This writing highlights a critical skills
gap regarding the development and deployment of AI systems. AI is not solely
about machine learning algorithms, but a complex interplay of software,
sophisticated electronics, connectivity, machines, and infrastructure. AI can
realize its full potential in Engineering applications if this broader AI
systems engineering skill shortage is addressed. There is an urgent need for
collaborative efforts between industry, government, and academia to create
relevant educational and training programs, and for upskilling the current
workforce to meet the demands of the AI era.
AI is a revolutionary technology with the
potential for profound impact on the economy, society, and environment. One should make a critical distinction
between focusing solely on machine learning algorithms and the broader AI
ecosystem. AI systems combine algorithms with sophisticated electronics,
pervasive connectivity, electro-mechanical modules and machines, and physical
infrastructure. A complete view of AI should include an understanding of the
end-to-end systems that integrate AI. There is a serious gap between the skills
needed for the future and those currently available. While there is a focus on
training and development within the broader AI skill sets, a gap has been noted
in specifically training for AI systems.
The integration of AI requires a systems engineering mindset,
with engineers prepared to work across different compute engines, understand
algorithms, and have skills in hardware, software, and compilers. Engineers
should also develop skills in digital signal processing (DSP), and other
aspects of electronics. This can be accomplished through collaborative efforts
between industry and academia. A
closer relationship between industry and higher education can help with keeping
the course contents current, and providing students with relevant experience. There are significant benefits to be realized
from development of apprenticeships, especially at the degree level, as
a way of bringing new talent into the AI field. Most companies are planning to
up-skill and re-skill their current workforce to deal with this technological
shift. Companies in the technology sector (semiconductors, electronics,
sensors) are driving the evolution of AI systems. The future of AI will depend
on these sectors, so focus should be placed on them.
In conclusion, the
focus at the government level needs to be on AI
systems and engineering skill sets, not just AI algorithms. It is crucial to
adapt educational and training programs to meet the diverse skills required for
AI systems, creating graduates that are ready for the industry. Greater
collaboration between industry and academia is necessary to ensure training and
education is relevant to current and future needs, for both those entering the
workforce and those needing to up-skill.
