It is an exciting time for computer vision. With the success of new computational architectures for visual processing, such as deep neural networks (e.g., ConvNets) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. Even when no examples are available, Generative Adversarial Networks (GANs) have demonstrated a remarkable ability to learn from images and are able to create nearly photorealistic images. The performance achieved by convNets and GANs is remarkable and constitute the state of the art on many tasks. But why do convNets work so well? what is the nature of the internal representation learned by a convNet in a classification task? How does a GAN represent our visual world internally? In this talk I will show that the internal representation in both convNets and GANs can be interpretable in some important cases. I will then show several applications for object recognition, computer graphics, and unsupervised learning from images and audio.
The large amounts of both structured and unstructured data created in manufacturing and operations today present enormous opportunities to apply advanced analytics, machine learning and deep learning. This talk will describe specific use cases in process control and optimization; yield prediction and enhancement; defect inspection and classification and anomaly detection in time series data. Additionally, some of the unique manufacturing and operations challenges like: class imbalance, concept drift and complex multivariate time dynamics will be described. This research has led to the creation of MIT MIMO (Machine Intelligence for Manufacturing and Operations) which will be described during this talk.
If AI succeeds in eclipsing human general intelligence within decades, as many leading AI researchers predict, then how can we make it the best rather than worst thing ever to happen to humanity? I argue that this will require planning and hard work, and explore challenges that we need to overcome as well as exciting opportunities. How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? How can we make machines understand, adopt and retain our goals, and whose goals should should they be? What future do you want? Welcome to the most important conversation of our time!
Artificial intelligence has the potential to radically reshape business and society, and transform the way we work and live -- unlike anything we’ve seen since the Industrial Revolution. Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Based on research gathered from 1,500 organizations revealed in the book Human + Machine: Reimagining Work in the Age of AI, this talk will shed light into key research that is needed, how organizations are deploying AI to work with humans in fundamentally new ways, and how the “Missing Middle” is the secret to humans powerfully harnessing the opportunity and the promise of AI for greater good.
Construction Tech is one of the fastest growing areas of venture capital funding in the US. With over three billion in investments over the past year it is clear that Construction Tech will soon impact the ways we deliver building of all sizes. Moving forward we need new, rich ideas in software development to solve many of the building industries toughest problems. The talk will present a framework for home delivery directly from computers. Larry will show how builders will design and construct buildings from digital files using systems similar to 3D Printing.
This lecture will detail the creation of ultrasensitive sensors based on electronically active conjugated polymers (CPs) and carbon nanotubes (CNTs). A central concept that a single nano- or molecular-wire spanning between two electrodes would create an exceptional sensor if binding of a molecule of interest to it would block all electronic transport. The use of molecular electronic circuits to give signal gain is not limited to electrical transport and CP-based fluorescent sensors can provide ultratrace detection of chemical vapors via amplification resulting from exciton migration. Nanowire networks of CNTs provide for a practical approximation to the single nanowire scheme. These methods include abrasion deposition and selectivity is generated by covalent and/or non-covalent binding selectors/receptors to the carbon nanotubes. Sensors for a variety of materials and cross-reactive sensor arrays will be described. The use of carbon nanotube based gas sensors for the detection of ethylene and other gases relevant to agricultural and food production/storage/transportation are being specifically targeted and can be used to create systems that increase production, manage inventories, and minimize losses.
The impetus for the SENSE.nano is the recognition that novel sensors and sensing system are bound to provide previously unimaginable insight into the condition of individuals, as well as built and natural world, to positively impact people, machines, and environment. Advances in nano-sciences and nano-technologies, pursued by many at MIT, now offer unprecedented opportunities to realize designs for, and at-scale manufacturing of, unique sensors and sensing systems, while leveraging data-science and IoT infrastructure.
Understanding the brain could lead to new kinds of computational algorithms and artificial intelligences, as well as treatments for intractable disorders that affect over a billion people worldwide. However, the brain is a very complex, densely wired circuit, and understanding how it works has remained elusive. In order to map how these circuits are organized, and control their complex dynamics, we are building new tools, which include methods for physically expanding brain circuits so that we can see their building blocks, as well as molecules that make neural circuits controllable by light. Through these tools we aim to enable the systematic analysis and repair of the brain.
Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.
Early and accurate detection of cancer represents an enormous opportunity for sensing technologies to impact patients' lives. I will discuss several examples of diagnostic technologies developed in the Bhatia lab that employ nanosensors to detect tumors using a simple urine test for readout. This platform technology uses nanosensors to detect enzyme activity associated with cancer invasion, and generate bar-coded reporters that can be detected by multiplexed mass spectrometry or antibody-based methods such as lateral flow assays. I will close the presentation with an introduction to the Marble Center for Cancer Nanomedicine, a new growing resource for the nanomedicine community.