artificial intelligence phd in usa

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2024 for a start date of August 2025 in the Machine Learning PhD program, GRE Scores are OPTIONAL. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2023, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2019, will not be accepted.)

Graduate Online Application

• Admissions application opens September 4, 2024
• Early Application Deadline – November 20, 2024 (3:00 p.m. EST)
• Final Application Deadline - December 11, 2024 (3:00 p.m. EST)

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Online Doctor of Engineering in Artificial Intelligence & Machine Learning

We are now accepting applications for the cohort beginning in January 2025.

The application deadline is November 1, 2024

Program Description

The online Doctor of Engineering in Artificial Intelligence & Machine Learning is a research-based doctoral program. The program is designed to provide graduates with a solid understanding of the latest AI&ML techniques, as well as hands-on experience in applying these techniques to real-world problems. Graduates of this program are equipped to lead AI&ML projects and teams in a wide range of industries, including healthcare, finance, and manufacturing. Having developed advanced research skills, graduates are also well-prepared for academic research and teaching roles.

The degree requires completion of eight graduate-level courses (listed below) and a minimum of 24 credit hours of Praxis Research (SEAS 8588). During the research phase, the student writes and defends a research praxis on a topic related to AI&ML. The topic is selected by the student and approved by the research advising committee.

SEAS 6414 Python Application for Data Analytics:  Introduction to Python programming tailored for Data Analytics. This course covers Python’s applications in automating data cleaning, feature engineering, outlier detection, implementing machine learning algorithms, conducting text mining, and performing time series analysis. (3 credit hours)

SEAS 8500 Fundamentals of AI-Enabled Systems:  Operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. Systems perspective in designing AI systems. Full-lifecycle of creating AI-enabled systems. Ethics and biases in AI systems (3 credit hours)

SEAS 8505 Applied Machine Intelligence and Reinforcement Learning:  Theory and practice of machine learning leveraging open-source tools, algorithms and techniques. Topics include intelligent model training, support vector machines, deep learning, transformer methods, GANs, and reinforcement learning (3 credit hours)

SEAS 8510 Analytical Methods for Machine Learning:  Mathematical tools for building machine learning algorithms: linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics (3 credit hours)

SEAS 8515 Data Engineering for AI:  Developing Python scripts to automate data pipelines, data ingestion, data processing, and data warehousing. Machine learning applications with Python including text mining and time series analysis (3 credit hours)

SEAS 8520 Deep Learning and Natural Language Processing:  Fundamentals of deep learning and Natural Language Processing (NLP). Techniques for designing modern deep learning networks using Keras and TensorFlow. NLP topics include sentiment analysis, bag of words, TFIDF, and Large Language Models (3 credit hours)

SEAS 8525 Computer Vision and Generative AI: Explore AI's visual realm. Learn image processing object detection, and models in generative adversarial networks and neural networks. Master tools for creating AI applications in art, design, ethical considerations, and societal impacts of generative AI technology (3 credit hours)

SEAS 8599 Praxis Development for AI & Machine Learning:  Overview of research methods. Aims and purpose of the praxis. Development of praxis research strategies, formulation, and defense of a praxis proposal (3 credit hours)

SEAS 8588 Praxis Research for D.Eng. in AI & Machine Learning:  Research leading to the degree of Doctor of Engineering in AI and Machine Learning (24 Credit Hours)  

Classroom courses last 10 weeks each and meet on Saturday mornings from 9:00 AM—12:10 PM and afternoons from 1:00—4:10 PM (all times Eastern). All classes meet live online through synchronous distance learning technologies (Zoom). All classes are recorded and available for viewing within two hours of the lecture. This program is taught in a cohort format in which students take all courses in lockstep. Courses cannot be taken out of sequence, live attendance at all class meetings is expected, and students must remain continuously enrolled. Leaves of absence are permitted only in the case of a medical or family emergency, or deployment to active military duty.  Please see below for the dates of our upcoming cohort.

No classes on  Memorial Day and Fourth of July weekends 

To proceed to the research phase, students must earn a grade point average of at least 3.2 in the eight classroom courses, and no grade below B-. Students are then registered for a minimum of 24 credit hours of SEAS 8588 Praxis Research: 3 ch in Fall 2025 (Session 2), 9 ch in Spring 2026, 3 ch in Summer 2026, and 9 ch in Fall 2026. Throughout the research phase, students develop the praxis under the guidance of a designated faculty advisor. Faculty research advisors are assigned by the program office and meet individually with students every two weeks.

Sample research areas are listed below:

•    Developing algorithms and methods that can explain how AI systems reach their decisions or predictions, making them more transparent and trustworthy •    Investigating how reinforcement learning can improve robotic performance and control, particularly in complex environments •    Examining how to ensure that AI systems are fair and unbiased in their decision-making, particularly in areas such as hiring, lending, and criminal justice •    Developing more advanced natural language processing models and algorithms that can understand and interpret human language more accurately and effectively •    Investigating how to apply transfer learning techniques to improve the performance of AI systems in new and different domains, with less data and less training time 

Tuition is $1,750 per credit hour for the 2024-2025 year and is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $995, which is applied to tuition due the first semester, is required when the applicant accepts the offer of admission.

Admissions Process

• Bachelor’s and master’s degrees in engineering, applied science, business, computer science, or a related field from accredited institutions.
• A minimum graduate-level GPA of 3.2
• Capacity for original scholarship.
• TOEFL, IELTS, Duolingo, or PTE scores are required of all applicants who are not citizens of countries where English is the official language.  Check our  International Students Page  to learn about the SEAS English language requirements and exemption policy. Test scores may not be more than two years old.

Note: GRE and GMAT scores are not required

Please note that our doctoral programs are highly selective; meeting minimum admissions requirements does not guarantee admission.  

• Attach up-to-date Resume 
• Attach Statement of Purpose – In an essay of 250 words or less, state your purpose in undertaking graduate study at The George Washington University. Describe your academic objectives, research interests, and career plans. Discuss your qualifications including collegiate, professional, and community activities, and any other substantial accomplishments not mentioned.
• Online Engineering Programs The George Washington University 170 Newport Center Drive Suite 260 Newport Beach, CA 92660

Normally, all transcripts must be received before an admission decision is rendered for the Doctor of Engineering program. 

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Artificial intelligence (ph.d., m.s., minor).

Artificial Intelligence (AI) is the study of intelligent artifacts and the principles behind their design, construction, and analysis. Oregon State has a long history of excellence in AI since the early days of computer science. The field traces its origin to many disciplines, including philosophy, psychology, mathematics, and engineering.

Today, AI is making contributions to all areas of science, engineering and humanities. To encompass this diversity, the AI program offers a direct pathway for well-motivated and computationally oriented students from any discipline to enter the field of AI and start making contributions.

  Artificial Intelligence Website

  College of Engineering

 Corvallis

Corvallis Campus Contact

Admissions requirements, required tests, english language requirements .

English language requirements for international applicants to this program are the same as the standard Graduate School requirements .

Additional Requirements

Course or experience in computer programming, college level courses in linear algebra and calculus.

Application Process

Please review the graduate school application process and Apply Online .

Dates & Deadlines ?

Admissions deadline for doctoral applicants, admissions deadline for masters applicants, admissions deadline for meng applicants.

Check the program website for exact dates

MAIS Participation

This program is not offered as a MAIS field of study.

AMP Participation ?

This program participates in the Accelerated Masters Platform (AMP)

AMP Contact

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Phone: 541-737-4881 Fax: 541-737-3313

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MastersInAI.org

PhD in Artificial Intelligence Programs

On This Page:

Universities offer a variety of Doctor of Philosophy (Ph.D.) programs related to Artificial Intelligence (AI.) Some of these are titled as Ph.D.s in AI, whereas most are Ph.D.s in Computer Science or related engineering disciplines with a specialization or focus in AI. Admissions requirements usually include a related bachelor’s degree and, sometimes, a master’s degree. Moreover, most Ph.D. programs expect academic excellence and strong recommendations. The AI Ph.D. programs take three to five or more years, depending on if you have a master’s and the complexity of your dissertation. People with Ph.D.s in AI usually go on to tenure track professorships, postdoctoral research positions, or high-level software engineering positions.

What Are Artificial Intelligence Ph.D. Programs?

Ph.D. programs in AI focus on mastering advanced theoretical subjects, such as decision theory, algorithms, optimization, and stochastic processes. Artificial intelligence covers anything where a computer behaves, rationalizes, or learns like a human. Ph.D.s are usually the endpoint to a long educational career. By the time scholars earn Ph.D.s, they have probably been in school for well over 20 years.

People with an AI Ph.D. degree are capable of formulating and executing novel research into the subtopics of AI. Some of the subtopics include:

• Environment adaptation in self-driving vehicles
• Natural language processing in robotics
• Cheating detection in higher education
• Diagnosing and treating diseased in healthcare

AI Ph.D. programs require candidates to focus most of their coursework and research on AI topics. Most culminate in a dissertation of published research. Many AI Ph.D. recipients’ dissertations are published in peer-reviewed journals or presented at industry-leading conferences. They go on to lead careers as experts in AI technology.

Types of Artificial Intelligence Ph.D. Programs

Most AI Ph.D. programs are a Ph.D. in Computer Science with a concentration in AI. These degrees involve general, advanced level computer science courses for the first year or two and then specialize in AI courses and research for the remainder of the curriculum.

AI Ph.D.s offered in other colleges like Computer Engineering, Systems Engineering, Mechanical Engineering, or Electrical Engineering are similar to Ph.D.s in Computer Science. They often involve similar coursework and research. For instance, colleges like Indiana University Bloomington’s Computing and Engineering have departments specializing in AI or Intelligent Engineering. Some colleges, however, may focus more on a specific discipline. For example, a Ph.D. in Mechanical Engineering with an AI focus is more likely to involve electric vehicles than targeted online advertising.

Some AI programs fall under a Computational Linguistics specialization, like CUNY . These programs emphasize the natural language processing aspect of AI. Computational Linguistics programs still involve significant computer science and engineering but also require advanced knowledge in language and speech.

Other unique programs offer a joint Ph.D. with non-engineering disciplines, such as Carnegie Mellon’s Joint Ph.D. in Machine Learning and Public Policy, Statistics, or Neural Computation .

How Ph.D. in Artificial Intelligence Programs Work

Ph.D. programs usually take three to six years to complete. For example, Harvard lays out a three+ year track where the last year(s) is spent completing your research and defending your dissertation. Many Ph.D. programs have a residency requirement where you must take classes on-campus for one to three years. Moreover, most universities, such as Brandeis , require Ph.D. students to grade and/or teach for one to four semesters. Despite these requirements, several Ph.D. programs allow for part-time or full-time students, like Drexel .

Admissions Requirements

Ph.D. programs in AI admit the strongest students. Most applications require a resume, transcripts, letters of recommendation, and a statement of interest. Many programs require a minimum undergraduate GPA of 3.0 or higher, although some allow for statements of explanation if you have a lower GPA due to illness or other excusable causes for a low GPA.

Many universities, like Cornell , recently made the GRE either optional or not required because the GRE provides little prediction into the success of research and represents a COVID-19 risk. These programs may require the GRE again in the future. However, many schools still require the IELTS/TOEFL for international applicants.

Curriculum and Coursework

The curriculum for AI Ph.D.s varies based on the applicants’ prior education for many universities. Some programs allow applicants to receive credit for relevant master’s programs completed prior to admission. The programs require about 30 hours of advanced research and classes. Other programs do not give credit for master’s programs completed elsewhere. These require over 60 hours of electives, in addition to the 30-hours of fundamental and core classes in addition to the advanced courses.

For programs with more specific specialties, the courses are usually narrowly focused. For example, Duke’s Dynamics, Robotics, and Controls track requires ten classes, at least three of which are focused on AI as it relates to robotics. Others allow for non-AI-specific courses such as computer networks.

Many Ph.D. programs have strict GPA requirements to remain in the program. For example, Northeastern requires PhD candidates to maintain at least a 3.5 GPA. Other programs automatically dismiss students with too many Cs in courses.

Common specializations include:

• Computational Linguistics
• Automotive Systems
• Data Science

Artificial Intelligence Dissertations

Most Ph.D. programs require a dissertation. The dissertation takes at least two years to research and write, usually starting in the second or third year of the Ph.D. curriculum. Moreover, many programs require an oral presentation or defense of the dissertation. Some universities give an award for the best dissertation of the year. For example, Boston University gave a best dissertation award to Hao Chen for the dissertation entitled “ Improving Data Center Efficiency Through Smart Grid Integration and Intelligent Analytics .”

A couple of programs require publications, like Capitol Technology , or additional course electives, like LIU . For example, The Ohio State University requires 27 hours of graded coursework and three hours with an advisor for non-thesis path candidates. Thesis-path candidates only have to take 18 hours of graded coursework but must spend 12 hours with their advisors.

Are There Online Ph.D. in Artificial Intelligence Programs?

Officially, the majority of AI Ph.D. programs are in-person. Only one university, Capitol Technology University , allows for a fully online program. This is one of the most expensive Ph.D.s in the field, costing about $60,000. However, it is also one of the most flexible programs. It allows you to complete your coursework on your own schedule, perhaps even while working. Moreover, it allows for either a dissertation path or a publication path. The coursework is fully focused on AI research and writing, thus eliminating requirements for more general courses like algorithms or networks.

One detail you should consider is that the Capitol Technology Ph.D. program is heavily driven by a faculty mentor. This is someone you will need consistent contact with and open communication. The website only lists the director, so there is a significant element of uncertainty on how the program will work for you. But doctoral candidates who are self-driven and have a solid idea of their research path have a higher likelihood of succeeding.

If you need flexibility in your Ph.D. program, you may find some professors at traditional universities will work with you on how you meet and conduct the research, or you may find an alternative degree program that is online. Although a Ph.D. program may not be officially online, you may be able to spend just a semester or two on campus and then perform the rest of the Ph.D. requirements remotely. This is most likely possible if the university has an online master’s program where you can take classes. For example, the Georgia Institute of Technology does not have a residency requirement, has an online master’s of computer science program , and some professors will work flexibly with doctoral candidates with whom they have a close relationship.

What Jobs Can You Get with a Ph.D. in Artificial Intelligence?

Many Ph.D. graduates work as tenure track professors at universities with AI classes. Others work as postdoc research scientists at universities. Both of these roles are expected to conduct research and publish, but professors have more of an expectation to teach, as well. Universities usually have a small number of these positions available. Moreover, postdoc research positions tend to only last for a limited amount of time.

Other engineers with AI-focused-Ph.D.s conduct research and do software development in the private sector at AI-intensive companies. For example, Google uses AI in many departments. Its assistant uses natural language processing to interface with users through voice. Moreover, Google uses AI to generate news feeds for users. Google, and other industry leaders, have a strong preference for engineers with Ph.D.s. This career path is often highly sought by new Ph.D. recipients.

Another private sector industry shifting to AI is vehicle manufacturing. For example, self-driving cars use significant AI to make ethical and legal decisions while operating. Another example is that electric vehicles use AI techniques to optimize performance and power usage.

Some AI Ph.D. recipients become c-suite executives, such as Chief Technology Officers (CTO). For example, Dr. Ted Gaubert has a Ph.D. in engineering and works as a CTO for an AI-intensive company. Another CTO, Dr. David Talby , revolutionized AI with a new natural language processing library, Spark. CTO positions in AI-focused companies often have decades of experience in the AI field.

How Much Do Ph.D. in Artificial Intelligence Programs Cost?

The tuition for many Ph.D. programs is paid through fellowships, graduate research assistantships, and teaching assistantships. For example, Harvard provides full support for Ph.D. candidates. Some programs mandate teaching or research to attend based on the assumption that Ph.D. candidates need financial assistance.

Fellowships are often reserved for applicants with an exceptional academic and research background. These are usually named for eminent alumni, professors, or other scholars associated with the university. Receiving such a fellowship is a highly respected honor.

For programs that do not provide full assistance, the usual cost is about $500 to $1,000 per credit hour, plus university fees. On the low end, Northern Illinois University charges about $557 per credit hour . With 30 to 60 hours required, this means the programs cost about $30,000 to over $60,000 out of pocket. Typically, Ph.D. programs that do not provide funding for any Ph.D. candidates are less reputable or provide other benefits, such as flexibility, online programs, or fewer requirements.

How Much Does a Ph.D. in AI Make?

Engineers with AI Ph.D.s earn well into the six-figure range in the private sector. For example, OpenAI , a non-profit, pays its top researchers over $400,000 per year. Amazon pays its data scientists with Ph.D.s over $200,000 in salary. Directors and executives with Ph.D.s often earn over $1,000,000 in private industry.

When considering working in the private industry, professionals usually compare offers based on total compensation, not just salary. Many companies offer large stock and bonus packages to Ph.D.-level engineers and scientists.

Startups sometimes pay less in salary, but much more in stock options. For example, the salary may be $50,000 to $100,000, but when the startup goes public, you may end up with hundreds of thousands in stock options. This creates a sense of ownership and investment in the success of the startup.

Computer science professors and postdoctoral researchers earn about $90,000 to $160,000 from universities. However, they increase their competition by writing books, speaking at conferences, and advising companies. Startups often employ professors for advice on the feasibility and design of their technology.

Schools with PhD in Artificial Intelligence Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

Ph.D. in Computer Science (Artificial Intelligence Research)

Ph.d. in computing and information sciences (artificial intelligence research), university of california-riverside.

Department of Electrical and Computer Engineering

Riverside, California

Ph.D. in Electrical Engineering - Intelligent Systems Research Area

University of california-san diego.

Electrical and Computer Engineering Department

La Jolla, California

Ph.D. in Intelligent Systems, Robotics and Control

Colorado state university-fort collins.

The Graduate School

Fort Collins, Colorado

Ph.D. in Computer Science - Artificial Intelligence Research Area

University of colorado boulder.

Paul M. Rady Mechanical Engineering

Boulder, Colorado

PhD in Robotics and Systems Design

District of columbia, georgetown university.

Department of Linguistics

Washington, District of Columbia

Doctor of Philosophy (Ph.D.) in Linguistics - Computational Linguistics

The university of west florida.

Department of Intelligent Systems and Robotics

Pensacola, Florida

Ph.D. in Intelligent Systems and Robotics

University of central florida.

Department of Electrical & Computer Engineering

Orlando, Florida

Doctorate in Computer Engineering - Intelligent Systems and Machine Learning

Georgia institute of technology.

Colleges of Computing, Engineering, and Sciences

Atlanta, Georgia

Ph.D. in Machine Learning

Northern illinois university.

Dekalb, Illinois

Ph.D. in Computer Science - Artificial Intelligence Area of Emphasis

Ph.d. in computer science - machine learning area of emphasis, northwestern university.

McCormick School of Engineering

Evanston, Illinois

PhD in Computer Science - Artificial Intelligence and Machine Learning Research Group

Indiana university bloomington.

Department of Intelligent Systems Engineering

Bloomington, Indiana

Ph.D. in Intelligent Systems Engineering

Ph.d. in linguistics - computational linguistics concentration, capitol technology university.

Doctoral Programs Department

Laurel, Maryland

Doctor of Philosophy (PhD) in Artificial Intelligence

Offered Online

Johns Hopkins University

Whiting School of Engineering

Baltimore, Maryland

Doctor of Philosophy in Mechanical Engineering - Robotics

Massachusetts, boston university.

College of Engineering

Boston, Massachusetts

PhD in Computer Engineering - Data Science and Intelligent Systems Research Area

Phd in systems engineering - automation, robotics, and control, brandeis university.

Department of Computer Science

Waltham, Massachusetts

Ph.D. in Computer Science - Computational Linguistics

Harvard university.

School of Engineering and Applied Sciences

Cambridge, Massachusetts

Ph.D. in Applied Mathematics

Northeastern university.

Khoury College of Computer Science

Ph.D. in Computer Science - Artificial Intelligence Area

University of michigan-ann arbor.

Electrical Engineering and Computer Science Department

Ann Arbor, Michigan

PhD in Electrical and Computer Engineering - Robotics

University of nebraska at omaha.

College of Information Science & Technology

Omaha, Nebraska

PhD in Information Technology - Artificial Intelligence Concentration

University of nevada-reno.

Computer Science and Engineering Department

Reno, Nevada

Ph.D. in Computer Science & Engineering - Intelligent and Autonomous Systems Research

Rutgers university.

New Brunswick, New Jersey

Ph.D. in Linguistics with Computational Linguistics Certificate

Stevens institute of technology.

Schaefer School Of Engineering & Science

Hoboken, New Jersey

Ph.D. in Computer Engineering

Ph.d. in electrical engineering - applied artificial intelligence, ph.d. in electrical engineering - robotics and smart systems research, cornell university.

Ithaca, New York

Linguistics Ph.D. - Computational Linguistics

Ph.d.in computer science, cuny graduate school and university center.

New York, New York

Ph.D. in Linguistics - Computational Linguistics

Long island university-brooklyn campus.

Graduate Department

Brooklyn, New York

Dual PharmD/M.S. in Artificial Intelligence

Rochester institute of technology.

Golisano College of Computing and Information Sciences

Rochester, New York

North Carolina

Duke university.

Duke Robotics

Durham, North Carolina

Ph.D in ECE - Robotics Track

Ph.d. in mems - robotics track, ohio state university-main campus.

Department of Mechanical and Aerospace Engineering

Columbus, Ohio

PhD in Mechanical Engineering - Automotive Systems and Mobility (Connected and Automated Vehicles)

University of cincinnati.

College of Engineering and Applied Science

Cincinnati, Ohio

PhD in Computer Science and Engineering - Intelligent Systems Group

Oregon state university.

Corvallis, Oregon

Ph.D. in Artificial Intelligence

Pennsylvania, carnegie mellon university.

Machine Learning Department

Pittsburgh, Pennsylvania

PhD in Machine Learning & Public Policy

Phd in neural computation & machine learning, phd in statistics & machine learning, phd program in machine learning, drexel university.

Philadelphia, Pennsylvania

Doctorate in Mechanical Engineering - Robotics and Autonomy

Temple university.

Computer & Information Sciences Department

PhD in Computer and Information Science - Artificial Intelligence

University of pittsburgh-pittsburgh campus.

School of Computing and Information

Ph.D. in Intelligent Systems

The university of texas at austin.

Austin, Texas

Ph.D. with Graduate Portfolio Program in Robotics

The university of texas at dallas.

Erik Jonsson School of Engineering and Computer Science

Richardson, Texas

University of Utah

Mechanical Engineering Department

Salt Lake City, Utah

Doctor of Philosophy - Robotics Track

University of washington-seattle campus.

Seattle, Washington

Ph.D. in Machine Learning and Big Data

PhD in Artificial Intelligence

To enter the Doctor of Philosophy in Artificial Intelligence, you must apply online through the UGA  Graduate School web page . There is an application fee, which must be paid at the time the application is submitted.

There are several items which must be included in the application:

• Standardized test scores, including the GRE. 
• 3 letters of recommendation, preferably from university faculty and/or professional supervisors. We encourage you to submit the letters to the graduate school online as you complete the application process.
• A sample of scholarly writing, in English. This can be anything you've written but should give an accurate indication of your writing abilities. The writing sample can be a term paper, research report, journal article, published paper, college paper, etc.
• A completed  Application for Graduate Assistantship , if you are interested in receiving funding. 
• A Statement of Purpose.
• A Resume or Curriculum Vitae.

Further information on program admissions is found in the AI Institute Frequently Asked Questions (FAQ) . 

International Students should also review the links on the  Information for International Students  page for additional information relevant to the application process.

Graduate School Policies

University of Georgia Graduate School policies and requirements apply in addition to (and, in cases of conflict, take precedence over) those described here. It is essential that graduate students familiarize themselves with Graduate School policies, including minimum and continuous enrollment  and other policies contained in the Graduate School Bulletin.

Students should also familiarize themselves with Graduate School Dates and Deadlines relevant to the degree.

Degree Requirements

Students of the doctoral program must complete a minimum of 40 hours of graduate coursework and 6 hours of dissertation credit (for a total of 46 credit hours), pass a comprehensive examination, and write and defend a dissertation. In addition, the University requires that all first-year graduate students enroll in a 1-credit-hour GradFirst seminar . Each of these requirements is described in greater detail below.

The degree program is offered using an in-person format, and classes are in general scheduled for full-time students. There are currently no special provisions for part-time, online, or off-campus students. Students are expected to attend all meetings of classes for which they are registered.

Program of Study

The Program of Study must include a minimum of 40 hours of graduate course work and a minimum of 6 hours of dissertation credit. Of the 40 hours of graduate course work, at least 20 hours must be 8000-level or 9000-level hours.

Required Courses

The following courses must be completed unless specifically waived for students entering the program with a master’s degree in Artificial Intelligence or a related field, or for students with substantially related graduate course work. All waived credits may be replaced by an equal number of doctoral research or doctoral dissertation credits (ARTI 9000, Doctoral Research or ARTI 9300, Doctoral Dissertation). Substitutions must be approved for a particular student by that student's Advisory Committee and by the Graduate Coordinator.

• PHIL/LING 6510  Deductive Systems (3 hours)
• CSCI 6380  Data Mining (4 hours) or CSCI 8950  Machine Learning (4 hours)
• CSCI/PHIL 6550  Artificial Intelligence (3 hours)
• ARTI 6950  Faculty Research Seminar (1 hour)
• ARTI/PHIL 6340 Ethics and Artificial Intelligence (3 hours)

Elective Courses

In addition to the required courses above, at least 6 additional courses must be taken from Groups A and Group B below, subject to the following requirements. 

• At least 2 courses must be taken from Group A, from at least 2 areas.
• At least 2 courses must be taken from Group B, from at least 2 areas.
• At least 3 courses must be taken from a single area comprising the student’s chosen area of emphasis .

Since not all courses have the same number of credit hours, Ph.D. students may need to take additional graduate courses to complete the 40 hours.

AREA 1: Artificial Intelligence Methodologies

• CSCI 6560  Evolutionary Computing (4 hours)
• CSCI 8050  Knowledge Based Systems (4 hours)
• CSCI/PHIL 8650  Logic and Logic Programming (4 hours)
• CSCI 8920  Decision Making Under Uncertainty (4 hours)
• CSCI/ENGR 8940  Computational Intelligence (4 hours)
• CSCI/ARTI 8950  Machine Learning (4 hours)

AREA 2: Machine Learning and Data Science

• CSCI 6360  Data Science II (4 hours)
• CSCI 8360  Data Science Practicum (4 hours)
• CSCI 8945  Advanced Representation Learning (4 hours)
• CSCI 8955  Advanced Data Analytics (4 hours)
• CSCI 8960  Privacy-Preserving Data Analysis (4 hours)

AREA 3: Machine Vision and Robotics

• CSCI/ARTI 6530  Introduction to Robotics (4 hours)
• CSCI 6800  Human Computer Interaction (4 hours)
• CSCI 6850  Biomedical Image Analysis (4 hours)
• CSCI 8850  Advanced Biomedical Image Analysis (4 hours)
• CSCI 8820  Computer Vision and Pattern Recognition (4 hours)
• CSCI 8530  Advanced Topics in Robotics (4 hours)
• CSCI 8535  Multi Robot Systems (4 hours)

AREA 4: Cognitive Modeling and Logic

• PHIL/LING 6300  Philosophy of Language (3 hours)
• PHIL 6310  Philosophy of Mind (3 hours)
• PHIL/LING 6520  Model Theory (3 hours)
• PHIL 8310  Seminar in Philosophy of Mind (max of 3 hours)
• PHIL 8500  Seminar in Problems of Logic (max of 3 hours)
• PHIL 8600  Seminar in Metaphysics (max of 3 hours)
• PHIL 8610  Epistemology (max of 3 hours)
• PSYC 6100  Cognitive Psychology (3 hours)
• PSYC 8240  Judgment and Decision Making (3 hours)
• CSCI 6860  Computational Neuroscience (4 hours)

AREA 5: Language and Computation

• ENGL 6885  Introduction to Humanities Computing (3 hours)
• LING 6021  Phonetics and Phonology (3 hours)
• LING 6080  Language and Complex Systems (3 hours)
• LING 6570  Natural Language Processing (3 hours)
• LING 8150  Generative Syntax (3 hours)
• LING 8580  Seminar in Computational Linguistics (3 hours)

AREA 6: Artificial Intelligence Applications

• ELEE 6280  Introduction to Robotics Engineering (3 hours)
• ENGL 6826  Style: Language, Genre, Cognition (3 hours)
• ENGL/LING 6885  Introduction to Humanities Computing (3 hours)
• FORS 8450  Advanced Forest Planning and Harvest Scheduling (3 hours)
• INFO 8000  Foundations of Informatics for Research and Practice
• MIST 7770  Business Intelligence (3 hours)

Students may under special circumstances use up to 6 hours from the following list to apply towards the Electives group requirement. 

• ARTI 8800  Directed Readings in Artificial Intelligence
• ARTI 8000  Topics in Artificial Intelligence

Other courses may be substituted for those on the Electives lists, provided the subject matter of the course is sufficiently related to artificial intelligence and consistent with the educational objectives of the Ph.D. degree program. Substitutions can be made only with the permission of the student's Advisory Committee and the Graduate Coordinator.

In addition to the specific PhD program requirements, all first-year UGA graduate students must enroll in a 1 credit-hour GRSC 7001 (GradFIRST) seminar which provides foundational training in research, scholarship, and professional development. Students may enroll in a section offered by any department, but it is recommended that they enroll in a section offered by AI Faculty Fellows for AI students. More information is available at the  Graduate School website .

Core Competency

Core competency must be exhibited by each student and certified by the student’s advisory committee. This takes the form of achievement in the required courses of the curriculum. Students entering the Ph.D. program with a previous graduate degree sufficient to cover this basic knowledge will need to work with their advisory committee to certify their core competency. Students entering the Ph.D. program without sufficient graduate background to certify core competency must take at least three of the required courses, and then pursue certification with their advisory committee. A grade average of at least 3.56 (e.g., A-, A-, B+) must be achieved for three required courses (excluding ARTI 6950). Students below this average may take the fourth required course and achieve a grade average of at least 3.32 (e.g., A-, B+, B+, B).

Core competency is certified by the unanimous approval of the student's Advisory Committee as well as the approval by the Graduate Coordinator. Students are strongly encouraged to meet the core competency requirement within their first three enrolled academic semesters (excluding summer semester).  Core Competency Certification must be completed before approval of the Final Program of Study.

Comprehensive Examination

Each student of the doctoral program must pass a Ph.D. Comprehensive Examination covering the student's advanced coursework. The examination consists of a written part and an oral part. Students have at most two attempts to pass the written part. The oral part may not be attempted unless the written part has been passed.

Admission to Candidacy

The student is responsible for initiating an application for admission to candidacy once all requirements, except the dissertation prospectus and the dissertation, have been completed.

Dissertation and Dissertation Credit Hours

In addition to the coursework and comprehensive examination, every student must conduct research in artificial intelligence under the direction of an advisory committee and report the results of his or her research in a dissertation acceptable to the Graduate School. The dissertation must represent originality in research, independent thinking, scholarly ability, and technical mastery of a field of study. The dissertation must also demonstrate competent style and organization. While working on his/her dissertation, the student must enroll for a minimum of 6 credit hours of ARTI 9300 Doctoral Dissertation spread over at least 2 semesters.

Advisory Committee

Before the end of the third semester, each student admitted into the program should approach relevant faculty members and form an advisory committee. Until the committee is formed, the student will be advised by the graduate coordinator. The committee consists of a major professor and two other faculty members, as follows:

• The major professor and at least one other member must be full members of the Graduate Program Faculty.
• The major professor and at least one other member must be Institute for Artificial Intelligence Faculty Fellows.

Deviations from the 3-member advisory committee structure, including having more members, are in some cases permitted but must conform to Graduate School policies. 

The major professor and advisory committee shall guide the student in planning the dissertation.  The committee shall agree upon, document, and communicate expectations for the dissertation. These expectations may include publication or submission requirements, but, should not exceed reasonable expectations for the given research domain. During the planning stage, the student will prepare a dissertation prospectus in the form of a detailed written dissertation proposal. It should clearly define the problem to be addressed, critique the current state-of-the-art, and explain the contributions to research expected by the dissertation work. When the major professor certifies that the dissertation prospectus is satisfactory, it must be formally considered by the advisory committee in a meeting with the student. This formal consideration may not take the place of the comprehensive oral examination.

Approval of the dissertation prospectus signifies that members of the advisory committee believe that it proposes a satisfactory research study. Approval of the prospectus requires the agreement of the advisory committee with no more than one dissenting vote as evidenced by their signing an appropriate form to be filed with the graduate coordinator’s office.  

Graduation Requirements - Forms and Timeline

Before the end of the third semester in residence, a student must begin submitting to the Graduate School, through the graduate coordinator, the following forms: (i) a Preliminary Program of Study Form and (ii) an Advisory Committee Form. The Program of Study Form indicates how and when degree requirements will be met and must be formulated in consultation with the student's major professor. An Application for Graduation Form must also be submitted directly to the Graduate School. Forms and Timing must be submitted as follows:

• Advisory Committee Form (G130)—end of third semester
• Core Competency Form (Internal to IAI)—beginning of fourth semester
• Preliminary Doctoral Program of Study Form—Fourth semester
• Final Program of Study Form (G138)—before Comprehensive Examination
• Application for Admission to Candidacy (G162)—after Comprehensive Examination
• Application for Graduation Form (on Athena)—beginning of last semester
• Approval Form for Doctoral Dissertation (G164)—last semester
• ETD Submission Approval Form (G129)—last semester

Students should frequently check the Graduate School Dates and Deadlines webpage to ensure that all necessary forms are completed in a timely manner.

Student Handbook

Additional information on degree requirements and AI Institute policies can be found in the AI Student Handbook .

For information regarding the graduate programs in IAI, please contact: 

Evette Dunbar [email protected] Boyd GSRC, Room 516 706-542-0358

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Stanford AI Lab

The Stanford Artificial Intelligence Laboratory (SAIL) has been a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963.

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Congratulations to Marco Pavone for winning the best paper award at the Robotics: Science and Systems Conference on AI Safety for autonomous systems.

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Carlos Guestrin has been elected to the National Academic of Engineering “for scalable systems and algorithms enabling the broad application of machine learning in science and industry.”

Congratulations to Chris Manning on being awarded 2024 IEEE John von Neumann Medal!

Chris Manning has been awarded the 2024 IEEE John von Neumann Medal “for advances in computational representation and analysis of natural language.” This is one of IEEE’s top awards in computing, given with very broad scope “for outstanding achievements in computer-related science and technology.”

SAIL Faculty and Students Win NeurIPS Outstanding Paper Awards

Congratulations to Sanmi Koyejo and his students for winning the NeurIPS Outstanding Paper Award, and congradulations to Chris Manning, Stefano Ermon, Chelsea Finn, and their students for winning Outstanding Paper Runner Up at NeurIPS!

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I’m co-leading a project that studies how AI benchmarks are being used by stakeholders. If you’ve ever used a benchmark (e.g. results from a benchmark to make a decision or ran a benchmark on a model to understand its performance), or decided not to, we’d love to talk to you!

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Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.

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• Doctoral Degrees

Intelligent Systems, PhD

The Intelligent Systems Program (ISP) is a multidisciplinary graduate program at the University of Pittsburgh dedicated to applied artificial intelligence (AI). Many of Pitt’s acclaimed schools are represented through our associated faculty, including the School of Medicine, the School of Law, the School of Education, the Swanson School of Engineering, and the Kenneth P. Dietrich School of Arts and Sciences.

What Do We Offer?

• Broadly interdisciplinary approach : We offer a strong, well-balanced foundation in the fundamentals of AI and many opportunities for advanced research and training in many disciplines, including computer science, biomedical informatics, cognitive psychology, information science, education, law, and more.
• Focused, customized curricula : Building on the core curriculum, you design your own personalized curricula that prepares you for interdisciplinary research in your areas of interest.
• Collaborative atmosphere : Faculty members and students present their research in regular program seminars, exposing you to a broad range of research topics and methods and affording you the opportunity to present your own research.
• Highly motivated faculty : Pitt’s widely published ISP faculty are leaders in their fields. Drawing on the strengths of diverse sectors of the university, and participating in over 30 funded research projects, they support graduate students through collaborative research, personal mentoring, and external research funding.

Degree Requirements

Students are expected to have the pre-requisites needed to take the courses necessary to obtain the PhD degree in ISP. These may be required if not already taken.

General Intelligent Systems Track

First-year students

• ISSP 2020 - TOPICS IN INTELLIGENT SYSTEMS
• INFSCI 3005 - INTRODUCTION TO THE DOCTORAL PROGRAM
• ISSP 2030  - ADVANCED TOPICS IN INTELLIGENT SYSTEMS
• ISSP 2160 / CS 2710 - FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

AND Choose two of the following:

• ISSP 2170 / CS 2750 - MACHINE LEARNING  
• ISSP 2230 / CS 2731 - INTRO NATURAL LANGUAGE PROCESSING
• ISSP 2180 / CS 2770 - COMPUTER VISION

Applied or mathematical statistics.  Choose one of the following:

• BIOST 2041 - INTRODUCTION TO STATISTICAL METHODS 1
• BIOST 2042 - INTRODUCTION TO STATISTICAL METHODS 2
• BIOINF 2118 - STATISTICAL FOUNDATIONS OF BIOMEDICAL INFORMATICS
• STAT 2131 - APPLIED STATISTICAL METHODS 1
• STAT 2132 - APPLIED STATISTICAL METHODS 2

Theory of computation, algorithms. Choose one of the following:

• CS 2110 - THEORY OF COMPUTATION
• CS 2150 - DESIGN & ANALYSIS OF ALGORITHMS

One additional course required. Any of the theory courses listed above are acceptable.

Advanced courses

Four ISSP advanced elective courses, from the list below with the approval of the student’s academic advisor and Program Director. Contact the ISP Administrator after completing an advisor-approved elective course that falls outside of the ISSP course offerings, as a waiver may need to be submitted.

Biomedical Informatics Track (ISP/MI)

This assumes that a student already has training in a health care field; if this is not so, then the faculty will select a set of courses that teach the student basic medical knowledge, and the student may take these courses as electives.

• ISSP 2030 - ADVANCED TOPICS IN INTELLIGENT SYSTEMS
• ISSP 2083 / BIOINF 2032 - BIOMEDICAL INFORMATICS JOURNAL CLUB
• ISSP 2016 / BIOINF 2070 - FOUNDATIONS OF BIOMEDICAL INFORMATICS 1  
• ISSP 2160 / CS 2710 - FOUNDATIONS OF ARTIFICIAL INTELLIGENCE  

Then choose:

One of the following:

• ISSP 2170 / CS 2750 - MACHINE LEARNING

AND choose one of the following:

• CS 1510 - ALGORITHM DESIGN
• CS 2150 - DESIGN & ANALYSIS OF ALGORITHMS
• CS 3150 - ADV TOPICS DESIGN & ANALYSIS OF ALGORITHMS
• STAT 2132 - APPLIED STATISTICAL METHODS 2  

AND choose two of the following:

• ISSP 2070 / BIOINF 2101 - PROBABILISTIC METHODS
• ISSP 2017 / BIOINF 2071 - FOUNDATIONS OF BIOMEDICAL INFORMATICS 2
• ISSP 2240 / INFSCI 2130 - DECISION ANALYSIS AND DECISION SUPPORT SYSTEMS
• BIOINF 2121 - HUMAN-COMPUTER INTERACTION AND EVALUATION METHODS
• BIOINF 2117  - APPLIED MEDICAL INFORMATICS
• BIOINF 2016 - FOUNDATIONS OF TRANSLATIONAL INFORMATICS
• BIOINF 2124 - PRINCIPLES OF GLOBAL HEALTH INFORMATICS

Advanced Courses

Three ISSP advanced elective courses, from the list below with the approval of the student’s academic advisor and Program Director. Contact the ISP Administrator after completing an advisor-approved elective course that falls outside of the ISSP course offerings, as a waiver may need to be submitted.

Students will register for three credits of the BIOINF 3998 - Doctoral Teaching Practicum. Special enrollment permission must be obtained from BMI training program coordinator.

For more degree requirements details, visit the Intelligent Systems course catalog .

Admissions Requirements

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

PhD Admissions

Main navigation.

The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

Eligibility

To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

Application Checklist

An completed online application must be submitted by the CS Department application deadline and can be found  here .

Application Deadlines

The online application can be found here . You may submit one application for a PhD program per respective academic term.

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Doctor of Philosophy in Artificial Intelligence and Data Science

The Doctor of Philosophy (Ph.D.) degree is a research-oriented degree requiring a minimum of 64 semester credit hours of approved courses and research beyond the Master of Science (M.S.) degree [96 credit hours beyond the Bachelor of Science (B.S.) degree]. The university places limitations on these credit hours in addition to the requirements of the Department of Civil Engineering.

A complete discussion of all university requirements is found in the current Texas A&M University Graduate Catalog .

NOTE: All documents requiring departmental signatures must be submitted to the Civil Engineering Graduate Office in DLEB 101 at least one day prior to the Office of Graduate Studies deadline.

Artificial Intelligence and Data Science Faculty Members

• Dr. Mark Burris
• Dr. Nasir Gharaibeh
• Dr. Stefan Hurlebaus
• Dr. Dominique Lord
• Dr. Xingmao “Samuel” Ma
• Dr. Ali Mostafavi
• Dr. Arash Noshadravan
• Dr. Stephanie Paal
• Dr. Luca Quadrifoglio
• Dr. Scott Socolofsky
• Dr. Yunlong Zhang

Admission Admission to the AI/DS track is conditional upon meeting the general admission requirements. Also, students may only be admitted to the AI/DS track if a faculty member affiliated with the track is willing to supervise (and provide funding support via GAT or GAR or Fellowship) for the student. If a current student is approved to change from one track to another, they must complete the Track Change Request Form and send it to the CVEN Graduate Advising Office so notification can be sent to their original area coordinator. Please read the CVEN department policy on changing tracks.

Departmental Requirements In addition to fulfilling the University requirements for the Doctor of Philosophy (Ph.D.) degree, a student enrolled in the Civil Engineering graduate program in the area of Artificial Intelligence and Data Science area must satisfy the following department requirements.

• A minimum of 32 credit hours of graduate-level coursework taken through Texas A&M University [a minimum of 24 credit hours if the student already has taken at least another 24 credit hours of graduate course work for the Master of Science (M.S.) or Master of Engineering (MEng) degree].
• Remaining coursework requirement can be met by 32 hours of CVEN 691.
• Qualifying Exam
• Degree Plan
• Written Preliminary Exam
• Research Proposal
• Oral Preliminary Exam
• Completion of Dissertation
• Final Defense

Dissertation Topic Students pursuing the AI/DS track would work on dissertation topics with a great extent of interdisciplinary elements spanning across civil engineering and computer science/AI. Such interdisciplinary research would require a student to develop depth of knowledge and skills across both domains.

Committee The committee of Ph.D. students in the AI/DS track can be composed of faculty from different departments with backgrounds and skills related to the subject matter of the dissertation research.

Students in the AI/DS track are strongly encouraged to form their dissertation committee prior to the qualification exam. If the dissertation committee is formed prior to the qualification exam, the exam questions will be developed by the committee in coordination with the AI/DS Track Coordinator. If the student's dissertation committee is not formed at the time of the qualification examination, the Track Coordinator and the student advisor will handle the development of the qualification examination.

Artificial intelligence

As a world leader in artificial intelligence with a history of challenging convention, UC Berkeley is shaping the future of this burgeoning field while exploring the larger implications of AI on society.

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Fall 2023 - berkeley lectures on the status and future of ai.

The Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) , the College of Computing, Data Science and Society (CDSS) and Berkeley Artificial Intelligence Research Lab (BAIR) together continue Berkeley’s distinguished speaker series exploring the recent innovations in AI, its broader societal implications and its future potential at Berkeley and beyond. Read more about the lecture series on the CDSS website .

Data Dignity and the Inversion of AI

Date: 09/13/2023 12:00pm Speaker: Jaron Lanier, Prime Unifying Scientist, Microsoft Sponsor: CITRIS Research Exchange, CDSS and BAIR Link to view: Watch Lanier's talk

Imitation and Innovation in AI: What Four-year-olds Can Do and AI Can’t (Yet)

Date: 09/27/2023 12:00pm Speaker: Alison Gopnik, Distinguished Professor of Psychology, UC Berkeley Sponsor: CITRIS Research Exchange, CDSS and BAIR Link to view: Watch Gopnik's talk

AI Agents That Do What We Want: Progress and Open Challenges

Date: 10/04/2023 12:00pm Speaker: Anca Dragan, Associate Professor of Electrical Engineering and Computer Sciences, UC Berkeley Sponsor: CITRIS Research Exchange, CDSS and BAIR Link to view: Watch Dragan's talk

Independent Community-rooted AI Research – Postponed; will be rescheduled

Speaker: Timnit Gebru, Founder and Executive Director, Distributed AI Research Institute Sponsor: CITRIS Research Exchange, CDSS and BAIR

Past lectures - Spring 2023

During the 2023 spring semester, the primary architect of ChatGPT and leading Berkeley AI faculty presented insights and viewpoints in a series of seven public lectures presented by the CITRIS and the Banatao Institute , BAIR , Electrical Engineering and Computer Sciences (EECS) , the Academic Senate and UC Berkeley. Read more about the spring AI lecture series on Berkeley News .

The Sensorimotor Road to Artificial Intelligence

Speaker: Jitendra Malik, Arthur J. Chick Professor of Electrical Engineering & Computer Sciences Sponsor: Martin Meyerson Berkeley Faculty Research Lectures Link to view: Watch Malik's lecture

How Not to Destroy the World With AI

Speaker: Stuart Russell, Professor, Electrical Engineering and Computer Sciences, UC Berkeley Sponsor: CITRIS Research Exchange and BAIR Link to view: Watch Russell's lecture

Reinforcement Learning with Large Datasets: a Path to Resourceful Autonomous Agents

Speaker: Sergey Levine, Associate Professor, Electrical Engineering and Computer Sciences Sponsor: CITRIS Research Exchange and BAIR Link to view: Watch Levine's talk

How AI Fails Us, and How Economics Can Help

Speaker: Mike Jordan, Pehong Chen Distinguished Professor, Electrical Engineering and Computer Sciences, UC Berkeley Sponsor: CITRIS Research Exchange and BAIR Link to view: Watch Jordan's lecture

Reinforcement Learning from Human Feedback: Progress and Challenges

Speaker: John Schulman, Research Scientist and cofounder of OpenAI Sponsor: EECS and BAIR Event details: Registration Link to view: Watch Schulman's talk

Generative AI Meets Copyright Law

Speaker: Pam Samuelson, Richard M. Sherman Distinguished Professor of Law, UC Berkeley Sponsor: CITRIS Research Exchange and BAIR Link to view: Watch Samuelson's lecture

Exploration vs Exploitation: Different Ways of Pushing AI and Robotics Forward

Speaker: Rod Brooks, MIT Professor Emeritus and Robust.AI Sponsor: BAIR Robotics Symposium Link to view: Watch Brooks' talk

“The promise of AI lies in its ability to help us solve some of the biggest challenges facing humanity, from climate change to disease prevention. But we must also recognize that these systems have the potential to exacerbate existing inequalities and biases if not designed and deployed thoughtfully.” Jennifer Chayes, Dean, College of Computing, Data Science, and Society at UC Berkeley

AI technologies in practice

Using AI and satellites to monitor California wildlife

Governor asks UC Berkeley, Stanford to assess impacts of generative AI on state

New brain implant helps paralyzed woman speak using a digital avatar

From tort law to cheating, what is ChatGPT’s future in higher education?

‘Raw’ data show AI signals mirror how the brain listens and learns

Massive traffic experiment pits machine learning against ‘phantom’ jams

Evolution on fast forward: Grace Gu engineers AI-optimized, bioinspired materials

New UC Berkeley initiative uses AI research to solve climate problems

What can psychology teach us about AI’s bias and misinformation problem?

For more stories and research on AI at Berkeley, visit Berkeley News

Academics and research.

AI is a significant focus for many areas around campus. Below are some examples of labs, programs, previous lectures, and more.

• Berkeley Artificial Intelligence Research Lab (BAIR) | The BAIR Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, control, and robotics.
• Berkeley Law AI Institute | A multi-day, online executive academy to help lawyers understand AI technology and how companies use it, as well as the risks and ethical issues raised by autonomous systems.
• Berkeley AI Policy Hub | A collaboration between the UC Berkeley Center for Long-Term Cybersecurity (CLTC) and its AI Security Initiative and the CITRIS Policy Lab, the AI Policy Hub is a multidisciplinary initiative training forward-thinking graduate student researchers to develop effective governance and policy frameworks to guide artificial intelligence, today and into the future.
• Berkeley [Emergent Space Tensegrities | Energy and Sustainable Technologies | Expert Systems Technologies ] (BEST) Lab | The BEST Lab conducts research at the intersection of cutting-edge frontiers in design research, computational design, sustainability, gender equity, human-machine cognition, supervisory control, soft robotics, sensor fusion, design research and intelligent learning systems.
• The Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) | CITRIS and the Banatao Institute is a University of California research center focused on creating IT solutions that generate societal and economic benefits for everyone.
• Computing, Data Science, and Society (CDSS) | CDSS leverages Berkeley’s preeminence in research and excellence across disciplines to propel data science discovery, education, and impact.
• Electrical Engineering and Computer Sciences (EECS) | EECS offers one of the strongest research and instructional programs anywhere in the world with an array of cross-disciplinary, team-driven projects.
• Haas ExecEd: AI Strategies and Applications | Participants in this program learn about AI’s current capabilities and gain an understanding into the variety of ways AI can benefit different business functions.
• Tech Policy Fellows | Offers scholars and practitioners the opportunity to spend six months to a year as a non-residential fellow at UC Berkeley to conduct research, share expertise and experiences with faculty, staff, and students and develop technical or policy interventions that support responsible technology development and use.
• Our Better Web | An independent interdisciplinary initiative at Berkeley that brings together leadership from the Schools of Information; Journalism; Law; and Public Policy; the Division of Computing, Data Science, and Society; and the CITRIS Policy Lab. Our Better Web researches and provides guidance on technical and policy strategies to mitigate harms from algorithmic amplification and algorithmic bias online.

AI and the arts

Cal Performances: Illuminations – “Human and Machine”

Generative Art and Deep Learning AI

Emerging AI technology has the potential to replicate some of the processes used by artists when creating their work. Dr. Nettrice Gaskins uses AI-driven software such as deep learning to train machines to identify and process images. Her approach puts the learning bias of race to the forefront by using AI to render her artwork using different source images and image styles.

Speaker Biography

Dr. Nettrice R. Gaskins is an African American digital artist, academic, cultural critic and advocate of STEAM fields. In her work she explores "techno-vernacular creativity" and Afrofuturism.

Dr. Gaskins teaches, writes, "fabs”, and makes art using algorithms and machine learning. She has taught multimedia, visual art, and computer science with high school students. She earned a BFA in Computer Graphics with Honors from Pratt Institute in 1992 and an MFA in Art and Technology from the School of the Art Institute of Chicago in 1994. She received a doctorate in Digital Media from Georgia Tech in 2014. Currently, Dr. Gaskins is a 2021 Ford Global Fellow and the assistant director of the Lesley STEAM Learning Lab at Lesley University. She is an advisory board member for the School of Literature, Media, and Communication at Georgia Tech. Her first full-length book, Techno-Vernacular Creativity and Innovation is available through The MIT Press. Gaskins' AI-generated artworks can be viewed in journals, magazines, museums, and on the Web. Her series of 'featured futurist' portraits are on view at the Smithsonian Arts and Industries Building through early July 2022.

Gaskins served as Board President of the National Alliance for Media Arts and Culture (The Alliance) and was on the board of the Community Technology Centers Network (CTCNet). She is currently on the board of Artisan’s Asylum.

CITRIS Tech Museum

Artwork on this page

The below image is a UC Berkeley-inspired collage constructed from images generated in Dall-E-2. The prompts used to generate the imagery included specific campus landmarks, such as “the Campanile,” “Sproul Hall,” “Doe Library,” and “Memorial Stadium.”

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Doctor of Philosophy (PhD) in Artificial Intelligence

• Request Information

Earn a doctorate degree in Artificial Intelligence, help lead innovation in a growing industry

The PhD in Artificial Intelligence is centered upon how computers operate to match the human decision making process in the brain. Your research will be led by AI experts with both research and industrial expertise. This emerging subject is starting to attract attention on the wider issues as the IOT and other advanced computer systems work in our lives.

This is a research based doctorate PhD degree where you will be assigned an academic supervisor almost immediately to guide you through your program and is based on mostly independent study through the entire program. It typically takes a minimum of two years but typically three years to complete if a student works closely with their assigned academic advisor. Under the guidance of your academic supervisor, you will conduct unique research in your chosen field before submitting a Thesis or being published in three academic journals agreed to by the academic supervisor.  If by publication route it will require original contribution to knowledge or understanding in the field you are investigating.

As your PhD progresses, you move through a series of progression points and review stages by your academic supervisor. This ensures that you are engaged in a process of research that will lead to the production of a high-quality Thesis and/or publications and that you are on track to complete this in the time available. Following submission of your PhD Thesis or accepted three academic journal articles, you have an oral presentation assessed by an external expert in your field.

Why Capitol?

Learn around your busy schedule

Program is 100% online, with no on-campus classes or residencies required, allowing you the flexibility needed to balance your studies and career.

Proven academic excellence

Study at a university that specializes in industry-focused education in technology fields, with a faculty that includes many industrial and academic experts.

Expert guidance in doctoral research

Capitol’s doctoral programs are supervised by faculty with extensive experience in chairing doctoral dissertations and mentoring students as they launch their academic careers. You’ll receive the guidance you need to successfully complete your doctoral research project and build credentials in the field. 

Key Faculty

Vice President

Dissertation Chair

Degree Details

This program may be completed with a minimum of 60 credit hours, but may require additional credit hours, depending on the time required to complete the dissertation/publication research. Students who are not prepared to defend after completion of the 60 credits will be required to enroll in RSC-899, a one-credit, eight-week continuation course. Students are required to be continuously enrolled/registered in the RSC-899 course until they successfully complete their dissertation defense/exegesis.

The student will produce, present, and defend a doctoral dissertation after receiving the required approvals from the student’s Committee and the PhD Review Boards.

Prior Achieved Credits May Be Accepted

Doctor of Philosophy - 60 credits

Program Objectives:

• Students will integrate and synthesize alternate, divergent, or contradictory perspectives or ideas fully within the field of Artificial Intelligence.
• Students will demonstrate advance knowledge and competencies in Artificial Intelligence.
• Students will analyze existing theories to draw data-supported consultations in Artificial Intelligence.
• Students will analyze theories, tools, and frameworks used in Artificial Intelligence.
• Students will execute a plan to complete a significant piece of scholarly work in Artificial Intelligence.
• Students will evaluate the legal, social, economic, environmental, and ethical impact of actions within Artificial Intelligence and demonstrate advance skill in integrating the results in to the leadership decision-making process.

Learning Outcomes:

Upon graduation, graduates will:

• integrate the theoretical basis and practical applications on Artificial Intelligence in to their professional work;
• demonstrate the highest mastery of Artificial Intelligence;
• evaluate complex problems, synthesize divergent/alternative/contradictory perspectives and ideas fully, and develop advanced solutions to Artificial Intelligence challenges; and
• contribute to the body of knowledge in the study of Artificial Intelligence.

Tuition & Fees

Tuition rates are subject to change.

The following rates are in effect for the 2024-2025 academic year, beginning in Fall 2024 and continuing through Summer 2025:

• The application fee is $100
• The per-credit charge for doctorate courses is $950. This is the same for in-state and out-of-state students.
• Retired military receive a $50 per credit hour tuition discount
• Active duty military receive a $100 per credit hour tuition discount for doctorate level coursework.
• Information technology fee $40 per credit hour.
• High School and Community College full-time faculty and full-time staff receive a 20% discount on tuition for doctoral programs.

Find additional information for 2024-2025 doctorate tuition and fees.

When I was comparing multiple universities, Capitol Tech's admissions staff was responsive, helpful, and caring. This was valuable my deciding factor in choosing Capitol Tech.

-Dr. Jason Collins-Baker PhD in Artificial Intelligence

I chose Capitol for its great reputation in the field I am interested in, as well as its flexibility offering a fully Online program that works very well with my work and family commitments. Another reason was its European PhD program, with which I am familiar since I grew up and studied in Europe (Spain), although I currently live in the United States (California).

-Hector Garcia Villa PhD in Artificial Intelligence

Need more info, or ready to apply?

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Found 76 Doctorate Artificial Intelligence Courses in United States

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• THE World Ranking: 34
• San Diego, United States
• Next intake: 23.09.2024
• Entry Score: IELTS 7.0
• USD29118 (2024)
• Cleveland, United States
• Next intake: 26.08.2024
• Entry Score: IELTS 6.5
• USD24966 (2024)
• Detroit, United States
• USD27702 (2024)
• THE World Ranking: 801
• Orlando, United States
• Next intake: 19.08.2024
• Entry Score: IELTS 6.0
• USD17780 (2024)
• THE World Ranking: 119
• Houston, United States
• USD57210 (2024)
• Charleston, United States
• Next intake: 10.01.2025
• USD22030 (2024)
• THE World Ranking: 36
• Atlanta, United States
• USD29140 (2024)
• THE World Ranking: 138
• Colorado Springs, United States
• USD38736 (2024)
• Eugene, United States
• Next intake: 18.09.2024
• USD20628 (2024)

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Selected highlights (2012-)

Best paper awards.

• 2024: CVPR Best Paper Award
• 2023: EMNLP Outstanding Paper Award
• 2023:  ACL Best Paper Award
• 2023: ACL Outstanding Demo Paper Award
• 2022: EMNLP Best Paper Award
• 2022: EMNLP Best Demo Paper Award
• 2022:  CIKM Test of Time Award
• 2022: IROS RoboCup Best Paper Award
• 2022: EC Exemplary Applied Modeling Track Paper
• 2021: ACL Test of Time Award
• 2021:  NAACL Outstanding Short Paper Award
• 2021: FAccT Best Paper Award
• 2020: SIGKDD Dissertation Award
• 2020:  SIGIR Best Paper Award
• 2020: Design Automation Conference (DAC) Best Paper Award
• 2020: CVPR Workshop on Text and Documents in the Deep Learning Era Best Paper award
• 2019: ICCV Helmholtz prize (test of time)
• 2019:  KDD best research paper award
• 2019: Best paper award, ICML Time Series Workshop
• 2019:  Best paper award, ICCV workshop on Computer Vision for Fashion, Art and Design (CVFAD)
• 2019: Best student paper award, COLT
• 2018: Caspar Bowden Award for Outstanding Rsearch in Privacy Enhancing Technologies
• 2018: INFORMS Data Mining Best Applied Paper
• 2018:  COLT best student paper award
• 2017: KDD Test of Time Award
• 2017:  ACL Best Resource Paper
• 2017:  CVPR Best Paper Award
• 2017: WSDM Best Paper Award
• 2017: CSCW Best Paper Award
• 2017 INFORMS Undergraduate OR Prize
• 2016: ICTIR Best Presentation Award
• 2016:  SIGIR Test of Time award
• 2016:  KDD Test of Time award
• 2016:  CVPR best student paper award
• 2016:  AAAI Classic Paper Award  
• 2016: INFORMS 2016 Data Mining and Decision Analytics workshop
• 2016: IJCAI "NLP meets journalism" workshop best paper award
• 2015:  ICCV Helmholtz Prize  (formerly Test of Time Award) 
• 2015: EMNLP best paper award
• 2015:  KDD Test of Time award
• 2015: KDD best student paper award
• 2015: EC best paper award
• 2015:  WACV vision and learning best paper award
• 2015: AAAI-RSS Special Workshop on the 50th Anniversary of Shakey Blue Skies Ideas Track Competition best paper
• 2015: INFORMS Social Media Analytics Best Paper competition
• 2014: NIPS best paper award
• 2014:  ISMIR best student paper award
• 2014:  IROS CoTeSys Cognitive Robotics Best Paper Award
• 2014:  Best paper award, ECCV
• 2014: Inaugural Test of Time award, KDD
• 2014:  Best paper award, EC 2014
• 2013:  Best student paper award, SIGIR 
• 2013:  Best student paper award, RSS (Robotics: Science and Systems)
• 2012:  ECCV Koenderink Prize
• 2012: Ray Reiter prize, KR 
• 2012: ICCV Test of Time award
• 2010: Best paper, International Conference on Image Processing (ICIP)
• 2010: Best student paper, Constraint Programming (CP)
• 2010: Longuet-Higgins (10-year paper) prize, CVPR
• 2010: Best paper, International Conference on Machine Learning and Applications (ICMLA)
• 2010: Best paper, Electronic Commerce (EC)
• 2009: Best paper, ECML
• 2009: Dijkstra Prize
• 2009: Best 10-year paper award, ICML
• 2008: Best student paper, COLT
• 2007: Best student paper, ECML
• 2006: Best paper, Constraint Programming (CP)
• 2006: Best paper, KDD
• 2006: Best paper, KR
• 2006: Best paper, AAAI
• 2005: Best student paper, KDD
• 2005: Best paper, ICML
• 2005: Best student paper, ICML (Cornell won 2 of 4)
• 2005: Citation, Technology Research News Top Picks
• 2004: Best paper, Constraint Programming
• 2004: Best paper, HLT-NAACL
• 2003: Best research paper, KDD
• 2002: Best paper, ECCV

Other awards (2012-)

• All: Multiple  NSF CAREER / Young Investigator Awards
• All: Five  AAAI Fellows
• All: multiple  Sloan Fellows
• All: multiple  ACM Fellows
• All: multiple  AAAS Fellows
• All: Two NAE Fellows
• All: Two IEEE Fellows
• All: Two  Microsoft Faculty Fellows
• All: 2  ACL fellow s
• All: 1 IMS fellow
• All: I inaugural SIGIR Academy member
• 2023: SIGecom Mid-Career Award
• 2023:  Brouwer Medal Award
• 2022: Schmidt Futures AI2050 Early Career fellow
• 2022: Forbes 30 under 30
• 2022: CIFAR Azrieli GLobal Scholar
• 2022: PAMI Young Researcher Award
• 2022: ACM AAAI Allen Newell Award
• 2021: ACL Distinguished Service Award
• 2021:  Technology Review 35 Under 35
• 2021: ALFRED Challenge win
• 2021: AAAI Feigenbaum Prize
• 2020: EMNLP 2020 Best Demo award
• 2020: SIGKDD Innovation Award
• 2018: Bloomberg's 2018 "Ones to Watch"
• 2018:  Forbes 30 under 30 in Science
• 2018: Election to AAAI President 
• 2018: ACL 2002-2012 Test of Time Award
• 2017: MyBridge's  30 Amazing Machine Learning Projects
• 2017:  IAAI Deployed Applications Award
• 2017 INFORMS Undergraduate Operations Research Prize
• 2017:  CB Insights AI 100 list
• 2017:  Arnet Miner 2016 Top 10 Most Influential Scholars
• 2017: INFORMS George B. Dantzig Dissertation Award
• 2016: PAMI Everingham Prize
• 2016:  LDV Vision Summit Entrepreneurial Computer Vision Challenge
• 2016:  Kampé de Fériet Award
• 2016: NSF Expeditions
• 2016: MIT Technology Review top 10 breakthrough technologies
• 2015: Smithsonian Top 8 Innovators
• 2015: Microsoft COCO Detection Challenge Best Student Entry
• 2015: Outstanding Reviewer Award, WSDM
• 2014: ACM-AAAI Allen Newell Award
• 2013: SIGKDD Innovation Award
• 2011: Technology Review's TR35
• 2011: ACM SIGART Autonomous Agents Research Award
• 2010: White House Leading Practices Award
• 2009: ONR Young Investigator Award
• 2009: Fraunhofer-Bessel Research Award
• 2008: NSF Expeditions in Computing Award
• 2008: Allen Newell Award
• 2007: DARPA Urban Challenge Finalists
• 2004: MIT Technology Review TR100

Press mentions (2012-)

• 2023: The Atlantic
• 2023: also  The Atlantic  
• 2023: Nature News
• 2023: GQ  (UK)
• 2023:  Psychology Today
• 2023:  Nature Computational Science
• 2023:  NPR
• 2022: Forbes
• 2022: New York Times
• 2021: New York Times
• 2021:  IEEE Specitrum
• 2021:  Applied Physics Review Scilight
• 2020: Knowable Magazine (Annual Reviews)
• 2020:  Computer Vision News
• 2020: Now at the Met
• 2020:  Washington Post
• 2020: Shine
• 2020: Enterprise AI
• 2020: Computer Vision News
• 2020: Science Magazine
• 2020: Forbes
• 2020: The Verge
• 2020:  Forbes
• 2020: Corriere Della Serra
• 2019: New York Times
• 2019:  Forbes
• 2019: Boston Globe
• 2019:  MIT Technology Review
• 2018: Nature Materials
• 2018: This Week in Machine Learning & AI 
• 2018:  Forbes
• 2018:  Futurity
• 2018:  Live Science
• 2018:  CGTN
• 2018:  CNN
• 2018: Campus Technology /THE Journal
• 2018: The Observer
• 2018:  New York Times
• 2018:  TechCrunch
• 2017:  Wired
• 2017:  TechCrunch
• 2017: NLP Highlights
• 2017: New York Times
• 2017: Wired
• 2017: Technology Review
• 2017: The Visionary
• 2017: NPR's Planet Money
• 2017: Austria's radio station Oe1
• 2017: Gizmodo
• 2017: Engadget
• 2017: The Verge
• 2017:  Nat and Friends
• 2017: TechCrunch
• 2017: The Atlantic
• 2017: Public Radio International's Innovation Hub
• 2017: Wall Street Journal
• 2017:  Information Week
• 2017:  Sirius XM Insight News and Issues
• 2016: Wall Street Journal
• 2016: Wired
• 2016: Science News
• 2016:  You Are Not So Smart
• 2016:  ScienceDaily
• 2016:  Inverse
• 2016:  Technology Review
• 2016: Pacific Standard Magazine
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• 2010: New Scientist
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• 2009: NYTimes
• 2009: NYTimes  (front page)
• 2009: BBC Today
• 2009: CNN.com

Since the early 1990's, the Cornell CS department has developed one of the leading AI groups in the world, as can be seen by our record of awards, press mentions, and other recognition. Yet, our relatively small size makes for a collaborative and cooperative environment within which a broad set of research groups flourish. 

Some research groups

Terminology notes: a "CS field member" is a Cornell faculty member who can serve as the official PhD advisor (in Cornell lingo, "Special Committee chair") of Cornell CS PhD students.

AI, ethics, and policy

• Group pages:  Artificial Intelligence, Policy, and Practice (AIPP) ; Ethical and Social Issues in AI
• CS field members:  Sarah Dean ,   Joe Halpern ,  Jon Kleinberg , Bart Selman
• Affiliated faculty : Allison Koenecke  (Information Science),  Karen Levy (Information Science), Helen Nissenbaum (Information Science)

Combinatorial search, connections to operations research and statistical physics

• CS field members: Carla Gomes , Bart Selman , David Shmoys

Computational sustainability

• Group page: Institute for Computational Sustainability (ICS)
• CS field members: Carla Gomes , John Hopcroft  (emeritus), Bart Selman , David Shmoys
• Affiliated faculty : (many across Cornell and other institutions --- see ICS homepage link above)

Center for Data Science for Enterprise and Society

This new Center aims to unify programs and curricula in data science with an initial emphasis on questions grounded in data that are generated by human activity, including computational social science (e.g., sociology and government), the economics/computer science interface, the aspects of digital agriculture in the production and management of agriculture, digital platforms supporting urban infrastructure (e.g., the sharing economy), and as a theme that is cross-cutting in many of these areas, the corresponding issues of privacy, security, and fairness; more generally, the Center will enhance other programmatic areas associated with data science in an entrepreneurial and opportunistic fashion.

Webpage :  https://datasciencecenter.cornell.edu/ Director :  David Shmoys Executive Committee : David Matteson, David Mimno, Francesca Molinari 

Game and decision theory, connections to economics

• CS field members:   Siddhartha Banerjee ,  Joe Halpern , Jon Kleinberg , Robert Kleinberg , Rafael Pass , Eva Tardos
• Affiliated faculty : Larry Blume (economics), David Easley (economics)

Knowledge representation and reasoning

• CS field members:   Ron Brachman ,  Joe Halpern , Bart Selman

Machine learning

• Group page: Machine learning homepage
• CS field members:   Jayadev Acharya ,  Yoav Artzi , Kavita Bala ,  Serge Belongie ,  Claire Cardie , Yudong Chen ,  Sanjiban Choudhury ,   Tanzeem Choudhury ,   Cristian Danescu-Niculescu-Mizil , Christopher De Sa ,  Shimon Edelman , Kevin Ellis ,  Carla Gomes ,  Haym Hirsch ,  Thorsten Joachims , Bobby Kleinberg ,  Jon Kleinberg , Volodymyr Kuleshov ,  Daniel Lee ,  Lillian Lee ,  David Mimno , Emma Pierson ,  Alexander M. Rush ,   Karthik Sridharan , Wen Sun ,  Kilian Weinberger , Christina Lee Yu
• Affiliated faculty : Sumanta Basu (stats),  Florentina Bunea (stats),   Morten Christiansen (psychology), Silvia Ferrari (MechE),   Peter Frazier (ORIE),  Giles Hooker (stats), Nathan Kallus (ORIE),   David Matteson (stats), Yang Ning (stats),   Mats Rooth (linguistics), David Ruppert (ORIE/stats), Mert Rory Sabuncu (ECE, biomedical engineering),  Martin Wegkamp (stats),  Qing Zhao (ECE)

Natural language processing, computational linguistics and information retrieval

• Group pages: NLP homepage ,  Computational linguistics lab
• CS field members: Yoav Artzi ,  Claire Cardie , Cristian Danescu-Niculescu-Mizil ,  Shimon Edelman , Tanya Goyal ,  Thorsten Joachims ,  Lillian Lee ,   David Mimno , Martha Pollack,  Alexander M. (Sasha) Rush ,   Immanuel Trummer ,  Marten van Schijndel , Kilian Weinberger
• Affiliated faculty : Morten Christiansen (psychology), Paul Ginsparg (physics, information science),   Mats Rooth (linguistics)
• Group pages: Robotics homepage , Robots in Groups homepage
• CS field members:   Yoav Artzi ,  Tapomayukh Bhattacharjee ,  Mark Campbell ,  Sanjiban Choudhury ,  Silvia Ferrari ,  Guy Hoffman ,  Wendy Ju ,  Malte Jung ,  Hadas Kress-Gazit , Daniel Lee ,  Kirstin Peterson ,  Wen Sun  
• Affiliated faculty :   Andy Ruina (mechanical and aerospace engineering), Robert Shepherd  (mechanical and aerospace engineering)
• Group pages: Computer vision homepage
• CS field members:   Kavita Bala ,  Serge Belongie ,   Shimon Edelman ,   Bharath Hariharan ,  Noah Snavely , Ramin Zabih
• Affiliated faculty : Anthony Reeves (ECE)

AI Research at UCF

Unlocking the future of artificial intelligence.

Artificial Intelligence (AI) is transforming the world and everyday lives – from facial recognition on phones to smart home devices to security measures implemented for online banking. By some estimates, the global artificial intelligence market will grow twentyfold by 2030, reaching nearly $2 trillion.

What is AI?

Artificial Intelligence (AI) describes the simulation of human intelligence in machines that are conditioned to think and learn like humans. It is a multidisciplinary discipline that combines computer science, mathematics, psychology, and other areas to develop intelligent systems. AI systems use algorithms, which are sets of rules and instructions, along with large amounts of data to simulate human-like reasoning and behavior. This allows machines to analyze complex data, recognize patterns, and make autonomous decisions, leading to advancements in various fields such as healthcare, finance, transportation, and entertainment. According to Next Move Strategy Consulting, the market for artificial intelligence (AI) is expected to show strong growth in the coming decade. Its value of nearly 100 billion U.S. dollars is expected to grow twentyfold by 2030, up to nearly two trillion U.S. dollars.

Branches of AI

Artificial Intelligence encompasses several branches, each focusing on different aspects of intelligent behavior and problem-solving. Through interdisciplinary collaboration and cutting-edge research, UCF explores the intersection of these branches, unlocking new possibilities and pushing the boundaries of what AI can achieve.

• Computer Vision
• Data Analytics
• Machine Learning
• Natural Language Processing (NLP)

As UCF continues to make strong strides to be the University for the Future, we’re playing an important role in exploring how Artificial Intelligence (AI) technologies can analyze more and deeper data with incredible accuracy, as well as greatly improve efficiency by expediting or even automating certain tasks. AI and its many implications present an enormous opportunity — and responsibility — for purposeful, impactful innovation at UCF.

A Network of AI Researchers

UCF’s Artificial Intelligence Initiative (Aii) aimed at strengthening AI expertise across key industries such as engineering, computer science, medicine, optics, photonics, and business. With plans to onboard nearly 30 new faculty members specializing in AI, this initiative signals UCF’s commitment to driving innovation and progress in AI-related fields.

Through Aii, an interdisciplinary team will harness the power of AI and computer vision to expand into emerging areas such as robotics, natural language processing, speech recognition, and machine learning. By bridging diverse industries, this collaborative effort seeks to pioneer groundbreaking technologies with wide-ranging societal impact.

A Top University for Artificial Intelligence

Articles presented at Computer Vision and Pattern Recognition Conference in 2023

UCF101 dataset is the research benchmark for all papers on human action recognition.

UCF has been the U.S. National Science Foundation REU site in computer vision.

Funded by Intelligence Advanced Research Projects Activity.

Powering AI Innovation from the Heart of Florida

Nestled among Research Park, downtown Orlando, and vibrant research hubs like the Lake Nona Medical City, UCF has a unique advantage in tapping into the diverse resources fueling AI research and development. Orlando’s dynamic tech scene fosters close-knit collaborations with industry partners and embraces cultural diversity, driving interdisciplinary efforts with real-world impact. Whether we’re delving into cutting-edge technologies within our local community or forging connections with global leaders, UCF’s position sets the stage for unparalleled growth in AI, shaping the future of innovation.

Transforming Lives Through AI Research

Much like electricity transformed the 20th century, AI is set to revolutionize the 21st. The adoption of AI isn’t just about technological change; it’s a catalyst for an industrial revolution fundamentally reshaping how we live and thrive.

From making medicine more accessible to building more sustainable cities, AI impacts nearly every aspect of our lives, and UCF’s faculty, students, and alumni are at the heart of it.

Autonomous Vehicles

Self-driving cars were once science fiction fantasy. Today, UCF researchers are making them a reality, promising safer roads, reduced congestion, and increased accessibility, revolutionizing how people and goods are transported.

• Driving the Future
• Developing a Computer Vision-based Navigation System
• AVs and the Future of Transportation

Managing Healthcare

From personalized treatment recommendations to optimizing resource allocation in hospitals, AI-driven solutions enhance efficiency and improve patient outcomes while reducing costs.

• Advanced Medicine
• Expanded Reality in Healthcare
• Using AI in Medicine to Better Predict Disease

Planning Cities and Economies

As smart cities become increasingly popular nationwide, UCF researchers are bringing cutting-edge AI tools and technology to one of the most heavily traveled areas in the state — improving mobility, business and safety for future generations here and across the nation.

• The New Era of Simulation
• Where Artificial Intelligence Meets Urban Planning

Reshaping Education

UCF researchers explore ways to learn from AI chatbots, like ChatGPT, to improve the learning experience for students and faculty. Through innovative approaches, they aim to revolutionize the educational landscape, fostering more interactive and personalized learning experiences.

• Could AI Save Education?
• Using AI to Help Children on the Autism Spectrum

Preserving Cultural Heritage

UCF researchers are leveraging AI to preserve cultural heritage, ensuring the protection of historical sites for future generations.

• Using Satellites to Protect Ancient Sites in Syria, Iraq
• Documenting and Triaging Cultural Heritage (DATCH) Project

Protecting Wildlife

Emphasizing the significance of proactive conservation efforts for future challenges UCF researchers work on the development of effective wildlife management strategies.

• Monitoring Genetic Mutations to Manage Florida Panther

Renewable Energy

UCF researchers are making renewable energy sources like solar and wind power more accessible and reliable, contributing to a greener and more sustainable future.

• The Truth About the Future of Energy
• Developing Floating Offshore Wind Turbine Simulators

A Network of Leading AI Experts

At UCF, our educators and researchers are mentors and leaders, thinkers and doers, big dreamers and problem-solvers. Here, different approaches to exploring and advancing AI also lead to unique collaborations with a variety of industry experts.

• Recognizing Human Action
• Accelerating Drug Development
• Harvesting the Potential of AI

Mubarak Shah Ph.D.

Trustee Chair Professor of Computer Science

The director of UCF’s Center for Research in Computer Vision, Shah also leads the Artificial Intelligence Initiative’s interdisciplinary team in pursuing new AI technologies. Recently, he and a team of UCF researchers received a prestigious prize for their pioneering human action recognition dataset.

Called UCF-101, the dataset includes videos with a range of actions taken with large variations in video characteristics — such as camera motion, object appearance, pose and lighting conditions. This footage provides better examples for computers to train with due to their similarity to how these actions occur in reality.

Find out more about this widely cited dataset

Ozlem Garibay Ph.D.

Assistant Professor of Industrial Engineering and Management Systems

Fusing AI with medicine, Garibay and a team of UCF researchers devised a new, more accurate prediction method that could accelerate the development of life-saving medicines and new treatments for various diseases. Both of which otherwise take decades of time and billions of dollars to produce.

The method models drug and target protein interactions using natural language processing techniques — and the team achieved up to 97% accuracy in identifying promising drug candidates. Garibay says this innovation has the potential to slow down diseases like Alzheimer’s, cancer and the next global virus.

Explore the details on this drug-screening method

Yunjun Xu, Ph.D.

Professor of Mechanical and Aerospace Engineering

By combing nature with technology, Xu and a team of researchers are exploring the use of autonomous robots in agriculture.

Supported by a grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture, the project will enhance the agricultural applications produced by the AI Institute for Transforming Workforce and Decision Support.

Xu’s team of researchers are applying AI to a variety of concepts to improve mobility, autonomy, precision, and analysis by agricultural robots. Advancing this technology will make farming more efficient, sustainable and cost effective.

Discover how this team will revolutionize agriculture

Artificial Intelligence Degrees and Academic Programs

UCF offers a comprehensive range of degrees related to Artificial Intelligence, including bachelor’s, master’s, doctoral and online programs that equip students with the knowledge and skills needed to excel in the rapidly evolving field of AI.

Bachelor’s Degrees

Best bachelor’s degrees for a career in artificial intelligence and data science

• Computer Science BS
• Computer Engineering BSCpE
• Data Science BS
• Statistics BS

Graduate Degrees

Top master’s and doctoral degrees for artificial intelligence and data science

• Computer Science MS
• Computer Vision MS
• Big Data Analytics Ph.D.
• Data Analytics MS
• Mathematical Sciences MS
• Statistics and Data Science MS

UCF Online Degrees

Nationally recognized fully online data analytics programs

• Healthcare Informatics MS
• Travel Technology and Analytics MS
• Data Analytics Certificate
• Data Modeling Certificate

Meeting the AI Demand with Top Talent in Orlando and Nationwide

Industry-leading companies throughout Florida and across the country have come to rely on UCF’s talent pipeline to advance their own efforts and positively impact their fields. Orlando’s top technology employers, including L3Harris and Northrop Grumman, are connected directly to UCF’s talent pipeline helping to cement the region as Florida’s technology and innovation hub. From computer science to engineering to optics and photonics, UCF alumni are making powerful contributions through fulfilling careers.

AI-Related Companies Employing UCF Alumni

• AstraZeneca
• Mayo Clinic
• Northrop Grumman

Areas of Excellence

Innovation. Access. Impact. Our integrated approach to teaching and learning prepares students for the future of work and lifelong careers, making a difference in their communities and around the world.

US, Britain, EU to sign first international AI treaty

• Medium Text

• AI Convention was adopted in May
• Convention covers human rights aspects of AI
• It was negotiated by 57 countries

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Reporting by Rishabh Jaiswal in Bengaluru, Supantha Mukherjee and Martin Coulter; Editing by Raju Gopalakrishnan, Philippa Fletcher and MarkPotter

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Conservative challenge to Brazil's ban on Musk's X may escalate feud

A conservative party in Brazil is seeking to reverse a judge's ban on Elon Musk's X platform, potentially escalating the months-long feud over censorship and hate speech in South America's largest country.

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Combinatorial artificial intelligence for defence applications PhD

• Cranfield's Doctoral Network

Fully funded Ph.D. opportunity in Aerospace AI. Sponsored by EPSRC and BAE Systems covering tuition, fees and a bursary of up to £19,569 (tax free). Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within Artificial Intelligence.  This stems from the two main styles of AI development over the last two decades. This research topic aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context of Defence applications.

Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within Artificial Intelligence.  This stems from the two main styles of AI development over the last two decades. 

'First Wave AI' is used to describe the rules/logic based AI used heavily in the 1990's and 2000's and still in wide use today.  This involves 'handcrafted' expert systems, which are good at reasoning about narrowly defined problems, but poor at handling uncertainty and have no ability to learn or abstract/generalise. In that sense, these systems serve as complex functional approximators trained over an input-output data set.  

‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data.  This include 'supervised learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks).  Some of the main problems with Second Wave AI are 'explainability' and trust - as the machines learn, they are based upon statistical outcomes on large data sets, rather than human intuitive information.  Another problem lies with the fragility of the systems, 'illogical' outcomes can sometimes be generated due to biases, gaps or pollution of the training sets.  They typically lack the ability to generalise and to reason beyond what it has been trained over.  

It is an emerging opinion that the next advances (on the way to ‘general’ intelligence) will be achieved through combinations of these alternate approaches.  These may be loosely coupled (novel applications of existing techniques) or tightly coupled, which involves new ways of defining and developing these intelligences to combine both approaches. As such, recent advances on techniques such as Meta Learning, One-shot/Few-shot Learning and Distributed/Decentralized Federated Learning not only provide approaches to combine intelligence but also ensure computational tractability of exponentially growing and unbounded variable and instance sets. In addition, novel approaches such as Physics Informed/Guided Learning allows the learning models to capture the underlying physics/patterns and to generate physically consistent regression (or classification) which is applicable not only to the limited physical envelope of the data, but to a wider extend and thus generalise. Such approaches provide a balance between infinite extent models and limited extend data based on trust over particular sets, and naturally create explainable AI structures which can further be analysed from a verification and validation perspective.  

This PhD research aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context of Defence applications. Such applications are expected to include:  • Robust and “functionally explainable” machine-aided decision support for Safety and Mission Critical objectives e.g. fault detection/tracing, evasive manoeuvring, target selection etc.  • Detailed semantic understanding of operational environments for Machine Situational Awareness, particularly within contested, congested and degraded scenarios.  • Fully autonomous robust intelligence data processing to significantly reduce the reliance upon human analysts and counter huge increases in data volumes.  • Improved synthetic training utilising machine-based instructors, matched to individual training needs.  • Improved “Virtual Assistants” for the next generation of platform-operator interfaces.  You will be working world-leading scholar (Prof. Gokhan Inalhan) on the topic partnered with one of the industrial giants from the defense sector (BAE Systems)  The work is envisioned to have great impact on design and development of intelligent autonomous agents. 

Fully funded Ph.D. covering not only tuition, fees and bursary but opportunity to attend conferences and to link with industrial experts in the field.  The student is envisioned to further enhance and develop world class skills in AI and Machine Learning with application to hard and challenging defense problems providing a great skill set for employability after the degree in both industry but also academia as well. 

At a glance

• Application deadline 28 Aug 2024
• Award type(s) PhD
• Start date 30 Sep 2024
• Duration of award 4 years
• Eligibility UK, Rest of world
• Reference number SATM475

Explore research at Cranfield

Applicants must have a B.Sc. in engineering or a related area and must either have or close to having a Master’s degree (must be completed by the time of the start of the iCASE Award). A demonstrated background in aerospace, autonomy and AI/ML would be a distinct advantage.

Diversity and Inclusion at Cranfield 

At Cranfield, we value our diverse staff and student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. 

We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum. 

About the sponsor

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network.  This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact:

Name:  Professor Gokhan Inalhan Email:  [email protected]

If you are eligible to apply for this studentship, please complete the online application form.

This vacancy may be filled before the closing date so early application is strongly encouraged.  Please ensure that your fully completed online application form is submitted by the application closing date. All requested documentation should be uploaded to the online form before submission. Note, your application will not be considered unless all relevant documents have been uploaded. For more information please visit Applying for a research degree .  

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• T: +44 (0)1234 758540

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Professor Gokhan Inalhan

BAE Systems Chair, Professor of Autonomous Systems and Artificial Intelligence

Research Excellence Framework (REF) results

We are ranked 7th in the UK for Engineering research power and 88% of our research is rated world-leading or internationally excellent.

How Russia is using artificial intelligence to interfere in elections

Simon Ostrovsky Simon Ostrovsky

Yegor Troyanovsky Yegor Troyanovsky

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• Copy URL https://www.pbs.org/newshour/show/how-russia-is-using-artificial-intelligence-to-interfere-in-elections

Moscow's attempts to interfere in U.S. and other elections are nothing new, though their tactics and strategy are constantly evolving. Special Correspondent Simon Ostrovsky recently sat down with an investigative journalist who's spent years uncovering Russian operations about yet another effort to sow doubt and chaos, this time using artificial intelligence.

Read the Full Transcript

Notice: Transcripts are machine and human generated and lightly edited for accuracy. They may contain errors.

Geoff Bennett:

Today's Justice Department indictments alleging ongoing Russian efforts to spread disinformation come just two months before Election Day. Moscow's attempts to interfere in U.S. and other elections are nothing new, though their tactics and strategy are constantly evolving.

Before today's announcement, special correspondent Simon Ostrovsky recently sat down with an investigative journalist who spent years uncovering Russian operations about yet another effort to sow doubt and chaos, this time using artificial intelligence.

Simon Ostrovsky:

Russia's Foreign Intelligence Service has a new plan to influence Western countries in this election year. Russia's CIA, which goes by its initials too, SVR, plans to use artificial intelligence to mask a sophisticated effort to interfere in a third straight U.S. presidential election.

First, a quick tour of 2016.

Donald Trump, Former President of the United States (R) and Current U.S. Presidential Candidate: Russia, if you're listening, I hope you're able to find the 30,000 e-mails that are missing.

Russian intelligence hacked the Democratic National Committee and Hillary Clinton's campaign chair. Thousands of e-mails were then dumped in a coordinated fashion to influence the race.

And that's not to mention the operations of Russian troll farms that created thousands of digital sock puppet soldiers to repeat and repeat and repeat messages that boosted Donald Trump, at Hillary Clinton's expense.

Christo Grozev, The Insider:

They have declared war, full-scale, hot war, information war on the rest of the world.

The man who helped uncover this new effort is Christo Grozev, an investigative journalist who's unmasked many intelligence operations. Perhaps most famously, he found the operatives who poisoned the late Russian opposition leader Alexei Navalny in 2020 and helped Navalny, who died in a Russian prison this past February, confront one of his assailants.

You have discovered a program that the Russian security services are trying to develop. What is this program actually about?

Christo Grozev:

We got access to a mailbox belonging to a senior intelligence officer working under cover of a commercial company in Russia.

And that mailbox contained initially reports that criticized the handling of the global propaganda effort by Russia, saying, we're losing to the West, we're losing to the Ukrainians, everybody loves Ukraine, everybody hates Russia. We have to change something about this.

They have decided to use A.I. and use all kind of new methods to make it indistinguishable from the regular flow of information we're getting.

We know that Russia has been trying to pit Western societies against themselves at least since the 2016 election. What's different about this new effort that you have discovered?

They will infiltrate Western organizations, some of them organizations that are even in defense of Western values. They're going to infiltrate pro-Ukrainian organizations and within those organizations create disruption.

They're going to make unreasonable demands to Western leaders, making Western societies tire and get annoyed with these "Ukrainian demands" — in quotation marks. They're no longer going to defend Russia. They're going to just cause disruption within Western societies.

They're no longer going to be trying to convince our societies that Russia's great. They're just going to use various different methods to make us angry at each other, angry at our allies, angry at Ukraine?

That is exactly so. And that's both bad news and good news. The good news part of it is that they realize that the ship has sailed on trying to convince the rest of the world that Russia is a powerful good.

Who's taking the lead in this project? Who's running it?

This is the Foreign Intelligence Service of Russia.

And, traditionally…

They're essentially criticizing the other agencies as having failed so far. And they have propositioned this to the Kremlin as, let us take care of this. We know how to do it better.

And, initially, we thought this may be just one proposal that may not have been accepted, but full of documents that we found show us that the program has been approved, recruitment has started, and there's even a document which is a letter to the head of the SVR, Naryshkin, which instructs him to allocate particular people to this program who will work under the cover of Kremlin officials.

And is there anything you can tell me about how you got this information?

Since the war started, the source of data from Russian databases has become both harder to get and easier to get, harder because there's a lot of clampdown on data providers, and easier because there are so many whistle-blowers and hacktivists, actually Russian hackers, who go and hack mailboxes of government officials.

And the latter is what happened with us right now. The name of the author of the program is Mikhail Kolesov. And, interestingly, he admits to being a high level SVR officer in his own C.V. that I have made available to you.

In his resume?

In his resume.

Incredible.

Kolesov's resume states that he's worked for the Foreign Intelligence Service of Russia since 2001 and oversees 40 agents. He lists among his achievements the rollout of 1,500 propaganda campaigns that supported achieving Russia's goals in the international arena.

He also boasts of receiving a medal in 2019 for developing new sources of information for the country's top leadership. Grozev was also able to obtain Kolesov's I.D. badge from an e-mail attachment, revealing the SVR agent's face for the first time.

Notably, he has two different resumes in his mailbox, one for the common people like you and us.

And for the real bosses, he actually admits to having worked for the last 19 years as a senior officer in the SVR.

And the SVR officer who is delegated to this program, seconded to this program and travels around the world, his name is Andrey Shcherbakov (ph). And he has a diplomatic passport, and we expect he will be able to travel to Western Europe and the United States, maybe not after this program.

I read in one of the documents that you got access to that they plan on actually hijacking our personal communication devices. What does that mean?

They plan to do insertion of advertising, which is in fact hidden as news, and in this way bombard the target population with things that may be misconstrued as news, but are in fact advertising content.

They plan to disguise that advertising content on a person-to-person level as if it is content from their favorite news sites. Now, we haven't seen that in action, but it's an intent, and they claim they have developed the technology to do that.

They're very explicit that they're not going to use Russia-related platforms or even separate platforms. They're going to infiltrate the platform that the target already uses. And that is what sounds scary.

If they have developed anything like that, then we would not know that one sentence from what we use on the read in The New York Times, for example, has been altered just for you as a reader to mislead you in what the content, the meaning of the article is.

Then the target is you and I and the general public?

The target is the general public on a mass, but custom — custom-made scale. They specifically talk about using A.I. to customize the message based on the biases and preferences of each individual user.

And while, before, they couldn't do that even with a troll farm run by Prigozhin in St. Petersburg of 10,000 people, because you can only customize it to 10,000 targets, now with A.I., you can do that to tens of million of people.

The documents hacked from Kolesov's inbox describe in stilted bureaucratic language an ambitious plan to shake the so-called main adversary, AKA the West, to its very foundations by secretly influencing key figures with new disinformation techniques.

The text reads:

"It is proposed that the theme of our campaign and countries of the main adversary be the stimulation of fear in recipients, the strongest emotion in human psychology."

The same team from Russia's foreign intelligence that is behind this global program is doing specific hit jobs on specific enemies of the Russian state.

This — these hit jobs go under the cover program named Ledorub, which means ice pick, because ice pick is what Stalin organized the assassination of Trotsky with last century. And there's no doubt that this is the meaning of this character assassination tool.

Now it seems like they're being much more targeted and trying to essentially send disinformation to specific, key individuals around a target.

They have a term in Russian, which is to make him unhandshakeable, somebody that nobody will want to engage with on a day-to-day basis.

The program's goals are, of course, much broader than individual Kremlin opponents, and is one more thing for U.S. news consumers to watch for as the election approaches.

For the "PBS News Hour," I'm Simon Ostrovsky in New York.

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North Carolina musician arrested, accused of Artificial Intelligence-assisted fraud caper

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NEW YORK (AP) — A North Carolina musician was arrested and charged Wednesday with using artificial intelligence to create hundreds of thousands of songs that he streamed billions of times to collect over $10 million in royalty payments, authorities in New York said.

Michael Smith, 52, of Cornelius, North Carolina, was arrested on fraud and conspiracy charges that carry a potential penalty of up to 60 years in prison.

U.S. Attorney Damian Williams said in a news release that Smith’s fraud cheated musicians and songwriters between 2017 and this year of royalty money that is available for them to claim.

He said Smith, a musician with a small catalog of music that he owned, streamed songs created with artificial intelligence billions of times “to steal royalties.”

A lawyer for Smith did not immediately return an email seeking comment.

Christie M. Curtis, who leads New York’s FBI office, said Smith “utilized automatic features to repeatedly stream the music to generate unlawful royalties.”

“The FBI remains dedicated to plucking out those who manipulate advanced technology to receive illicit profits and infringe on the genuine artistic talent of others,” she said.

An indictment in Manhattan federal court said Smith created thousands of accounts on streaming platforms so that he could stream songs continuously, generating about 661,000 streams per day. It said the avalanche of streams yielded annual royalties of $1.2 million.

The royalties were drawn from a pool of royalties that streaming platforms are required to set aside for artists who stream sound recordings that embody musical compositions, the indictment said.

According to the indictment, Smith used artificial intelligence to create tens of thousands of songs so that his fake streams would not alert streaming platforms and music distribution companies that a fraud was underway.

It said Smith, beginning in 2018, teamed up with the chief executive of an artificial intelligence music company and a music promoter to create the songs.

Smith boasted in an email last February that he had generated over four billion streams and $12 million in royalties since 2019, authorities said.

The indictment said that when a music distribution company in 2018 suggested that he might be engaged in fraud, he protested, writing: “This is absolutely wrong and crazy! ... There is absolutely no fraud going on whatsoever!”

Melissa Smith, PhD, helps workplaces embrace AI

Vol. 55 No. 6 Print version: page 29

• Applied Psychology
• Managing Human Capital
• User Experience Design
• Artificial Intelligence

As artificial intelligence (AI) and automation revolutionize work, employers worldwide are striving to keep pace with the latest developments, maintain productivity, and reduce employee stress.

Applied cognitive psychologist Melissa Smith, PhD, is studying the best ways to help companies and organizations do that as a senior user experience (UX) researcher at Google Workspace, based in Raleigh, North Carolina. The group designs and integrates Google’s vast suite of productivity tools, including Gmail, Google Docs, and Google Meet, into a cohesive service. Using the latest cognitive science, Smith and her team are building more intuitive, user-friendly programs, such as the mobile versions of popular applications like Google Drive and Calendar. Their goal is to boost both employee performance and well-being.

Smith underscores the need for workplaces to adapt to AI and other emerging technologies. She sees these advances not as threats to replace people but as tools to aid in mundane or risky tasks, enabling people to prioritize what truly defines human work: collaboration and creativity. “The beauty of user experience research is discovering what makes someone care deeply about a product, then developing that technology to support their learning and growth,” said Smith.

The Monitor talked with Smith about how she came to UX research and its implications for the future workforce.

How do your team’s strategies and goals stand out from those of other companies developing tools to improve how people work?

Google Workspace products have always been known for their collaborative nature. When I was in early college and Google first introduced Docs, it was revolutionary to be able to have multiple people working on one document at the same time. Today, those collaborative features are an industry norm, and our team is still pushing the cutting-edge boundaries of collaborative work. We are currently incorporating generative AI features across Gmail and Workspace to simplify organization tasks. Soon, you will be able to use Gmail’s side panel to summarize emails and highlight the most important action items. Also, the “Help me write” feature in Gmail and Docs, which uses AI to draft messages based on your prompts, will support Spanish and Portuguese.

Our team also prioritizes tech accessibility as we build new features, making sure that we don’t inadvertently exclude people who, for instance, rely on screen readers or high-contrast screens to interact with our services. Accessibility considerations can be easily overlooked if you don’t actively engage with the many types of consumers who use your services. There are always opportunities for us to improve in creating technology that caters to people with diverse needs or disabilities.

How is your research at Google enhancing employee well-being and shaping how the next generation will work?

User experience research is vital in product development because we are actively incorporating the voices of customers and users. My work focuses on talking with people who use our products to accomplish the diverse tasks relevant to their roles. For example, the needs of a general consumer using our products to complete schoolwork or organize family events differ from those of a small business owner who uses Google Workspace to manage a team.

By making productivity tools more user-friendly, our services streamline workflows and reduce employee stress. Overly complex software and information overload can cause mental fatigue. If we can simplify these processes and present information more clearly, we can help workers focus on essential tasks. This is especially important as workplaces increasingly adopt hybrid work models and communication among workers is fragmented. Our research helps us develop products that better support remote work, such as improved virtual collaboration and scheduling tools that help employees maintain work-life balance.

For example, my team has gained valuable information from users about the importance of seamless connection across multiple platforms and devices which has inspired us to improve the mobile interface for Google Workspace products. Just 5 years ago, I would have never opened a Google Doc on my phone. Now, mobile Docs is far more accessible and offers expanded features for collaboration among employees working from many different locations and platforms.

What led you to user experience research?

During middle school and high school, I was involved with a nonprofit organization called FIRST, which fosters excitement for science and technology among K–12 students through annual robotics competitions. It’s been more than 20 years since I first participated in the program, but that excitement hasn’t stopped. I serve on the FIRST Robotics board and help connect FIRST students with alumni at Google.

One of my goals is to show students the diverse STEM (science, technology, engineering, and mathematics)-related careers available to them, beyond the already well-known roles like engineer, lab scientist, or doctor. This is partly influenced by my own experiences. I spent my undergraduate years as a mechanical engineering major because I wanted to work in robotics. But when I discovered human-robot interactions, I found that exploring how people engage with and trust artificial agents, and how robots can improve human lives, interested me far more. So, I changed my major and pursued a PhD in applied cognitive psychology and eventually realized that my research interests aligned with the user experience field.

Your dissertation looked at people’s trust in automation and robotics. How do you bring that knowledge into your current work?

No matter what the technology is—you could insert whichever technology buzzword you want, whether it’s AI, machine learning, or big data—people’s fundamental approaches to adopting new systems follow a similar pattern. There will be the early adopters, who embrace the new technology and trust it even if it’s still being workshopped. Then, there is a larger chunk of intermediary users, who prefer to test the waters and wait for the technology to take off before they immerse themselves in it. Finally, there are the people who resist change altogether—the “if it’s not broken, why fix it?” users, who probably wouldn’t mind using an old-school flip phone.

That research taught me that you need to adapt to each set of users. I emphasize that perspective in every product my team creates because most of us on the development team belong to that first group, who generally trust and understand technology. But we aren’t representative of most consumers, so it’s essential to reach out to our end users, not to convince them to trust our product but to hear their concerns so we can build a product worth trusting.

How will AI continue to influence UX research?

AI is unique in that it doesn’t just offer incremental improvements over existing technologies; it represents a whole new paradigm in how people think about and interact with technology. Consequently, we need to exercise much greater caution when building new products and proactively anticipate how users will interact with these systems. At the same time, AI opens many more opportunities to create magical moments—to push productivity, problem-solving, and collaboration forward. That kind of entirely new technology hasn’t emerged in many years, so it is an incredibly interesting time to be a user experience researcher.

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