MIT Makes Probability-Based Computing a Bit Brighter

MIT Makes Probability-Based Computing a Bit Brighter The p-bit harnesses photonic randomness to explore a new computing frontier By EDD GENT      MARGO ANDERSON

In a noisy and imprecise world, the definitive 0s and 1s of today’s computers can get in the way of accurate answers to messy real-world problems. So says an emerging field of research pioneering a kind of computing called probabilistic computing. And now a team of researchers at MIT have pioneered a new way of generating probabilistic bits (p-bits) at much higher rates—using photonics to harness random quantum oscillations in empty space.

The deterministic way in which conventional computers operate is not well-suited to dealing with the uncertainty and randomness found in many physical processes and complex systems. Probabilistic computing promises to provide a more natural way to solve these kinds of problems by building processors out of components that behave randomly themselves.

The approach is particularly well-suited to complicated optimization problems with many possible solutions or to doing machine learning on very large and incomplete datasets where uncertainty is an issue. Probabilistic computing could unlock new insights and findings in meteorology and climate simulations, for instance, or spam detection and counterterrorism software, or next-generation AI.

The team can now generate 10,000 p-bits per second. Is the p-circuit next?

The fundamental building blocks of a probabilistic computer are known as p-bits and are equivalent to the bits found in classical computers, except they fluctuate between 0 and 1 based on a probability distribution. So far, p-bits have been built out of electronic components that exploit random fluctuations in certain physical characteristics.

But in a new paper published in the latest issue of the journal Science, the MIT team have created the first ever photonic p-bit. The attraction of using photonic components is that they operate much faster and are considerably more energy efficient, says Charles Roques-Carmes, a science fellow at Stanford University and visiting scientist at MIT, who worked on the project while he was a postdoc at MIT. “The main advantage is that you could generate, in principle, very many random numbers per second,” he adds.    ..'

Computer Vision That Works More Like a Brain Sees More Like People Do

Towards better vision. 

ACM TECHNEWS

Computer Vision That Works More Like a Brain Sees More Like People Do

By MIT News, July 13, 2023

When deep Learning computer vision systems establish efficient ways to solve visual problems, they end up with artificial circuits that work similarly to the neural circuits that process visual information in our own brains.

Researchers made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition.

Credit: iStock

James DiCarlo and colleagues at the Massachusetts Institute of Technology trained an artificial neural network to function more like the human and primate brain's inferior temporal (IT) cortex to improve computer vision.

The researchers constructed a computer vision model based on neural data from primate vision-processing neurons, and tasked it to recognize objects.

DiCarlo said this made the artificial neural circuits process visual information differently.

The researchers found the biologically informed model IT layer aligned better with the IT neural data than a similarly-sized network model that lacked neural-data training.

They also discovered the neurally aligned model was more resilient against adversarial attacks for assessing computer vision and artificial intelligence systems.

From MIT News

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Integrated Biosciences’ Platform has potential to fuel advances in senolytic anti-aging compounds and longevity research

Integrated Biosciences’ new platform has potential to fuel advances in senolytic anti-aging compounds and longevity research

AI-driven platform built on work pioneered at MIT identifies three candidates with comparable efficacy and superior medicinal chemistry relative to current investigational compounds

Treatment of aged mice reduced number of senescent ‘zombie’ cells and lowered expression of senescence-associated genes.

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated senescent cells (left, with red stain) without affecting other cells (right). Using artificial intelligence, researchers from Integrated Biosciences have, for the first time, identified three senolytics with comparable efficacy and superior drug-like properties relative to leading investigational compounds. (Photo: Business Wire)

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated senescent cells (left, with red stain) without affecting other cells (right). Using artificial intelligence, researchers from Integrated Biosciences have, for the first time, identified three senolytics with comparable efficacy and superior drug-like properties relative to leading investigational compounds. (Photo: Business Wire)

May 04, 2023 11:00 AM Eastern Daylight Time

SAN CARLOS, Calif.--(BUSINESS WIRE)--Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, in collaboration with researchers at the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, today announced results demonstrating the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small molecules under intense study for their ability to suppress age-related processes such as fibrosis, inflammation and cancer. A new publication authored by company founders in Nature Aging, “Discovering small-molecule senolytics with deep neural networks,” describes the AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and superior medicinal chemistry properties than those of senolytics currently under investigation.

“This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. “These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today.”

Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that are no longer dividing. A hallmark of aging, senescent cells have been implicated in a broad spectrum of age-related diseases and conditions including cancer, diabetes, cardiovascular disease, and Alzheimer’s disease. Despite promising clinical results, most senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences was founded in 2022 to overcome these obstacles, target other neglected hallmarks of aging, and advance anti-aging drug development more generally using artificial intelligence, synthetic biology and other next-generation tools.  ... ' 

Packing Just Got Faster, Easier

 Packing is always a place you should search for better.

Packing Just Got Faster, Easier

MIT News, Steve Nadis. July 6, 2023

Researchers at the Massachusetts Institute of Technology (MIT) and MIT spinout company Inkbit created a packing algorithm that determines ways to pack items most densely within a given object. The Scalable Spectral Packing model constructs an arrangement for solid three-dimensional (3D) objects to fill a fixed container, then positions them within it by voxelizing the container into a three-dimensional grid. The algorithm also voxelizes each object to be packed, then calculates the collision metric at each voxel to determine the available space for the object. The researchers use the fast Fourier transform method to locally maximize packing density by computing another metric at each voxel to find the best placement position while minimizing gaps between objects. In one demonstration, the algorithm packed 670 objects with a roughly 36% density in just 40 seconds. ... ' 

MIT-based AI apps startup aims to Block Supply Chain Attacks with advanced Cybersecurity

 More ...

MIT-based AI apps startup aims to block supply chain attacks with advanced cybersecurity

Louis Columbus  @LouisColumbus

June 15, 2023 10:07 AM

Join top executives in San Francisco on July 11-12 and learn how business leaders are getting ahead of the generative AI revolution. Learn More

The digital pandemic of increasing breaches and ransomware attacks is hitting supply chains and the manufacturers who rely on them hard this year. VentureBeat has learned that supply chain-directed ransomware attacks have set records across every manufacturing sector, with medical devices, pharma and plastics taking the most brutal hits. Attackers are demanding ransoms equal to the full amount of cyber-insurance coverage a victim organization has. When senior management refuses, the attackers send them a copy of their insurance policy. 

Disrupting supply chains nets larger payouts 

Manufacturers hit with supply chain attacks say attackers are asking for anywhere between two and three times the ransomware amounts demanded from other industries. That’s because stopping a production line for just a day can cost millions. Many smaller to mid-tier single-location manufacturers quietly pay the ransom and then scramble to find cybersecurity help to try to prevent another breach. Still, too often, they become victims a second or third time.  ... '

MIT Scientists Develop Flexible Metal-Free Electrode

MIT Scientists Develop Flexible Metal-Free Electrode

These Jell-O-like electrodes could eventually connect electronic implants with the human body.

By Ryan Whitwam June 20, 2023

The inexorable march of technological innovation continuously turns science fiction into science fact, and the next target may be the human body. With multiple companies moving toward human testing of bioelectronic implants, our cyberpunk future could be here before we know it, and a new project from MIT could play a crucial part. A team working with conductive polymers has succeeded in designing a soft metal-free electrode that can be 3D printed and attached directly to tissues.

The future of implantable technology could take many paths, but whether you're trying to restore vision, enhance mobility, or build a brain computer interface, you'll need an electrode to mediate the connection between gooey human and cold, unfeeling technology. Most electrodes, like the ones Elon Musk's Neuralink robot "sews" into the brain are composed of metal, which is naturally conductive. However, metals can cause tissue irritation over time, which leads to signal degradation.

Most polymers are natural insulators, so they cannot conduct signals. However, there is a class of conductive polymers, the discovery of which won a Nobel Prize in chemistry in the 1970s. Researchers working with mechanical engineering professor Xuanhe Zhao have experimented with several materials to produce soft, flexible electrodes. The team found some success by mixing conductive polymers with hydrogel, a gelatin-like polymer that is high in water content. However, this mixture didn't offer the right mix of conductivity and durability. "In gel materials, the electrical and mechanical properties always fight each other,” says collaborator Hyunwoo Yuk. “If you improve a gel’s electrical properties, you have to sacrifice mechanical properties, and vice versa."  .... ' 

MIT-based AI apps startup aims to block supply chain attacks with advanced cybersecurity

IT-based AI apps startup aims to block supply chain attacks with advanced cybersecurity

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More   in Venturebeat

The digital pandemic of increasing breaches and ransomware attacks is hitting supply chains and the manufacturers who rely on them hard this year. VentureBeat has learned that supply chain-directed ransomware attacks have set records across every manufacturing sector, with medical devices, pharma and plastics taking the most brutal hits. Attackers are demanding ransoms equal to the full amount of cyber-insurance coverage a victim organization has. When senior management refuses, the attackers send them a copy of their insurance policy. 

Disrupting supply chains nets larger payouts 

Manufacturers hit with supply chain attacks say attackers are asking for anywhere between two and three times the ransomware amounts demanded from other industries. That’s because stopping a production line for just a day can cost millions. Many smaller to mid-tier single-location manufacturers quietly pay the ransom and then scramble to find cybersecurity help to try to prevent another breach. Still, too often, they become victims a second or third time.  ... '

Asia's Opportunity for Generative AI

Expected, especially with regard to delivering multi lingual Generative results. 

ACM NEWS

Asia's Opportunity for Generative AI

By MIT Technology Review, June 16, 2023

The skyline of Shinjuku, Tokyo, Japan, with a view of Mt. Fuji.

The key promise of generative AI is to streamline virtually any routine language- or process-driven task, supporting the capabilities of humans while freeing up more creative and productive uses of time.

Credit: Morio/Wikimedia Commons

Suddenly, everybody is talking about generative artificial intelligence (AI). (Disclaimer: this article is written by a human.) The idea of software that generates dynamic, customized content is exciting. While chatbots have existed for years, a rapidly expanding suite of generative AI-based image, video, and text generators such as DALL-E 2, Fotor, Runway, AlphaCode, and ChatGPT (just to name a few) have the potential to democratize AI and put it into the hands of every person and every organization. Integrating these into mainstream software products in the form of "co-pilots" to assist in everyday tasks hold even more promise.

Generative AI offers particularly strong potential as an economic catalyst across Asia, building on advanced levels of digital adoption. Already, India and China are global centers of tech talent. Japan, Korea, and Singapore lead in smart cities and robotics, while a vibrant and growing startup ecosystem flourishes in Beijing, Jakarta, Bangkok, and beyond. All this provides a foundation for the region's developers to create the next wave of locally relevant solutions.

Implemented responsibly, generative AI stands to create a ripple effect—one that transforms industries, fosters productivity and innovation, and improves billions of lives. So, as the technology reaches an inflection point, what are some of its main uses and early success stories in Asia? And how should the region's organizations prepare to innovate?

From MIT Technology Review

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AI Helps Scientists Develop New Antibiotic To Kill Superbug

AI Helps Scientists Develop New Antibiotic To Kill Superbug

By Decrypt, May 31, 2023

Acinetobacter bacteria

Researchers trained a neural network and performed in silico predictions for new molecules with activity against A. baumannii.

Scientists from McMaster University and the Massachusetts Institute of Technology used machine learning to find a new antibiotic to kill Acinetobacter baumannii, a bacteria often found in hospitals that can cause serious infections, according to a study published in Nature Chemical Biology.

Acinetobacter baumannii, commonly transmitted via the hands of healthcare workers, has become resistant to most antibiotics and was deemed a "critical" threat by the World Health Organization, which said in 2017 that new antibiotics were "urgently needed" to fight it. 

AI may help achieve that goal. In this case, the scientists screened 7,500 molecules to find a new antibacterial compound named abaucin, which was shown to treat infected wounds on mice. 

The researchers call it a "promising lead" that "highlights the utility of machine learning in antibiotic discovery."

From Decrypt

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Successfully Deploying Machine Learning

Successfully Deploying Machine Learning

By focusing on data, value propositions, and talent, enterprises can get ready to take the next step beyond experimentation.

By MIT Technology Review Insights archive page

June 12, 2023

In association with JPMorgan Chase & Co.

After decades of research and development, mostly confined to academia and projects in large organizations, artificial intelligence (AI) and machine learning (ML) are advancing into every corner of the modern enterprise, from chatbots to tractors, and financial markets to medical research. But companies are struggling to move from individual use cases to organization-wide adoption for several reasons, including inadequate or inappropriate data, talent gaps, unclear value propositions, and concerns about risk and responsibility.

Successfully deploying machine learning

Download the report

This MIT Technology Review Insights report, commissioned by and produced in association with with JPMorgan Chase, draws from a survey of 300 executives and interviews with seven experts from finance, health care, academia, and technology to chart elements that are enablers and barriers on the journey to AI/ML deployment.

The following are the report’s key findings:

Businesses buy into AI/ML, but struggle to scale across the organization. The vast majority (93%) of respondents have several experimental or in-use AI/ML projects, with larger companies likely to have greater deployment. A majority (82%) say ML investment will increase during the next 18 months, and closely tie AI and ML to revenue goals. Yet scaling is a major challenge, as is hiring skilled workers, finding appropriate use cases, and showing value.

Deployment success requires a talent and skills strategy. The challenge goes further than attracting core data scientists. Firms need hybrid and translator talent to guide AI/ML design, testing, and governance, and a workforce strategy to ensure all users play a role in technology development. Competitive companies should offer clear opportunities, progression, and impacts for workers that set them apart. For the broader workforce, upskilling and engagement are key to support AI/ML innovations.

Centers of excellence (CoE) provide a foundation for broad deployment, balancing technology-sharing with tailored solutions. Companies with mature capabilities, usually larger companies, tend to develop systems in-house. A CoE provides a hub-and-spoke model, with core ML consulting across divisions to develop widely deployable solutions alongside bespoke tools. ML teams should be incentivized to stay abreast of rapidly evolving AI/ML data science developments. .... ' 

Training Machines for Uncertain Real-World Simulations

 Uncertainty a key aspect.  

Training Machines for Uncertain Real-World Situations

MIT News

Adam Zewe, May 31, 2023

An algorithm developed by researchers at the Massachusetts Institute of Technology (MIT) and Technion—Israel Institute of Technology can determine automatically and independently whether and when imitation learning or reinforcement learning is more effective for training a "student" machine. The algorithm is adaptive, allowing the machine to move between both types of learning throughout the training process based on which would achieve better, faster results. MIT's Idan Shenfeld said, "This combination of learning by trial-and-error and following a teacher is very powerful. It gives our algorithm the ability to solve very difficult tasks that cannot be solved by using either technique individually."

Peripheral Vision for Machines

Beyond robotics

The Benefits of Peripheral Vision for Machines

Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes.

Adam Zewe | MIT News Office

Publication Date:March 2, 2022

Perhaps computer vision and human vision have more in common than meets the eye?

Research from MIT suggests that a certain type of robust computer-vision model perceives visual representations similarly to the way humans do using peripheral vision. These models, known as adversarially robust models, are designed to overcome subtle bits of noise that have been added to image data.

The way these models learn to transform images is similar to some elements involved in human peripheral processing, the researchers found. But because machines do not have a visual periphery, little work on computer vision models has focused on peripheral processing, says senior author Arturo Deza, a postdoc in the Center for Brains, Minds, and Machines.

“It seems like peripheral vision, and the textural representations that are going on there, have been shown to be pretty useful for human vision. So, our thought was, OK, maybe there might be some uses in machines, too,” says lead author Anne Harrington, a graduate student in the Department of Electrical Engineering and Computer Science.

The results suggest that designing a machine-learning model to include some form of peripheral processing could enable the model to automatically learn visual representations that are robust to some subtle manipulations in image data. This work could also help shed some light on the goals of peripheral processing in humans, which are still not well-understood, Deza adds.  ... '

Yann LeCun's Bold New Vision for the Future of AI

Like LeCun's views they are always interesting, inspired us early on in AI.   His connection to Meta is interesting given his generally very practical views.

Yann LeCun's Bold New Vision for the Future of AI

By MIT Technology Review, June 28, 2022,  Meta AI Chief Scientist Yann LeCun.

"This idea that we're going to just scale up the current large language models and eventually human-level AI will emerge—I don't believe this at all, not for one second." -Yann LeCun

Yann LeCun, chief scientist at Meta's artificial intelligence (AI) lab and one of the world's most influential AI researchers, has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world.

"Getting machines to behave like humans and animals has been the quest of my life," he says. LeCun thinks that animal brains run a kind of simulation of the world, which he calls a world model.

From MIT Technology Review

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Exploring Material Interiors with Deep Learning

Previously Mentioned, now in more detail..

Machine learning & AI

Deep-learning system explores materials' interiors from the outside   by David L. Chandler , Massachusetts Institute of Technology 

Maybe you can't tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what's going on inside simply by observing properties of the material's surface.

The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials' external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.

The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

"It's a very common problem in engineering," Buehler explains. "If you have a piece of material—maybe it's a door on a car or a piece of an airplane—and you want to know what's inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. But you can't really look inside the material. The only way you can do that is by cutting it and then looking inside and seeing if there's any kind of damage in there."

It's also possible to use X-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. "So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what's going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what's actually going on inside?" That inside information might include any damages, cracks, or stresses in the material, or details of its internal microstructure.

The same kind of questions can apply to biological tissues as well, he adds. "Is there disease in there, or some kind of growth or changes in the tissue?" The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.

Achieving that goal involved addressing complexities including the fact that "many such problems have multiple solutions," Buehler says. For example, many different internal configurations might exhibit the same surface properties. To deal with that ambiguity, "we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular [surface] scenario."

The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. This included not only uniform materials but also ones with different materials in combination. "Some new airplanes are made out of composites, so they have deliberate designs of having different phases," Buehler says. "And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances."  ... ' 

Training Machines to Learn More Like Humans Do

Training computer Vision models

MIT News, By Adam Zewe  May 9, 2023

Scientists at the Massachusetts Institute of Technology (MIT) and Toyota subsidiary Woven Planet found computer vision models can be trained to produce more stable, predictable visual representations, similar to those humans learn through perceptual straightening. The researchers taught the models millions of examples via adversarial training, which enhanced their perceptual straightness while reducing their reactivity to slight errors within images. They discovered the models trained on more perceptually straight representations could correctly classify objects in videos with greater consistency. MIT's Vasha DuTell said, "One of the take-home messages here is that taking inspiration from biological systems, such as human vision, can both give you insight about why certain things work the way that they do and also inspire ideas to improve neural networks."

Full Article  

Training Machines to Learn More Like Humans Do

 Computer Vision Models.

Training Machines to Learn More Like Humans Do   By MIT News  May 10, 2023

The researchers found a specific training technique can enable certain types of computer vision models to learn more stable, predictable visual representations.

Scientists at the Massachusetts Institute of Technology (MIT) and Toyota subsidiary Woven Planet found computer vision models can be trained to produce more stable, predictable visual representations, similar to those humans learn through perceptual straightening.

The researchers taught the models millions of examples via adversarial training, which enhanced their perceptual straightness while reducing their reactivity to slight errors within images.

They discovered the models trained on more perceptually straight representations could correctly classify objects in videos with greater consistency.

MIT's Vasha DuTell said, "One of the take-home messages here is that taking inspiration from biological systems, such as human vision, can both give you insight about why certain things work the way that they do and also inspire ideas to improve neural networks."

From MIT News  

Free MIT Press Book on Deep Learning

 An MIT Press book   https://www.deeplearningbook.org/ 

Ian Goodfellow and Yoshua Bengio and Aaron Courville

Exercises   Lectures   External Links  

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

The deep learning textbook can now be ordered on Amazon.

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Citing the book

To cite this book, please use this bibtex entry:

@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
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    year={2016}
}

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Configurable Robots Can Be Mixed, Matched for Tasks in Space

Configurable Robots Can Be Mixed, Matched for Tasks in Space

ACM TECHNEWS

By Interesting Engineering

March 22, 2023

The Walking Oligomeric Robotic Mobility System could revolutionize how we approach space robotics, according to MIT.

Credit: MIT

Massachusetts Institute of Technology (MIT) researchers engineered the Walking Oligomeric Robotic Mobility System (WORMS) to revolutionize space robotics.

MIT's Brooke Bensche said, "Our idea was that, with just a few parts, combined in different ways, you could mix and match and get all these different robots."

The university's Michael Brown said the system's appendages were inspired by the humble worm because each can make "similar movements as an arm, or a leg, or a backbone, or a tail."

Each configurable articulated robot carries its own motors, sensors, computer, and battery; they can be assembled individually to perform small tasks or together for more complex functions.

From Interesting Engineering

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Humans get X-Ray Vision in Augmented Reality

 Seen this in previous worlds we experimented with.   Concept interesting. 

MIT researchers invented an augmented reality headset that gives humans X-ray vision. The invention, dubbed X-AR, combines wireless sensing with computer vision to enable users to see hidden items. X-AR can help users find missing items and guide them toward these items for retrieval. This new technology has many applications in retail, warehousing, manufacturing, smart homes, and more.

For more information, check out:

Website: https://Xar.media.mit.edu

Paper: https://www.mit.edu/~fadel/papers/XAR-paper.pdf

Instagram: @mit_sk_lab (https://instagram.com/mit_sk_lab?igshid=YmMyMTA2M2Y=)

Authors: Tara Boroushaki, Maisy Lam, Laura Dodds, Aline Eid, Fadel Adib

Video Production: Maisy Lam, Jimmy Day

UI design: Maisy Lam, Yuechen Wang

Funding: NSF, Sloan Foundation, MIT Media Lab  .... 

VR Headset Gives you X-Ray Vision!

Possibilities are considerable in VRworld

MIT researchers invented an augmented reality headset that gives humans X-ray vision. The invention, dubbed X-AR, combines wireless sensing with computer vision to enable users to see hidden items. X-AR can help users find missing items and guide them toward these items for retrieval. This new technology has many applications in retail, warehousing, manufacturing, smart homes, and more.

For more information, check out:

Website: https://Xar.media.mit.edu

Paper: https://www.mit.edu/~fadel/papers/XAR-paper.pdf

Instagram: @mit_sk_lab (https://instagram.com/mit_sk_lab?igshid=YmMyMTA2M2Y=)

Authors: Tara Boroushaki, Maisy Lam, Laura Dodds, Aline Eid, Fadel Adib

Video Production: Maisy Lam, Jimmy Day

UI design: Maisy Lam, Yuechen Wang

Funding: NSF, Sloan Foundation, MIT Media Lab

VISIT WEBSITE   https://www.youtube.com/watch?v=bdUN21ft7G0