WHAT AI WILL DO — AND WHAT IT STILL CAN’T
There are a multitude of skills which are and will be important when working with AI. Having strong technical skills in computer programming, machine learning, statistics and data management are essential. Additionally, soft skills are also in high demand, including problem solving, critical thinking, communication, teamwork and collaboration. Elements of both hard and soft skills will be needed to successfully understand and implement AI tools and processes.
Today’s AI tools can detect and recognize specific patterns, but they may not respond precisely to human emotions. True emotional intelligence involves interpreting subtle cues, understanding the context and depth of languages, and the ability to respond appropriately. So, can AI learn to “feel” as a human? This capability may not be unilaterally available across all sectors today, but it will likely evolve with the technology. In the meantime, humans still have the greater edge and depth to connect and interpret emotionally.
CRITICAL SKILLS FOR WORKING IN AI, ACCORDING TO AI
So, what are the top skills that programmers or engineers might need to be successful when working in AI? I asked ChatGPT to list the top 10 skills necessary to be successful in AI, and in about 10 seconds, it responded with the following:
Machine learning: Understanding machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning, is essential, in addition to being familiar with techniques such as regression, classification, clustering and neural networks.
Statistics and mathematics: A solid foundation in statistics and mathematics is necessary to understand the theoretical aspects of machine learning algorithms, probability theory, linear algebra, calculus and optimization techniques.
Data handling: Skills in data preprocessing, cleaning and manipulation are important for preparing data sets for training machine learning models, including knowledge of databases, SQL and big data technologies.
AI frameworks and libraries: Familiarity with popular AI frameworks and libraries such as TensorFlow, PyTorch, Keras and scikit-learn is essential for building and deploying AI models efficiently.
Problem-solving skills: Ability to define problems, formulate them into machine learning tasks, and devise appropriate solutions is critical.
Domain knowledge: Understanding the domain in which you’re applying AI is crucial for interpreting results correctly and making informed decisions.
Ethical considerations: Awareness of ethical issues related to AI, such as bias in algorithms, data privacy and transparency, is increasingly important.
Communication skills: Ability to communicate effectively with stakeholders, including explaining AI concepts to non-technical audiences, presenting findings and collaborating with cross-functional teams, is vital for successful AI projects.
Continuous learning: AI is a rapidly evolving field, so a willingness to stay updated with the latest research, trends and technologies is crucial.
Colleges and universities can assist in teaching prospective students these 10 skills. Notably, curriculum in STEM-related coursework covers at least half of the listed skills, but the other half could be derived from a liberal-arts perspective. The University of London’s School of Oriental and African Studies specifically addresses this in a blog from June 2023, titled “How a Global Liberal Arts degree offers critical skills in the era of AI.” Their program highlights the importance of critical thinking, communications, teamwork, empathy and decision-making in building more accurate, powerful, robust, and perhaps more “human-like” AI.
Additional skills are also available through certifications. For those who already have a college degree or lack the time or financial support to obtain one, AI certifications could potentially make them more attractive to employers in a wide variety of industries. AI certifications could redefine their career goals and open up new employment opportunities. TechRepublic, a trade publication for IT professionals, recently compiled a list of some of the best AI courses available that “offer a wide variety of hands-on experiences with generative AI, machine learning and AI algorithms.” The list provides information on cost, course duration, skill level achieved and type of certification.
AI REPLACING JOBS IN HIGHER ED
While higher education plays a key role in educating and training students for prospective careers in AI, ironically, traditional jobs within the education environment could be changing at the same time. Offering higher productivity and efficiencies, AI could assist or replace workers in areas such as marketing and communications, entry-level accountancy jobs, administrative assistants, jobs related to librarian reference services, introductory college courses, some basic services provided for student advising and tutoring, and IT help desks. As higher education trains our students for the AI jobs of tomorrow, it will be simultaneously changing its own workforce environment. Education could prove to be the guiding factor on how we learn, embrace, and effectively utilize AI in all aspects of our jobs and lives. As the author Nicky Verd wrote in her book, Disrupt Yourself or Be Disrupted, “Schools should be at the forefront of innovation and technological progress(,) NOT a place for preserving obsolete learning methods and clinging onto archaic practices that are no longer relevant for the world we live in.” Higher education has much potential, and work to do, in crafting our future.
