AI Researcher: what they do and how to become one
Are you passionate about Artificial Intelligence? Would you like to work in the field as a Researcher and help shape AI based systems? Discover how to pursue this career path.
Greeted with as much enthusiasm as awe, the rise of Artificial Intelligence is unquestionably revolutionising our personal and professional lives, influencing everyday behaviour (such as online purchasing choices) and work processes. Its rapid spread in the most diverse professional fields has led to the emergence of a series of new figures in charge of researching and implementing new algorithms, models, and increasingly advanced AI systems.
One of these is the AI Researcher, a professional who conducts important research in the field, developing innovative solutions to make AI-equipped systems more effective, powerful, and capable of solving complex problems. This is an important role, which requires a solid foundation in computer science and mathematics, theoretical and practical foundations of how AI works, rigorous research methodologies and the ability to write advanced algorithms that enable machines to learn, reason and adapt in increasingly sophisticated ways.
Starting from theory, then, AI Researchers develop Artificial Intelligence systems capable of optimising all kinds of processes in every imaginable field – from healthcare to finance, via entertainment and education.
Often specialised in specific areas such as machine learning, natural language processing or computer vision, the AI researcher has well-defined responsibilities:
- Experimenting with AI to identify limits and unexplored potential;
- Developing algorithms that optimise and enhance the capabilities of Artificial Intelligence and Machine Learning systems, creating prototypes and practical demonstrations;
- Carefully document the results of his research in order to disseminate them through peer-reviewed journals, collaborations with other experts and academic conferences;
- Review and verify research conducted by other experts to contribute to the validation and progression of AI studies;
- Collaborate with interdisciplinary teams and enter into commercial agreements with industrial partners to translate the research conducted into tangible commercial applications;
- Apply for grants and funding to support its research projects.
To enter the world of AI Research, it is important to undertake a targeted training path, through ad hoc university courses and academic masters that delve into topics such as artificial intelligence, machine learning, data science and advanced computational methods. The next step is to identify internships or roles as Junior AI Researcher in companies and start-ups dedicated to the tech world, research institutes or academic laboratories, to participate in collaborative projects that allow them to gain practical experience and contacts with other researchers in the field, which are useful for the creation of a solid professional base.
Continuous training through specialisation programmes, semester courses, Academic Masters or short courses dedicated to new trends, which provide additional specific skills and certifications to enrich one’s cultural background and curriculum vitae, is a must: a constantly evolving sector such as AI needs up-to-date professionals.
Specific skills and knowledge are required to become AI Researchers, including:
- Advanced mathematical and statistical knowledge, enabling them to develop new algorithms and interpret complex data;
- Solid foundations in programming, machine learning, data engineering and Big Data analysis, useful for implementing and testing advanced AI models, conducting simulations and processing large sets of insights;
- Ability to apply rigorous research methodologies and verification of results obtained;
- Aptitude for problem-solving and critical thinking, essential to creatively address the many problems that may arise during the research phase;
- Ability to communicate efficiently and work in extended teams, as the AI Researcher will always have to collaborate and interface with other researchers, engineers and stakeholders;
- Strong ethical judgement and social awareness, enabling consideration of the ethical implications of the social impact of AI;
- Ability to propose their innovations to the business world.
The software and tools used on a daily basis by AI Researchers vary depending on the specific projects and application areas. However, some fundamental tools and software are widely used in the field of AI research.
Among the software and tools most widely utilised by AI Researchers to develop, test and implement advanced AI models are open-source libraries, such as PyTorch and TensorFlow, which enable the construction of deep neural networks, and interactive environments that allow code to be written and shared, such as Jupyter Notebook.
Of great importance are tools for managing data accumulated during research, such as NumPy, and machine learning tools that offer ready-to-use algorithms useful for optimising the machine learning of systems under development, such as Scikit-learn. To work on large-scale AI models and manage their implementation, AI researchers also use isolated environments such as Docker. Instead, platforms such as GIt, which tracks every change made, and GitHub, which simplifies collaboration between multiple users, are used to share work between researchers.
A multifaceted and increasingly in-demand figure, AI Researchers can work in different contexts, such as:
- Large and small companies where develop and implement AI solutions to improve production processes, products and services offered. They’re often included in R&D teams, but can also be attached to IT;
- Tech companies and start-ups, where they develop AI apps and machine learning tools to propose to an industry audience;
- Research centres and universities, where they conduct advanced AI research that he shares with other experts;
- Public institutions and non-profit organisations, within which it exploits AI to contribute to research projects with social impact and automates recurring processes.