[🇧🇩] Artificial Intelligence-----It's challenges and Prospects in Bangladesh

[Saif]

Can AI help mitigate long-pending legal cases?

Bangladesh’s legal system is overwhelmed by a staggering backlog of cases, leaving many people waiting for justice for years, sometimes even decades.

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Can AI help mitigate long-pending legal cases?

Bangladesh's legal system is overwhelmed by a staggering backlog of cases, leaving many people waiting for justice for years, sometimes even decades. The legal process has become slow and frustrating with nearly 49 lakh cases pending and a severe shortage of judges, according to a newspaper report. Public trust in the judiciary has eroded, and finding solutions to this crisis has become critical. About 70 percent of the cases are backlogged at the witness hearing stage for at least three or more years, whereas 22 percent are backlogged at the investigation stage for one year and above, media reports say. One potential answer lies in using Artificial Intelligence (AI), which could offer much-needed efficiency and innovation to tackle these challenges.

SCALE OF THE PROBLEM

With only one judge for every 95 thousand citizens, Bangladesh's courts are stretched to the limit. As a result, cases drag on for years. This inefficiency is not just inconvenient, it is a denial of timely justice, which affects individuals, families and businesses alike.

HOW AI CAN HELP

Automating document creation and management: AI can help lawyers draft legal documents quickly and accurately. By generating first drafts of contracts or legal papers using templates, AI tools can save lawyers valuable time. Instead of being bogged down in repetitive tasks, legal professionals can focus on more complex issues, helping to move cases forward faster.

Enhancing legal research: Legal research is time-consuming, but AI can change that. AI-powered tools can skim through enormous amounts of data, case laws and statutes, providing quick access to relevant information.

Task management and scheduling: AI can take over the mundane yet critical task of managing lawyers' and judges' schedules. It can remind them of deadlines, upcoming court dates, and pending tasks.

Training junior lawyers: AI can act as a virtual mentor for junior lawyers, helping them learn faster. AI tools can simulate courtroom scenarios, provide real-time feedback on legal drafts and even conduct mock trials.

LOCAL SOLUTIONS FOR LOCAL PROBLEMS

Bangladesh has the potential to create AI solutions tailored to the specific needs of its legal system. Local tech companies are in a unique position to design tools that understand the context of Bangladeshi law. By investing in these technologies, Bangladesh can develop affordable solutions that will help clear the case backlog. Collaboration between the legal community, tech companies, and institutions like the Supreme Court and Bangladesh Bar Council is crucial for success.

Oleyn, a Bangladeshi-Singaporean tech company, is already developing AI-driven solutions through its product "superattorney.ai". Salman Sayeed, co-founder and CEO of Oleyn, said their innovative platform transforms legal services by scaling up operations at a low cost, addressing the high demand for legal assistance while keeping expenses low for clients and increasing revenue for lawyers.

VOICES FROM THE LEGAL INDUSTRY

Lawyer Raiyan Amin points out that "AI can help automate repetitive tasks such as case management, legal research and data entry," but adds that "AI should just assist and mustn't replace human judgment." Barrister Rafaelur Rahman Mehedi agrees, saying, "AI can help with drafting, legal databases and recording court statements, but trust and confidentiality are crucial in law, and we must be careful with AI's role in this."

CONCLUSION

AI has the potential to bring long-overdue changes to Bangladesh's legal system. By streamlining routine tasks, improving research and supporting lawyers, AI can help clear the backlog of cases and speed up justice. As local companies like Oleyn step up to provide innovative solutions, Bangladesh's legal landscape could soon see a much-needed transformation, ensuring that justice is delivered on time.

The author is the chief of staff of a leading startup and a former president of Junior Chamber International (JCI) Bangladesh​

 

[Saif]

Is AI modern Frankenstein?

A cognitive psychologist and computer scientist, the British-Canadian, Geoffrey E. Hinton who along with the American, John J. Hopfield, was awarded the Nobel Prize in Physics by the Royal Swedish Academy of Sciences on October 8 is himself fearful of the invention that brought him the honour. Upon

thefinancialexpress.com.bd

Is AI modern Frankenstein?
SYED FATTAHUL ALIM
Published :
Oct 14, 2024 21:56
Updated :
Oct 14, 2024 21:56

A cognitive psychologist and computer scientist, the British-Canadian, Geoffrey E. Hinton who along with the American, John J. Hopfield, was awarded the Nobel Prize in Physics by the Royal Swedish Academy of Sciences on October 8 is himself fearful of the invention that brought him the honour. Upon leaving Google in May 2023 after working there for a decade, he admitted that he left the job to speak freely about the dangers of AI. To him, AI is outpacing human's ability to control it. Consider the frustration of the AI buffs, not less Google, who held him in high regard for his pioneering work on deep learning!The reason the Nobel Committee considered them for the Prize (in Physics) is their use of statistical physics concepts in the development of artificial intelligence. John J. Hopfield, a physicist- turned-chemist-turned-biologist at the California Institute of Technology (Caltech) in 1982 proposed a simple (neural) network on how memories are stored in the brain. He later returned to Princeton as a molecular biologist.That means, neither scientist was a practising physicist when they got the Nobel Prize in Physics. Interestingly, though these two Nobel laureates in Physics got the prize for their seminal works in the advancement of AI, yet both of them expressed concerns about further development of the field they dedicated their career for. However, unlike Geoffrey Hinton, John Hopfield was less dramatic, though no less apprehensive, about expressing his fears about neural network he worked for that mimics the function of the human brain. Maybe, AI does it better than human brain and, what is alarming, even faster! He also warned of potential catastrophes if the advancements in AI research are not properly managed. So, he emphasised the need for deeper understanding of the deep learning systems so the technological development in the field may not go out of control.

The concerns raised by these two lead researchers in AI's advancement, call to mind the Asilomar conference (organised at the Asilomar State Beach in California, USA) of biotechnologists on recombinant DNA molecules in 1975. They discussed potential hazards and the need for regulation of biotechnology. Some 140 biologists, lawyers and physicists participated in the conference and they drew up a set of voluntary guidelines to ensure safety of the recombinant DNA technology, which is about genetic engineering technique that involves combining DNA from different species or creating new genes to alter an organism's genetic makeup.

Geoffrey Hinton in his interview with the website of Nobel Prize stressed thatAI is indeed an existential threat but we still do not know how to tackle it. There are some existential threats like climate change. But not only scientists, the general public also knows that by not burning fossil fuels and cutting down trees, the danger can be averted. That means, humanity knows the answer to the threat posed by the climate change, but it is the greedy businesses and politicians lacking the will who are coming in the way of addressing the threat.

To avert the threat to humanity originating from unregulated AI, mobilising resources by tech companies to conduct research on safety measures is necessary.

Hinton thinks that the linguistics school of Noam Chomsky, for instance, is quite dismissive about AI's capacity for understanding things the way humans do. They (neural networks of AI) cannot process language like humans, the Chomsky School holds.

But Geoffrey Hinton thinks this notion is wrong, since, in his view, neural nets do the job (of processing language) better than what the Chomsky School of Linguistics might imagine.

The harm AI can do is already before all to see. These include AI-generated photos, videos and texts that are flooding the internet. The problem is it is hard to tell the real contents from the fake ones. It can replace jobs, build lethal autonomous weapons by themselves and so on. Here lies the existential threat.​

 

[Saif]

Exploiting the potential of AI integration into Bangladesh university curriculums

AI has become the number one cause of change in industries all over the world, be it in health, manufacturing, agriculture, or even financial services. Its transformative potential is huge, especially in education, as it empowers personalised learning, data-based insights, and the acquisition of cr

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Exploiting the potential of AI integration into Bangladesh university curriculums
Serajul I Bhuiyan
Published :
Nov 11, 2024 22:04
Updated :
Nov 11, 2024 22:04

AI has become the number one cause of change in industries all over the world, be it in health, manufacturing, agriculture, or even financial services. Its transformative potential is huge, especially in education, as it empowers personalised learning, data-based insights, and the acquisition of critical skills demanded by the modern workforce. This inclusion of AI-based courses in university curricula is not only innovative but also highly essential for Bangladesh. Moving with the tide of time, Bangladeshi universities, especially private-sector ones, should seize the opportunity and get their students prepared with the competencies they will need in the AI-centric future.

RISE OF THE DEMAND FOR AI SKILLS: A 2023 report by the World Economic Forum emphasised the acute need for digital literacy, machine learning, data analytics, and other skills related to AI as integral to future employability. In Bangladesh, at least, this means industries such as textiles, telecommunication, banking, and logistics have already established their firm plans for integrating AI tools into their work. Bangladeshi universities have started integrating practical learning in AI, such as coding, predictive analytics, and automation tools. With the inclusion of AI across the curriculum, universities can ensure that students develop theoretical knowledge with a mix of practical skills in multidisciplinary areas that render job-ready adaptability in a fast-changing job market.

GOVERNMENT-INDUSTRY SYMBIOSIS: The post-Sheikh Hasina interim government now envisages Smart Bangladesh as a fundamental promise to establish a digitally competitive economy. This is an ambitious plan that epitomises the realisation that integrating artificial intelligence skills into the workforce is no longer an option but a critical driver for future economic growth and innovation. Correspondingly, the Bangladeshi government has also emphasised the development of relevant skills on AI and encouraged active partnerships between industries and academia in keeping with international trends.

This collaboration between the academia and the industry provides students with excellent opportunities to apply AI in practice, through internships, hands-on projects, and so on. The partnerships introduce students to the latest AI technologies firsthand, enabling them to explore various applications in fields such as telecommunications and finance, among others. Drawing inspiration from successful models in India and Singapore—where the integration of AI ranges from research to governance to skill-building—Bangladesh is well-placed to understand how to nurture the AI economy.

CHALLENGES OF INTEGRATING AI IN BANGLADESHI EDUCATION: While promising, the road to AI-integrated education in Bangladesh is beset with daunting challenges.

Infrastructure and skilled faculty shortages: Most universities in Bangladesh, especially the public ones, lack high-performance computing infrastructure, data labs, and software tools which are essential for effective AI education. Furthermore, a severe shortage of AI-trained faculty is also an added concern. To that effect, investment in faculty development, collaboration with globally top institutions, and integration in a knowledge-sharing platform is crucial.

Implementation Cost: There is a huge cost involved in setting up courses on AI, which includes building laboratory facilities as well as buying software licences and training the faculty on a continuous basis. It is expected that universities can have this challenge through public-private partnerships, international funding, and grants so that all institutions could have the required resources for the inclusion of AI in their programmes.

STRATEGIC RECOMMENDATIONS FOR FUTURE-READY AI EDUCATION SYSTEM: The collaboration of universities, industries, and government in tandem holds the key to realising the full potential of AI in education. Here are some strategic recommendations that can help create a more enabling AI integration in Bangladeshi Universities:

AI centers of excellence: Setting up AI-focused research, industry partnerships, and skill development can help build an innovation-oriented culture. For instance, such projects at United International University can set examples by involving students and faculty in policymaking related to advanced AI projects.

Encourage multidisciplinary AI learning: The study of AI should be open to students from all disciplinary backgrounds, including but not limited to business, healthcare, and engineering, so that inter-disciplinarity in AI innovation is facilitated. This will ensure that students of all different fields have a preliminary understanding of where AI is taking their industry.

Leveraging AI-enhanced learning tools Intelligent learning platforms can enable customized learning through knowledge gap identification and adaptivity within learning content. These kinds of adaptive learning tools create an inclusive and effective learning environment, catering to the diverse learning styles and pace of the students.

Offering online and blended learning: Courses and certifications in AI online would increase access to students from all corners of the country, including those living in the most remote areas. This will enable Bangladeshi universities to provide top-class AI content irrespective of geographical barriers.

Organising AI hackathon and competition: Let the hackathons and competitions be the motivator for the students in designing solutions with AI applications aimed at solving real-world problems. This will enable ease of collaboration, creative problem-solving, and entrepreneurial mindset development.

Encouraging international collaboration: An internationally aligned AI curriculum, and collaboration with other institutions abroad, would continue to keep Bangladeshi universities competitive and current with global standards regarding AI education.

AI Education should be included at all levels: Initiative building concepts of AI should be prioritised at the primary and secondary levels to nurture AI literacy from a young age. This long-term approach will help cultivate a generation of tech-savvy individuals prepared to drive Bangladesh’s digital future.

BREAKING ACADEMIC SILOS— UNIVERSITIES AS PIONEERS IN AI INTEGRATION: Bangladeshi universities like North South University, BRAC University, Independent University, Bangladesh, American International University Bangladesh, the Institute of Business Administration at Dhaka University, East-West University, University of Liberal Arts Bangladesh, Daffodil International University, and United International University can be uniquely leading the AI revolution in the country. These institutions have both the resources and academic influence to enable a national movement for AI literacy and skill development. However, one widely held misconception stands in the way of such an outcome: far too many university leaders consider AI a tool relevant only to technical disciplines, and principally within computer science and engineering programmes. This is a very narrow perception, considering that AI’s potential benefits actually span widely, from economics and business to the social sciences. For instance, students of economics might use AI to create predictive market analytics, while students of social sciences might use data to understand behaviors pertinent to a community project. University leaders need to understand that AI is fundamentally multidisciplinary and that its applications go far beyond technical fields: AI tools are transforming the social sciences, the liberal arts, business, health care, and many other areas. For that reason, all academic leaders need to understand how AI applies to each discipline. In this way, universities can hope not only to raise the academic bar but also to prepare the students with the right skills to address prevailing challenges in the real world. Therein lies the opportunity for university leaders to get acquainted with the range of applications that AI serves so that they may strategically consider using it to create curricula positions that will place their institutions on the front row in academic excellence and prepare their students for meaningful contributions to the future of Bangladesh.

With a firm commitment to partnership, cross-disciplinary learning, and strategic investments, universities in Bangladesh could build the bedrock for a resilient and adaptable AI workforce. Standing in line with the likes of Satya Nadella’s vision, where he underlines the harmonious coexistence of humans and technology, Bangladesh has every reason to claim itself as a digital leader in the South Asian region. The journey toward AI literacy—one of transformation—charts the route towards a smart, sustainable future in which citizens of this nation are prepared to leverage the world for opportunities.

Dr. Serajul I. Bhuiyan is professor and former chair of the Department of Journalism and Mass Communications and Georgia Governor’s AI Teaching Fellow, Savannah State University, Savannah, Georgia, USA.​

 

[Saif]

AI beyond engineering

In Bangladesh and much of South Asia, artificial intelligence (AI) is still commonly perceived as the domain of technical fields like computer science and engineering. However, this view limits the true potential of AI, which has become a transformative force across numerous disciplines, from medic

thefinancialexpress.com.bd

AI beyond engineering
Serajul I Bhuiyan
Published :
Nov 14, 2024 21:08
Updated :
Nov 14, 2024 21:08

In Bangladesh and much of South Asia, artificial intelligence (AI) is still commonly perceived as the domain of technical fields like computer science and engineering. However, this view limits the true potential of AI, which has become a transformative force across numerous disciplines, from medicine and business to environmental science, social sciences, and the arts. Recognising AI's broad, interdisciplinary applications, visionary universities worldwide are embedding AI into a diverse array of academic fields. This integration equips students not only with foundational AI knowledge but also with practical experience, preparing them to succeed in an evolving, technology-driven world.

This article highlights AI's applications beyond technical fields, showing how cross-disciplinary AI education prepares students for innovation, complex problem-solving, and adapting to a global workforce. By embracing AI across the curriculum, educational institutions can foster a new generation of professionals ready to lead in an AI-powered future.

AI IN BUSINESS AND MANAGEMENT: In today's globalised economy, AI is reshaping business by enabling data-driven decision-making, improving efficiency, and keeping companies competitive. Recognising AI's pivotal role, leading business schools worldwide are incorporating AI applications-ranging from predictive analytics and consumer behaviour modelling to financial forecasting and process automation-into their curricula. This equips students with essential skills to apply AI in marketing, finance, HR, and logistics, preparing them to thrive in technology-driven business environments. By blending theory with practical AI applications, these programs develop business professionals who can leverage AI for strategic advantage.

Business schools across the US, Europe, Asia, and Australia lead the way in AI education. Harvard Business School uses AI-driven analytics for marketing and strategic decisions, allowing students to analyse consumer behaviour and optimize pricing using real-world case studies. INSEAD, with campuses in France and Singapore, emphasizes AI's role in market analysis, consumer segmentation, and supply chain efficiency, tailoring AI applications to diverse business settings across Europe and Asia. Similarly, the National University of Singapore integrates AI for finance and customer management, preparing graduates for roles in the expanding FinTech sector. Other institutions, like London Business School and the University of Melbourne, offer courses on AI in digital transformation and personalised customer experiences, providing hands-on experience through industry collaborations.

The integration of AI in business education extends to areas like customer relationship management (CRM), supply chain logistics, financial forecasting, and HR. AI-enabled CRM tools let students design personalized customer experiences, essential in e-commerce and retail. AI-driven logistics models help students optimize supply chains, while in finance, students learn predictive modelling and risk analysis. HR applications allow students to use AI to predict workforce trends and enhance talent management. Mastering these applications, graduates from AI-empowered business programs gain a competitive edge, ready to lead AI initiatives aligned with strategic business goals.

AI IN HEALTHCARE AND LIFE SCIENCES: AI applications in healthcare are transformative, enhancing diagnostics, treatment planning, and patient care. Medical programs at institutions like Stanford University and Imperial College London integrate AI, training students in areas like image analysis in radiology, genomic sequencing, and predictive healthcare modelling. AI-based diagnostic tools, for instance, can detect diseases like cancer with remarkable precision. Students trained on such tools develop expertise in AI-driven healthcare, equipping them to improve patient outcomes in efficient, technology-enhanced healthcare systems. This hands-on approach prepares future professionals for tackling complex medical challenges, contributing to global health improvements.

AI IN ENVIRONMENTAL SCIENCE AND SUSTAINABILITY: Environmental science programs are increasingly using AI to address challenges such as climate change, resource management, and conservation. Institutions like ETH Zurich and Australia's University of Queensland incorporate AI into environmental studies, where students learn to monitor ecosystems, forecast weather patterns, and create renewable energy solutions. For example, students may use AI models to analyse satellite images for tracking deforestation, helping governments and NGOs respond to environmental degradation in real-time. This training enables graduates to apply data-driven insights toward sustainable environmental solutions, making meaningful contributions to global issues.

AI IN ENGINEERING AND MANUFACTURING: In fields like manufacturing, civil infrastructure, and robotics, AI is driving innovation and efficiency. Institutions such as MIT and the Technical University of Munich incorporate AI into their engineering programs, allowing students to simulate processes, optimize structures, and develop automated systems. In manufacturing, AI applications include predictive maintenance and logistics optimization, enhancing production efficiency and quality control. Robotics students, for example, design AI-powered robots for complex tasks like assembly or hazardous material handling. These real-world AI applications provide students with the practical skills needed to lead advancements in automation and smart manufacturing.

AI IN SOCIAL SCIENCES AND HUMANITIES: AI is also making an impact in social sciences and humanities, fields that were traditionally data-limited but are now rich with insights through AI analytics. Universities like Oxford and the University of Toronto use AI tools in sociology, psychology, and political science to study social patterns, behavioural trends, and policy impacts. Political science students can use predictive analytics to model election outcomes or assess public opinion shifts, while psychology students may apply AI-driven sentiment analysis to evaluate mental health trends on social media, identifying community needs. This interdisciplinary approach allows students to ground theoretical knowledge in empirical data, fostering a nuanced understanding of societal issues and enabling evidence-based solutions.

AI IN LAW AND PUBLIC POLICY: AI is also transforming law and public policy, fields that were traditionally considered resistant to technology. Institutions like the National University of Singapore and Stanford Law School now offer courses on AI applications in legal research, contract analysis, and case prediction. Students learn how AI can automate document reviews, identify precedents, and predict case outcomes based on past rulings. In public policy, AI enables the analysis of healthcare access, crime rates, and education quality, providing empirical evidence for data-driven policy decisions. This integration equips future professionals to address the ethical and regulatory challenges of AI, preparing them to create informed legislation that balances innovation with responsible AI use.

By embracing AI across disciplines, universities worldwide are preparing a generation of graduates ready to lead in an AI-powered world.

AI IN ARTS AND DESIGN: AI introduces an exciting new dimension to arts and design, opening frontiers for creativity and innovation. Programs at institutions like the Royal College of Art in the UK and Parsons School of Design in the US are incorporating AI into courses that explore generative design, digital art, and music composition. Through AI, students create immersive virtual reality experiences, develop algorithmic art, and even compose music using machine learning models. In media arts, AI enhances special effects, personalizes viewer experiences, and optimizes content recommendations. By merging AI with creative disciplines, universities empower students to push the boundaries of traditional art forms, creating a unique blend of technology and artistic expression that redefines the possibilities in arts.

AI IN JOURNALISM AND MASS COMMUNICATIONS: AI is redefining journalism and mass communications, reshaping how news is gathered, produced, and delivered in an age of rapid digital transformation. Top media organisations and journalism schools globally are adopting AI-powered tools to create a more agile, responsive, and insightful media landscape. AI tools analyze vast datasets, automate content generation, personalize news delivery, and detect misinformation, enabling journalists to produce content more quickly, accurately, and with greater impact. From automating routine news to enhancing fact-checking, AI allows newsrooms to cover stories efficiently, freeing journalists to delve into complex investigative work. Journalism students, in turn, learn to use AI technologies to meet emerging challenges and capitalise on AI-driven opportunities in media

In practice, AI advances news automation, personalised content, investigative data analysis, and multimedia storytelling. Organisations like The Associated Press and Reuters employ AI algorithms to automate routine reports on financials, sports, and weather, allowing journalists to focus on in-depth stories. AI personalises content to readers' preferences, as seen with The New York Times and BBC, which recommend articles based on reader interests, enhancing engagement. AI-powered visual tools also streamline video editing, create immersive AR and VR content, and enable social listening on platforms like Crimson Hexagon, tracking public sentiment on key issues. Predictive analytics helps outlets like The Guardian forecast trends, while translation tools like Google Translate broaden reach globally. Journalism programs now integrate training in these applications, preparing students for a field where tech-savvy complements traditional reporting skills. As students consider the ethical aspects of AI-addressing privacy, bias, and responsible use-they are positioned to lead with integrity in a fast-evolving digital world.

TRANSFORMATIVE IMPACT: By embedding AI across disciplines, universities create a space where students experience AI's transformative potential in varied contexts. This cross-functional approach produces graduates who are not only experts in their fields but also proficient in AI, ready to work in interdisciplinary teams and adapt to fast-changing, tech-driven environments. Real-world projects, internships, and AI-driven lab work help students to bridge academic knowledge with industry practice, equipping them to contribute meaningfully from day one in the workforce. This blend of theory and practice prepares students to drive change and innovation across industries.

BUILDING A RESILIENT WORKFORCE FOR AN AI-DRIVEN WORLD: In a global economy that values digital proficiency and technological expertise, universities that embrace AI across disciplines position their graduates for lasting success. Integrating AI into diverse fields ensures that students gain foundational knowledge while also learning to apply AI in industry-relevant ways. This mix of theory and hands-on experience fosters agile, innovative graduates equipped to navigate the complexities of an AI-powered future. As AI advances, this approach cultivates a resilient workforce capable of adapting and harnessing AI responsibly to address real-world challenges and contribute to societal progress.

Dr Serajul I Bhuiyan is a professor and former chair of the Department of Journalism and Mass Communications at Savannah State University, Savannah, Georgia. USA; and a Georgia Governor's AI Teaching Fellow at Louise

McBee Institute of Higher Education, University of Georgia, Athens, USA.​

 

[Saif]

Is AI’s meteoric rise beginning to slow?

A quietly growing belief in Silicon Valley could have immense implications: the breakthroughs from large AI models -– the ones expected to bring human-level artificial intelligence in the near future –- may be slowing down.

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Is AI’s meteoric rise beginning to slow?

OpenAI CEO Sam Altman (L) shakes hands with Microsoft Chief Technology Officer and Executive VP of Artificial Intelligence Kevin Scott during an event in Seattle. OpenAI recently raised $6.6 billion to fund further advances. Photo: AFP

A quietly growing belief in Silicon Valley could have immense implications: the breakthroughs from large AI models -– the ones expected to bring human-level artificial intelligence in the near future –- may be slowing down.

Since the frenzied launch of ChatGPT two years ago, AI believers have maintained that improvements in generative AI would accelerate exponentially as tech giants kept adding fuel to the fire in the form of data for training and computing muscle.

The reasoning was that delivering on the technology's promise was simply a matter of resources –- pour in enough computing power and data, and artificial general intelligence (AGI) would emerge, capable of matching or exceeding human-level performance.

Progress was advancing at such a rapid pace that leading industry figures, including Elon Musk, called for a moratorium on AI research.

Despite the massive investments in AI, performance improvements are showing signs of plateauing.

Yet the major tech companies, including Musk's own, pressed forward, spending tens of billions of dollars to avoid falling behind.

OpenAI, ChatGPT's Microsoft-backed creator, recently raised $6.6 billion to fund further advances.

xAI, Musk's AI company, is in the process of raising $6 billion, according to CNBC, to buy 100,000 Nvidia chips, the cutting-edge electronic components that power the big models.

However, there appears to be problems on the road to AGI.

Industry insiders are beginning to acknowledge that large language models (LLMs) aren't scaling endlessly higher at breakneck speed when pumped with more power and data.

Despite the massive investments, performance improvements are showing signs of plateauing.

"Sky-high valuations of companies like OpenAI and Microsoft are largely based on the notion that LLMs will, with continued scaling, become artificial general intelligence," said AI expert and frequent critic Gary Marcus. "As I have always warned, that's just a fantasy."

One fundamental challenge is the finite amount of language-based data available for AI training.

According to Scott Stevenson, CEO of AI legal tasks firm Spellbook, who works with OpenAI and other providers, relying on language data alone for scaling is destined to hit a wall.

"Some of the labs out there were way too focused on just feeding in more language, thinking it's just going to keep getting smarter," Stevenson explained.

Sasha Luccioni, researcher and AI lead at startup Hugging Face, argues a stall in progress was predictable given companies' focus on size rather than purpose in model development.

"The pursuit of AGI has always been unrealistic, and the 'bigger is better' approach to AI was bound to hit a limit eventually -- and I think this is what we're seeing here," she told AFP.

The AI industry contests these interpretations, maintaining that progress toward human-level AI is unpredictable.

"There is no wall," OpenAI CEO Sam Altman posted Thursday on X, without elaboration.

Anthropic's CEO Dario Amodei, whose company develops the Claude chatbot in partnership with Amazon, remains bullish: "If you just eyeball the rate at which these capabilities are increasing, it does make you think that we'll get there by 2026 or 2027."

Nevertheless, OpenAI has delayed the release of the awaited successor to GPT-4, the model that powers ChatGPT, because its increase in capability is below expectations, according to sources quoted by The Information.

Now, the company is focusing on using its existing capabilities more efficiently.

This shift in strategy is reflected in their recent o1 model, designed to provide more accurate answers through improved reasoning rather than increased training data.

Stevenson said an OpenAI shift to teaching its model to "spend more time thinking rather than responding" has led to "radical improvements".

He likened the AI advent to the discovery of fire. Rather than tossing on more fuel in the form of data and computer power, it is time to harness the breakthrough for specific tasks.

Stanford University professor Walter De Brouwer likens advanced LLMs to students transitioning from high school to university: "The AI baby was a chatbot which did a lot of improv'" and was prone to mistakes, he noted.

"The homo sapiens approach of thinking before leaping is coming," he added.​