By Jess Campitiello

In the last year, artificial intelligence (AI) art generators such as Stable Diffusion, DALL-E 2, and Midjourney have been making a splash across the headlines. To the general public, what started as a fun, harmless, and novel way to pass some time quickly turned into professional discourse over artistic integrity.

Generative AI programs are trained by scraping large online public datasets to learn what words relate to corresponding images. Once this has been done, users can input a text prompt which causes the program to compile what it has learned and composite it into a single image output. Björn Ommer, who worked on Stable Diffusion, explains its workings in more detail here.

Accusations of art theft arose surrounding the use of creatives’ works to train these models, as image outputs emulated prominent artists’ styles without their consent. Backlash grew as an AI-generated piece won first place in an art competition. On Reddit, one user was banned for posting their own work because it too closely resembled AI art. Ultimately, the outrage has led to multiple lawsuits unfolding against these programs.

Simultaneously, the generative AI platform Scenario raised $6 million in seed funding and Microsoft made a ‘multiyear, multibillion-dollar investment’ in OpenAI, the company behind Dall-E 2 and ChatGPT.

“There’s a Cambrian explosion right now with this technology,” said Scott Belsky, Adobe’s Chief Product Officer and Executive Vice President of Creative Cloud, at the most recent Cornell Tech @ Bloomberg Speaker Series event. Alongside over 800 partners, Adobe has been working to add generative AI to its products while still maintaining user trust.

When asked about how artificial intelligence and machine learning fit into the future of art, there were three main points that Belsky mentioned:

1. Creative confidence is only going to grow.

   Our creative confidence typically peaks as kids, Belsky explained. Starting off with simple crayon drawings, many children find a creative outlet within art. Crude approximations of houses, animals, and loved ones are held in high esteem as they are tacked up on the fridge. However, as we grow older and we meet critics — those who insist on colors within the lines and accurate representations on the page — our confidence goes down. And with that, so too does many peoples’ efforts to pursue artistic endeavors. This technology not only makes creativity widely accessible, but also allows individual artistic confidence to grow since the barrier to entry is quite low.

2. Professionals can access endless creative possibilities.

   Generative AI allows artists to create countless visual concepts at the click of a button, making the process of fielding potential design ideas with clients exponentially more efficient. Giving clients a couple of quick AI ‘thumbnail sketch’ options will allow artists to finalize their work faster, as they will not be spending time creating preliminary visuals. The fabricated art is not meant to be used as a replacement but rather as a springboard for the artist’s own creative output. AI can even offer design choices that the artist wouldn’t have considered otherwise, mentioned Belsky. Integrating this technology into artists’ tools has the potential to grant them an “edge to a breakthrough.”

3. Outcome-oriented versus process-oriented people.

   Belsky described two different generative AI user types: those who are outcome-oriented, and those who are process-oriented.

   Outcome-oriented individuals are less interested in the details of the final product and more focused on receiving the product itself. For example, if an outcome-oriented person were to ask an AI program to generate images of a ‘cowboy in space,’ they would be happy to use the first image presented that suits their needs. The speed of the output is key.

   On the other hand, process-oriented individuals look at what is generated by the AI program and use it as a tool to create something unique for their needs. Inputting the same prompt, they may be drawn to and emulate the overall composition of one of the outputs but would move the cowboy’s arms into a specific position or make any other number of edits to create a final piece that is precisely what they want.

   We are all on a spectrum of outcome-to-process-oriented, explained Belsky.

Do artists have a right to be scared? Belsky says that while AI will not be replacing creatives in their spaces as it cannot emulate a human eye for aesthetics, their concerns are justified and there are many important questions that need to be considered moving forward with these powerful technologies. “Has their content been trained on these models? If so, how are they being compensated? Can you generate things in the style of a specific person? People can be ethical and say ‘no,’ but other players can say ‘it’s a free for all.’”

Belsky concluded his thoughts on the subject by stating that nothing in Adobe’s Cloud has ever been stored for the use of training a generative AI, but that the company plans to be very specific about this in its future terms and policies so that there is no ambiguity on the matter.

Watch the full Cornell Tech @ Bloomberg interview:

The Cornell Tech @ Bloomberg Speaker Series is hosted by Scarlet Fu of Bloomberg Television and Bloomberg QuickTake in partnership with Bloomberg, Cornell Tech, and Tech:NYC.

Jess Campitiello is the Digital Communications Specialist at Cornell Tech.

The post AI vs. Artist: The Future of Creativity appeared first on Cornell Tech.

At the recent Cornell Blockchain Conference, Professor of Law James Grimmelmann talked about how ambiguities in natural language that have proven contentious in courts of law for centuries can manifest in smart contracts as well.

To start the conversation on how language can affect a legal contract, Grimmelmann described the famous case of Frigaliment Importing Co. v. B.N.S. International Sales Corp. This case focused on a disagreement on how to define the word “chicken.” The buyer defined it as “a young chicken suitable for broiling and frying,” whereas the seller said it was “any bird… of the species Gallus gallus domesticus” — and depending on the context, both definitions could be considered correct.

As language evolves, the common usages of words change and, subsequently, statements containing those words are altered in meaning. According to Grimmelmann, smart contracts were created in an attempt to remove this ambiguity but even contracts made of code can be uncertain.

“All natural language contains ambiguities,” said Grimmelmann. “The problem is that language is social. Words have meanings based upon how they are used and that can change — language is a stream that is always moving and changing, and that means the contracts that try to rely upon language to fix parties’ obligations can be ambiguous.” This was true, he emphasized, even for smart contracts.

For a basic example, take the expression “2 + 2” in the programming language, Python. The result will always be “4,” and any other answer than that would be unanimously considered  wrong. However, the program only knows this because humans have agreed that the sequence of symbols “2 + 2″ results in 4 in Python. It is a social convention, just like choosing to use the word “chicken” to refer to a specific type of bird.

“It’s only when there’s no serious disagreement about the social facts that the technical facts [based upon] them are unambiguous.”

If people collectively decided that “2 + 2″ in Python should result in 5 in Python” then all instances of “2 + 2″ producing 4” would become incorrect. This is also the case for the programming languages, like Solidity, used to write smart contracts on blockchains. However, Grimmelmann believes blockchain developers shouldn’t allow the risk of ambiguity to dissuade them from pursuing the use of smart contracts in the future.

“All is not lost,” he said. “Smart contracts don’t have to be perfect. If they are better than legal contracts for some applications, that’s enough.”

The post Linguistic Ambiguity in the World of Blockchain appeared first on Cornell Tech.

Aatonomy’s modular software simplifies the deployment of machine-learning and computer-vision capabilities to hardware products and robots. Their platform can add autonomous functionality to any kind of machine, whether it be cleaning a house or carrying out military maneuvers.

Built at Cornell Tech

Pulling’s trajectory from child inventor to robotics pioneer began at the University of Pennsylvania in the M&T Program, where he earned dual undergraduate degrees in Mechanical Engineering and Business. He went into management consulting but transitioned into a mission to acquire expertise in the third part of the robotics entrepreneur trifecta: programming skills. Cornell Tech, with its focus on technology and business, was the perfect fit.

At Cornell Tech, Pulling met Yang Hu, a microelectronics specialist who had studied at Tsinghua University in China; Hu is co-founder and now CTO of Aatonomy. Brainstorming ideas along with Kuowei Tseng and Jeehyun (Inna) Kim, both Master’s of Computer Science ‘16 and the other members of their Startup Studio team, they decided that rather than build a one-task robot, they would produce infrastructure technology to enable many robots.

Pulling and Hu asked themselves what it would take for robotics to become mainstream: for robots to be found in every home and business. Currently, robotics faces a core problem as hardware and software development are tightly paired.“Robots are purpose-built and inflexible. It’s difficult to add new functionalities or even to keep them updated with the best solutions. Hardware companies that want to add ‘smart’ functionality to their products shouldn’t have to become algorithmic experts,” Pulling said. To become mainstream, they concluded, the deployment of intelligence across any form factor had to be simplified.

Powered by Aatonomy

The first “Powered by Aatonomy” application adds a “Follow-Me” selfie mode to a $100 drone sold by a Canadian company, LiteHawk, at The Source stores in Canada. The drone’s new mode allows the machines to fly themselves, to lock onto and follow users, and to shoot videos and close-ups.

The code has been added to LiteHawk’s existing iOS and Android apps, meaning it runs on users’ smartphones. “We change the iOS code and all of a sudden the drone can fly autonomously. We don’t have a special chip they have to put in, and the drone doesn’t need to have GPS. The fact that we don’t have to change their hardware at all is mind-bending to them,” Pulling said.

A Viable Business

At Cornell Tech, Pulling, Hu, and their teammates developed Aatonomy’s initial architecture and showed they could control different robots with the same code base, but they also worked on their business model. “Cornell Tech inherently forces you to evaluate the viability of your product as a business, not just as a technology, from the very beginning,” said Pulling.

Building a competitive edge in robotics is notoriously challenging since software and hardware change quickly and become outdated. Aatonomy tackles this by focusing on integrations. “Hardware can be copied in six months, and algorithms become depreciated in 18 months. By making it really easy to cycle new algorithms into new or even existing hardware, we serve a need that will never go away,” said Pulling.

Receiving a 2016 Cornell Tech Startup Award allowed Pulling and Hu to take Aatonomy to the next level in a real-world setting and when they graduated later that year, the pair relocated to California. With further assistance from Alchemist Accelerator, they identified B2B selling opportunities and robotics applications that could be enabled with their software. The company is now based in Houston where they’ve been able to acquire 800 sq. ft. of office space. “It’s important [to have more space] because we’re flying the robots around. We have the robots running around the office,” said Pulling.

The LiteHawk drone is the first “Powered by Aatonomy” product, and retail drones have served as their initial wedge into the market. But Pulling and Hu are already preparing to expand to other categories, from cleaning robots, to security cameras, and even to military applications with the Department of Defense. “We did a week-long experiment with them in the desert and we just said, ‘Bring us your robots and we’ll make them autonomous,’ Pulling said. “It’s the same code for military ground robots as it is for the retail drones. That’s the whole point.”

The post How To Easily Make Any Robot Autonomous appeared first on Cornell Tech.

This is among the core issues being explored at the Sept. 28-29 Speed Conference, hosted by Cornell Tech’s Digital Life Initiative (DLI), a cross-disciplinary and cross-campus program aimed at uniting students and scholars to analyze technology’s impact on society. Faculty from Cornell’s Ithaca and New York City campuses, as well as Stanford University, Columbia University, Northeastern University and other schools, will speak on panels examining the role of technology and speed in areas like content moderation, finance, warfare and policing, and labor and manufacturing.

“There’s a very strong sense that, in all the important ways of applying technology, everything’s going to be fine as long as we can insert a human in the loop,” said Helen Nissenbaum, Cornell Tech professor of information science, director of DLI and co-chair of the Speed Conference. “What we are worried about is that because humans and machines think and process at such enormously different speeds, this particular protection that we think we can build in is actually infeasible. And that raises all sorts of societal questions, ethical questions, legal questions and probably technical questions, too.”

Panelists at the Speed Conference will include Nissenbaum; conference co-chair James Grimmelmann, professor of law at Cornell Tech and Cornell Law School; Steven Jackson, associate professor of information science; and Wendy Ju, assistant professor of information science in the Jacobs Technion-Cornell Institute at Cornell Tech.

“There’s a real mismatch between how fast computers operate and how fast people react,” Grimmelmann said. “We want to come collaboratively across fields to understand the place for humans to respond to this new challenge.”

The Speed Conference is one of the first major events from DLI, which launched in 2017. The initiative also holds a weekly seminar series at Cornell Tech, open to the public, focusing on the development and application of digital technology. A DLI doctoral fellowship began this semester, drawing students from across Cornell’s campuses. As the effort continues ramping up, Nissenbaum said she hopes DLI can support cross-disciplinary research projects on related topics, as well as additional classes.

“Our goal is not necessarily to question the activity of technological development – we’re not opposed to that – it’s more to raise awareness that tech can be developed in many different directions, and it can take many different shapes,” she said. “I think many people just accept technology and think it’s God-given, but they may not be aware that choices are made all along the way to shape the technology from the very beginning. Especially with students, I want them to be aware that every step of the way, from the beginning, you can integrate ethical and societal thinking.”

The Speed Conference is supported by Microsoft.

The post Fast and out of control? Conference examines the role of speed in tech appeared first on Cornell Tech.

Thread Learning aims to radically improve that process.

Two years ago, Greg Brill, Johnson Cornell Tech MBA ’16, and Sam Raudabaugh, Master in Computer Science ’16, won a $100,000 investment from Cornell Tech to create an electronic health record for the therapists and other caretakers working with children with autism.

Until recently, therapists and teachers primarily analyzed and tracked students’ progress with a pen and paper in a painstaking process. Brill and Raudabaugh’s product Thread allows them to collect, graph, and analyze academic and behavioral data on an intuitive mobile application and online portal.

Through the power of technology, caregivers can now coordinate care for the first time by sending instructions and automatic progress reports to parents and other caregivers. Thread even gives caregivers reminders and advice to help increase the quality of instruction. Brill and Raudabaugh are now hoping the data collected through the platform proves that coordinated care among caregivers along with improved parental involvement will increase the rate of learning in the children.

At the same time, Thread also helps therapists and teachers save many hours per week in paperwork, which opens up more time with the children.

“Our goal is to bring efficient, top-quality care to the millions of people around the world on the autism spectrum,” said Brill, who worked in autism education prior to pursuing his MBA.

Only 3 months after the company started, the Thread platform was ready to deploy as a successful pilot in New York City autism classrooms. This traction, and the fact that the system was already piloted successfully in real classrooms, aided the team when they presented for one of Cornell Tech’s Startup Awards in front of 300+ audience of venture capitalists, students, investors, and judges. Since then, Brill and Raudabaugh’s dream of doing social good while building a business has evolved far beyond their initial vision. After Cornell and the success of their first product, they raised an additional $1.23 million Seed Round to build a second product for practice management, to help simplify the complicated world of insurance reimbursement, session notes, and healthcare management.

“Everyone knows someone affected by complicated insurance systems,” said Raudabaugh. He feels good about building a program that will allow insurance companies to more efficiently understand and process claims, reducing stress for the affected families.

Thread Learning’s team and business are growing quickly. They recently added one of the largest autism therapy providers in the United States to their customer list, Proud Moments ABA. Thread Learning expects to add at least eight New York area schools to its customer list in September and expand to ten campuses of NYC Autism Charter School this school year. They also hope to hire two more employees to add to their four full-time and five part-time workers.

An Emphasis On Simplifying User Experience

As soon as they met on the first day of Cornell Tech’s Admitted Students Day, Brill and Raudabaugh knew they wanted to collaborate.

“I even wrote about autism in my personal statement for Cornell Tech,” said Raudabaugh, whose family includes teachers and computer scientists. “I was inspired by the inclusive technologies I saw for people with autism on the app store, but talking to Greg, I realized that there were still huge problems in autism that needed solving.”

Cornell Tech’s curriculum encourages students to solve problems from a customer’s perspective by focusing on user feedback and experimentation. For example, Brill and Raudabaugh worked closely with their potential customers and found that therapists want to be able to work on multiple lesson plans with multiple children at the same time because it’s been shown to better engage children, leading to measurably improved outcomes. Thread allows teachers to instruct and measure up to six lesson plans simultaneously with many students .

“That inspired us to make being user-friendly a core value of the company,” said Raudabaugh.

Despite advances in inclusive technology, the digital revolution has largely passed by the autism space. Thread Learning aims to fix that by creating an intuitive and easy-to-use platform that helps therapists and teachers provide cutting-edge care.

“Everyone in the space does instruction a little differently,” said Brill. “The fact that Thread is built to be super intuitive means we can support all the different instructional methodologies that our customers use.”

Unlike other products, Thread tries to support all special-needs therapists and teachers, including behavioral analysts who use a variety of Applied Behavior Analysis (ABA) based methodologies. In recent years, ABA has gained widespread adoption from government and insurance, leading to a triple-digit increase in the number of children receiving autism-related services. Thread’s aforementioned multiple-student data tracking surpasses competing products built under the false assumption that caretakers are testing one data point with one student at a time.

The platform’s intuitive user interface was created with the help of Stephen Lang, the User Experience designer-in-residence at Cornell Tech’s The Foundry. “Cornell Tech gave us access to a great network,” said Raudabaugh.

What’s Next?

After seeing success in home care, clinics, and classrooms, the Thread Learning team now has its eyes set on making scalable improvements to the autism ecosystem. With Thread, they are collecting millions of data points on how children with special needs learn and now plan on using data analytics and deep learning to create insights for caregivers.  

“Almost everything we know about special-needs therapy was learned in studies of small groups of children, which takes years to conduct and disseminate,” said Brill. “We’ll be able to look at how hundreds of thousands of children learn, which I think will give us real insight into things like path-of-treatment, learning styles, and instructional techniques.”

Thread Learning’s mission has a  global scope. “There are probably at least 7 million people around the globe on the autism spectrum,” said Brill. “While modern treatment is extremely effective, the vast majority of people living with autism aren’t getting it. We want to push the boundaries of cutting-edge care, while also using technology to bring the best research, the best tools, and the best care to people all around the world.”

The post How Thread Learning is Modernizing the Complex World of Autism Care appeared first on Cornell Tech.

Jha and Hachuel’s team won a spot in the 6-8 month incubator program at MIT for their AI algorithms that can characterize human stool samples based on visual input. This can help manage and prevent certain aspects of chronic digestive disorders like Irritable Bowel Syndrome (IBS) and Inflammatory Bowel Disease (IBD) that affect over 60 million Americans.

Hachuel and Jha’s technology has potential to scale beyond IBS and IBD given the product is purely software based. One application that shows promise is helping drug developers and researchers collect higher quality data that is less subjective to patient reports.

In the coming months, they will focus their efforts in perfecting the technology and polishing the business plan leveraging resources both from Cornell Tech and MIT.

The post Jacobs Institute Students Win Top Prize at MIT Grand Hack appeared first on Cornell Tech.

This question fascinated Dr. Wendy Ju, who joined the Jacobs Technion-Cornell Institute at Cornell Tech as Assistant Professor of Information Science last year. Ju is setting up a robotics research lab for human-and-robot interaction, where she will seek how to design machines that can easily and naturally interact with humans.

Watching what humans do

Dr. Ju has a PhD in mechanical engineering from Stanford and a master’s degree in media arts and sciences from MIT. She has spent years observing human interactions. “They’re basically very small conversations that enable everyday life: Who’s going to open the door? Who’s going to go in first or second? With cars we do it at four-way stops,” she said.

Watching what happens when things go wrong during these interactions can also be useful, she said. “I’m just a person that really likes to watch people. I really love looking at breakdowns, I like being in public places and seeing when these things get tripped up.”

Ju explained that machines do not automatically pick up cues that people use every day; rather, everything they do requires button pushing or explicit asking. Humans are constantly signaling to one another, so when a robot is added to the mix, how is it given the skills it needs to interact?

“I’m interested in that kind of interaction, knowing that is how people interact with one another, and thinking about what that means for how we design machines.”

According to Ju, these interactions are not always completely obvious to the people making robots because they work so invisibly that it’s not often noticed “I feel like I have an obligation, since I can see these things, to design the machines so that they work that way as well,” she said.

On the road with ghost drivers

One of Ju’s principal areas of research is autonomous cars. She’s carried out field experiments to see how people behave around them. To gauge these reactions, Ju and her team use what she calls “ghost drivers.” In light of a recent accident in Tempe, Arizona, where a woman was killed by an autonomous Uber car undergoing testing, Ju’s methods have particular significance. “We basically have a person dressed in a costume to look like a car seat driving on the road, and then we can do experiments to see how people react, but with the safety of an actual human controlling the car,” she said.

Through her work, Ju aims to answer specific questions about set scenarios. For example, how quickly will people react if a car hands control over to its passenger? What will people do when they encounter an autonomous car on the road? The results could be surprising. People tend to understand that autonomous cars and robots are learning, Ju said, and they often want to help. “People do different things to enforce norms about where the car’s supposed to be and when it’s supposed to stop,” she said.

Ju’s other work on robot-and-human interactions is focused on everyday situations. The machines she deploys are not actually autonomous, they’re controlled by people. The point is to try to anticipate how people might behave around robots in the future. How will they react to a robot collecting trash, cooking food, or running on the sidewalk?

Some people test the robots to see what they are capable of, while others try to help them. “If the robot plays dumb, if it doesn’t pick up a signal, people will do different things to really exaggerate what they’re doing, like wave the garbage or point at empty spaces,” Ju said. These, she said, are implicit cues which people amplify to teach the robot the correct way to do something.

By observing all of the different cues and behaviors, Ju can then incorporate them into machine-learning algorithms. This way, robots can learn from the natural things that humans do.

A new context in NYC

To date, Ju has carried out most of her experiments around Stanford. She has found that people there are pretty nonchalant when they encounter robots. “A lot of people are very pro-technology and they also work in technology so they’re very much like, ‘Oh yeah, this is happening now,’” she said.

Having recently moved to New York City, she is curious to find out whether people will behave differently or the same way. One of her first projects at Cornell Tech will explore how people interact with a troupe of chair robots -— essentially chairs that can reconfigure themselves in a space. She will also carry out further on-road experiments with ghost drivers.

Ju relishes the opportunity to continue her work in this new context. The diverse department at the Jacobs Technion-Cornell Institute also really appealed to her. “There are so many people with shared interests in both technology and the social issues around that,” she said. The city played its part too, “New York City was a big draw because I’m so interested in observing people,” she said. “I’m working at the intersection of technology and design; New York City’s such a great place for that.”

The post Can Humans and Robots Interact Naturally? appeared first on Cornell Tech.

The negative health effects of poor sleep are well documented, said CEO and Startup Postdoc at Runway and Elisha M. Friedman Postdoctoral Fellow, Dr. Amir Reuveny. If you have a sleep disorder, your risk of developing diabetes can be up to three times higher than normal and the chance of having high blood pressure can be up to four times higher.

Further, sleep deprivation and disorders are estimated to cost the economy over $400 billion annually due to factors such as accidents and loss of productivity. And, of course, disturbed sleep can have a serious effect on a person’s emotional and personal life.

Perhaps most surprising, most people who suffer from sleep deprivation and sleep disorders are undiagnosed, “It was really staggering for us, why such a huge epidemic is left with so many underdiagnosed and untreated patients,” said Reuveny.

Reuveny, who studied imperceptible electronics and earned his PhD from Tokyo University, was drawn to the problem not least because of the sheer scale of it, but also for personal reasons: his own father, he said, hasn’t had a good night’s sleep in more than twenty years.

The other members of the team are: CTO, Ahud (Adi) Mordechai; data products consultant, Noam Schwartz; and sleep specialist, Stephanie Zandieh. They talked to dozens of sleep physicians and technicians in a bid to understand how the process and economics of sleep diagnostics currently work, then they set about improving things.

The existing diagnostic procedure is labor-intensive and expensive, making it inaccessible to many people. It is also cumbersome and unpleasant; sleep labs haven’t changed much since the 1970s, said Reuveny, “You sleep outside your house. You are wired to 10-20 different electrodes. People are watching you, monitoring you during the night.”

While home diagnostic tests are cheaper ($200 per night compared to $1,000-$5,000 in-lab) there is still a lot of friction in the process: waiting lists can be long, patients may damage the device or fail to return it to the clinic, or they may have difficulty assembling it at home.

“If you combine all things together, you understand that something doesn’t work properly in the way people diagnose and manage sleep disorders today and this is where we come into play,” said Reuveny.

A Seamless and Economic Solution

TATCH is a sleep diagnosis and monitoring patch that replaces bulky wired devices; the patent-pending, disposable product is both easy-to-use and cost-effective. To develop the prototype, the team focused on sleep apnea — the most common sleep test done in the United States today (3.5-4 million tests per year).

The one-use patch combines flexible electronics, machine learning and wearable technology to monitor diagnostic parameters for sleep apnea, such as sleep position and oxygen saturation levels.

“The patch has sensors and a communication module inside. All the signals are communicated from the patch to your smartphone securely and from the phone they are saved to the cloud. Then the clinic can easily pull the results of the test,” said Reuveny.

The patch is aimed at sleep clinics and the team believes it will dramatically reduce diagnostic costs for several reasons: patients can easily carry out the test at home; they do not require complex instructions; the risk of damaging or failing to return an expensive wired device is removed.

Further, said Reuveny, clinics can distribute multiple patches and carry out tests simultaneously, “With disposable patches you can do much more and you can also remove the waiting lists, you remove the friction.”

When Technology Innovation Meets Entrepreneurship

Reuveny has embraced the opportunities offered by the Runway Startup Postdoc Program at the Jacobs Institute. The program’s mission of taking digital science specialists and helping them become entrepreneurs was a perfect fit for him.

“They really help you to start things,” he said, “They give you all the initial services and support to start a company such as legal advice, IP advice and business mentorship, which is really very helpful at the first stages.”

TATCH recently won acceptance to a New York City Economic Development Corporation (NYCEDC) and HITLAB Digital Health Breakthrough Network program for healthcare, innovation, and technology in New York City. “This program provides us with a clinical pilot in a sleep clinic in New York City, and they are very helpful in administering the pilot and testing the device. This will be the first meet of the patch with real patients which is very exciting for us,” Reuveny said.

The next steps for the patch include bringing the prototype to product stage, starting clinical pilots, and investigating its application in the diagnoses of other sleep disorders.

The post Could This Smart Patch Help People Finally Get a Good Night’s Sleep? appeared first on Cornell Tech.

Winternships give these young women an immersive experience in different tech businesses/industries as well as a resume credential that will make them more competitive when applying for a summer tech internship.

Winterns and company sponsors were so excited about the experience they wrote about it. Here are a few of the first-hand experiences:

LinkedIn: Making Strides to Encourage More Women in Tech

Learn about LinkedIn’s three rewarding nonstop weeks with the Winterns. Students were fully integrated with the video team, where they built a product that will actually be used on LinkedIn’s platform.

Two Sigma Ventures: Thank You to Our Three Winterns

Two Sigma Ventures welcomed three CUNY students to their team through the Winternship program.

Turner Broadcasting: Let’s Talk About Tech

Macaulay Honors College at Baruch sophomore, Annmarie Gajdos, talks about her Winternship with Turner Broadcasting.

Paul, Weiss: Hosts CUNY Women in Tech Interns for Winter Break

Paul, Weiss welcomed five CUNY students to their team through the Winternship program.

Intersection: The Most Productive Winter Break of My Life

Macaulay Honors College at Baruch freshmen, Justina Hilbert, talks about her Winternship with Intersection.

Thank you to the following companies that participated in the 2018 Winternship Program:

The post Winternship Recap 2018 appeared first on Cornell Tech.

The framework, developed by Longqi Yang, a PhD candidate at Cornell Tech and Deborah Estrin, Associate Dean and Robert V. Tishman ’37 Professor, was developed for researchers to develop and evaluate algorithms across a range of use cases and practitioners to customize state-of-the-art solutions.

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