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Developing and adopting AI (II)

Without enough electricity, pontificating about AI strategies and policies constitutes an exercise in futility.

RESEARCH into optimising artificial intelligence (AI) algorithms and hardware for energy efficiency is ongoing.

Techniques like model pruning (removing unnecessary parts of a model), quantisation (reducing the precision of calculations), and energy-aware scheduling can help reduce the energy footprint of AI systems without significantly sacrificing performance.

Countries in the Global South must radically invest in energy and power infrastructure. Without enough electricity, pontificating about AI strategies and policies constitutes an exercise in futility.

Governance, regulatory framework

AI development and adoption can only be built based on clean, efficient, transparent and accountable governance. All these African countries riddled with corruption, incompetence, and mismanagement — in the public and private sectors — are not ready for the AI revolution.

Good governance is crucial for responsible AI development, deployment, and use. An enabling AI regulatory framework is essential.

Specific aspects foundational to AI include establishing ethical guidelines and principles for AI development and deployment, regulatory oversight, transparency and accountability, inclusive public engagement and participation, risk management and resilience. Good AI governance frameworks must support capacity-building initiatives to enhance AI literacy among policymakers, regulators, and society.

There is a need for continuous monitoring, evaluation, and updating of governance mechanisms to address emerging challenges, ensure compliance with evolving regulations, and maintain public trust in AI technologies.

Indeed, good governance and an enabling regulatory framework are essential for fostering ethical, transparent, and accountable AI ecosystems. By integrating principles of fairness, accountability, transparency, and inclusiveness, policymakers can promote innovation while safeguarding against potential risks and ensuring that AI technologies contribute positively to sustainable socio-economic development.

Globally, national AI policies and regulations must be developed and harmonised in regional blocs and continents.

AI ecosystem

An ecosystem approach is required to drive the development and adoption of AI. An AI ecosystem refers to a complex network of interconnected entities, including organisations, institutions, technologies, and individuals, that collaborate, interact, and influence each other within the field of AI.

This ecosystem encompasses various components that collectively contribute to the development, deployment, and utilisation of AI technologies. The main constituents of an AI ecosystem include AI researchers and developers, technology companies and startups, financiers and investors (including venture capital firms), academic institutions, government agencies and regulators, industry partners and collaborators, data providers and data infrastructure.

Data is the lifeblood of AI

The ecosystem relies on data providers, aggregators, and infrastructure, such as data centres and cloud services to collect, store, manage, and analyse vast amounts of data for AI applications.

Further participants in the ecosystem include ethics and policy think tanks, end users and consumers, and global collaborators and partners. All these interconnected and interdependent entities form a dynamic AI ecosystem that evolves.

Technological breakthroughs, regulatory developments, market trends, societal demands, and ethical considerations influence the configuration.

Effective collaboration and coordination among ecosystem stakeholders are essential for realising AI’s transformative potential while addressing challenges related to fairness, transparency, privacy, and societal impact.  Every country must strive to establish a national AI ecosystem. These can then be expanded and integrated into regional and continental AI ecosystems.

Talent and expertise

AI development and adoption require diverse talent and expertise across various disciplines.

Building and deploying AI systems involves a multidisciplinary approach that integrates expertise in computer science, mathematics, data science, engineering, and domain-specific knowledge.

Skills required for AI development and adoption include data scientists, Machine Learning engineers, software engineers, data engineers, AI researchers, domain experts (e.g., agriculture, education, healthcare, and finance), ethics and policy experts, prompt engineers, project managers, AI trainers/teachers and AI product managers.

There is a need for dynamic and effective collaboration among these multiple disciplines for successful AI development and adoption. Countries and institutions (private and public) must invest in developing AI talent, skills, and expertise in a transdisciplinary and interdisciplinary fashion.

They must create environments that encourage innovation and knowledge sharing to leverage the full potential of AI technologies.

Organisational preparedness

As all organisations prepare to embrace the AI revolution, they must seek to create the AI transformation roadmap  — a treasure map to a new AI-driven entity.

They must build a talent bench with the requisite expertise and skills. More significantly, organisations must realise that their current way of doing things will not cut it.

They must develop and adopt new and innovative operating models that are fit for purpose in the AI context. Furthermore, organisations and countries must nurture decentralised and distributed technology environments that enable teams to innovate across institutions and sectors.

As explained earlier, data is the key driver of AI systems. Consequently, there must be efforts to embed data everywhere in institutions. There must be ubiquitous access to and use of this data. The proof of the pudding is in the eating. Countries and organisations must drive and unlock AI user adoption and enterprise scaling.

Financing and investment

Financing and investment are critical in driving AI development and adoption by providing the necessary resources, funding, and support for research, innovation, and commercialisation.

The AI industry requires significant investments to advance the technology, build scalable solutions, and integrate AI into various sectors.

Specifically, financing and investments are essential for research and development, fostering and promoting innovations, startups and entrepreneurship, AI infrastructure and technology platforms such as cloud computing, AI chips, and data centres.

The funding and investment from the private sector will be critical. Universities or governments cannot fund extremely expensive AI innovations, such as large language models. There is also a need to fund industry-specific applications, talent acquisition and skills development, ethical AI and governance, market adoption and commercialisation.

Indeed, financing and investment are catalysts for driving AI innovation, accelerating technology development, and fostering widespread adoption across sectors.

All countries, industries, and businesses must pursue and promote strategic investments in AI to maximise its transformative impact.

Right mindset

The right mindset is critical in the development and adoption of AI. First, there must be self-belief and confidence, solution orientation, a can-do mentality, entrepreneurial disposition, possibility thinking, an affinity for technology, multi-disciplinarity, transdisciplinarity, and an enduring appetite for continuous learning.

A positive and open mindset encourages innovation and creativity in AI development. An experimental mindset leads to the early adoption of AI systems.

Embracing new ideas and approaches fosters breakthroughs in algorithms, models, and applications, leading to advancements in AI technology.

The right mindset for AI must embrace a problem-solving orientation, where developers and adopters of AI focus on addressing real-world challenges and opportunities.

A thoughtful, ethical mindset is essential to ensure responsible AI development and adoption. Being mindful of potential biases, fairness issues, privacy concerns, and societal impacts helps guide ethical decision-making throughout the AI lifecycle.

As already asserted, adaptability and continuous learning are critical. AI technologies evolve rapidly, and having a growth mindset encourages individuals and organisations to embrace change, attain new skills, and stay abreast of the latest innovations in AI.

Developing and adopting AI often requires collaboration across diverse disciplines and stakeholders. Hence, an interdisciplinary and collaborative attitude within diverse knowledge development and sharing teams will be the most effective way to tackle complex AI challenges.

Adopting a user-centric mindset ensures that AI solutions are designed with end users in mind. Prioritising user needs, experiences, and feedback leads to more intuitive, accessible, and user-friendly AI applications.

The right mindset encourages calculated risk-taking and resilience in AI development. Innovation involves experimentation, and not being afraid of failure allows for iterative improvements and breakthroughs in AI technology.

When all is said and done, cultivating the right mindset among AI developers, researchers, policymakers, and industry leaders is essential for unleashing the transformative power of AI. However, mindset cannot be legislated or decreed into existence. It has to be built and developed diligently and judiciously over time.

Basic education and literacy

Basic education and literacy are crucial in developing and adopting AI systems. Basic education lays the groundwork by equipping individuals with foundational skills in mathematics, statistics, computer science, and critical thinking — all essential for understanding AI concepts and applications.

Literacy and competence in science, technology, engineering and mathematics (STEM) subjects are fundamental to nurturing the next generation of AI researchers, data scientists, engineers, and developers.

Basic education provides the necessary knowledge and skills to pursue careers in AI-related fields. Education fosters AI literacy among the general population, enabling individuals to understand AI systems’ capabilities, limitations, and societal implications.

AI literacy enables and empowers individuals to make informed decisions about AI adoption and usage. Basic education instils a culture of lifelong learning, which is essential in the rapidly evolving field of AI.

Furthermore, education promotes ethical awareness and responsible behaviour in AI development and adoption. Basic education emphasises ethical principles such as fairness, transparency, accountability, and privacy, which are critical for ethical AI governance.  Literacy and education foster creativity and innovation, enabling individuals to apply AI technologies to solve complex problems in various domains such as agriculture, mining, healthcare, finance, agriculture, and environmental sustainability.

Access to quality education and literacy programmes reduces socio-economic disparities in AI adoption. Equitable educational opportunities ensure that individuals from diverse backgrounds can participate in and benefit from the AI-driven economy.

AI is transforming industries and reshaping job roles. Basic education equips individuals with adaptable skills and competencies needed to thrive in AI-enabled workplaces, fostering career readiness and employability.  Education empowers citizens to engage in informed discussions and policymaking about AI governance, regulation, and societal impact.

AI-literate individuals can advocate for ethical AI practices and contribute to shaping responsible AI policies.

Basic education and literacy are essential enablers for AI development and adoption, as they foster foundational skills, promote AI literacy, nurture ethical awareness, and prepare individuals for the future of work.

Concluding remarks

Countries and organisations must not put the cart before the horse when developing and adopting AI.

They must address critical pillars, such as basic infrastructure, digital infrastructure, governance and regulatory framework, financing and investment, basic education and literacy, talent and expertise, energy/power, the semi-conductor industry, “compute” resources infrastructure, organisational preparedness, the AI ecosystem, and the right mindset.

These related, interdependent, and mutually reinforcing foundational matters must be attended to in a holistic, systemic, and structured manner.

There must be an AI national vision, strategy, implementation plan and policy framework, all premised on resolving the above challenges. Furthermore, AI regional, continental and global partnerships must be developed and leveraged. Indeed, AI can drive all economic sectors and improve the quality of life for all of Earth’s inhabitants. However, the journey to the Promised Land must start with the basics.

  • Mutambara is the director and full professor of the Institute for the Future of Knowledge at the University of Johannesburg in South Africa. He is also an independent technology and strategy consultant and former deputy prime minister of Zimbabwe.

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