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Rendering of the Syracuse University Center for Advanced Semiconductor Manufacturing

Syracuse University today announced its plans to launch the Syracuse University Center for Advanced Semiconductor Manufacturing, an interdisciplinary center that will bring together expertise in artificial intelligence (AI), cybersecurity, manufacturing processes, optimization and robotics to advance the science of semiconductor manufacturing. The center will be funded by a $10 million investment from the University, as well as a $10 million grant from Onondaga County. The center is part of a more than $100 million investment in strategically transforming STEM and expanding the College of Engineering and Computer Science (ECS) at Syracuse University over the next five years.

Housed in the Universitys Center for Science and Technology and situated within ECS, the new center will position the University and Central New York as a global leader in research and education on the intelligent manufacturing of semiconductors.

Syracuse University and Onondaga County have a longstanding history of collaborating in ways that are mutually beneficial for our students, faculty and staff; the Central New York community and the economic prosperity of our region, says Chancellor Kent Syverud. I am grateful for the countys support. I look forward to the teaching and research that will occur at this new center as well as the meaningful ways that its educational outcomes will contribute to a thriving advanced semiconductor manufacturing industry in Central New York.

Todays announcement comes as Micron Technology continues its $100 billion investment in Central New York, which is expected to create 50,000 new jobs in the region, including 9,000 high-paying jobs directly with Micron. Micron will also invest $500 million in community and workforce development, focusing on assisting traditionally underrepresented and disadvantaged populations while training or retraining the regions workforce.

Onondaga County Executive Ryan McMahon, who was central to attracting Micron to Central New York, says this new facility will play a significant role in helping to drive economic development, cultivate the talent pipeline, attract federal research and development funding and build the semiconductor supply chain in Syracuse.

As Onondaga County prepares to become the hub for memory technology chip production, we know that we will need our partners in higher education to help develop the necessary workforce critical to ensuring our success, says McMahon. With this historic investment by Onondaga County and Syracuse University to launch the Center for Advanced Semiconductor Manufacturing, we are taking a huge step forward in that effort. This new center will serve as a vital workforce pipeline as Micron proceeds with the largest investment in the country at White Pine Business Park. I want to thank Chancellor Syverud and the entire team at Syracuse University for their commitment and partnership in making this important initiative a reality.

The new Syracuse University Center for Advanced Semiconductor Manufacturing will drive progress in manufacturing processes across the semiconductor supply chain. A state-of-the-art teaching and research facility, it will replicate an autonomous-advanced manufacturing floor enabling research and design that will make Syracuse and the United States globally competitive in semiconductor manufacturing technologies. Students will be trained in the manufacturing technologies of today and create the new ideas that will drive the industry tomorrow. This universitys ongoing partnership with Micron and the county will ensure that Onondaga County can deliver chips through the most high-quality and cost-effective manufacturing processes possible for years to come.

Central New York is about to undergo a once-in-a-generation transformation and Syracuse University will play a critical role as one of the regions key higher education partners, says Vice Chancellor for Strategic Initiatives and Innovation J. Michael Haynie. We are proud to partner with the county, Micron and other community and business leaders to prepare a workforce in a way that capitalizes on all of the economic opportunities facing our region today.

Rendering of the Syracuse University Center for Advanced Semiconductor Manufacturing

The new center is aligned with the Universitys academic strategic plan and leverages the investment it has already made in AI, manufacturing, quantum technologies and precision measurement. Over the next five years, the University will hire more than 10 new faculty at various ranks with expertise in manufacturing process engineering and automation, optimization and artificial intelligence, materials science engineering and other related fields.

Not only will this center support economic and workforce development, it will also generate significant academic opportunities for both our students and our faculty from a teaching, learning and research perspective, says Vice Chancellor, Provost and Chief Academic Officer Gretchen Ritter. There is huge demand for trained professionals in and across these fields and Syracuse University will be at the forefront of preparing the next generation of scientists, engineers and leaders in the advanced semiconductor manufacturing space.

The centers research will drive the improvements in manufacturing needed to give designers the ability to create tomorrows most advanced chips. It will also deliver the skill sets needed by todays semiconductor industry by educating graduate and undergraduate students in cutting-edge manufacturing and supply-chain technologies.

ECS Dean Cole Smith, who is leading the efforts to expand engineering at Syracuse, says the new center will allow the University to attract and retain diverse and talented student scholars from across the globe who will come to Syracuse to live, learn, study and work. The University will also work closely with the county and the City of Syracuse to recruit students from area high schools, including the new STEAM High School. These efforts directly support the College of Engineering and Computer Sciences plan to grow its undergraduate enrollment by 50% by 2028.

We want to make advanced manufacturing tangible, exciting and accessible for all students, even if they have not yet seen engineering and computer science as a potential career field, says Dean Smith. One of the most exciting aspects of this center is in its dual use for research and education. Prospective students, especially those coming from Central New York, will see amazing opportunities for themselves in the field of semiconductor manufacturing. Instead of just reading about the industry, they will both witness exciting research and interact with an automated, intelligent factory floor when they visit the center.

Work to transform existing space into the new facility is underway.

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Syracuse University, Onondaga County Make Combined $20M Investment to Launch the Syracuse University Center ... - Syracuse University News

This week, a group of computer scientists from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) flew to sunny Honolulu, Hawaii for the Association of Computing Machinery (ACM) CHI conference on Human Factors in Computing Systems, one of the most important international conferences on Human-Computer Interaction.

There they presented papers on a range of topics, including the use of chatbots in LGBTQ+ communities, algorithms for social services, AI for text summarization and more. Among the hundreds of papers selected for the conference, 40 received awards for Best Paper, including research led by Priyan Vaithilingam, a PhD candidate in Assistant Professor Elena Glassmans Variation Lab.

The paper, primarily advised by Microsoft researchers Jeevana Priya Inala and Chenglong Wang during Priyans summer internship, describes a new program that allows users to create and edit data visualizations using natural language commands.

A lot of people who aren't experts in data visualization are still required to create and edit visualizations for their work, said Vaithilingam. Current software tools such as Tableau and Microsofts PowerBI and Charticulator have reduced the effort it takes to create visualizations, but those tools provide a one-size-fits-all solution to millions of users with different needs and workflows. Our goal was to provide personalized tools to users.

The program, named Dynavis, uses large-language models, including Chat GPT4, to generate personalized user interfaces by automatically creating widgets on the fly based on the users needs. This AI-generated dynamic UI can be added to any existing software tools.

Imagine, for example, you are creating a chart on Tableau to track sales over time and want to tweak the label on the X-axis. Today, a user would need to know exactly where to click in the complicated editing window to make the change.

With Dynavis, the user would either type rotate x-axis label 45 degrees or simply ask for a widget to perform the edits with, for example: give me a slider to control x-axis label angles.

Instead of a static user interface, users can now have dynamic and contextual UI for their tasks, said Glassman. This research could allow us to build software that is malleable and contextual truly from the ground up re-defining user experience completely.

Two other papers by members of Glassmans team received honorable mentions at the conference.

In ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing, the research team, which included Glassman, former Variation Lab postdoctoral fellow Ian Arawjo, PhD students Chelse Swoopes and Priyan Vaithilingam, and Martin Wattenberg, Gordon McKay Professor of Computer Science at SEAS, described a new program to support people testing large language models (LLM).

Evaluating the outputs of large language models is challenging, requiring making and making sense of many responses, said Arawjo, who is currently an Assistant Professor at the University of Montral. But tools that go beyond basic prompting tend to require advanced knowledge that most users interested in using LLMs for general tasks dont have.

To address that gap, the researchers developed an open-source tool called ChainForge and deployed it to get feedback from real users. The team used that feedback in addition to feedback from in-lab user studies to iterate and refine the program.

Our goal is to support people in testing large language model behavior on the tasks that matter the most to them, said Arawjo.

LLMs, as demonstrated by ChainForge, sometimes generate a lot of responses that can feel like a wall of text, but have a lot of utility for system designers, and even end-users. In Supporting Sensemaking of Large Language Model Outputs at Scale, authors Glassman, postdoctoral fellow Katy Ilonka Gero, PhD students Chelse Swoopes and Ziwei Gu and collaborator Jonathan Kummerfeld, of the University of Sydney, describe a new program that can present many LLM responses at once in a way that is easier for the human brain to parse.

Large language models are capable of generating multiple different responses to a single prompt, yet little effort has been expended to help end-users or system designers make use of this capability, said Swoopes. For instance, users may want to select the best option from among many responses or compose their own response by combining a few different LLM responses or audit a model by looking at the variety of possible responses.

The researchers began by interviewing designers, model characterizers, and model auditors and found that the methods currently available for comparing dozens of responses were slow and painstaking.

To address that challenge, the researchers designed and implemented text analysis algorithms and rendering techniques to capture possible variations and consistencies of LLM responses. The algorithm highlights similar text across LLM responses and aligns and grays out redundancies so users can easily compare responses.

As LLMs are increasingly adopted, supporting end-users, system developers, and system examiners in making sense of LLM responses is becoming an increasingly important area of study, said Swoopes.

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SEAS at CHI - Harvard School of Engineering and Applied Sciences

Laws move slow, technology moves fast. Thats one challenge when regulating AIsometimes the damage is already done by the time lawmakers pass legislation. Like the European ban on using images scraped from the web to build facial recognition databases, a ban that was approved years after a company had already collected billions of pictures of our unsuspecting, unconsenting faces to do exactly that.

The stakes also vary wildly depending on the context where AI is deployed. For instance, how do you regulate a dual-use technology that some might use to sort handbags on an e-commerce site, others to direct military drones in a warzone? And regulation requires anticipating and countering risksbut its difficult to anticipate risk when we cant possibly predict every novel situation an AI might encounter, nor how it will behave when it does. During a grisly incident in San Francisco, a self-driving car struck a pedestrian and proceeded to pull overa reasonable response to a collision, but not when the victim is still in harms way.

The versatile, unpredictable, and rapidly evolving nature of AI presents a challenge for regulators tasked with keeping us safe as the technology becomes both more sophisticated and more entrenched in our day-to-day lives.

This is not a problem with just the machine. Its a problem with how the machine interacts with us.

Earlier this month, the 2024 Behavioral Science & Policy Association convened a panel at their annual conference to discuss the role behavioral science can play in regulating AI. Ronnie Chatterji, professor of business and public policy at Duke University, moderated the conversation, which featured perspectives from the worlds of business, academia, and government.

The panel included Paula Goldman, ethical and humane use officer at Salesforce; Kristian Hammond, professor of computer science at Northwestern University and chief scientist at Narrative Science; and Elizabeth Kelly, director of the U.S. Artificial Intelligence Safety Institute.

The core question that Goldman, Hammond, and Kelly are grappling with in their respective domains is how to mitigate harm without hampering innovation. All three agree that behavioral science is essential to these efforts. This is not a problem with just the machine, Hammond said. Its a problem with how the machine interacts with us.

Weve curated a portion of their discussion. You can watch the full discussion here.

The transcript has been edited for clarity and brevity.

What about safety and AI keeps you up at night?

Paula Goldman, Salesforce: I spend a lot of time thinking about how to apply safety to the world of AI agents. In a world where were moving from generative AIgoing from generating content to taking action on our behalf, to these interfaces where you can ask a million different things and a million different actions result from ithow do you know in advance, when you ask for an action, what its going to look like? If one prompt is going to launch a million emails, for example, how do you in advance check the quality of that? And then ex-post check the quality of that?

Kristian Hammond, Northwestern: I worry that were going to end up trying to solve the wrong problems. There are some really flashy AI fears, but the thing I worry about is that if we ignore the genuine reality of how this impacts individuals and groups in society, well end up with, Oh, we have regulations around transparency, We have regulations around explanation, We have a focus on being responsible, but without actually getting into the concrete places where there are genuine harms. The fact is that there is a rise in depression among young women ages 13 to 23. Theres a rise in online addiction. We allow the production of false pornography thats humiliating women across the country, and were like, Well, we dont know what to do.

Lets focus on the places where there are real harms because they are rampant. The thing I genuinely worry about is that well focus on, you know, evil drones blowing people up instead of the fact that we are creating a nation of people who are being humiliated, addicted, and pushed into depression, and were not doing anything about it.

Elizabeth Kelly, U.S. AI Safety Institute: I totally agree with what Kris said, but I push back a little on the not doing anything about it, because federal agencies are pretty hard at work trying to make sure theyre addressing a lot of these harms. And theres honestly more that Congress needs to do, and we were very clear about that.

This is light speed for government, but its still slower than the technology.

The thing that keeps me up at night is just how quickly this is moving. If the technology is evolving exponentially, we have no idea what 2025 or 2026 will be. Its hard to say these are the harms we should anticipate. And I think its even harder to say that we as policymakers, we as government, will be able to stay on top of it.

Weve seen the global community move pretty quickly for government. [The executive order] came together in a couple of months, the G7 Code of Conduct. This is light speed for government, but its still slower than the technology. And for all the reasons that weve talked about, weve got to stay ahead of it.

What else is on your mind?

Paula Goldman, Salesforce: I spend a lot of time in this AI bubble. When I step out of it and talk to someone, like a friend I havent talked to in a long time, I hear a lot of fear, and honestly, a lot of mysticism about AI. I think its incumbent on all of us to break that down and to give people a mental model for how to interact with AI. How do we build that into these systems? Accounting for not only the strengths and weaknesses of where AI is right now and where its going, but also human strengths and human cognitive biases. And thats, I think, where the magic is. Thats where we unlock not only avoiding harm with AI but actually using AI for good.

Kristian Hammond, Northwestern: We have to embrace the notion that this is sociotechnical, that this is not a problem with just the machine. Its a problem with how the machine interacts with us. And that means we have to understand and admit who we are and how were hurt, and realize that youre not going to solve the problem by telling people to act differently. Youre going to solve the problem by making sure the machine is built so that when people do what people do, they dont hurt themselves.

Elizabeth Kelly, U.S. AI Safety Institute: Agreed, and thats why my leadership team includes both an anthropologist and an ethicist. For me, the question is: How do we shift away from AI that is easily monetized, that produces a lot of the harms that Kris has talked about, to AI that is actually able to tackle a lot of our most pressing societal problems. Drug discovery and development, carbon capture and storage, education. How can we together work to shift the narrative?

Disclosure: BSPA is an organizational partner of Behavioral Scientist. Organizational partners do not play a role in the editorial decisions of the magazine.

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The Challenges of Regulating AI and the Role of Behavioral Science - by Heather Graci - Behavioral Scientist

Joseph Casale 24 (computational mathematics and computer science) had hardly been on a plane when he traveled to Malaysia to do research a year ago. Now, he gets the opportunity to do it again.

Casale, who is from Rochester, N.Y., is one of RITs record-setting six 2024 Fulbright U.S. awardees. He will be traveling back to Malaysia after previously going there with Chester F. Carlson Center for Imaging Science professor Tony Vodacek to work with the Universiti Teknologi Mara.

On his previous trip to Malaysia, Casale was part of a group of students who spent time in the Taman Negara National Park. The research team was looking to quantify biodiversity in the rainforest with audio processing. Casales future project will be analyzing aerial hyperspectral imagery to map the species of trees.

The great thing about doing machine learning is it allows you to be a scientist and allows you to work with plenty of different people across all different types of fields, said Casale. Being able to go to Malaysia with Dr. Vodacek directly formed the connections that led to this project.

Casale originally wanted to study aerospace engineering and began his academic career at Monroe Community College, but then realized he was more interested in pure analytical mathematics. When he transferred to RIT, his interest in machine learning and optimization started him on the path to earning the prestigious international experience that a Fulbright Scholarship brings.

Having the opportunity to travel around the world as a RIT student has broadened Casales interests and has shown him the possibilities that are available through academia. Earning a Fulbright scholarship serves to enhance what he has already experienced.

Before I left for Malaysia, I didnt really see the potential of becoming an international researcher, said Casale. But there is room for people to do that, it is something that can be achieved with a little bit of luck.

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Graduate Joseph Casale ready to return to Malaysia as a Fulbright awardee - Rochester Institute of Technology