Alien Minds, Immaculate Bullshit, Outstanding Questions: College in the Age of ChatGPT

 From the Penn Alumni Mag,   Where I was much interested in AI. Considerable piece I am reading.  

 Considerble pieice, I do NOT agree in much of it, below an intro the whole thing follows 

Alien Minds, Immaculate Bullshit, Outstanding Questions

26 Apr 2023   Page 22-33

College in the age of ChatGPT.

By Trey Popp | Illustration by Chris Gash   Sidebar | The Coming Economic and Ethical Earthquake

In June 2021, Chris Callison-Burch typed his first query into GPT-3, a natural language processing platform developed by the San Francisco-based company OpenAI. Callison-Burch, an associate professor of computer and information science, was hardly new to AI chatbots or the neural networks that power them. He’s been at the forefront of machine translation since the early 2000s, and at Penn he teaches courses in computational linguistics and artificial intelligence. Besides, digital assistants like Siri and Alexa had already woven NLPs into the fabric of everyday life. But the jaw-dropping fluency of OpenAI’s new model pitched him into a “career existential crisis.”

It could respond to prompts with cogent, grammatically impeccable prose. It could turn plain language into Python code. It could expand bullet-point outlines into five-paragraph essays—or theatrical dialogues.

“I was like, ‘Is there anything left for me to do? Should I just drop out of computer science and become a poet?’” he later recollected. “But then I trained the model to write better poetry than me

On November 30, 2022, OpenAI publicly released a refined version called ChatGPT. Its shock-and-awe debut quickly gave Callison-Burch plenty of company on campus. On February 1 he went to a meeting convened by Penn’s Center for Teaching & Learning (CTL) to address the anxiety and excitement racing through faculty lounges—especially after the bot had passed a Wharton operations management exam administered to it by Christian Terwiesch, the Andrew M. Heller Professor. “It was probably the best-attended CTL meeting ever,” Callison-Burch recalled, with a wry chuckle, a couple weeks later. So many people came that CTL director Bruce Lenthall split them into three sessions—two comprising social sciences and humanities faculty and one that blended professors of math, engineering, and physical sciences with counterparts from the University’s health schools.

They’d come for varied reasons. “Some people were just alarmed,” Lenthall said. Having ingested vast swathes of internet text, ChatGPT and other so-called generative AI tools are exquisitely adapted to serve as “sophisticated plagiarism machines,” in the words of Eric Orts, the Guardsmark Professor in Wharton’s department of legal studies and business ethics, who’d experimented with ChatGPT in an MBA course and discussed it within the Faculty Senate executive committee. Other attendees had yet to engage with the tools at all and simply wanted to learn about them. A third group sensed a chance to get in on the ground floor of a revolutionary change. “They suggested that this could be exciting and open up possibilities,” Lenthall recalled, “but they didn’t really have a good idea of what those might be.”

Most participants fell somewhere in the middle—worried about the threats ChatGPT posed to established modes of teaching and evaluation, but curious about its potential to advance the scope or pace of instruction. “What was the most gratifying to me,” said Lenthall, who is also an adjunct associate professor of history, “was that all the faculty came to the conclusion that they really needed to think through the question: What is it most critical that my students learn to do on their own? And when is it most appropriate for them to do something with another tool?”

Their search for answers gave the spring semester a hothouse atmosphere of probing and experimentation.

Wharton associate professor Ethan Mollick, a Ralph J. Roberts Distinguished Faculty Scholar and academic director of Wharton Interactive, not only permitted but in some cases required students in his innovation and entrepreneurship courses to use generative AI platforms, which he likened to “analytic engines.” Meanwhile, on the other end of campus, astronomy professor Masao Sako and his analytical mechanics students asked ChatGPT to solve homework problems. “It returns answers and explanations that sound plausible,” Sako said, but “failed on every single one.” Given the confident authority with which ChatGPT announced its defective solutions, Sako concluded that the tool might indeed have some utility in the realm of upper-level physics. “I’ve told my students to continue using it to get some practice on identifying errors, which is a useful skill.”

Penn Integrates Knowledge (PIK) University Professor Konrad Kording, who teaches psychology and neuroscience with a focus on neural networks and machine learning, emerged as a pithy generative AI maximalist. “It’s just a mistaken opportunity for any student to not use ChatGPT for any possible project they’re working on,” he declared at a late-February panel discussion sponsored by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI). “It’s just incompetent. We should give them bad grades for not using ChatGPT.” Yet Eric Orts was finding that when he let his MBA students use it for some assignments, it tended to lead them toward bad grades—in the form of “deadening” prose—all on its own. “I’m convinced that there are positive uses emerging for this in the real world,” he told me. “But in general I was not impressed by the answers I got from students using it.”

Neither was Karen Rile C’80, a fiction writing teacher in the English department whose experimentation with chatbots goes back to a primitive model developed by AOL at the turn of the century. “What I value in writing is specificity, sharpness, clarity—and it fails on every level,” she reflected. “It’s like a bad student writer who writes in a way that’s very generic, with lots of vague cliches and phrases. It feels blurry. I think that it’ll probably get sharper and better, but I can’t imagine it’s ever going to do anything that’s literary quality. It’ll be very formulaic.”

Yet Rile kicked off her fiction seminar this spring by assigning her students a piece by a writer who’d used GPT-3 as a kind of a coauthor. “I wanted to get ahead of it at the beginning of the semester,” she told me. Then, in mid-March, she brought in Callison-Burch and his PhD student Liam Dugan EAS’20 GEng’20, who focuses on natural language processing, to give a guest lecture about generative AI and creative writing.

All 10 professors I interviewed, plus another four who participated in that DDDI panel discussion and several with whom I spoke informally, expressed a similarly open attitude. Sako’s dim view of ChatGPT’s analytical chops didn’t keep him from seeing its potential to boost the conceptual sophistication of his mid-level coding class. Mollick mixed breathless boosterism with a running list of warnings about its boundless propensity to deceive users. Skeptics were on the lookout for positive use cases, and enthusiasts frequently offered insights about generative AI’s limitations.   .... ' '

Real Time Photo Editing from DragGAN

Has received significant praise,  plan to examine.

From YouTube:     https://www.youtube.com/watch?v=bQxfBE5n1oA

1,201 views  May 23, 2023  #draggan #ai

Introducing DragGAN: an AI Image Manipulation Tool for real-time photo editing. Developed by the renowned Max Planck Institute, DragGAN allows you to manipulate images in real-time by dragging and dropping points, resulting in stunning results. With its innovative features and versatile capabilities, DragGAN is set to redefine the world of AI photo editing.

Become a Member of the channel and Supporter of AI Revolution →   

https://www.analyticsvidhya.com         

 / @airevolutionx    #draggan #ai

DragGAN: Google Researchers Unveil AI Technique for Magical Image Editing

download  Share    From Penn and 

Yana Khare — Published On May 22, 2023 and Last Modified On May 23rd, 2023

Artificial Intelligence GANs Generative AI Image News

Researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL have recently released a new AI editing tool called DragGAN

Researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL have recently released a new AI technique. It allows users to manipulate images in seconds with just a click and drag. The new DragGAN is an AI editing tool that leverages a pre-trained GAN (Generative Adversarial Network) to synthesize ideas that precisely follow user input while remaining on the manifold of realistic images.

Learn More: An End-to-End Introduction to Generative Adversarial Networks(GANs) 

The Power of DragGAN

DragGAN is an interactive approach for intuitive point-based image editing far more powerful than Photoshop’s Warp tool. Unlike Photoshop, which merely smushes pixels around, DragGAN uses AI to regenerate the underlying object. With DragGAN, users can rotate images as if they were 3D, change the dimensions of cars, manipulate smiles into frowns, and adjust reflections on lakes. Moreover, they can change the direction someone faces.

Also Read: How to Use Generative AI to Create Beautiful Pictures for Free?

General Framework and Optimisation of Latent Codes

What sets DragGAN apart from other approaches is its general framework which does not rely on domain-specific modeling or auxiliary networks. To achieve this, the researchers used an optimization of latent codes that incrementally moved multiple handle points toward their target locations alongside a point-tracking procedure to trace the trajectory of the handle points faithfully. Both components use the discriminative quality of intermediate feature maps of the GAN to yield pixel-precise image deformations and interactive performance.

Outperforming SOTA in GAN-Based Manipulation

DragGAN uses Generative Adversarial Network to edit images while ensuring that they remain realistic | AI

According to the researchers, DragGAN by Google outperforms the state-of-the-art (SOTA) in GAN-based manipulation. Furthermore, it opens new directions for powerful image editing using generative priors. They look to extend point-based editing to  .. .' 

Microlaser Chip Adds Dimensions to Quantum Communication

Discovered late through an alumni connection ... 

Microlaser Chip Adds Dimensions to Quantum Communication

Penn Engineering Today

Devorah Fischler,  November 21, 2022 

A multi-institutional team led by researchers at the University of Pennsylvania School of Engineering and Applied Science (Penn Engineering) developed a chip that doubles the quantum information space of any previous on-chip laser by communicating in qudits (quantum bits in a state of superposition greater than two levels). The hyperdimensional microlaser-produced qudits boost the maximum secrete key rate for information exchange from 1 bit to 2 bits per pulse, supporting four levels of superposition and clearing a path for further dimensional enlargement. The researchers realized the four-level system by devising a method to manipulate and couple the orbital angular momentum and spin of photons.

Full Article

Re a Further Look at BioPhysics

How does life and physics fit together?    Just received in an Alumni mailing discussing,

THE PHYSICS OF Us

Physicists are studying how living matter works, and find that it breaks the standard rules and produces fascinating new phenomena.

Monday, December 5, 2022, By Susan Ahlborn. Illustrations by Marina Muun,   UPenn Omnia 

The James Webb telescope is showing us our universe in vibrant new detail. Some physicists, though, are looking in another direction: at us and other living matter here on Earth, from the cilia in lungs to the vasculature in leaves to the neurons in brains. What they’re finding is equally marvelous, and it’s challenging some of the current understanding of physics. 

Ultimately, they’re working to discover the rules that govern how matter lives and evolves, and their research may lead to better medicine, robotics based on biology, and an expanded understanding of the physical and biological world. 

How can an intelligent system arise from the collective dynamics of its basic components?

“We’re using physics principles to understand life and living matter,” says Eleni Katifori, Associate Professor of Physics and Astronomy, who studies vasculature in plants and animals. “But we are also using living matter as an inspiration to discover new physics, for asking the right questions or new questions.”

“Biology has already invented a lot of things. Living matter, from bacteria to leaves to humans, works in ways that physicists don’t understand, much less can duplicate,” adds Arnold Mathijssen, Assistant Professor of Physics and Astronomy. His goal is to unravel the physics of pathogens, design biomedical materials, and understand the collective functionality of living systems out of equilibrium. “It’s fundamental research. For example, how can an intelligent system arise from the collective dynamics of its basic components? It’s also directly relevant to our society, as in, what is the probability of SARS-CoV-2 transmission within a food supply chain?”

Leading the Way

The study of biophysics is not new; Luigi and Lucia Galvani were already investigating animal electricity in the late 1700s. In the last 20 years, though, technological advances have allowed researchers to see microscopic phenomena in living tissue with unprecedented detail, record simultaneously from thousands of neurons, and even track the large-scale behavior of ecosystems. All of these new methods produce vast amounts of quantitative data from which we can infer the laws of living matter. But it wasn’t until 2022 that the National Academies of Science, Engineering, and Medicine recognized biological physics as a separate field. 

Penn Arts & Sciences physicists have been studying living matter for decades, bringing Penn to the front of this area. Philip Nelson, Professor of Physics and Astronomy, wrote key books in the field, starting with Biological Physics: Energy, Information, Life in 2014. He’s been honored with the Emily Gray Award of the Biophysical Society for his “far-reaching and significant contributions.” Arjun Yodh, James M. Skinner Professor of Science, has received the Michael S. Feld Biophotonics Award of the Optical Society of America for his pioneering work in demonstrating and clinically translating biomedical optics. A.T. Charlie Johnson, Rebecca W. Bushnell Professor of Physics and Astronomy, is using biological molecules as chemical recognition elements in disease diagnosis, security, and environmental monitoring. Marija Drndic, Fay R. and Eugene L. Langberg Professor of Physics, explores mesoscopic and nanoscale structures, including the detection and analysis of DNA and microRNA.

In 2021, Penn Arts & Sciences and Penn Engineering made a unique investment in this interdisciplinary study with the new Center for Soft and Living Matter. Led by Director Andrea J. Liu, Hepburn Professor of Physics, and Associate Director Douglas J. Durian, Mary Amanda Wood Professor of Physics and Astronomy, the center brings together more than 60 faculty from the two schools. And Penn’s Computational Neuroscience Initiative, cofounded by Vijay Balasubramanian, Cathy and Marc Lasry Professor of Physics and Astronomy, involves researchers from Arts & Sciences, the Perelman School of Medicine, and Engineering. 

“If you look at a different scale or regime, new phenomena always pop up,” says Balasubramanian, a theoretical physicist who also holds a secondary appointment in neuroscience in the Perelman School of Medicine. “It’s the interactions between the components of living systems that make them so interesting, unlike, for example simple gases in a room. The brain contains a hundred billion interacting neurons. Molecules can talk to the whole organism, pheromones can change the behavior of entire colonies of organisms, stress can change gene expression. So, living systems interact across scales of organization unlike most physical systems that we are used to.”  .... ' 

Rethinking the Computer Chip in the Age of AI

 New designs for Computer chips. 

Rethinking the Computer Chip in the Age of AI,    via U of Penn

Posted on September 29, 2022   Author Devorah Fischler 

The transistor-free compute-in-memory architecture permits three computational tasks essential for AI applications: search, storage, and neural network operations.

Artificial intelligence presents a major challenge to conventional computing architecture. In standard models, memory storage and computing take place in different parts of the machine, and data must move from its area of storage to a CPU or GPU for processing.

The problem with this design is that movement takes time. Too much time. You can have the most powerful processing unit on the market, but its performance will be limited as it idles waiting for data, a problem known as the “memory wall” or “bottleneck.”

When computing outperforms memory transfer, latency is unavoidable. These delays become serious problems when dealing with the enormous amounts of data essential for machine learning and AI applications.

As AI software continues to develop in sophistication and the rise of the sensor-heavy Internet of Things produces larger and larger data sets, researchers have zeroed in on hardware redesign to deliver required improvements in speed, agility and energy usage.

A team of researchers from the University of Pennsylvania’s School of Engineering and Applied Science, in partnership with scientists from Sandia National Laboratories and Brookhaven National Laboratory, has introduced a computing architecture ideal for AI.

Deep Jariwala, Xiwen Liu and Troy Olsson

Co-led by Deep Jariwala, Assistant Professor in the Department of Electrical and Systems Engineering (ESE), Troy Olsson, Associate Professor in ESE, and Xiwen Liu, a Ph.D. candidate in Jarawala’s Device Research and Engineering Laboratory, the research group relied on an approach known as compute-in-memory (CIM).

In CIM architectures, processing and storage occur in the same place, eliminating transfer time as well as minimizing energy consumption. The team’s new CIM design, the subject of a recent study published in Nano Letters, is notable for being completely transistor-free. This design is uniquely attuned to the way that Big Data applications have transformed the nature of computing.

“Even when used in a compute-in-memory architecture, transistors compromise the access time of data,” says Jariwala. “They require a lot of wiring in the overall circuitry of a chip and thus use time, space and energy in excess of what we would want for AI applications. The beauty of our transistor-free design is that it is simple, small and quick and it requires very little energy.”

The advance is not only at the circuit-level design. This new computing architecture builds on the team’s earlier work in materials science focused on a semiconductor known as scandium-alloyed aluminum nitride (AlScN). AlScN allows for ferroelectric switching, the physics of which are faster and more energy efficient than alternative nonvolatile memory elements.

“One of this material’s key attributes is that it can be deposited at temperatures low enough to be compatible with silicon foundries,” says Olsson. “Most ferroelectric materials require much higher temperatures. AlScN’s special properties mean our demonstrated memory devices can go on top of the silicon layer in a vertical hetero-integrated stack. Think about the difference between a multistory parking lot with a hundred-car capacity and a hundred individual parking spaces spread out over a single lot. Which is more efficient in terms of space? The same is the case for information and devices in a highly miniaturized chip like ours. This efficiency is as important for applications that require resource constraints, such as mobile or wearable devices, as it is for applications that are extremely energy intensive, such as data centers.”  ... ' 

Algorithm for 2D-to-3D Engineering Integrates Art, Nature, Science

 Interesting, How does this integrate these? 

Algorithm for 2D-to-3D Engineering Integrates Art, Nature, Science

Penn Engineering Today

Devorah Fischler, October 31, 2022

Researchers at the University of Pennsylvania School of Engineering and Applied Science (Penn Engineering) and the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory have developed what they are calling a universal algorithm that permits two-dimensional materials to retain their lightness and durability when converted into three-dimensional (3D) structures. The algorithm allows hard materials to keep their mechanical strength after cutting by mimicking the structure of nacre, mollusks' natural shell coating. The algorithm can generate a computational map of cuts that are optimized for stacking, ensuring they never overlap with one another to compensate for necessary defects; fortifying tabs further bolster mechanical strength.

Full Article  

New Computing Architecture

At a school I attended, worth a look.   Technical

Researchers at the University of Pennsylvania Propose a New Computing Architecture Ideal for Artificial Intelligence (AI)

By Khushboo Gupta- October 5, 2022

Conventional computing architectures severely constrain artificial intelligence’s ability to improve technology. In traditional models, memory storage and computing occur in separate areas of the machine. This is why data must be transported from its storage area to a CPU or GPU for processing. The most significant disadvantage of this design is that this movement takes time, which reduces the performance of even the most potent processing units available. There is no avoiding lag when compute performance exceeds memory transfer. These delays become a severe issue when dealing with the massive amounts of data required for machine learning and AI applications. 

Researchers have focused on hardware innovation to achieve the necessary increases in speed, agility, and energy efficiency as AI software advances in sophistication and the rise of the sensor-heavy Internet of Things produces larger datasets. A team of researchers from the University of Pennsylvania’s School of Engineering and Applied Science, in collaboration with researchers from Sandia National Laboratories and Brookhaven National Laboratory, have created a new computing architecture based on compute-in-memory (CIM), which is ideal for AI. Processing and storage take place simultaneously in CIM systems, which helps to reduce energy consumption and eliminate transfer time. The new CIM design from the team stands out for containing no transistors. This design is specifically adapted to how Big Data applications have changed how computing works today.

Transistors limit the speed at which data may be accessed, even in a compute-in-memory architecture. They utilize more time, space, and energy than is ideal for AI applications since they require much wire in a chip’s overall circuitry. The transistor-free design by the team is distinctive since it is straightforward, quick, and uses less energy. The researchers clearly emphasize that the advancement is not limited to circuit-level design. Their earlier materials science research on a semiconductor known as scandium-alloyed aluminum nitride (AlScN) was the foundation for the new computing architecture. Ferroelectric switching is possible with AlScN, making it faster and more energy-efficient than other nonvolatile memory components. Another crucial feature is the material’s ability to be deposited at temperatures low enough to work with silicon foundries. This makes it possible for the architecture to be space-efficient, which is crucial for small chip designs. .... '    (much more, Computing Technical) 

Chip Proccess and Classification

 Impressive speed for detecting and classifying images

Chip Processes, Classifies Nearly Two Billion Images per Second

Penn Engineering Today

Melissa Pappas, June 1, 2022

University of Pennsylvania (Penn) engineers have designed a 9.3-square-millimeter chip that can detect and classify images in less than a nanosecond. The chip directly processes light received from objects of interest using an optical deep neural network. "Our chip processes information through what we call 'computation-by-propagation,' meaning that unlike clock-based systems, computations occur as light propagates through the chip," explained Penn's Firooz Aflatouni. "We are also skipping the step of converting optical signals to electrical signals because our chip can read and process optical signals directly, and both of these changes make our chip a significantly faster technology." Penn's Farshid Ashtiani said direct processing of optical signals makes a large memory unit unnecessary. ... 

Vaccine Nudging Tested

LISTEN TO THE PODCAST:  (from Kowledge@Wharton) 

Wharton’s Mitesh Patel speaks with Wharton Business Daily on SiriusXM about how text-based nudges increase vaccination rates.

The best way to get patients to take their vaccinations is to send text message reminders that a shot has been “reserved” for them at their upcoming doctor’s appointment, according to a study from Wharton and Penn Medicine.

Personalized messages using the word “reserved” was enough to boost vaccination rates by 11% in the mega-study of more than 47,000 patients.

“It turns out one of the most simple, straightforward messages worked the best. Instead of telling people that the vaccine was available for them, we said it was ‘reserved for you,’” said Mitesh Patel, professor of health care management at Wharton and former director of the Penn Medicine Nudge Unit, which is the world’s first behavioral design team embedded in a health care system. He is also national lead for behavioral insights at Ascension Health.

Patel joined scientists from Penn, Harvard, Yale, and several other universities to collaborate with Penn Medicine and Geisinger Health on the mega-study. The researchers developed 19 different text messaging protocols to discover which ones were most effective at nudging participants to get their shots. The messages ran the gamut — from jokes to short videos to allowing participants to dedicate their shots with a loved one’s initials — but nothing worked as well as the reservation reminder.

Although the study was designed to boost uptake of the flu vaccine, the researchers said the method can be easily adapted to encourage COVID-19 vaccinations.

During the 2019–2020 flu season, less than half the U.S. population took the influenza vaccine and an estimated 35,000 people died from the virus. By comparison, COVID-19 has killed more than 635,000 Americans since it first appeared early last year. About 74% of Americans eligible for the vaccine have taken at least one dose, according to the Centers for Disease Control and Prevention.

“The most important takeaway is that the way that we communicate the vaccine to people is going to have a huge impact on whether or not they’re going to be motivated to get it, and that really subtle changes can have a big impact,” Patel said during an interview with Wharton Business Daily on SiriusXM. (Listen to the podcast above.) “Now that we have the evidence on what works and what doesn’t, we can actually leverage this to help motivate more people to get vaccinated quickly.”  ... ' 

Collaborating Robotic Teams

 Continuing to look at this space,   now that robotics is getting more advanced, the potential expands.  We examined some very early warehouse management approaches.  Mentioned previously:  

Helping Robots Collaborate to Get the Job Done, By MIT News

An algorithm developed by researchers at the Massachusetts Institute of Technology (MIT), the University of Pennsylvania, and the University of California, San Diego aims to foster cooperation between information-gathering robot teams.

The algorithm balances data collection and energy expenditure to avoid having robots perform maneuvers that waste energy to gain only a small amount of data.

Using the researchers' Distributed Local Search approach, each robot proposes a potential trajectory for itself as part of the team, and the algorithm accepts or rejects them based whether it will increase the likelihood of achieving the team's objective function.

A test involving a simulated team of 10 robots showed that the algorithm required more computation time, but guaranteed their mission would be completed successfully.

 .... Massachusetts Institute of Technology researchers have developed an algorithm that coordinates the performance of robot teams for missions like mapping or search-and-rescue in complex, unpredictable environments.

From MIT News

Pandemic Eviction Modeling

 More models accurately done, with the right data, address the results of specific decisions.

Modeling Shows Pandemic Eviction Bans Protect Entire Communities From Covid-19 Spread

Johns Hopkins Medicine Newsroom

April 19, 2021

Researchers at institutions including Johns Hopkins University and the University of Pennsylvania used computer modeling to determine that eviction bans during the Covid-19 pandemic lowered infection rates, shielding entire communities from the virus. The scientists said they used simulations to predict additional virus infections in major U.S. cities if bans were not authorized in fall 2020. The team initially calibrated its model to reproduce the most common epidemic patterns observed in major cities last year, accounting for infection-rate changes due to public health measures. Another iteration factored in the lifting of eviction bans, determining that people who are evicted or who live in a household that hosts evictees are 1.5 to 2.5 times more likely to become infected than with such bans in place.  .... '

Intel Using Federated Learning to Preserve Privacy of Brain Tumor Training Data

The importance of such a method, is noted, and also the ability to use 'Federated learning'  to preserve the privacy of the large amount of data being used to train the method.  See Google's efforts on this at the tag below.

Intel Works with University of Pennsylvania in Using Privacy-Preserving AI to Identify Brain Tumors

Intel and the Perelman School of Medicine at the University of Pennsylvania are setting up a federation to train AI models that identify brain tumors.

Federated learning is a distributed machine learning approach that enables organizations to collaborate on machine learning projects without sharing sensitive data such as patient records. (Credit: Intel Corporation)

SANTA CLARA, Calif.--(BUSINESS WIRE)--What’s New: Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) are co-developing technology to enable a federation of 29 international healthcare and research institutions led by Penn Medicine to train artificial intelligence (AI) models that identify brain tumors using a privacy-preserving technique called federated learning. Penn Medicine’s work is funded by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), through a three-year, $1.2 million grant awarded to principal investigator Dr. Spyridon Bakas at the Center for Biomedical Image Computing and Analytics (CBICA) of the University of Pennsylvania.

“AI shows great promise for the early detection of brain tumors, but it will require more data than any single medical center holds to reach its full potential. Using Intel software and hardware and support from some of Intel Labs' brightest minds, we are working with the University of Pennsylvania and a federation of 29 collaborating medical centers to advance the identification of brain tumors while protecting sensitive patient data.”

– Jason Martin, principal engineer, Intel Labs  .... '

Superforecasting Short Course from The Edge

Quite interesting, free, have followed the concept for some time, but have yet to apply it.  Videos embedded at the link.   Good to see this followup.  Is enough said about embedded risk analysis?  Following.

A Short Course in Superforecasting—Philip Tetlock: An EDGE Master Class
(ED. NOTE: In 2015, Edge presented "A Short Course in Superforecasting" with political and social scientist Philip Tetlock. Superforecasting is back in the news this week thanks to the UK news coverage of comments by Boris Johnson's chief adviser Dominic Cummings, who urged journalists to "read Philip Tetlock's Superforecasters [sic], instead of political pundits who don't know what they're talking about.")

PHILIP E. TETLOCK, political and social scientist, is the Annenberg University Professor at the University of Pennsylvania, with appointments in Wharton, psychology and political science. He is co-leader of the Good Judgment Project, a multi-year forecasting study, author of Expert Political Judgment, co-author of Counterfactual Thought Experiments in World Politics (with Aaron Belkin), and co-author of Superforecasting: The Art & Science of Prediction (with Dan Gardner). Further reading on Edge: "How to Win at Forecasting: A Conversation with Philip Tetlock" (December 6, 2012). Philip Tetlock's Edge Bio Page.

CLASS I — Forecasting Tournaments: What We Discover When We Start Scoring Accuracy
It is as though high status pundits have learned a valuable survival skill, and that survival skill is they've mastered the art of appearing to go out on a limb without actually going out on a limb. They say dramatic things but there are vague verbiage quantifiers connected to the dramatic things. It sounds as though they're saying something very compelling and riveting. There's a scenario that's been conjured up in your mind of something either very good or very bad. It's vivid, easily imaginable.

It turns out, on close inspection they're not really saying that's going to happen. They're not specifying the conditions, or a time frame, or likelihood, so there's no way of assessing accuracy. You could say these pundits are just doing what a rational pundit would do because they know that they live in a somewhat stochastic world. They know that it's a world that frequently is going to throw off surprises at them, so to maintain their credibility with their community of co-believers they need to be vague. It's an essential survival skill. There is some considerable truth to that, and forecasting tournaments are a very different way of proceeding. Forecasting tournaments require people to attach explicit probabilities to well-defined outcomes in well-defined time frames so you can keep score.   ... " 

Will Augmented Reality Change Everything we See?

From the Penn Alumni Mag, a broad, descriptive and mostly academic oriented view of the world of augmented reality.  Below the intro and at the link more...

Augmenting Reality
Will augmented reality change everything we see? A growing number of Penn alumni, staff, and faculty think so. And even as they bump up against its challenges and limitations, they’re still committed to pulling AR further into our lives.

By Molly Petrilla | Illustration by Roman Klonek

Sidebar: Need a HoloLens? Try the Library

I’m killing scorpions inside Stephen Lane’s office.

They skitter out of openings in the wall, crawling up and down, sometimes even jumping right at me. I try zapping them away but sometimes I miss, blasting holes around Lane’s door and exposing white pipes and other slivers of the building’s bones.

Lane doesn’t seem to mind. In fact, he’s laughing. The computer and information science professor who was just sitting behind his desk, earnestly answering questions about the future of technology, has transformed into a jazzed-up gamer who’s excited to share his toy with a novice.

“Are you getting them?” he asks as scorpions dance around us. “Just keep firing!”

Even as giant predatory arachnids are scurrying in front of me, and even as my fingers trigger laser beams to destroy them, I’m still inside Lane’s real office. If I turn around, I can see his dark wood desk with its stacks of papers and cup of coffee. I can see his crowded bookshelves and his whiteboard. I can see Lane himself—plaid shirt, bristly mustache, belt with colorful little sailboats. But thanks to the Microsoft HoloLens I’m wearing—a headset that wraps around my forehead and hangs down over my eyes, resting on the bridge of my nose—the game is with us too, turning a plain white wall into a scorpion lair.

And that’s augmented reality.

RoboRaid is a simple introduction for the uninitiated. “It’s a game, but it really shows off some of the capabilities,” Lane says just before passing me the HoloLens.

AR isn’t about fully losing yourself in an imaginary world the way virtual reality is. It’s about enhancing your actual surroundings with computer-generated images and objects. At its most ambitious, AR can require programming expertise and a $3,500 headset like the HoloLens. But it can also be as simple as pulling out your smartphone and turning yourself into a bunny with an Instagram filter.

On that morning inside Lane’s office, I’m not the only one at Penn testing out augmented reality’s abilities and discovering its limits. Across campus and well beyond, a number of faculty, alumni, staff, and students are all focused on applying AR to their fields. You’ll find them inside operating rooms and classrooms, heading up teams at Google and the New York Times, and working for a leading AR headset company.

They’re all convinced that augmented reality can change our world—that it will change our world—in every area from medicine and education to research, retail, and entertainment. But they’re also starting to wonder: What will it take to get past the tipping point, and what might life be like after we do? ... " 

Towards Better Forecasting: Less Noise

A forecast prediction is key for any business decision. Short intro, then the entire podcast follows at the link.

Wharton’s Barbara Mellers and Ville Satopӓӓ from INSEAD discuss their research on the impact of noise on forecast accuracy.

From predicting the weather to possible election outcomes, forecasts have a wide range of applications. Research shows that many forces can interfere with the process of predicting outcomes accurately — among them are bias, information and noise. Barbara Mellers, a Wharton marketing professor and Penn Integrates Knowledge (PIK) professor at the University of Pennsylvania, and Ville Satopӓӓ, assistant professor of technology and operations management at INSEAD, examined these forces and found that noise was a much bigger factor than expected in the accuracy of predictions. The professors recently spoke with Knowledge@Wharton about their working paper, “Bias, Information, Noise: The BIN Model of Forecasting.” (Listen to the podcast at the top of this page.)

An edited transcript of the conversation follows.

Knowledge@Wharton: In your paper, you propose a model for determining why some forecasters and forecasting methods do better than others. You call it the BIN model, which stands for bias, information and noise. Can you explain how these three elements affect predictions?

Ville Satopӓӓ: Let me begin with information. This describes how much we know about the event that we’re predicting. In general, the more we know about it, the more accurately we can forecast. For instance, suppose someone asked me to predict the occurrence of a series of future political events. If I’m entirely ignorant about this, I don’t really follow politics, I don’t follow the news, I barely understand the questions you’re asking me, I would predict around 50% for these events.

On the other hand, suppose I follow the news and I’m interested in the topic. My predictions would be then more informed, and hence they would be not around 50% anymore. Instead, they would start to tilt in the direction of what would actually happen.

At the extreme case, we could think of me having some sort of a crystal ball that would allow me to see into the future. This would make me perfectly informed, and hence I would predict zero or 100% for each one of the events, depending on what I see in the crystal ball. This just illustrates how information can drive our predictions. It introduces variability into them that is useful because it is based on actual information. Because of that, it correlates with the outcome.   .... " 

Examining Smart City Backlash

A look at how people are reacting to 'smart' and trust, and surveillance and privacy stretching that are parts of smart city plans and implementations.  Is it best to see these ideas as primarily cost saving and life improving?  Or addressing outliers like solving and preventing crime in city spaces?     Both are happening today.

https://penniur.upenn.edu/  Penn Institute for Urban Research

What’s Fueling the Smart City Backlash?

A new phase of pause and double-check assumptions seems to have gripped the three-decades-old global movement of overstressed urban centers transitioning to so-called smart cities with innovative, technology-led promises. The latest phase is marked by scattered, local-level resistance by residents to smart-city programs in big cities like Toronto and New York to small towns such as Ross, California — near San Francisco — with less than 2,500 residents.

Other cities have banned specific technologies such as facial recognition software, amid doubts over its accuracy or concerns over cities stealthily collecting such data on their citizens through video surveillance. In some cases, they see technology companies forming opaque partnerships with city-level agencies to profit from projects at their expense, using public resources such as land and development rights.

Fears over privacy intrusions in today’s digital age and unbridled development compromising the public interest have been heightened by the erosion of trust between residents, city administrations and private companies leading “smart” projects. With increased transparency, and stronger citizen engagement, the smart-city movement could regain lost credibility and continue its growth, according to experts who spoke with Knowledge@Wharton.

For the most part, residents are wary about how city governments and big technology companies involved in the projects will track and collect data about their daily activities while not compromising their privacy and security by selling data without their consent. In several cases, legislators in many U.S. states have enacted or are considering laws to ban or limit the erection of 5G cell towers because of health concerns.

Data privacy and security issues are more sensitive in some settings than others. “Smart cities mean different things to different people, but big data is intrinsic to these initiatives and thus privacy concerns arise,” notes Susan Wachter, Wharton professor of real estate and finance. “However, some initiatives such as coordinated traffic lights are high on efficiency and low on privacy issues — and they are no brainers. Others, such as tracking people — much as is done in private places such as malls — provoke a backlash because they undermine the anonymity privilege of public spaces.” ... '

Microbots

Smaller and more of them to swarm to a problem.

The Microbots Are on Their Way   By The New York Times

A researcher at the University of Pennsylvania has developed tiny robots.

Credit: Marc Miskin/University of Pennsylvania and Cornell University

Marc Miskin, a researcher at the University of Pennsylvania, has developed tiny robots, thousands of which can fit side by side on a single silicon wafer.

The microbots, which are each about 100 atoms thick, rely on a technique used to put layers of platinum and titanium on a silicon wafer.  ....  "

Designing Robots with Personality

We always attribute a bit of personality into our devices,  but what amount and kind is useful for the best results, and minimal unintended consequences??

Character Engineer: Designing robots with a touch of personality.

So, Mark Palatucci EAS’00 wants to put a robot in every home.

That might sound familiar. After all, you may even already have one. But Palatucci, a cofounder of the San Francisco-based robotics company Anki, isn’t thinking about task-oriented automatons or self-directed vacuum cleaners. He’s not even thinking about smart speakers. He’s designing robots with “character”—enough to spark an emotional connection with their owners.

“People are much more willing to put a character in their home than they are just some smart cylinder or smart speaker that doesn’t have any emotion or character built around it,” he says. “It creates a sense of trust that a lot of other products don’t necessarily have.”

And if that trust leads to more engagement with the robot—whether it’s playing games with a robot called Cozmo, or getting Vector, another model, to take a picture when your hands are full—all the better.

Anki’s aim in building robots is to enable people to “build relationships with technology that feel a little more human.” Palatucci, who earned a computer science and engineering degree at Penn, is the company’s head of cloud artificial intelligence and machine learning. Their products have been getting notice. .... " 

Saving Lives with Swarms of Drones

Self assembling and autonomous drones for safety and rescue platforms.

Saving Lives with Aerial Drones in CACM

Researchers at the University of Pennsylvania General Robotics, Automation, Sensing & Perception Laboratory have created a system of aerial drones that can connect to form rigid structures for emergency rescues. .... " 

Separating Better Data from Big Data

Interesting Podcast and transcript that leads towards operational considerations for analytics:

Separating Better Data from Big Data: Where Analytics Is Headed

Wharton's Eric Bradlow, Peter Fader and Raghuram Iyengar discuss what's next for customer analytics.

Ten years ago, the most forward-thinking companies were just starting to dive into the potential of data and analytics. Since then, brands have moved from using analytics to answer what customers are doing to exploring the how and why, and also to figure out what they will do in the future.

The Wharton Customer Analytics Initiative (WCAI) is celebrating its 10th anniversary this year and has seen every step of that evolution. Knowledge@Wharton recently sat down with Wharton marketing professors Eric Bradlow, Peter Fader and Raghuram Iyengar to discuss how the field has developed over time, and what they expect to be the key trends over the next decade. Bradlow and Fader are the founding directors of WCAI, and Bradlow and Iyengar are the current co-directors.

An edited transcript of the conversation follows. ... "