The Future is Now: Harnessing AI, Data Science, and Machine Learning

AI Learning and Artificial Intelligence conceptual - Icon Graphic Interface showing computer, machine thinking and AI Artificial Intelligence of Digital Robotic Devices.

Empowering employees with professional education from Johns Hopkins Whiting School of Engineering Lifelong Learning will enable corporations to leverage technological advances to meet future challenges and drive success.

Technology continues to advance at a breathtaking pace. These advancements have become more pronounced with the advent and increased use of artificial intelligence (AI), data science, and machine learning (ML). Now, there is a new AI and ML-induced revolution every week. This also coincides with the growing importance of the management and analysis of vast swathes of data that have become the lifeblood of many corporations.

Bloomberg Intelligence predicts that the generative AI market will be worth $1.3 trillion by 2032. Corporations worldwide will compete—or already are competing—for their share of this market.

Corporations and global enterprises ignore these modern technologies at their peril. Much like those who struggled to adapt to the Internet of Things (IoT), the risk of getting passed on the superhighways technological progress is too great.

Hidden under these shiny advancements, however, are the ethical dilemmas and risks of AI adoption and data mismanagement. Ignoring these risks can result in numerous negative downstream impacts and places added importance on knowing how to best utilize these revolutionary technologies.

As such, it is more important than ever before for corporations to harness these advances and provide professional and executive education about AI, data science, and machine learning to employees. These educational opportunities will not only ensure that corporations can use these technologies to positively impact their bottom line, but also use them in an ethical way that benefits society.

What is the difference between AI, data science, and machine learning?

AI, data science, and machine learning have become part of our lexicon, but these terms have often been treated as interchangeable, which is not the case. While many of these fields are related, they do have their specific tasks. It is important to understand these terms to best determine how to empower your employees with these advanced and revolutionary technologies.

For instance, computers—and how humans interact with machines—have evolved. This is due to advances in data science and analytics, or how we process data and analyze data to gain insight and understanding. These advances in data have led to an explosion in AI. These advances have now made it possible for increasingly intelligent machines to perform autonomous tasks and mirror human abilities, including speech, image recognition, and problem-solving. Machine learning, on the other hand, is usually considered a subset of AI that occurs when machines learn from data and analysis without being specifically programmed to do so.

All three fields have a vast potential to positively impact our society and have become vital instruments for corporations to harness. Businesses that empower their employees with education in these fields will set themselves up for success.

 Johns Hopkins University, for instance, has found that these three fields have a variety of applications over a wide range of areas, including national security, societal safety, materials design, public health, neuroscience, space systems, and more. These fields have grown so much that the university is creating its own Data Science and AI Institute within the Whiting School of Engineering.

Importance of responsible AI and data usage

AI, data science, and ML have the potential to positively impact our global communities in multiple ways. For example, Johns Hopkins researchers recently used AI, teamed with medical professionals, to map the abdomen and create AbdomenAtlas-8K, a multi-organ dataset that can aid in medical diagnosis. This demonstrates how experts can leverage AI applications and tools to better the communities they serve.

However, not all AI and data are used responsibly. Even a casual look at recent headlines will show negative AI use cases, including fraudulent art, aiding plagiarism, or creating fake images that could be used as part of a disinformation campaign. A Johns Hopkins computer scientist even found that AI image generators could be tricked into making NSFW content.

AI aside, consumers have grown accustomed to sharing their data but have also become more aware of how data is used (or misused). Consumers want to do business with corporations that safeguard their vast amounts of data, especially those used in cloud computing. This raises the stakes for corporations as big data—and its ethical use and management—has become the lifeblood of any business and its reputation.

Harnessing AI and data science will drive success for corporations

Corporations must harness AI, data science, and machine learning to leverage these tools and drive future success. For instance, even as ChatGPT and other AI solutions have seeped into many facets of our lives, there remains a need to understand how these tools work and their limitations, including the wide variety of problems they may present, and the way humans interact with them.

AI, data science, and ML will continue to become more important to corporations. The Wall Street Journal reported that CFOs are beginning to incorporate AI and other automation tools to improve efficiencies within operations and are even using ML to help make predictions. Imagine the possibilities of incorporating these technologies into your supply chains, for instance.

If your corporation is looking to leverage these advances, it is more important than ever before that employees receive the most current professional education in these fields. It is equally important that this education also encourages the ethical use and management of AI, data science, and ML for the betterment of our shared communities.

Why does empowering employees with the latest professional education build resilient corporations?

Corporations that invest in professional and executive education for their employees can often point to a financial return on investment, but it’s not all dollars and cents.

Executive education or professional education programs provide business leaders with an educational background and skills that enable them—and the teams they lead—to solve complex challenges. It also provides a valuable opportunity for leaders to broaden their horizons by working with—and learning from—subject matter experts and their industry peers.

Additionally, a corporation that invests in the development of its people is better positioned to succeed. In constantly evolving and expanding fields like AI or data science, this could be the difference between bringing the next breakthrough to market or watching a competitor take a share of the market.

By providing a strong AI, data science, or machine learning foundation—centered on positive human values and ethics— corporations are setting their employees up for future success and tapping into the AI and data revolution.

Join the AI Revolution with Johns Hopkins Whiting School of Engineering Lifelong Learning

Partnering with the Johns Hopkins Whiting School of Engineering Lifelong Learning for executive and professional education ensures employees are empowered to stay at the forefront of constantly evolving fields in AI, data science, machine learning, cybersecurity, computer science, and more. Current courses include:

  • Assurance of AI-Enabled Systems
  • AI in Healthcare
  • AI for Executives
  • Leading Data and AI-Enabled Organizations: For Senior Leaders (United States government)

These intensive programs go beyond typical professional development or executive education and provide real-world use cases and hard-won lessons taught by world-renowned faculty and subject matter experts—including those trusted by the federal government—with flexible learning schedules and modalities for working professionals.

Reach out today to learn more about the Johns Hopkins Whiting School of Engineering Lifelong Learning, including course offerings, types of educational modalities, and more.

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Instructors

Andrew Ball
Instructor
Andy Ball is the human and complex systems design strategist in JHU/ APL’s Design Thinking Group, where he leads cross-functional teams in the delivery of amazing solutions in the service of national security and space exploration.
Dave Barsic
Instructor
Dave Barsic is an Assistant Program Manager in the Force Projection Sector at JHU/APL. He is a member of the JHU/APL Principal Professional Staff and has 19 years of experience focusing on machine learning and signal processing applications for various U.S. Navy efforts.
Steve Biemer
Systems Engineering Program Instructor
Steve Biemer is a systems engineer for the Johns Hopkins University Applied Physics Laboratory. He works with both the Department of Defense and the Department of Homeland Security defining and conducting analytical end-engineering assessments of systems, platforms, architectures, and networks.
John Callahan
Chief Technology Officer
Dr. John Callahan is Chief Technology Officer (CTO) at VeridiumID.com, a leading biometric authentication company. He recently served as the Associate Director for Information Dominance at the U.S. Navy’s Office of Naval Research Global (ONRG) London office.
James Caroland
Captain
James Caroland is an active-duty Navy Captain in the Cyber Warfare Engineer community.  He is currently the Chair of the Cyber Science Department at the U.S. Naval Academy.
Anton T. Dahbura
Instructor
Anton Dahbura is the co-director of Johns Hopkins University’s Institute for Assured Autonomy and executive director of the Johns Hopkins University Information Security Institute.
Michael DiRossi
Principal Research Engineer
Mike DiRossi is a Principal Research Engineer at the Johns Hopkins University Applied Physics Laboratory, where he provides leadership, strategic vision, and technical oversight over a portfolio of cybersecurity research projects.
Ashutosh Dutta
Senior Scientist and Chair for Electrical and Computer Engineering for Engineering Professional Program
Ashutosh Dutta is currently a senior scientist and 5G chief strategist at Johns Hopkins University Applied Physics Labs (JHU/APL). He also serves as chair for Electrical and Computer Engineering for Engineering Professional Program at JHU.
Frank Fratrik
Instructor
Frank Fratrik is the senior director of safety solutions at Edge Case Research, where he manages a group of system safety engineers who provide system safety management and engineering expertise across a diverse customer base of developer, users, and assessors.
Kandice Garner
Academic Program Manager, Lifelong Learning
Kandice Garner serves as the first point of contact for assistance with your course.
John Gersh
Instructor
John Gersh is a principal cognitive engineer in JHU/APL’s Intelligent Systems Branch, where he focuses on human-machine teaming.
Will Gray-Roncal
Principal Research Scientist
Will Gray-Roncal is a Principal Research Scientist at the Johns Hopkins University Applied Physics Laboratory, with expertise in data science, neuroscience, artificial intelligence, precision medicine, and learning research, including leadership of the CIRCUIT Program.
Joseph Greenstein
Lecturer
Dr. Joseph Greenstein is a lecturer in the Department of Biomedical Engineering. He teaches courses that cover topics in precision medicine, biomedical data sciences, and computational physiology and medicine.
Erin Hahn
Instructor
Erin Hahn is a senior national security analyst and principal professional staff member in JHU/APL’s National Security Analysis Mission Area, where she supervises a group of analysts working on broad issues related to technology development and implementation.
David Handelman
Instructor
David Handelman is a Senior Roboticist at the Johns Hopkins University Applied Physics Laboratory. He is a member of the Robotics Group in the Research and Exploratory Development Department. His current research focus is adaptive human-robot teaming based on the emulation of human skill acquisition by robots using neuro-symbolic AI/ML.
Chad Hawthorne
Instructor
Chad Hawthorne is a principal investigator and autonomy researcher at JHU/APL and has 20 years of experience developing autonomy software for unmanned maritime systems. At APL, he oversees a research team that focuses on delivering autonomy and sensing solutions for our nation’s submarine and unmanned platforms.
Scott Hendrickson
Experimental Optical Scientist
Dr. Scott Hendrickson leads a physics group in the Research and Exploratory Development Department at the Johns Hopkins University Applied Physics Laboratory (JHU/APL). This group focuses on a range of topics including quantum information, electromagnetics, and biomedical imaging. He currently helps lead projects focused on quantum information hardware development in partnership with the government and…
Paul Huckett
Interim Associate Dean, Lifelong Learning
For information on other courses or general inquiries about Lifelong Learning, contact Paul.
Jenny Kelley
Chief Scientist
Jenny Kelley serves as the Chief Scientist of the Cyber Warfare Systems Group within the Asymmetric Operations Sector at the Johns Hopkins University Applied Physics Laboratory.
Ian McCulloh
Awesome Title, Lifelong Learning
Ian McCulloh is the [appropriate title] for Lifelong Learning.
Matthew Montoya
Instructor
Dr. Matthew (Matt) Montoya is an advisor, instructor, professor, academic program director, and researcher at Johns Hopkins Engineering for Professionals Systems Engineering, Healthcare Systems Engineering, and Lifelong Learning programs.
Bart Paulhamus
Instructor
Bart Paulhamus is the chief of the Intelligent Systems Center at Johns Hopkins University’s Applied Physics Laboratory.
Chris Ratto
Instructor
Dr. Christopher Ratto is a member of the Senior Professional Staff at The Johns Hopkins University Applied Physics Laboratory.
Jane Pinelis
Instructor
Dr. Jane Pinelis is the Chief of the Test, Evaluation, and Assessment branch at the Department of Defense Joint Artificial Intelligence Center (JAIC). She leads a diverse team of testers and analysts in rigorous test and evaluation (T&E) for JAIC capabilities, as well as development of T&E-specific products and standards that will support testing of…
Gregory Quiroz
Project Manager and Visiting Scientist
Dr. Gregory Quiroz is a project manager in the Research and Exploratory Development Department at the Johns Hopkins University Applied Physics Laboratory (JHU/APL).
Alan Ravitz
Instructor
Alan D. Ravitz is chief engineer in JHU/APL’s National Health Mission Area and is chair of the Whiting School of Engineering’s Engineering for Professionals MS program in Healthcare Systems Engineering.
Lynn Reggia
Instructor
Lynn Reggia is the supervisor of the Human Machine Engineering Group within JHU/APL’s Air and Missile Defense Sector.
Sarah Rigsbee
Instructor
Sarah Rigsbee is a senior human-centered design and innovation strategist and senior professional staff member at JHU/APL and is the lead human-centered design strategist for JHU’s Institute for Assured Autonomy (IAA).
Pedro Rodriguez
Instructor
Pedro A. Rodriguez is the principal technical leader of multiple deep learning projects at JHU/APL, where currently he focuses on developing and deploying deep learning algorithms at the tactical edge for the U.S. Army and the Joint AI Center (JAIC).
Sri Sarma
Associate Professor
Dr. Sri Sarma is an Associate Professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University.
Lia Scarince
Instructor
Lia Scarince leads program strategy and project execution initiatives within the National Health Mission Area at JHU/APL, where she leverages 20 years’ experience at the front lines of public, private, and military health systems.
Aurora Schmidt
Instructor
Aurora C. Schmidt is a project manager in JHU/APL’s Research and Exploratory Development Mission Area, and her research interests include sensor networks, estimation and coordination problems, signal processing, compressed sensing, optimization, multi-target tracking, control theory, and information and decision-making.
Christina Selby
Instructor
Christina Selby is a senior professional staff member and section supervisor at JHU/APL, with expertise in developing and analyzing mathematical methodologies to solve critical problems that are not well understood.
John Slotwinski
Senior National Security Analyst and Project Leader
Dr. John A. Slotwinski is a Senior National Security Analyst and Project Leader in The Applied Physics Laboratory’s (APL) National Security Analysis Department, where he leads and performs studies on national security topics for the U.S. Government.
Tamim Sookoor
Instructor
Tamim Sookoor is a researcher at JHU/APL, where his research interests include cyber physical systems (CPS), cyber security, the Internet of Things (IoT), and machine learning.
Adam Watkins
Instructor
Adam Watkins is a principal staff member of JHU/APL with over 15 years’ experience in autonomy and robotics.
Tony Wei
PhD Candidate
Tony Wei is a third year Ph.D. candidate in Biomedical Engineering at Johns Hopkins University in Dr. Sridevi Sarma’s Neuromedical Control Systems Lab.
Dan Yaroslaski
Instructor
Dan Yaroslaski is a senior professional staff member in the Tactical Intelligence Systems group within the Asymmetrical Operations Sector at Johns Hopkins Applied Physics Laboratory.
Kaliya Young
Instructor
Kaliya Young co-founded the Internet Identity Workshop in 2005 to bring together technologists who want to see decentralized identity come into being. This community is credited with creating internet standards such as OpenID Connect and OAuth and an initiation ground for collaborations that have led to multi-million-dollar projects. In 2010 she was recognized as a…
Reed Young
Instructor
Reed Young is a member of the senior professional staff in the Research and Exploratory Development Mission Area at JHU/APL, where he serves as the program manager for Robotics and Autonomy.
Kevin Ligozio
Instructor
Kevin Ligozio serves as the Technical Director and Assistant Group Supervisor of the Tactical Intelligence Systems Group within the Asymmetric Operations Sector at the Johns Hopkins University Applied Physics Laboratory.