Features
Artificial intelligence – the dream, the nightmare and the reality
The 33rd Susan George Pulimood Memorial Oration
by Prof. Shanika Karunasekera
Before I get to the main topic for my oration today, I would like to briefly touch upon an important topic, which is close to my heart: Empowering Women in Engineering-and-Technology, which I will refer to as, Women in Engineering for brevity from here.
The 2018 Pulimood orator, Professor Mrs Rathnayake, dedicated her oration to this topic. I wish to convey a message, on a particular aspect of the same topic, which I believe is pertinent to this occasion and audience, which consists of many educators who can make an impact, similar to the impact Mrs Pulimood made almost 70 years ago. It is somewhat sad to say, that in this 21st century, engineering remains male-dominated. This is the case both in industry and academia. A significant female under-representation in engineering exists worldwide.
I want to take a moment here to reflect on my journey through engineering. When I chose the physical sciences stream for A/Ls, I did not have any idea of engineering as a profession. I chose Physical Sciences merely for the love of mathematics. I did not want to give up mathematics. That year there was only one physical science class at Visakha, compared to 12 bioscience classes. That is less than 10% of the science cohort chose physical sciences. At that time, it did not occur to me, that I was just starting a lifelong journey in a minority club! It was during my A/L years, thanks to four great teachers, whom I would like to name here, Mrs. Chithra Malalasekera, Mrs Sitha Siriwardena, Mrs. Shyamalee Ariyarathne, Mrs Gunaseeli Wijerathne, that I started appreciating the use of science and mathematics, in real-world applications. This sparked my interest in engineering as a profession.
Upon completing A/Ls, I joined the University of Moratuwa to study engineering. As one of only 23 female students in a cohort of around 230 students, again in the less than 10% minority, I started to wonder if I had made the right choice. Let me give you one simple example of stereotyping in engineering, from my first year at the University. It was often the case, that I ended up being the only female student in the group, when it came to group work, for obvious reasons. However, it was not a coincidence, that my male colleagues assigned me as the note-taker, in every instance, while the male students did the more interesting engineering tasks.
I am not blaming any of my male colleagues here, because most probably they meant well, and thought that the engineering tasks were too hard for females, but this is a stereotype. The expectation of me being the default notetaker, changed in my latter years at the University, after I was able to prove that, I was as good as them in engineering tasks. However, my point here is, that females must always prove themselves in engineering, whereas it is taken for granted that males are good engineers. Now as a mentor to many female engineering students, I remind them that they must not be the default note-taker for their groups – a practice that continues to date in many places.
Fortunately for me, several female role models in engineering inspired me to keep going. Of them, the major inspiration for me was, Professor Mrs Indra Dayawanse, who was the Head of the Department of Electronics and Telecommunication Engineering, at the University of Moratuwa, during that time. She was an exceptional engineering educator, who demonstrated that females not only could succeed in engineering but could also lead and excel.
In my 35-year engineering career, that has spanned four continents, Asia, Europe, North America and Australia, both in industry and academia, being in the under 10% minority by gender most of the time, I have had first-hand experience of the challenges women in engineering face. Despite these challenges, my experience is that, the impact you can make on society as an engineer, gives you great intrinsic rewards and satisfaction, that outweigh all the negatives.
I hope you will not disagree with me if I say that engineers are largely responsible for the advancements that have enhanced our quality of life. The contributions of engineering are evident in all facets of life including communication, transport and healthcare. Engineering involves creativity and problem-solving. Engineers make a profound impact by changing lives and shaping the world. These are areas where females excel and exhibit profound passion. However, it’s disheartening that we haven’t achieved anywhere near parity in Women in Engineering. Consequently, society is deprived of the invaluable female perspective in this field.
Throughout my career, I’ve actively pursued opportunities to advocate for increased female participation in engineering. Introduce engineering as a viable, and appealing, career option for young girls, during their early school years and present girls with female role models in engineering from an early age, especially between the ages of 12 and 16, a critical period, when they contemplate their future subject choices and careers. By doing so, we can inspire the next generation of girls to take up engineering and pave the way, for a more inclusive and innovative future. I believe that the teachers can make a huge positive impact here.
Trailblazers like Mrs Pulimood have done an outstanding job, promoting STEM (science, technology, engineering and mathematics) among girls, which has made a difference in the biological sciences. However, more work is still needed in the physical sciences. Let us lead like Mrs Pulimood, to promote engineering and technology to the next generation of girls, to make a real change. In my opinion, that is the best tribute we can pay to Mrs Pulimood.
Let me now get to the main topic for today: Artificial Intelligence: The Dream, The Nightmare and The Reality. Artificial intelligence, commonly referred to as AI, is receiving widespread attention, and generating a spectrum of emotions among individuals. While some embrace it with excitement, others approach it with suspicion or even concern. My aim today is to address the diverse range of sentiments, and misconceptions, surrounding AI, and shed light on this transformative technology, to give you a better understanding of its potential implications.
Considering the mixed audience here today, I will keep my presentation mostly non-technical. I hope everyone will be able to follow it. During this talk, I will use the terms Artificial Intelligence and AI interchangeably.
AI has become ubiquitous, seamlessly integrating into our daily lives, whether we are aware of it or not. I will introduce AI with simple examples, which I believe most of you in the audience can relate to. Many of you, I believe, would have gone to YouTube to watch videos. Some others, who are avid movie lovers, nowadays subscribe to services like NetFlix to watch movies. In both these cases, when you access the service using the web, you enter a page, called the Homepage. The Homepage shows videos that the system automatically recommends for you. Have you ever noticed that the videos that are recommended to you, are different to what others get recommended? This is AI helping them. The service has learnt their preferences. This is an example of machine learning the foundation of AI.
Another simple example is AI-based navigation applications, such as Google Navigation application, which many of you may already be using. It can give you directions, to a desired destination from a starting location and also the travel times. These travel times normally are quite accurate. In fact, more accurate than what you could estimate yourself. While these estimates account for typical uncertainties in traffic, Google is also able to update the estimates dynamically, if something major unexpected happens on the road. In this case, not only does Google do this task more accurately than you could do, but it does it way more efficiently. That is another example of AI.
Let me now take you through the journey of AI, starting from the very beginning. At the time, Artificial Intelligence was just a dream! Although the concept of mimicing, or exceeding human intelligence, seems to have fascinated humans for centuries, machine intelligence, which is the basis of current AI, was only envisioned in the 1950s. Here is how it all started. Alan Turing is a British Computer Scientist, who is considered the father of Computer Science. In 1950, he envisioned the possibility of:
“A human, interacting with another human, and a computer, without knowing which one is which, and being unable to differentiate them from their responses”.
This test, then referred to as the Imitation Game, is now called the Turing Test, named after Alan Turing. This is the earliest known definition of machine intelligence, which came about around the same time computers were invented. The Turing Award considered the Nobel Prize in Computer Science, is named after this great scientist.
Another key milestone in this journey of AI, was the American Computer Scientist, John McCathy, coining the term Artificial Intelligence in 1956; John McCarthy is recognized as the father of Artificial Intelligence. He defined AI as: “The science and engineering of making intelligent machines”.
This seminal definition encapsulates the essence of AI, marking the beginning of the journey of AI. John McCarthy was awarded the Turing Award in 1971 for his work on AI. Since its introduction, the journey of AI has been characterized by, cycles of rapid advancement, referred to as “Booms”, followed by periods of stagnation, often referred to as “AI winters”. AI is currently in its third boom, marked by unprecedented progress, and widespread adoption of AI technologies.
Boom 1, in the very early days of computing, was about the programming and processing power of computers, being able to deliver intelligence. This approach is now referred to as “Good Old Fashioned AI”. Despite this being the early days, during this period there were a few bold predictions such as:
Within ten years a digital computer:
– “would become the world’s chess champion”,
– “discover and prove important new mathematical theorems” and
– “will even write music”.
and more.
Similar to today there was significant concern about AI and automation being a major risk to the American economy and society; people feared job losses. However, these bold predictions were not delivered during Boom 1, resulting in the first AI Winter! The second boom started in the 1980s. There was a shift in approach to achieving AI.
Scientists began exploring the idea of, encoding all human knowledge into programmable rules in a computer. This approach, known as Expert Systems, may be a term some of you in this audience have encountered. A notable achievement in Boom 2 was, IBM’s Chess Playing Computer, known as Deep Blue, defeating the reigning world chess champion, Garry Kasparov, in a highly publicized competition in 1997. I doubt this is a milestone Kasporov celebrates in his chess career, but this is a significant milestone in AI and machine learning. However, it happened almost 40 years after the initial prediction!
Despite this success, this approach to AI ultimately proved limited in its capabilities and failed to deliver sustained progress. As you can imagine, feeding the universal human knowledge, as rules to a computer proved impractical, leading to a second AI winter.
Let us now transport ourselves to 2016, during the era known as Boom 3. In a monumental event, Google’s computer program, known as AlphaGo, defeated the world champion Lee Sedol, in a complex strategy game, called Go. This is another significant milestone in the history of AI.
You might now be questioning: Is AI merely a champion of games? What is the big deal? But unlike the previous chess victory, this victory marked a pivotal moment that started a new era in AI. Scientists had unveiled a revolutionary AI technology, that could solve real-world challenges beyond games.
As one example, Google repurposed the computer program, that was originally used to win the Go competition, into a program called AlphaFold, to achieve a completely different objective. In 2020, AlphaFold made a breakthrough in a complex problem, that had challenged the scientific community for many decades: the protein-folding problem. Proteins are the building blocks of our body, which are responsible for many bodily functions. Currently, there are over 200 million known proteins, and more are being discovered daily. Understanding the structure of these proteins is vital for disease detection, drug discovery, and a myriad of other medical applications. AlphaFold, the AI-based solution, was able to rapidly and cost-effectively, understand the structure of a protein. Before this breakthrough, understanding the protein structure used to cost millions of dollars and years of research. This a pioneering leap in scientific discovery, a milestone duly recognised with publication in Nature, the premier journal at the forefront of scientific inquiry.
(To be continued)
(The orator, an alumnus of the Moratuwa University and a Ph.D Cambridge, is Professor of Software Engineering and School of Information Systems serving as Deputy Dean Academic, Faculty of Engineering and School of Computing Information Technology, University of Melbourne.)
Features
The Digital Underground
Illegal Foreign Exchange, Undiyal, Hawala and Money Laundering, A Four-Part Investigative Series
Forex Platforms, Cryptocurrency, AI and the New Financial Battlefield
THE INVISIBLE FINANCIAL EMPIRE – PART III
The Boyfriend Who Was Never Real
Priya, a 34-year-old professional in Colombo, met “David” on LinkedIn. He claimed to work in fintech in Singapore. For six weeks they exchanged messages daily, about work, about life, about a recent trip he had taken to the Maldives. Eventually, the conversation turned, gently and naturally, to money.
“I’ve been trading on this platform, let me show you,” he said, sharing a screenshot of a sleek trading dashboard showing consistent, impressive returns.
Priya invested a small amount first, $500. Within days, her dashboard showed it had grown to $650. She withdrew $100 successfully, just to test it. It worked. Encouraged, she invested more. Then more. Over two months, she transferred a total of $42,000 into the platform.
When she tried to withdraw her full balance, the platform demanded a “regulatory release fee” of $8,000 before funds could be unlocked. She paid it. Then another fee appeared. Then the platform stopped responding altogether. “David” vanished. The trading dashboard, the customer support chat, the entire brokerage, all of it had never been real.
This is what investigators now call “pig butchering”, and, in 2026, the most disturbing development is not the scam itself, which has existed for years, but what now powers it: artificial intelligence has industrialised the entire operation.
From Manual Fraud to Machine-Generated Deception
For most of the past decade, romance-and-investment scams, like the one that targeted Priya, required enormous manual labour. Scam operations, many of them staffed by trafficked workers held against their will in compounds across Myanmar, Cambodia, and Laos, needed real humans to build relationships with victims over weeks, manage fake trading platforms, and respond convincingly to questions.
That labour-intensive model has now been substantially automated. According to financial-crime researchers tracking this shift through 2026, threat actors are standing up entire AI-generated “brokerage” experiences end-to-end, complete with KYC onboarding, branded customer-service chat, animated portfolio dashboards, and falsified live market data feeds, and operating them at industrial scale against multiple victims simultaneously. Generative-AI relationship managers now front the WhatsApp and Telegram conversations that once required real human scammers. AI-cloned regulator letters are generated on demand to justify the fake “release fees” that drain victims a final time before the platform disappears.
What has changed is not the deception itself, it is the production economics. The cost of running a credible synthetic brokerage against one additional victim has collapsed, meaning a single criminal network can now run hundreds of “Davids” simultaneously, each one indistinguishable from a genuine fintech professional until it is too late. (Figure 01)

Sri Lanka: From Victim Pool to Operating Base
Sri Lanka’s relationship to this global scam economy has shifted in an alarming direction over the past two years. The country is no longer only a source of victims, it has become an operating base for the criminal networks themselves.
In April, 2026, Sri Lankan police raided a five-star hotel property, in Ambakandavila, and arrested 150 individuals, including 133 Chinese nationals, 13 Vietnamese nationals, and one Malaysian national, allegedly running a cyber fraud centre with links to international criminal syndicates, based in Myanmar and Cambodia. Investigators say the operation followed a now-familiar regional pattern: recruiters advertise “online marketing” or “data entry” jobs on social media to lure foreign workers to Sri Lanka, confiscate their passports on arrival, and force them to operate scam campaigns under threat.
The Central Bank of Sri Lanka has formally flagged pig-butchering scams as a “developing threat,” warning that foreign scam networks are increasingly targeting overseas nationals through scam farms operating from Sri Lankan soil. A 2026 United Nations report estimated that at least 300,000 people have been trafficked into scam centres across Southeast Asia.
This is not an abstract international problem. It is unfolding in hotels and rented properties across the country, exploiting the same infrastructure, high-speed internet, affordable accommodation, accessible tourist visas, that Sri Lanka has built to attract legitimate digital businesses and tourists.
Where the Money Actually Goes: The Stablecoin Pipeline
Behind every successful pig-butchering scam sits a laundering pipeline that has been transformed almost as dramatically as the scams themselves, and the transformation has a single dominant feature: stablecoins.
According to the Financial Action Task Force’s March 2026, report, drawing on analysis from blockchain intelligence firms Chainalysis and TRM Labs, stablecoins accounted for 84% of the USD 154 billion in illicit virtual asset transaction volume recorded in 2025, the highest share ever observed, and a dramatic jump from just 15% only a few years earlier. TRM Labs separately found that illicit entities received USD 141 billion in stablecoins, in 2025 alone, the highest level observed in five years. (See Table 01)

The scale of state-level abuse is striking. A Russian sanctions-evasion network built around the ruble-pegged stablecoin A7A5 processed more than USD 72 billion in total volume in 2025.
Fighting Fire with Fire: AI on the Defensive Side
The same artificial intelligence reshaping financial crime is also, out of necessity, reshaping the defence against it. Legacy anti-money laundering systems, built on static, rule-based thresholds, have proven badly outmatched by AI-generated fraud operating at machine speed. Research cited by compliance technology analysts suggests that between 90% and 95% of alerts generated by legacy AML systems are false positives, consuming enormous investigator time while genuinely suspicious activity slips through.
This is not a frictionless transition. AI models are notoriously difficult to explain to regulators and examiners in the way traditional rule-based systems are. The practical compromise emerging across the industry is a hybrid model: AI handles the initial scoring and prioritisation of risk, while documented rule-based logic still governs the final decision that must be defensible to a regulator.
The Regulatory Response: Catching Up to the Digital Frontier
Regulators worldwide have begun moving to close the most dangerous gaps exposed by this digital transformation of financial crime. (See Table 02)

What Comes Next
We have now traced this investigation from the centuries-old mechanics of Hawala and Undiyal, through the three-stage architecture that turns criminal proceeds into apparently legitimate wealth, to the AI-generated frontier of digital financial crime reshaping all of it at machine speed.
In our concluding instalment, Part IV: “Sri Lanka at the Crossroads: Economic Consequences, Organised Crime and the Road Ahead”, we bring this series home. We examine precisely what all of this costs Sri Lanka in hard economic terms: lost remittances, exchange rate pressure, tax revenue forgone, and the 2026 FATF evaluation that will determine whether the country’s institutions can demonstrate, with evidence rather than legislation alone, that they are equal to this challenge. We close with a practical policy roadmap.
(The writer, a senior Chartered Accountant and professional banker, is Professor at SLIIT, Malabe.
Views expressed in this article are personal.)
Features
‘There are no private universities in Sri Lanka’ – some considerations for higher education reform
Academics involved in education policy like to say that there is no such thing as a private university in Sri Lanka. The only ‘universities’ in the country are state universities; anything else offering degrees is a private higher education institution (HEI). This position is technically accurate. Yet, in the discourse and imagination of the public, private universities are very real – people teach in them, students register in them, families pay fees, and such degree holders enter job markets in Sri Lanka and outside.
For decades, activists concerned for public higher education have ignored or resisted looking at private HEIs, as if such scrutiny would taint them. Others have worked in both types of institutions, carrying practices from each to the other. The apex body governing state universities, the UGC, has, meanwhile, ignored the concept of conflict of interest and appointed individuals in private higher education in committees and leadership positions. It is unsurprising then that some of the ideologies informing private higher education appear in reform agendas in the state sector.
This is a good time then to consider the varying types of private HEIs around us, and to take a look at some of the issues within them in the hope that higher education reform agendas will include private, as well as state higher education.
What is a ‘private university’?
First, some clarifications. In the public imaginary, a ‘private university’ is typically an institution that provides a foreign or local degree for which the student makes a payment. But this broad classification encompasses a host of diverse institutions and types of degrees which I detail below.
The Non-State Higher Education Division (NSHE) of the Ministry of Education has recognised 295 degrees by 32 institutions. Most of these are private companies and include a handful of established, well-known private HEIs that are ‘university like’. The degrees are local degrees conferred by the institutions accredited by the NSHE Division. While private HEIs conferring local degrees must be accredited by the NSHE Division, there appears to be no legal consequence for not doing so. In addition, there are several permutations of the private degree that miss the net of this Division and the Standing Committee on Accreditation and Quality Assurance (SCAQA) that assists this Division.
For one, degrees conferred by foreign universities offered, via these same private HEIs, are not vetted by the NSHE Division. Secondly, there is a growing plethora of private HEIs which have either no physical presence locally or only a dubious presence. The University Grants Commission has notified the public, through their website, that foreign universities listed in the Commonwealth Universities Yearbook and the World Higher Education Database are recognised, but refrained from giving any other details – which degrees? Offered by what modes? These details are not known. Some of the foreign universities in the lists may be legitimate entities in their own land but the degrees conferred locally, in their name, may not adhere to curriculum or teaching specifications of the NSHE Division or the UGC.
Another troubling phenomenon is the ‘top up degree’, which appears to work on the same principle as that of a pre-paid mobile connection: if I have a Diploma or an HND of a sort, I am eligible to complete a course of study which provides me with a degree, usually from a foreign university. The idea that someone who does not initially qualify for a degree programme should be able to work their way towards one is a progressive notion. This is the concept that open and distance learning (ODL) was based on initially, but which is now sadly exploited. ODL models are expected to provide opportunity for learning for those who may be excluded from traditional learning institutions. In Sri Lanka, however, we have seen ODL become a marketplace offering easy to obtain, for-fee qualifications by institutions with little commitment to superior teaching and learning.
Finally, a perusal of the many types of private HEIs and their varied degrees bring to mind another question – how should the private degrees, provided by state institutions (that are not educational institutions), be regulated? Who should do so?
All of these create a host of problems for the public – for hopeful students and parents and trusting employers. For the higher education sector, recruitment of academic staff, too, has become difficult due to this plethora of ambiguous higher education qualifications, as I discussed in a previous Kuppi article (‘Recruiting academics to state universities’).
Some issues in private HEIs – a bellwether for change in state universities
In this second part of this article, I will discuss some aspects of work in private HEIs – albeit the more established institutions – given that such issues may appear in reform agendas in future.
Across state universities, all permanent staff of a specific category are paid according to the same criteria. The picture is not so clear when it comes to private HEIs since they are different entities legally, typically companies. Private HEIs have salary scales and financial incentives that are different to each other. The more established private HEIs reportedly have attractive renumeration packages, possibly a reason for academics of state universities migrating eagerly to such institutions during sabbatical years and on retirement. This may not of course be the case with other less established, or improperly registered HEIs of which we know little. Academic staff of these more accepted private HEIs seem to value the high financial remuneration they receive (in comparison to state universities) as something that makes their work rewarding.
Attractive remuneration is important to sustain the good life and is at times seen as the institution’s way of encouraging good work. Yet, this has implications for the future of the institution: to continue to deliver on promised financial packages, institutions must continue to have large profit margins. One strategy has been to enroll multiple cohorts of students per year, even up to three or four intakes per year. This can result in exploitative work conditions, since staff must cater to all these cohorts in that same year. If there is inadequate staff, employees are further burdened. On the other hand, if there is a sudden drop in enrolments (degrees can go out of fashion) unexpected layoffs occur. Similar to other sectors that employ short-term contract staff – including state universities – in private HEIs, too, individual teachers, who are on short term contracts that need regular renewal, can feel pressured to work under difficult or exploitative conditions.
At the same time, even in the more established private HEIs, work norms differ from those of state universities in that they include promotional work that keeps the institution’s name in the eye of the public. The Marketing (or similarly named) unit comes up in conversations as one of the most important departments. It appears to weigh in on decision-making related to the number of staff, the amount of re-sits per exams, and other pedagogically important matters. This is a worrying example of how financial rationales interfere with pedagogically or academically sound processes, resulting in problematic results in the classroom. On the plus side, junior colleagues, who had experience in both state and private HEIs, also felt that they faced less harassment in private HEIs – primarily due to the private HEIs ability to take swift action in reported cases of harassment. This is a real indictment on state institutions and their reluctance to address chronic issues of harassment in our universities.
Yet, while we hear much about problems in state universities, we hardly hear of problems that staff in private HEIs face. One rationale for a lack of public expressions by staff is that expressions of discontent might lead to trouble given the importance of reputation for private HEIs. The worry about reputational damage is a growing concern in state universities, too, as evidenced by social media policies and internal conversations on reputational damage, consequent to negative publicity. Institutional worries of reputational damage are harmful in the long run since these impact not only freedom of expression by student and staff, but also research that is possible in and about the education sector.
Some thoughts at the end…
A close look at the private higher education sector is important given its strong presence in the country. Impending reform needs to regulate this diverse array of higher education offerings in the private sector, as well as the state institutions that offer privately-funded options of higher education (a topic for a separate Kuppi on its own). It is time we carefully considered how to build a whole system of higher education out of this broken mess.
Kaushalya Perera is a senior lecturer at the University of Colombo.
Kuppi is a politics and pedagogy happening on the margins of the lecture hall that parodies, subverts, and simultaneously reaffirms social hierarchies.
Features
Ready for solo spotlight
Singer Nish Peiris is set to take the next big step in her music journey.
The talented vocalist, who has been seen and heard in the scene here for a short while, and was also featured with the now-defunct band, Inner Vision, has announced that she will be fully committing to her solo career, after completing her degree this year.
“I’m finishing my degree this year, and after that I’ll be fully committing to my solo music career,” Nish told The Island.
“I’ve already got a few tours lined up for next year, so I’m really excited for what’s ahead.”
Fans, no doubt, will remember Nish for her smooth voice and stage presence, and the good news is that she is now ready to chart her own path and bring new music to audiences at home and abroad.
With tours already planned for 2027, the year 2026 promises to be an exciting year for the young artiste as she steps into the spotlight on her own.
We wish Nish every success in this new chapter!
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