AI technology witnessed a surge in AI research, leading to the development of expert systems.
Since the initial commercial application of Artificial Intelligence (AI) in the late 1970s, the discourse surrounding the technology has undergone significant shifts. Initially, fears abounded that robots would decimate employment opportunities. However, rather than displacing jobs, they spurred productivity, ultimately contributing to the economic prosperity of many developed economies, including the Newly Industrialized Countries (NICs) of Asia.
In subsequent years, early adaptors of AI technology witnessed a surge in AI research, leading to the development of expert systems primarily deployed by large corporations in the global North. Although AI was among the fourth industrial revolution technologies, it initially did not grab headlines as Blockchain, the technology behind Bitcoin. The public debate on AI resurfaced towards the end of 2022 with the commercial application of generative AI.
Since the emergence of generative AI, conferences have been rife with skepticism, vilification, and a litany of excuses, all cautioning against the unbridled adoption of AI. Often overlooked amidst these discussions are the lessons gleaned from the process of digitalization. Different individuals resisted digitalization for various reasons, with studies revealing a common thread of resistance stemming from aversion to change or a lack of requisite skills. Additionally, senior managers frequently resist technological advancements in the corporate realm due to the disruption they pose to established business models and power structures.
As Albert Einstein famously asserted before AI, “The measure of intelligence is the ability to change.” Intelligence, therefore, is not an innate trait but rather a dynamic skill honed through adaptability to diverse circumstances and challenges. While change is inevitable, it necessitates courage and creativity, qualities often hindered by overanalyzing new ideas. While some nations embraced the new idea, others, including Africa, sought ways to curtail creativity and innovation through regulations.
The trouble with over analysis is that it tends to fixate on an idea’s potential risks and drawbacks, obscuring its benefits, opportunities, and strengths. This can impede progress, stifle innovation, and hinder intelligence. Moreover, over analysis can trigger what psychologists Parrish and Chapman describe as cognitive-emotive loops, reinforcing negative beliefs and emotions, thereby constraining adaptability and growth.
Widespread acceptance of AI is imperative to overcome these challenges. Achieving this requires collaboration between the research community, the private sector, and policymakers. Educating the public about AI’s potential and limitations, involving them in its design and implementation, and transparently addressing their concerns are essential to fostering trust and engagement.
However, achieving collaboration between research, the private sector, and policymakers in AI development faces challenges due to differing goals, interests, and perspectives. Common ground, trust, and mutual respect must be established to facilitate effective collaboration. It also helps to establish platforms for dialogue and consultation that can foster alignment on vision, principles, and standards. Promoting openness, transparency, and accountability in AI development can enhance collaboration by sharing data, code, methods, results, and best practices and disclosing objectives, assumptions, limitations, and risks.
Further, platforms can play a significant role in incentivising capacity building in AI to attract talent, foster skill development, and drive innovation. As it is now, Africa has left this role to foreign tech giants. The continent needs to change that model and build an ecosystem where public institutions are at the forefront of adapting these technologies and providing scholarships, internships, research funding, tax incentives, and recognition programs that create a supportive environment conducive to the widespread adoption of AI technologies. For example, AI could play a significant role in facilitating affordable healthcare through productivity improvement.
The young population the continent prides itself on can only become an asset if its capacity is strengthened through scholarships and grants that play a pivotal role in removing financial barriers for individuals seeking education and training in AI-related fields. By offering financial assistance, we can encourage more people to pursue careers in AI, thereby expanding the pool of talent and fostering diversity within the field. Additionally, internships and apprenticeship programs provide invaluable hands-on experience and mentorship opportunities, allowing aspiring AI professionals to apply their knowledge in real-world settings and gain practical skills.
Research funding is another critical incentive for capacity building in AI. By allocating resources to support research projects and initiatives, we can incentivize researchers to pursue innovative ideas and push the boundaries of AI technology. This drives scientific advancement and fosters collaboration between academia and industry, leading to the development of cutting-edge solutions with real-world applications.
Tax incentives can also significantly encourage investment in AI research and development. By providing tax breaks or other financial incentives to companies that invest in AI, we can stimulate innovation and job creation in the field. This encourages private sector involvement in AI and promotes economic growth and competitiveness.
Recognition and awards serve as powerful motivators for individuals and organisations to excel in the field of AI. Acknowledging their contributions and achievements can inspire others to pursue excellence and drive innovation in AI. Recognition programs can include awards for groundbreaking research, innovative applications of AI, and contributions to the AI community.
Public-private partnerships are essential for fostering collaboration and driving progress in AI. By bringing together government agencies, academic institutions, and private sector organisations, we can leverage resources and expertise from multiple stakeholders to address complex challenges and seize opportunities in AI. These partnerships facilitate knowledge sharing, technology transfer, and joint investment in AI research, education, and infrastructure, leading to robust AI ecosystems that benefit society.
Incentivizing capacity building in AI is essential for driving innovation, fostering talent development, and ensuring the widespread adoption of AI technologies. Scholarships, internships, research funding, tax incentives, recognition programs, and public-private partnerships are crucial in creating a supportive ecosystem that empowers individuals and organisations to harness AI’s transformative potential for society’s betterment. By embracing these incentives and working collaboratively across sectors, we can build a brighter future powered by AI innovation
