Advances in Technology | Touro University

What AI skills should today’s college students, specifically those in nontechnical fields, master before graduating?

College students in all fields need to master AI literacy, including ethical considerations. They also need to know how to collaborate with technical professionals. They need to understand basic AI concepts and terminology, and how to use AI tools effectively. This includes recognizing and addressing AI biases and understanding common risks associated with AI. Additionally, students should learn to use AI-powered software relevant to their field and be capable of collaborating with technical professionals to use AI effectively in their work.

How are you teaching AI skills across the Touro system? What is your goal?

AI@Touro is working to integrate AI education across all disciplines to prepare students for AI-driven careers. Touro now offers multiple new AI courses for students, including a new Master of Science in Artificial Intelligence Systems and an Advanced Certificate in Applied AI through the Graduate School of Technology. AI is also being integrated into existing courses across disciplines, including in the general education curriculum. The goals are to prepare students for working with AI, foster innovation and critical thinking, and ensure they can handle AI-related risks ethically and effectively. An essential component of our strategy is working with all of our faculty and administrators, training them in AI through a variety of bootcamps and workshops, and building a sense of collective teamwork around AI initiatives.

Will AI make certain jobs unnecessary? In what fields might that happen?

AI is more likely to transform jobs rather than eliminate them, with significant effects in some areas like manufacturing and transportation. While AI will not destroy jobs overall, it will change the nature of many roles. People who can effectively use AI will tend to replace those who cannot, leading to a shift in the job market. We are likely to see an increasing shift towards jobs that emphasize human interaction, such as in the service sector. Fields like manufacturing, where automation and robotics can handle repetitive tasks, and transportation, where self-driving vehicles could revolutionize trucking and logistics, are likely to see the most disruption.

How are professionals currently using AI in medicine and finance to benefit patients and clients?

AI enhances healthcare and financial services through advanced data analysis and AI-based automation. In medicine, AI assists in medical imaging analysis, supports radiologists, accelerates drug discovery, personalizes behavioral medicine with wearable technology, provides early-stage virtual health assistants for patients and improves surgical outcomes through robotics. It also streamlines administrative tasks, freeing up healthcare professionals to spend more time on patients. In finance, AI-driven algorithms optimize investment strategies, detect fraud, improve customer service with chatbots, automate routine tasks, analyze financial documents and provide predictive analytics for market trends.

Will colleges and universities need to incorporate AI into training for all disciplines? What about high schools?

Incorporating AI into education, starting at the high school level, is essential to prepare students for the future economy and society. Colleges and universities need to integrate AI education into various subjects to ensure students are ready to address AI use effectively in their careers. High schools should introduce basic AI concepts to create informed citizens and consumers. Vocational training should also include specific AI applications relevant to fields like manufacturing, construction and agriculture.

Does AI have biases in terms of race, gender or religion? What kind of impact does that have on the results people get when using AI?

AI systems work by incorporating large amounts of public or private information into a large language model (LLM). AI is “trained” by the data it incorporates from these sources. AI systems amplify biases in their training data, leading to potentially discriminatory outcomes. Biases in AI stem from the training data, which often reflects societal biases. If not properly managed, this can result in discriminatory outcomes in areas like hiring, lending and law enforcement. Mitigating these biases involves using diverse datasets and bias detection tools and adjusting how the tools are used to support human decision-making. While bias can never be entirely removed, it must be continuously monitored and addressed to minimize its impact.