By Oweyegha-Afunaduula
30 January 2025
I am a Ugandan academic in retirement 15 years on and aged just over 75 years. What has kept my mind productive is that I had the experience of being a public intellectual, which necessitated that I took reading and writing continuously and engaging in continuous learning outside the academe seriously. This influenced and continues to greatly influence my reading and writing culture and view of the world. I wrote just one book with a fallen colleague at Makerere University, Professor Foster Byarugaba when I was an active academic. It is a Google Book published by the Department of Political Science and Public Administration, Makerere University. However, in retirement I have been able to write 5 books. One is titled “History of Uganda’s Political Leadership: A Treatise of Political and Leadership History of Uganda from 1894 to the Present” which is being published by Penguin. The others are four
volumes in one titled “Writings, Thoughts and Meditations of Oweyegha-Afunaduula:
Comprehensive Exploration of A Ugandan Mind” being published by Panama Books of East Africa.
Because in retirement I am no longer constrained by the rigid disciplinary boundaries in the African university setting, I read broadly on a diversity of seemingly unrelated topics in a world in which everything is connected to every other thing except in the minds of men and women produced by the disciplinarily culture of knowledge production. Consequently, I have been able to use my gift of multigenre writing and prolific writing to write critically on a diversity of crosscutting topics. As a result, I am able to teach and or inform people within and outside the disciplines on diverse topics that do not respect disciplinary walls and make sense to everyone.
Recently I have been reading a lot on the challenge of changing universities in fast changing world dominated by the World Wide Web and Artificial Intelligence (AI). These applications of fine technological advancement in communication, are changing the relation of humanity to technology, let alone the processes and discourses in education, especially in higher education. They are particularly useful to education beyond disciplinary boundaries, which is now sought through the new and different knowledge production cultures still excluded on most university campuses dominated by the disciplinary knowledge culture. Disciplinary knowledge culture is extremely selfish and produces selfish, individualistic, often arrogant individuals that do not interact well with humanity outside their disciplines. Besides, they are not future ready and are becoming increasingly unemployable. The majority who get higher
qualifications in their disciplines can best be employed in the universities with strong
disciplinary orientation.
The new cultures of knowledge production are already producing graduates and scholars who can think critically, reason critically, analyse critically, read critically and write critically without fear or favour. They are producing future ready professionals who will be more influenced in their work by the World Wide Web and Artificial Intelligence, than the disciplinary restrictions on critical thinking critical reasoning, critical reading, critical writing and problem-solving that are encountered in the disciplines of knowledge and practice.
Frequently when the products of disciplinary knowledge culture write they write to
themselves and their reasoning is only useful to themselves, often for career development.
New and Different Knowledge Production Cultures
The African University in general and the Ugandan universities in particular are faced with the real danger of sticking to disciplinary knowledge production and discourses at a time when, globally, universities are beginning to accommodate the new and different knowledge production cultures of interdisciplinarity, crossdisciplinarity, transdisciplinarity and extradisciplinarity (or nondisciplinarity) on their campuses.
Interdisciplinarity involves or draws on two or more branches of knowledge.
Crossdisciplinarity is the practice of studying topics by applying methods from different disciplines and involves viewing one discipline from the perspective of another.
Transdisciplinarity is an adaptive and problem-focused approach that requires the input of different stakeholders and rights holders to address complex social-ecological problems. The relevance of transdisciplinarity has long been recognized as a convergent, integrative approach encompassing diverse disciplines. Extradisciplinary means beyond the boundaries of disciplines, and it describes a way of thinking, working, and sharing knowledge that crosses over different fields of knowledge and practice; it is a nondisciplinary approach to knowledge production and practice.
Virtually all African universities are proceeding in the 21st century towards the 22nd century as if these knowledge production and practice possibilities do not exist. Their graduates and scholars are in the disciplinary knowledge production chain. They cannot read, think, reason, write or even solve problems outside the rigid walls of their disciplines. Consequently, their solutions frequently become the new problems. Environmentally speaking, they are environmental pollutants as disconnected elements in an intricately interconnected world!
As if all this is not bad enough, African universities face a range of challenges with internet access, including the high-handed restrictions on internet use by dictatorial rulers, poor connectivity, high costs, and limited bandwidth. Where internet access is restricted by the powers that be the young generation is being separated from the rest of the world where internet use may even be free or subsidised by the governments. Besides, these challenges of internet use can make it difficult for universities to provide to create an adequate environment for meaningful and effective research, teaching, and administration.
Internet Use in Uganda
In Uganda, internet access is extremely expensive, and the instability of electricity supply makes its use problematic. Besides government does not deliberately encourage Ugandans to use internet and this is exemplified by the high taxes imposed on the users. This is unlike in Rwanda where internet use even by primary school children, even in rural areas, is actively promoted by the government.
So, in Uganda, even if internet access is broad through mobile broadband, fiber internet and satellite internet, just like hydropower, the issue of great concern is affordability; not accessibility. The deep sea of poverty means that huge numbers of people will not afford internet use.
This is complicated by the fact that the Uganda economy is a Magendo (black market
economy), whereby money that would otherwise be used to develop internet use is either lost in the global market or finds its way in the pockets and bank accounts of a few unscrupulous people connected to power. For example, the most recent report of the Auditor-General of Uganda states that Uganda exported gold worth 11 trillion shillings, but all this money is not reflected in the national budget. This means the Ugandan economy is so besieged by unscrupulous people connected to power that the country is at their mercy. The country can not commit adequate funds to universities to do research or to public facilities such as internet.
Since my article is on the challenge of changing Ugandan universities in the face of
increasing importance of Artificial Intelligence (AI) in higher education and the critical issues thereof, let me focus on artificial intelligence.
Artificial Intelligence
Artificial Intelligence (AI) is a word that was first coined by Professor John McCarthy,
American Computer Scientist, in 1956. He defined it as “the science and engineering of
making intelligent”. In today’s world, AI has become an indispensable tool in various
technological applications, holding immense significance. It has been used in almost every part of the world, automating most activities resulting in minimizing human interventions to ensure efficiency and eliminate errors. African governments in general and African universities in particular must be integral to this trend. If not our countries, or for that matter, the graduates of our education system, become a collective aberration in this century and beyond.
2017 was the year of hype for artificial intelligence and machine learning. Countless articles this year have extolled AI’s virtues, explored the automation of menial tasks, and prophesied the end of menial work altogether. While we have all heard about AI’s potential of endless press coverage, few Ugandans or Africans have a basic understanding of how the technology works. It is an integral component of machine learning (ML).
Machine learning is giving a computer the ability to learn without being explicitly
programmed ((Bennett Garner, 2017). Few who are aware of learning machines doubt that we are in the golden age of machine learning, which can no longer be ignored by both young and old in and outside the university. The future – a digitalised future – belongs to them. Although that future is here already it is sad that the Uganda is still firmly in the hands of people who belong to the analog age and only see power, glory and glory as well as wars and domination people as the prime reasons for governance of the country. This way, they are governing the country backwards, yet we want governors who will integrate our increasingly young population in the digital age. Our education system remains deeply buried in the analog age in an age of machine learning.
Machine learning requires a large dataset so that the computer can make hundreds or
thousands of mistakes while tuning its predictions; and lots of processing power to assemble and examine the data and run hundreds or thousands of tests. These two requirements are now readily available. Storage and processing power has increased exponentially over the decades in a phenomenon known as Moore’s Law (Bennett Garner, 2017).
Machine learning, however, has flaws. First, because machine learning is so complex, its code is notoriously difficult to debug and write correctly. When a machine learning algorithm fails, it can be difficult to pinpoint exactly why it failed. Second, machine learning inherits the biases that are present in one’s data set. If one’s data is imprecise, skewed, or incomplete, the algorithm will still try to make sense of it; not reject it. Consequently, conclusions drawn from too small a data set are often outright wrong. Insufficient data is one reason why machine learning projects fail to deliver. Otherwise, AI will certainly make our lives easier by automating menial tasks (e.g., Bennett Garner, 2017).
The role of AI in innovation and creativity is recognized worldwide. Its implementation in various sectors of the economy, including education, medical science, entertainment, transportation, industry, environment and ecology, and others has transformed our daily lives.
As machines are gaining human-like skills, the distinction between humans and machines is getting nearly obscured (Free Law, 2024). This is because AI is humanlike.
There are, however, differences between natural and AI. Michael Bennett has given three ways AI and human cognition diverge. After pointing out that Smartness, Understanding.
Brainpower. Ability to reason. Sharpness and Wisdom are terms usually used to indicate human intelligence, he observes that AI, or machines with the capacity to do things traditionally associated with and assumed to be within the exclusive domain of humans, has rattled human society. Since the second half of the 20th century, and with a vastly accelerated pace in the last two decades, machines have exhibited the ability to learn and to apply learning in ways that only humans had been able to previously (Bennett, 2024). However, human beings tend to be superior to AI in contexts and at tasks that require empathy. Human intelligence encompasses the ability to understand and relate to the feelings of fellow humans, a capacity that AI systems struggle to emulate. Activities such as judgment, intuition and imagination are subtle yet effective communication. They are all domains in which human intelligence is much more useful and valuable – and simply better – than AI in any of
its present forms.
AI systems, however, outperform the human brain in a range of important categories.
AI is strikingly effective at processing and integrating new information and sharing new knowledge among separate AI models. The endurance of AI is also superior to human intelligence; machines do not require rest and do not get distracted. Machine learning is an extremely powerful tool for detecting patterns in data. In numerous examples to-date spanning medical imagery, speech, digital fraud and plagiarism, AI has proved to be much more effective at most forms of pattern recognition than the average human brain.
Additionally, AI works at speeds well beyond those of human intelligence; in terms of pace, a machine will outperform the human brain at most tasks that both have been trained to complete by many orders of magnitude (Michael Bennett, 2024).
We will never create true AI (if we really want that) until we know more about how the human brain works (Max Bennett, 2024). What is easy for humans is hard for AI, but AI is better at doing some things that are quite hard for us. Mostly, what AI teaches us is just how remarkable the human brain is – it is much better at continued learning than AI is, and it requires less input to come to conclusions (Max Bennett, 2024). ChatGPT, a very popular AI technology. You can ask it any question and it will give you an answer. But how exactly does that happen, and how smart is it? GPT-4 has about 1 trillion connections. The human brain has about 100 trillion synaptic connections. GPT-3 had 150,000,000,000 connections! (Max Bennett, 2024).
One of the most miraculous qualities of humans is the ability to learn new concepts and ideas from a small number of samples, sometimes from a single one. Most humans are even able to understand and identify a pattern and to use it to generalize and extrapolate. Having been shown one or two images of a leopard, for example, and then being shown images of various types of animals, a human would be able to determine with high accuracy whether those images depicted a leopard. This ability is referred to as one-shot learning (Bennet, 2024).
Much more often than not, AI systems need copious examples to achieve comparable levels of learning. An AI system may require millions, even billions, of such samples to learn at a level beyond that of a human of average intelligence. This requirement for multishot learning distinguishes AI from human intelligence. Many researchers feel that this difference is a strong basis for describing humans as being, on average, much more efficient learners than AI systems (Michael Bennett, 2024). This must be recognised by all AI advocates.
Discourse often involves power dynamics, influencing beliefs, opinions, and decisions.
Similarly, AI systems have the potential to shape and influence human understanding and behaviour through their language-based interactions.
AI is important in the future of discourse analysis because it offers powerful tools to analyze complex discursive patterns at scale, uncover subtle linguistic features, and handle vast datasets efficiently. As discourse increasingly takes place in digital and global contexts, AI’s ability to process and analyze large amounts of data will be crucial for understanding the evolving dynamics of language, communication, and power in society. However, careful attention must be given to the challenges of interpretability, bias, and context sensitivity to ensure robust and ethical discourse analysis (Discourse analyser (2024).
We must all be aware that the use of AI has severely changed the format of how people work.
Similarly, its use in intellectual property has become increasingly prevalent. One of the most important applications of AI is the creation of new work as it can generate original work.
Besides, AI is beneficial to democracy but also poses risks. While it helps increase political engagement and access to information, its algorithms also create echo chambers, spread misinformation and even manipulate election results. For our increasingly AI-based society to thrive, we must regulate it and improve our digital literacy (Anura Singh, 2024) because in poor countries like Uganda the aim of adopting AI in elections may not be so much to improve the results of elections but to manipulate them in favour of the powers that be.
A critically important ethical issue facing the AI research community is how AI research and AI products can be responsibly conceptualised and presented to the public. A good deal of fear and concern about uncontrollable AI is now being displayed in public discourse. Public understanding of AI is being shaped in a way that may ultimately impede AI research. The public discourse as well as discourse among AI researchers leads to at least two problems: a confusion about the notion of ‘autonomy’ that induces people to attribute to machines something comparable to human autonomy, and a ‘sociotechnical blindness’ that hides the essential role played by humans at every stage of the design and deployment of an AI system.
Here, our purpose is to develop and use a language with the aim of reframing the discourse in AI and shed light on the real issues in the discipline of AI (Johnson and Verdicchio, 2023).
One thing is true. AI is not going to replace peoples jobs. It is going to make them 10 times more efficient. (David Goldberg, 2023).
Max Bennett (2024) still feels hopeful about the future of AI. There is no danger of AI taking over the world, but it is fundamentally going to change the world,” he says. “It’s a complex topic and it will take time to figure out the right way to approach it. However, 50% of AI researchers believe there’s a 10% or greater chance that humans go extinct from our inability to control AI (Bennett Garner (2023).
AI is here to stay. It will be used for good and bad. Increasingly it will find use in education, particularly higher education. Students, lecturers and professors will use it, but misuse is possible, which may result in the abuse of the human mind, as reasoning and thinking skills are surrendered to AI. Indeed, recently professors at one university publicly demonstrated because their students were using AI far more than necessary to the point of sacrificing critical thinking and critical reasoning. Therefore, if disciplinary education sabotaged critical thinking and critical reasoning, AI has the potential to sabotage these two goals of education even more.
Ugandan universities must not ignore what is going on in the area of AI. They must adopt the good and discard the bad of AI while continually introducing their staff and students to machine learning. What they must not undermine is that we need thinking and reasoning graduates who can also engage in problem solving in the internet and AI dominated century and beyond. We must have a critical mass of them. Today the opposite is true. It is an enduring challenge.
For God and My Country
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