimization of high-dimensional experimental conditions and design parameters, increase the accuracy and speed of physics-based simulations, or design new molecules and crystals automatically. By combining cutting-edge machine learning models on experimental data with automated theoretical simulations (molecular dynamics, electronic structure) the Gomez-Bombarelli addresses the design and optimization of novel materials in multiple areas such as small molecules (organic electrolytes for flow batteries), soft materials (lithium-conducting polymers, organic light-emitting diodes) and crystalline frameworks (zeolite catalysts). Here, we will describe recent results and ongoing work in using machine learning as the connector between multiple scales of simulation and experiment and automation of computer simulations.
Read More

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