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A Look Back on the Dartmouth Summer Research Project on Artificial Intelligence

At this convention that took place on campus in the summer of 1956, the term “artificial intelligence” was coined by scientists..

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For six weeks in the summer of 1956, a group of scientists convened on Dartmouth’s campus for the Dartmouth Summer Research Project on Artificial Intelligence. It was at this meeting that the term “artificial intelligence,” was coined. Decades later, artificial intelligence has made significant advancements. While the recent onset of programs like ChatGPT are changing the artificial intelligence landscape once again, The Dartmouth investigates the history of artificial intelligence on campus.   

That initial conference in 1956 paved the way for the future of artificial intelligence in academia, according to Cade Metz, author of the book “Genius Makers: the Mavericks who Brought AI to Google, Facebook and the World.” 

“It set the goals for this field,” Metz said. “The way we think about the technology is because of the way it was framed at that conference.”

However, the connection between Dartmouth and the birth of AI is not very well-known, according to some students. DALI Lab outreach chair and developer Jason Pak ’24 said that he had heard of the conference, but that he didn’t think it was widely discussed in the computer science department. 

“In general, a lot of CS students don’t know a lot about the history of AI at Dartmouth,” Pak said. “When I’m taking CS classes, it is not something that I’m actively thinking about.”

Even though the connection between Dartmouth and the birth of artificial intelligence is not widely known on campus today, the conference’s influence on academic research in AI was far-reaching, Metz said. In fact, four of the conference participants built three of the largest and most influential AI labs at other universities across the country, shifting the nexus of AI research away from Dartmouth.

Conference participants John McCarthy and Marvin Minsky would establish AI labs at Stanford and MIT, respectively, while two other participants, Alan Newell and Hebert Simon, built an AI lab at Carnegie Mellon. Taken together, the labs at MIT, Stanford and Carnegie Mellon drove AI research for decades, Metz said.

Although the conference participants were optimistic, in the following decades, they would not achieve many of the achievements they believed would be possible with AI. Some participants in the conference, for example, believed that a computer would be able to beat any human in chess within just a decade. 

“The goal was to build a machine that could do what the human brain could do,” Metz said. “Generally speaking, they didn’t think [the development of AI] would take that long.”

The conference mostly consisted of brainstorming ideas about how AI should work. However, “there was very little written record” of the conference, according to computer science professor emeritus Thomas Kurtz, in an interview that is part of the Rauner Special Collections archives. 

The conference represented all kinds of disciplines coming together, Metz said. At that point, AI was a field at the intersection of computer science and psychology and it had overlaps with other emerging disciplines, such as neuroscience, he added. 

Metz said that after the conference, two camps of AI research emerged. One camp believed in what is called neural networks, mathematical systems that learn skills by analyzing data. The idea of neural networks was based on the concept that machines can learn like the human brain, creating new connections and growing over time by responding to real-world input data.

Some of the conference participants would go on to argue that it wasn’t possible for machines to learn on their own. Instead, they believed in what is called “symbolic AI.” 

“They felt like you had to build AI rule-by-rule,” Metz  said. “You had to define intelligence yourself; you had to — rule-by-rule, line-by-line — define how intelligence would work.”

Notably, conference participant Marvin Minsky would go on to cast doubt on the neural network idea, particularly after the 1969 publication of “Perceptrons,” co-authored by Minsky and mathematician Seymour Paper, which Metz said led to a decline in neural network research.

Over the decades, Minsky adapted his ideas about neural networks, according to Joseph Rosen, a surgery professor at Dartmouth Hitchcock Medical Center. Rosen first met Minsky in 1989 and remained a close friend of his until Minsky’s death in 2016.

Minsky’s views on neural networks were complex, Rosen said, but his interest in studying AI was driven by a desire to understand human intelligence and how it worked.

“Marvin was most interested in how computers and AI could help us better understand ourselves,” Rosen said.

In about 2010, however, the neural network idea “was proven to be the way forward,” Metz said. Neural networks allow artificial intelligence programs to learn tasks on their own, which has driven a current boom in AI research, he added.

Given the boom in research activity around neural networks, some Dartmouth students feel like there is an opportunity for growth in AI-related courses and research opportunities. According to Pak, currently, the computer science department mostly focuses on research areas other than AI. Of the 64 general computer science courses offered every year, only two are related to AI, according to the computer science department website. 

“A lot of our interests are shaped by the classes we take,” Pak said. “There is definitely room for more growth in AI-related courses.”

There is a high demand for classes related to AI, according to Pak. Despite being a computer science and music double major, he said he could not get into a course called MUS 14.05: “Music and Artificial Intelligence” because of the demand.

DALI Lab developer and former development lead Samiha Datta ’23 said that she is doing her senior thesis on neural language processing, a subfield of AI and machine learning. Datta said that the conference is pretty well-referenced, but she believes that many students do not know much about the specifics.

She added she thinks the department is aware of and trying to improve the lack of courses taught directly related to AI, and that it is “more possible” to do AI research at Dartmouth now than it would have been a few years ago, due to the recent onboarding of four new professors who do AI research. 

“I feel lucky to be doing research on AI at the same place where the term was coined,” Datta said.

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Dartmouth Summer Research Project: The Birth of Artificial Intelligence

Held in the summer of 1956, the dartmouth summer research project on artificial intelligence brought together some of the brightest minds in computing and cognitive science — and is considered to have founded artificial intelligence (ai) as a field..

In the early 1950s, the field of “thinking machines” was given an array of names, from cybernetics to automata theory to complex information processing. Prior to the conference, John McCarthy — a young Assistant Professor of Mathematics at Dartmouth College — had been disappointed by submissions to the Annals of Mathematics Studies journal. He regretted that contributors didn’t focus on the potential for computers to possess intelligence beyond simple behaviors. So, he decided to organize a group to clarify and develop ideas about thinking machines.

“At the time I believed if only we could get everyone who was interested in the subject together to devote time to it and avoid distractions, we could make real progress”. John McCarthy

John approached the Rockefeller Foundation to request funding for a summer seminar at Dartmouth for 10 participants. In 1955, he formally proposed the project, along with friends and colleagues Marvin Minsky (Harvard University), Nathaniel Rochester (IBM Corporation), and Claude Shannon (Bell Telephone Laboratories).

Laying the Foundations of AI

The workshop was based on the conjecture that, “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

Although they came from very different backgrounds, all the attendees believed that the act of thinking is not unique either to humans or even biological beings. Participants came and went, and discussions were wide reaching. The term AI itself was first coined and directions such as symbolic methods were initiated. Many of the participants would later make key contributions to AI, ushering in a new era.

IBM First Computer

Nathaniel Rochester designs the IBM 701, the first computer marketed by IBM.

“Machine Learning” is coined

Attendee Arthur Samuel coins the term “machine learning” and creates the Samuel Checkers-Playing program, one of the world’s first successful self-learning programs.

Marvin Minsky Wins the Turing Award

Marvin Minsky wins the Turing Award for his “central role in creating, shaping, promoting and advancing the field of artificial intelligence.”

Claude Shannon: The Father of Information Theory

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Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., the meeting of the minds that launched ai, there’s more to this group photo from a 1956 ai workshop than you’d think.

black and white photo of seven smiling men, sitting on a lawn in front of a tree and a white school building with many windows.

At the 1956 Dartmouth AI workshop, the organizers and a few other participants gathered in front of Dartmouth Hall.

The Dartmouth Summer Research Project on Artificial Intelligence , held from 18 June through 17 August of 1956, is widely considered the event that kicked off AI as a research discipline. Organized by John McCarthy , Marvin Minsky , Claude Shannon , and Nathaniel Rochester , it brought together a few dozen of the leading thinkers in AI, computer science, and information theory to map out future paths for investigation.

A group photo [shown above] captured seven of the main participants. When the photo was reprinted in Eliza Strickland’s October 2021 article “The Turbulent Past and Uncertain Future of Artificial Intelligence” in IEEE Spectrum , the caption identified six people, plus one “unknown.” So who was this unknown person?

Who is in the photo?

Six of the people in the photo are easy to identify. In the back row, from left to right, we see Oliver Selfridge , Nathaniel Rochester, Marvin Minsky, and John McCarthy. Sitting in front on the left is Ray Solomonoff , and on the right, Claude Shannon. All six contributed to AI, computer science, or related fields in the decades following the Dartmouth workshop.

Between Solomonoff and Shannon is the unknown person. Over the years, some people suggested that this was Trenchard More , another AI expert who attended the workshop.

I first ran across the Dartmouth group photo in 2018, when I was gathering material for Ray’s memorial website . Ray and I had met in 1969, and we got married in 1989; he passed away in late 2009. Over the years, I had attended a number of his talks, and I had met many of Ray’s peers and colleagues in AI, so I was curious about the photo.

I thought, “Gee, that guy in the middle doesn’t look like my memory of Trenchard.” So I called up Trenchard’s son Paul More. He assured me that the unknown person was not his father.

More recently, I discovered a letter among Ray’s papers. On 8 November 1956, Nat Rochester sent a short note and a copy of the photo to some colleagues: “Enclosed is a print of the photograph I took of the Artificial Intelligence group.” He sent his note to McCarthy, Minsky, Selfridge, Shannon, Solomonoff—and Peter Milner.

So the unknown person must be Milner! This makes perfect sense. Milner was working on neuropsychology at McGill University , in Montreal, although he had trained as an electrical engineer. He’s not generally lumped in with the other AI pioneers because his research interests diverged from theirs. Even at Dartmouth, he felt he was in over his head, as he wrote in his 1999 autobiography: “I was invited to a meeting of computer scientists and information theorists at Dartmouth College…. Most of the time I had no idea what they were talking about.”

In his fascinating autobiography, Milner writes about his work in radar development during World War II, and his switch after the war from nuclear-reactor design to psychology. His doctoral thesis in 1954, “ Effects of Intracranial Stimulation on Rat Behaviour ,” examined the effects of electrical stimulation on certain rat neurons, which became widely and enthusiastically known as “pleasure centers.”

This work led to one of Milner’s most famous papers, “ The Cell Assembly: Mark II ,” in 1957. The paper describes how, when a neuron in the brain fires, it excites similar connected neurons (especially those already aroused by sensory input) and randomly excites other cortical neurons. Cells may form assemblies and connect with other assemblies. But the neurons don’t seem to exhibit the same snowballing behavior of atoms that leads to an exponential explosion. How neurons might inhibit this effect were among his ideas that led to new insights at the workshop.

Milner’s work contributed to the early development of artificial neural networks, and it’s why he was included in the Dartmouth meeting. There was considerable interest among AI researchers in studying the brain and neurons in order to reproduce its functions and intelligence.

But as Strickland notes in her October 2021 Spectrum article, a division was already forming in AI research. One side focused on replicating the brain, while the other was more interested in what the mind might do to directly solve problems. Scientists interested in this latter approach were also represented at Dartmouth and later championed the rise of symbolic logic, using heuristic and algorithmic processes, which I’ll discuss in a bit.

Where Was the Photo Taken?

Rochester’s photo from 1956 shows the left-hand side of Dartmouth Hall in the background. In 2006 Dartmouth convened a conference, AI@50 , to celebrate the 50th anniversary of the AI gathering and to discuss AI’s present and future. Trenchard More, the person most often misidentified as the “unknown person” in Nat’s photo, met with the organizers, James Moor and Carey Heckman, as well as Wendy Conquest, who was working on a movie about AI for the conference. None of the AI@50 organizers knew exactly where the 1956 meeting had taken place.

More led them across the lawn and to the left-hand side door of Dartmouth Hall. He showed them the rooms that were used, which in turn triggered an old memory. During the 1956 meeting, as More recalled in a 2011 interview , “Selfridge, and Minsky, and McCarthy, and Ray Solomonoff, and I gathered around a dictionary on a stand to look up the word heuristic , because we thought that might be a useful word.” On that 2006 tour of Dartmouth Hall, he was delighted to find that the dictionary was still there.

The word heuristic was invoked all through the summer of 1956. Instead of trying to analyze the brain to develop machine intelligence, some participants focused on the operational steps needed to solve a given problem, making particular use of heuristic methods to quickly identify the steps.

Early in the summer, for instance, Herb Simon and Allen Newell gave a talk on a program they had written, the logic theory machine . The program relied on early ideas of symbolic logic, with algorithmic steps and heuristic guidance in list form. They later won the 1975 Turing Award for these ideas. Think of heuristics as intuitive guides. The logic theory machine used such guides to initiate the algorithmic steps—that is, the set of instructions to actually carry out the problem solving.

Who Wasn’t in the Photo

There was one person who was at the Dartmouth Workshop from time to time but was never included in any of the lists of attendees: Gloria Minsky, Marvin’s wife.

But Gloria was definitely a presence that summer. Marvin, Ray, and John McCarthy were the only three participants to stay for the entire eight-week workshop. Everyone else came and went as their schedules allowed. At the time, Gloria was a pediatrics fellow at Children’s Hospital in Boston, but whenever she could, she would drive up to Dartmouth, stay in Marvin’s apartment, and visit with whoever was at the workshop.

Several years earlier, in the spring of 1952, Gloria had been doing her residency in pathology at New York’s Bellevue Hospital, when she began dating Marvin. Marvin was a Ph.D. student at Princeton, as was McCarthy, and the two were invited to Bell Labs for the summer to work under Claude Shannon. In July, just four months after their first meeting, Gloria and Marvin got married. Although Marvin was working nonstop for Shannon, Shannon insisted he and Gloria take a honeymoon in New Mexico.

Four years later, McCarthy, Shannon, and Minsky, along with Nat Rochester, organized the Dartmouth workshop . Gloria remembered a conversation between her husband and Ray, in which Marvin expressed a thought that later became one of his hallmarks: “You need to see something in more than one way to understand it.” In Minsky’s 2007 book The Emotion Machine , he looked at how emotions, intuitions, and feelings create different descriptions and provide different ways of looking at things. He tended to favor symbolic logic and deductive methods in AI, which he called “good old-fashioned AI.”

Ray, meanwhile, was focused on probabilities—the likelihood of something happening and predictions of how it might evolve. He later developed algorithmic probability, an early version of algorithmic information theory, in which each different description of something leads with a probabilistic likelihood (some more likely, some less likely) of a given outcome in the future. Probabilistic methods eventually became the underpinnings of machine learning.

These days, as chatbots enter the limelight, and compression methods are used more in AI, the value of understanding things in many ways and using probabilistic predictions will only grow in importance. That is, logic and probability methods are uniting. These in turn are being aided by new work on neural nets as well as symbolic logic. And so the photo that Nat Rochester took not only captured a moment in time for AI. It also offered a glimpse into how AI would develop.

The author thanks Gloria Minsky, Margaret Minsky, Nicholas Rochester, Julie Sussman, Gerald Jay Sussman, and Paul More for their help and patience.

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  • UNT AI Summer Research Program

The UNT AI/CS Summer Research Program brings together students from a variety of AI and CS-related academic programs to supplement their traditional course-based educational experiences with focused, project-oriented research efforts

A unique aspect of this program is the immersive 4-week effort which guides students through all stages from initial project selection to research poster presentation. This is accomplished through intensive group efforts, including brief twice-daily full group meetings ("huddles") and daily project group meetings lead by faculty and grad students. The intensive period culminates in a celebration and poster presentation event on the final Friday. This initial period demonstrates what can be accomplished with well-organized, focused, and intensive group effort, and accelerates the student preparation for more independent efforts throughout the summer and into the next academic year. Uniquely this year, we are accepting students with both AI and general computer science interest, with the goal of having project options across both domains to provide additional opportunities for teams.

Sections below:

  • Summary of the 2023 program
  • Who can apply?
  • Application
  • Past summer programs
  • Contact information
  • Related links
  • FAQ  (frequently asked questions)
  • Application opens: Friday, Feb 3rd, 2023. 
  • TAMS-only application deadline: Friday, Feb 10th  17th 2023 (to coincide with the TAMS summer research proposal deadline)
  • Acceptances are emailed by: Friday, April 14, 2023 (TAMS will be earlier)
  • Project selection FOR ALL SESSIONS 9am-12pm, Monday, May 22, 2023. Choices to be made online before 3pm.
  • June Session: Tuesday, May 23rd - Friday, June 16th
  • July Session: Wednesday, July 5th - Friday, July 28th
  • End of program presentations and poster session FOR ALL SESSIONS TBD: first weeks of Fall semester for greater reach

2023 SUMMER PROGRAM

  • The 2023 program summary document with projects, participant lists, posters, and pictures
  • The previous 2022 program summary document

WHO CAN APPLY?

A variety of UNT student and faculty populations are integrated in the program, with some participation more formally arranged than others. The includes faculty, PhD students, MS students, and undergrads including TAMS. The "How to Apply" section details general expectations for all students. Each of the participating groups has additional expectations, requirements, or enrollment limits described below.

APPLICATION

Review the following requirements and make sure you meet them before proceeding to the application:

Availability

  • You are available without interruption for the morning sessions (9am-12) every workday in the 4 weeks of the June/late-May session or the 4 weeks of the July session. You indicate your availability when applying and the selection of students will be dictated by availability and indicated preferences.
  • In-person attendance is required. You should not be taking concurrent summer classes during your session given demands of the program, and classes during the morning session are not permitted at all.

Experience expectations

  • You must be actively completing a degree program at UNT or through a previous arrangement with the coordinators.
  • You have substantial programming experience, ideally Python
  • For the AI sessions, you have taken an introductory AI/Machine Learning/Data Science course (e.g. you can explain what "cross validation" is, and why it's useful - or you understand train/validation/test splits)
  • Your GPA at UNT must be 3.0 or higher, and you must have completed at least one semester of courses at UNT prior to the summer.
  • If you are a TAMS student: these are not requirements - though similar experience is encouraged.

Materials to prepare before submission

  • Prepare a Resume or CV for upload for the online application.

After reviewing and preparing the above materials, please complete the online form

Application form   due by March 31st, 2023 (but Feb 10, 2023 for TAMS students only)    (note, a google account will be needed for verification)

Any questions, comments, or concerns please email one of the summer program coordinators: Mark Albert at [email protected] or Ting Xiao at [email protected]. If it is not urgent, please allow up to two workdays for a response.

The coordinators have been running summer research programs in AI and CS continuously since 2015 (moving to UNT in Fall 2019) with over 200 students and 50+ projects total so far. Here are the following program summaries which include brief program descriptions, research posters, participants, and pictures.

  • Summer 2023 - 58 students, 18 projects, 9 participating faculty (UNT)
  • Summer 2022 - 56 students, 17 projects, 8 participating faculty (UNT)
  • Summer 2021 - 48 students, 15 projects, 9 participating faculty (UNT)
  • Summer 2020  - 22 students, 9 projects, 10 participating faculty (UNT, primarily TAMS due to COVID-19)
  • Summer 2019  - 30 students, 9 projects, 5 participating faculty (Loyola CS)
  • Summer 2018  - 28 students, 10 projects, 8 participating faculty (Loyola CS)
  • Summer 2017  - 23 students, 7 projects, 8 participating faculty (Loyola CS)
  • Summer 2016  - 30 students, 12 projects, 6 participating faculty (Loyola CS)
  • Summer 2015  - 19 students, 5 projects, 2 participating faculty (Loyola CS)

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  • I have a question about the program, who do I ask? Any questions, comments, or concerns please email one of the coordinators: Mark Albert at [email protected] or Ting Xiao at [email protected]
  • Is there a difference between participating for CSCE 5900: Special Projects credit or not? Students will not be treated differently in the program based on whether or not they are enrolled in course credit. However, credit helps toward graduation and taking the course indirectly supports efforts like this, and so it is encouraged if it helps you complete your degree. Attendance expectations are clearly more important with course credit on the line, and a lack of participation is generally the only way Special Projects grading would be affected.
  • But what if I am not as interesting in AI - I just want to participate in this environment? That is perfectly fine. We also have CS-focussed projects about developing software, as well as more AI focussed project efforts. In fact, traditionally there have always been projects with a development focus in addition to AI model building/validation/testing. This is why we may refer to the program as the UNT AI/CS Summer Research Program.

Send comments to [email protected]. I sometimes make changes suggested in them. - John McCarthy

SBU News

Chancellor’s Summer Research Excellence Fund Focuses on AI

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As part of the 2024 State of the State agenda, Governor Kathy Hochul has proposed the Empire AI initiative, which will provide SUNY’s four university centers and other leading New York research institutions with the opportunity to conduct groundbreaking research and lead the nation in artificial intelligence research and economic development, with an emphasis on the use of AI to advance the public good.

To help support Empire AI and the nation leading work New York is doing around artificial intelligence, at least 22 of the 2024 Chancellor’s Summer Research Excellence Fund paid internship experiences will focus on roles in AI and the need to increase diversity, equity, and inclusion in AI research and development.

“Like the invention of the printing press, the radio, or the internet, once every generation, a technological wave comes along that changes how we live,” said King. “That is why it is critical we provide internship experiences to students in the growing field of Artificial Intelligence to apply AI to solve problems, to learn about AI ethics, and to increase diversity in STEM fields researching and innovating in this emerging technology. Internships are absolutely vital to student success, as they offer experiences students would not otherwise get in the classroom alone.”

The internship program will expand research opportunities to students with financial need, first-generation students, and others who may face barriers to securing research experiences. Participating campuses include Stony Brook, University at Albany, Binghamton University, University at Buffalo, Downstate Health Sciences University, SUNY College of Environmental Science and Forestry, SUNY Polytechnic Institute and Upstate Medical University.

The Summer Research Excellence Fund, which is supported by SUNY’s Empire Innovation Program, covers all student costs for the internship including, but not limited to, student stipend/salary, tuition/fees, housing, meal plans, childcare and transportation.

These internships will be in fields including biology, artificial intelligence, machine learning, cybersecurity, physics, astronomy, engineering, medicine, life sciences, chemistry, computer science and clean energy.

“As Stony Brook prepares its bright and talented students to lead the next generation of scientific research, ensuring the future of STEM education and innovation is inclusive, equitable, diverse and collaborative is a top priority,” said Stony Brook University President Maurie McInnis. “The generous and visionary support of the Chancellor’s Summer Research Excellence Fund makes it possible for our students to excel at the highest levels by obtaining hands-on research opportunities in critical fields, such as artificial intelligence, climate science, and clean energy.”

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  • Artificial Intelligence
  • Reading time 3 min.

Seven new research projects on cybersecurity and artificial intelligence TUM and Google strengthen cooperation

The Technical University of Munich (TUM) and Google are driving forward research into cybersecurity and artificial intelligence (AI). Thanks to funding from Google, seven new research projects are being launched at TUM to investigate critical questions at the interface of cybersecurity and AI.

summer research project on artificial intelligence

How can AI be used to moderate problematic content, such as hate speech online? What are the data protection risks of general-purpose AI models, and how are they perceived in Europe? Moreover, what are the patterns of cyberattacks on large language models (LLMs)? The work in the seven projects is led by TUM professors and implemented by their teams. Experts from Google are on hand to advise them. Seven new positions for doctoral students will also be created as part of the funding.

About the seven research projects

  • Secure compilation of high-performance concurrency models (Prof. Pramod Bhatotia): How do we ensure that software customized for parallel computing architectures is secure and free of vulnerabilities?
  • Large Language Models (LLMs) for the analysis of program code with regard to security vulnerabilities and data protection violations (Prof. Claudia Eckert): How can novel, LLM-based, automated methods be used to identify privacy and security vulnerabilities in very large codebases more efficiently and precisely?
  • Probe positioning (Prof. Georg Sigl): Hardware can reveal information that can be spied on by an electromagnetic probe. The positioning of the probe is an open problem. How can AI help to make side-channel measurements more reproducible and thus make the hardware more secure?
  • Side-channel analysis of post-quantum cryptography (Prof. Georg Sigl): How to automatically detect all side-channel leaks of implementations of quantum computer-resistant cryptography?
  • Understanding attitudes and promoting acceptance of AI-assisted approaches to content moderation (Prof. Jens Großklags): LLMs can be used to automate content moderation to detect hate speech, sexism, or cyberbullying online. However, how effective are such AI-driven approaches, and how do human users perceive them?
  • Data protection risks of general AI systems: Stakeholder perspectives in Europe (Prof. Florian Matthes). What data protection risks exist in new general-purpose AI (GPAI) systems like LLMs? How are these risks perceived in Europe?
  • Understanding attacks on language models (Prof. Stephan Günnemann): LLMs can be attacked - for example, by malicious requests to reveal private user data. We want to better understand how such attacks work in general, what triggers them in LLMs, and how they can be prevented.

AI permeates our everyday lives

Prof. Thomas F. Hofmann, TUM President: "AI has begun to permeate our everyday lives. That is why the further development of AI technologies is crucial. These technologies must be as reliable and safe as possible for this to be socially acceptable. Together with Google, we are driving these developments forward." Dr. Wieland Holfelder, Vice President Engineering & Site Lead Google Munich: "Issues relating to cyber security and artificial intelligence are becoming increasingly intertwined. How can we make AI as secure as possible? And how can AI help to better protect our digital infrastructure? These are key questions of our time, not least at the upcoming Munich Security Conference. We look forward to working with TUM to further advance research on these key topics."

Cooperation with Google

TUM and Google are deepening their partnership through new contract research in the field of cybersecurity and AI. In 2018, Google became the first non-European company to become an Excellence Partner of the university. In addition, Google has supported TUM initiatives since 2022 that promote women on their career paths in the STEM subjects of mathematics, computer science, natural sciences, and technology. In February 2023, TUM launched the TUM Innovation Network on cybersecurity using a non-earmarked donation from Google. Google has also established its global Safety Engineering Center in Munich.

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Penn Engineering Announces First Ivy League Undergraduate Degree in Artificial Intelligence

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The University of Pennsylvania   School of Engineering and Applied Science introduces its Bachelor of Science in Engineering (B.S.E.) in Artificial Intelligence (AI) degree , the first undergraduate major of its kind among Ivy League universities and one of the very first AI undergraduate engineering programs in the U.S. 

The rapid rise of generative AI is transforming virtually every aspect of life: health, energy, transportation, robotics, computer vision, commerce, learning and even national security. This produces an urgent need for innovative, leading-edge AI engineers who understand the principles of AI and how to apply them in a responsible and ethical way. 

“Inventive at its core, Penn excels at the cutting edge,” says Interim President J. Larry Jameson . “Data, including AI, is a critical area of focus for our strategic framework, In Principle and Practice, and this new degree program represents a leap forward for the Penn engineers who will lead in developing and deploying these powerful technologies in service to humanity. We are deeply grateful to Raj and Neera Singh, whose leadership helps make this possible.”

The Raj and Neera Singh Program in Artificial Intelligence equips students to unlock AI’s potential to benefit our society. Students in the program will be empowered to develop responsible AI tools that can harness the full knowledge available on the Internet, provide superhuman attention to detail, and augment humans in making transformative scientific discoveries, researching materials for chips of the future, creating breakthroughs in healthcare through new antibiotics, applying life-saving treatments and accelerating knowledge and creativity.

Raj and Neera Singh are visionaries in technology and a constant force for innovation through their philanthropy. Their generosity graciously provides funding to support leadership, faculty, and infrastructure for the new program.

“Penn Engineering has long been a pioneer in computing and education, with ENIAC, the first digital computer, and the first Ph.D. in Computer Science,” says Dr. Raj Singh, who together with his wife Neera have established the first undergraduate degree program in Artificial Intelligence within the Ivy League. “This proud legacy of innovation continues with Penn Engineering’s AI program, which will produce engineers that can leverage this powerful technology in a way that benefits all humankind.” “We are thrilled to continue investing in Penn Engineering and the students who can best shape the future of this field,” says Neera. 

Picture of Raj and Neera Singh sitting down

Preparing the next generation of AI engineers

The B.S.E. in Artificial Intelligence curriculum offers high-level coursework in topics including machine learning, computing algorithms, data analytics and advanced robotics.

“The timing of this new undergraduate program comes as AI poses one of the most promising yet challenging opportunities the world currently faces,” says Vijay Kumar , Nemirovsky Family Dean of Penn Engineering. “Thanks to the generosity of Raj and Neera Singh, Penn Engineering’s B.S.E. in Artificial Intelligence program, we are preparing the next generation of engineers to create a society where AI isn’t just a tool, but a fundamental force for good to advance society in ways previously unimaginable.”

Leading the program will be George J. Pappas , UPS Foundation Professor of Transportation in Penn Engineering. “Realizing the potential of AI for positive social impact stands as one of the paramount challenges confronting engineering,” says Pappas, a 2024 National Academy of Engineering inductee. “We are excited to introduce a cutting-edge curriculum poised to train our students as leaders and innovators in the ongoing AI revolution.”

Ivy League coursework equipping students for the future

The program’s courses will be taught by world-renowned faculty in the setting of Amy Gutmann Hall, Penn Engineering’s newest building. A hub for data science on campus and for the Philadelphia community when it officially opens this year, the state-of-the-art facilities in Amy Gutmann Hall will further transform the University’s capabilities in engineering education, research and innovation as Penn Engineering advances the development of artificial intelligence.

“We are training students for jobs that don’t yet exist in fields that may be completely new or revolutionized by the time they graduate,” says Robert Ghrist , Associate Dean of Undergraduate Education in Penn Engineering and the Andrea Mitchell University Professor. “In my decades of teaching, this is one of the most exciting educational opportunities I’ve ever seen, and I can’t wait to work with these amazing students.”

More details about the AI curriculum and a full list of courses available within the program can be reviewed here . 

“Our carefully selected curriculum reflects the reality that AI has come into its own as an academic discipline, not only because of the many amazing things it can do, but also because we think it’s important to address fundamental questions about the nature of intelligence and learning, how to align AI with our social values, and how to build trustworthy AI systems,” says Zachary Ives , Adani President’s Distinguished Professor and Chair of the Department of Computer and Information Science in Penn Engineering.

The B.S.E. in Artificial Intelligence program will begin in fall 2024, with applications for existing University of Pennsylvania students who would like to transfer into the 2024 cohort available this fall. Fall 2025 applications for all prospective students will be made available in fall 2024. Visit the new website at ai.seas.upenn.edu .

IMAGES

  1. A Proposal for the Dartmouth Summer Research Project on Artificial

    summer research project on artificial intelligence

  2. This week in The History of AI at AIWS.net

    summer research project on artificial intelligence

  3. Dartmouth Summer Research Project: The Birth of Artificial Intelligence

    summer research project on artificial intelligence

  4. Dartmouth

    summer research project on artificial intelligence

  5. A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL

    summer research project on artificial intelligence

  6. Artificial Intelligence Summer Workshop for Grades 7

    summer research project on artificial intelligence

VIDEO

  1. ITC FINAL PROJECT (Artificial Intelligence)

  2. FeedbackHHC

  3. How OR and AI can improve the travel experience

  4. Final Project

  5. Artificial intelligence introducing

  6. LINCS Exploratory: A LINCS Discord Server

COMMENTS

  1. Dartmouth workshop

    The Dartmouth Summer Research Project on Artificial Intelligence was a 1956 summer workshop widely considered [1] [2] [3] to be the founding event of artificial intelligence as a field. The project lasted approximately six to eight weeks and was essentially an extended brainstorming session.

  2. A Look Back on the Dartmouth Summer Research Project on Artificial

    For six weeks in the summer of 1956, a group of scientists convened on Dartmouth's campus for the Dartmouth Summer Research Project on Artificial Intelligence. It was at this meeting that the term "artificial intelligence," was coined. Decades later, artificial intelligence has made significant advancements.

  3. Dartmouth Summer Research Project: The Birth of Artificial Intelligence

    Held in the summer of 1956, the Dartmouth Summer Research Project on Artificial Intelligence brought together some of the brightest minds in computing and cognitive science — and is considered to have founded artificial intelligence (AI) as a field.

  4. Artificial Intelligence (AI) Coined at Dartmouth

    In 1956, a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence, which was the birth of this field of research. To celebrate the anniversary, more than 100 researchers and scholars again met at Dartmouth for AI@50, a conference that not only honored the past and assessed present accomplishments ...

  5. A Proposal for the Dartmouth Summer Research Project on Artificial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page.

  6. The Meeting of the Minds That Launched AI

    The Dartmouth Summer Research Project on Artificial Intelligence, held from 18 June through 17 August of 1956, is widely considered the event that kicked off AI as a research discipline.

  7. Artificial Intelligence · Issue 1.1, Summer 2019

    The plaque was hung in 2006, in conjunction with a conference commemorating the 50th anniversary of the Summer Research Project, and it enshrines the standard account of the history of Artificial Intelligence-that it was born in 1955 when these veterans of early military computing applied to the Rockefeller Foundation for a summer grant to fund ...

  8. A Proposal for The Dartmouth Summer Research Project on Artificial

    5. Self-lmprovement Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well. 6. Abstractions

  9. A Proposal for the Dartmouth Summer Research Project on Artificial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, along with the short autobiographical statements of the proposers. Expand View via Publisher aaai.org Save to Library Create Alert Cite Topics

  10. Dartmouth Milestones

    The Dartmouth Summer Research Project on Artificial Intelligence was a seminal event for artificial intelligence as a field. Read the story. Image. Howard Chivers, manager, and Polly Chinlund, who won the contest to name the "Skiway." Chinlund won a lifetime pass. (Photo by D. Cutter '73, courtesy of the Dartmouth Library)

  11. A Proposal for the Dartmouth Summer Research Project on Arti cial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude...

  12. PDF A OPOSAL PR OR F THE

    The abstractions together with certain ternal in habits or es driv vide: pro 2.1 A de nition of a problem in terms of desired condition to b e ed hiev ac in the future, a goal. 2.2 A suggested action to e solv the problem. 2.3 ulation Stim to arouse in the brain the engine h whic corresp onds to this situation. 3.

  13. A Proposal for the Dartmouth Summer Research Project on Artificial

    The proposal for the Dartmouth Summer Research Project on Artificial Intelligence was, so far as I know, the first use of phrase Artificial Intelligence. If you are interested in the exact typography, you will have to consult a paper copy. Download the article in PDF. Professor John McCarthy's page

  14. The Birthplace of AI. An essay about the 1956 "Dartmouth ...

    T he Dartmouth Summer Research Project on Artificial Intelligence was a summer workshop widely considered to be the founding moment of artificial intelligence as a field of research.

  15. A Proposal for the Dartmouth Summer Research Project on Artificial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page.

  16. UNT AI Summer Research Program

    The UNT AI/CS Summer Research Program. brings together students from a variety of AI and CS-related academic programs to supplement their traditional course-based educational experiences with focused, project-oriented research efforts. A unique aspect of this program is the immersive 4-week effort which guides students through all stages from ...

  17. About this document ...

    A PROPOSAL FOR Previous: A PROPOSAL FOR . About this document ... A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE. John McCarthy Wed Apr 3 19:48:31 PST 1996

  18. A Proposal for the Dartmouth Summer Research Project on Artificial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page.

  19. (PDF) The Dartmouth College Artificial Intelligence Conference: The

    Following Moor, the Dartmouth Summer Research Project of 1956 is possibly the event that has set artificial intelligence as a promising scholarly field [16]. Participants followed on the original ...

  20. A Proposal for the Dartmouth Summer Research

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page.

  21. PDF A proposal for the Dartmouth Summer Research Project on Artificial

    August 3 1, 1955 A Proposal for the DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE We propose that a 2 month. 10 man study of artificial intelligence be carried out during the summe r of 1956 at Dartmouth College in Hanover. New Hampshire. The study is to proceed On the basis of the conjecture that every

  22. Proposal for Dartmouth Summer Research Project on Artificial Intelligence

    The proposal for the Dartmouth Summer Research Project on Artificial Intelligence was, so far as I know, the first use of phrase Artificial Intelligence. If you are interested in the exact typography, you will have to consult a paper copy. Here is the html form of the Dartmouth Proposal . There are also .dvi, .ps and .pdf forms.

  23. Chancellor's Summer Research Excellence Fund Focuses on AI

    To help support Empire AI and the nation leading work New York is doing around artificial intelligence, at least 22 of the 2024 Chancellor's Summer Research Excellence Fund paid internship experiences will focus on roles in AI and the need to increase diversity, equity, and inclusion in AI research and development. "Like the invention of ...

  24. TUM and Google launch seven new research projects on cybersecurity and

    The Technical University of Munich (TUM) and Google are driving forward research into cybersecurity and artificial intelligence (AI). Thanks to funding from Google, seven new research projects are being launched at TUM to investigate critical questions at the interface of cybersecurity and AI. Astrid Eckert / TUM.

  25. Penn Engineering Announces First Ivy League Undergraduate Degree in

    Amy Gutmann Hall (Photo courtesy of Eric Sucar) The University of Pennsylvania School of Engineering and Applied Science introduces its Bachelor of Science in Engineering (B.S.E.) in Artificial Intelligence (AI) degree, the first undergraduate major of its kind among Ivy League universities and one of the very first AI undergraduate engineering programs in the U.S.

  26. A Proposal for the Dartmouth Summer Research Project on Artificial

    The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page.