Latest news with #ChristopherPissarides
The Independent
01-07-2025
- Business
- The Independent
The surprising job AI won't replace any time soon
As AI systems expand their already impressive capacities, there is an increasingly common belief that the field of computer science (CS) will soon be a thing of the past. This is being communicated to today's prospective students in the form of well-meaning advice, but much of it amounts to little more than hearsay from individuals who, despite their intelligence, speak outside of their expertise. High-profile figures like Nobel Prize -winning economist Christopher Pissarides have made this argument, and as a result, it has taken root on a much more mundane level – I have even personally heard high school careers advisers dismiss the idea of studying CS outright, despite having no knowledge of the field itself. These claims typically share two common flaws. First among them is that the advice comes from people who are not computer scientists. Secondly, there is a widespread misunderstanding of what computer science actually involves. AI and the myth of code replacement It is not wrong to say that AI can write computer code from prompts, just as it can generate poems, recipes and cover letters. It can boost productivity and speed up workflow, but none of this eliminates the value of human input. Writing code is not synonymous with CS. One can learn to write code without ever attending a single university class, but a CS degree goes far beyond this one skill. It involves, among many other things, engineering complex systems, designing infrastructure and future programming languages, ensuring cybersecurity and verifying systems for correctness. AI cannot reliably do these tasks, nor will it be able to in the foreseeable future. Human input remains essential, but pessimistic misinformation risks steering tens of thousands of talented students away from important, meaningful careers in this vital field. What AI can and can't do AI excels at making predictions. Generative AI enhances this by adding a user-friendly presentation layer to internet content – it rewrites, summarises and formats information into something that resembles a human's work. However, current AI does not genuinely 'think'. Instead, it relies on logical shortcuts, known as heuristics, that sacrifice precision for speed. This means that, despite speaking like a person, it cannot reason, feel, care, or desire anything. It does not work in the same way as a human mind. Not long ago, it seemed that 'prompt engineering' would replace CS. Today, however, there are virtually no job postings for prompt engineers, while companies like LinkedIn report that the responsibilities of CS professionals have actually expanded. Where AI falls short What AI provides is more powerful tools for CS professionals to do their jobs. This means they can now take concepts further – from ideation to market deployment – while requiring fewer support roles and more technical leadership. There are, however, many areas where specialised human input is still essential, whether for trust, oversight or the need for human creativity. Examples abound, but there are 10 areas that stand out in particular: Adapting a hedge fund algorithm to new economic conditions. This requires algorithmic design and deep understanding of markets, not just reams of code. Diagnosing intermittent cloud service outages from providers like Google or Microsoft. AI can troubleshoot on a small scale, but it cannot contextualise large-scale, high-stakes troubleshooting. Rewriting code for quantum computers. AI cannot do this without extensive examples of successful implementations (which do not currently exist). Designing and securing a new cloud operating system. This involves high-level system architecture and rigorous testing that AI cannot perform. Creating energy-efficient AI systems. AI cannot spontaneously invent lower-power GPU code or reinvent its own architecture. Building secure, hacker-proof, real-time control software for nuclear power plants. This requires embedded systems expertise to be mixed with the translation of code and system design. Verifying that a surgical robot's software works under unpredictable conditions. Safety-critical validation exceeds AI's current scope. Designing systems to authenticate email sources and ensure integrity. This is a cryptographic and multi-disciplinary challenge. Auditing and improving AI-driven cancer prediction tools. This requires human oversight and continuous system validation. Building the next generation of safe and controllable AI. Evolving towards safer AI cannot be done by AI itself – this is a human responsibility. Why Computer Science is still indispensable One thing is certain: AI will reshape how engineering and Computer Science are done. But what we are faced with is a shift in working methods, not a wholesale destruction of the field. Whenever we face an entirely new problem or complexity, AI alone will not suffice for one simple reason: it depends entirely on past data. Maintaining AI, building new platforms, and developing fields like trustworthy AI and AI governance, therefore, all require CS. The only scenario in which we might not need CS is if we reach a point where we no longer expect any new languages, systems, tools, or future challenges. This is vanishingly unlikely. Some argue that AI may eventually perform all of these tasks. It's not impossible, but even if AI became this advanced, it would place almost all professions at equal risk. One of the few exceptions would be those who build, control, and advance AI. There is a historical precedent to this: during the Industrial Revolution, factory workers were displaced at a 50 to 1 ratio as a result of rapid advances in machinery and technology. In that case, the workforce actually grew with a new economy, but most of the new workers were those who could operate or fix machines, develop new machines, or design new factories and processes around machinery. During this period of massive upheaval, technical skills were actually the most in-demand, not the least. Today, the parallel holds true: technical expertise, especially in CS, is more valuable than it ever has been. Let's not confuse the next generation with the opposite message.
Yahoo
01-07-2025
- Yahoo
AI won't replace computer scientists any time soon – here are 10 reasons why
As AI systems expand their already impressive capacities, there is an increasingly common belief that the field of computer science (CS) will soon be a thing of the past. This is being communicated to today's prospective students in the form of well-meaning advice, but much of it amounts to little more than hearsay from individuals who, despite their intelligence, speak outside of their expertise. High-profile figures like Nobel Prize-winning economist Christopher Pissarides have made this argument, and as a result it has taken root on a much more mundane level – I have even personally heard high school careers advisers dismiss the idea of studying CS outright, despite having no knowledge of the field itself. These claims typically share two common flaws. First among them is that the advice comes from people who are not computer scientists. Secondly, there is a widespread misunderstanding of what computer science actually involves. It is not wrong to say that AI can write computer code from prompts, just as it can generate poems, recipes and cover letters. It can boost productivity and speed up workflow, but none of this eliminates the value of human input. Writing code is not synonymous with CS. One can learn to write code without ever attending a single university class, but a CS degree goes far beyond this one skill. It involves, among many other things, engineering complex systems, designing infrastructure and future programming languages, ensuring cybersecurity and verifying systems for correctness. AI cannot reliably do these tasks, nor will it be able to in the foreseeable future. Human input remains essential, but pessimistic misinformation risks steering tens of thousands of talented students away from important, meaningful careers in this vital field. AI excels at making predictions. Generative AI enhances this by adding a user-friendly presentation layer to internet content – it rewrites, summarises and formats information into something that resembles a human's work. However, current AI does not genuinely 'think'. Instead, it relies on logical shortcuts, known as heuristics, that sacrifice precision for speed. This means that, despite speaking like a person, it cannot reason, feel, care, or desire anything. It does not work in the same way as a human mind. Not long ago it seemed that 'prompt engineering' would replace CS. Today, however, there are virtually no job postings for prompt engineers, while companies like LinkedIn report that the responsibilities of CS professionals have actually expanded. Leer más: What AI provides is more powerful tools for CS professionals to do their jobs. This means they can now take concepts further – from ideation to market deployment – while requiring fewer support roles and more technical leadership. There are, however, many areas where specialised human input is still essential, whether for trust, oversight or the need for human creativity. Examples abound, but there are 10 areas that stand out in particular: Adapting a hedge fund algorithm to new economic conditions. This requires algorithmic design and deep understanding of markets, not just reams of code. Diagnosing intermittent cloud service outages from providers like Google or Microsoft. AI can troubleshoot on a small scale, but it cannot contextualise large-scale, high-stakes troubleshooting. Rewriting code for . AI cannot do this without extensive examples of successful implementations (which do not currently exist). Designing and securing a new cloud operating system. This involves high-level system architecture and rigorous testing that AI cannot perform. Creating energy-efficient AI systems. AI cannot spontaneously invent lower power GPU code, or reinvent its own architecture. Building secure, hacker-proof, real-time control software for nuclear power plants. This requires embedded systems expertise to be mixed with the translation of code and system design. Verifying that a surgical robot's software works under unpredictable conditions. Safety-critical validation exceeds AI's current scope. Designing systems to authenticate email sources and ensure integrity. This is a cryptographic and multi-disciplinary challenge. Auditing and improving AI-driven cancer prediction tools. This requires human oversight and continuous system validation. Building the next generation of safe and controllable AI. Evolving towards safer AI cannot be done by AI itself – this is a human responsibility. One thing is certain: AI will reshape how engineering and Computer Science is done. But what we are faced with is a shift in working methods, not a wholesale destruction of the field. Whenever we face an entirely new problem or complexity, AI alone will not suffice for one simple reason: it depends entirely on past data. Maintaining AI, building new platforms, and developing fields like trustworthy AI and AI governance therefore all require CS. The only scenario in which we might not need CS is if we reach a point where we no longer expect any new languages, systems, tools, or future challenges. This is vanishingly unlikely. Some argue that AI may eventually perform all of these tasks. It's not impossible, but even if AI became this advanced, it would place almost all professions at equal risk. One of the few exceptions would be those who build, control, and advance AI. There is a historical precedent to this: during the industrial revolution, factory workers were displaced at a 50 to 1 ratio as a result of rapid advances in machinery and technology. In that case, the workforce actually grew with a new economy, but most of the new workers were those who could operate or fix machines, develop new machines, or design new factories and processes around machinery. During this period of massive upheaval, technical skills were actually the most in-demand, not the least. Today, the parallel holds true: technical expertise, especially in CS, is more valuable than it ever has been. Let's not confuse the next generation with the opposite message. Este artículo fue publicado originalmente en The Conversation, un sitio de noticias sin fines de lucro dedicado a compartir ideas de expertos académicos. Lee mas: AI won't take your job – but that doesn't mean you should ignore it Forget about a job for life. Today's workers need to prepare for many jobs across multiple industries Is AI a con? A new book punctures the hype and proposes some ways to resist Ikhlaq Sidhu no recibe salario, ni ejerce labores de consultoría, ni posee acciones, ni recibe financiación de ninguna compañía u organización que pueda obtener beneficio de este artículo, y ha declarado carecer de vínculos relevantes más allá del cargo académico citado.
TECHx
27-01-2025
- Business
- TECHx
AI, Inequality, and Labour Resilience
AI, Inequality, and Labour Resilience: Key Findings from Whiteshield's GLRI Whiteshield, in partnership with Google Cloud, has released the 9th edition of the Global Labour Resilience Index (GLRI) during the World Economic Forum (WEF) Annual Meeting in Davos. Drawing on over a decade of data and more than 70 indicators, the report offers deep insights into how 118 nations adapt their labour markets to external shocks, including the rapid rise of AI. The event, titled 'The Transformative Impact of AI on Global Economies & Labour Markets,' brought together over 50 senior policymakers, CEOs, academics, and international organizations. Nobel Laureate Sir Christopher Pissarides, Special Advisor at Whiteshield, and Anna Koivuniemi, Head of Google DeepMind Impact Accelerator, chaired the discussion. The report identifies the United States and Singapore as the top performers in labour market resilience for 2025, owing to their entrepreneurial ecosystems, flexible labour policies, and AI leadership. Sweden ranks third, driven by investments in education and R&D. Countries such as India, the UAE, and Saudi Arabia are emerging as key beneficiaries of AI investments, showcasing substantial progress in labour market adaptation. While AI offers transformative opportunities, including improved workforce efficiency and new skilled roles like AI ethics officers, the report highlights challenges such as job displacement, wage inequality, and the widening gap between nations. It emphasizes the need for proactive policies to ensure equitable benefits from AI's rapid integration into global labour markets. The GLRI outlines three resilience pathways: traditional models seen in Sweden and Norway, innovation-driven strategies led by the US, and blended approaches like Singapore's focus on governance and AI investments. It calls for governments to shift toward personalized, AI-driven policies and invest in digital infrastructure to navigate these transformations. Europe dominates the rankings with six of the top ten resilient economies, but disparities persist, particularly in Eastern and Southern Europe. Sub-Saharan Africa remains the least resilient region, hindered by structural and policy gaps, despite its demographic potential. The Middle East and North Africa (MENA) region showcases a mixed performance, with GCC countries like the UAE and Saudi Arabia excelling, while non-GCC nations lag in AI adaptability. The Asia-Pacific region, led by Singapore, China, and South Korea, demonstrates strength in governance, digital skills, and AI innovation. Karan Bhatia, Google's Global Head of Government Affairs, stated, 'The GLRI offers a roadmap for inclusive, forward-looking policies to address the challenges of automation while unlocking AI's potential.' Sir Christopher Pissarides added, 'This year's report provides actionable insights to tackle inequality and foster innovation for sustainable growth.' The GLRI concludes with a call to action for policymakers to embrace AI-driven strategies and prioritize investments in education, digital literacy, and workforce transformation. The report stresses that inaction risks deepening inequality and missing opportunities for sustainable economic growth, urging governments to act now to ensure labour market resilience in the age of AI.
The Guardian
27-01-2025
- Business
- The Guardian
AI-based automation of jobs could increase inequality in UK, report says
The automation of millions of jobs will increase inequality in the UK unless the government intervenes to support small businesses and workers through the transition, according to a major report into the future of work. Ministers need to act in the interest of those who will be made unemployed or whose jobs dramatically change, said the report by the Institute for the Future of Work (IFOW) thinktank, in order to prevent skills shortages hitting employers and workers from suffering a decline in job satisfaction and wellbeing. Artificial intelligence software is expected to become a widespread tool in factories, offices and in the public sector, demanding new skills, the IFOW said. However, a survey of 5,000 UK employees found 'a pervasive sense of anxiety, fear and uncertainty' about the introduction of AI technology, and what it could do to their work. Christopher Pissarides, a Nobel prize winner in economics and the report's main author, said ministers needed to consider 'how AI can bring productivity and prosperity, without putting people under more intense stress and pressure? How can it help us identify and deliver new opportunities, without exacerbating growing divides cross the country?' He said the three-year report, which also surveyed 1,000 businesses, discovered that while some major employers had developed tools to mitigate the effects of automation and AI to support staff, many smaller employers struggled to comprehend how they would transform the workplace and what skills and training staff would be needed in order to adapt over the next decade. The report makes a series of recommendations, including establishing science centres – like London's Crick Institute – in regional cities to prevent the capital and the arc between Oxford and Cambridge from dominating innovations in fast-growing bio-technologies and securing a disproportionate number of high paying jobs. Pissarides, professor at the London School of Economics, said devolving decision making to the regions would also be an important element of the reforms needed, while unions should also be given new powers of 'digital access, collective rights to information and new e-learning roles, backed by the Treasury'. He said this would be in 'recognition of the key role of unions to deliver meaningful partnership working'. James Hayton, professor of innovation at Warwick Business School, and a member of the report team, said the impact on jobs, skills, and job quality should not be blamed on AI, but how firms used it. Sign up to Business Today Get set for the working day – we'll point you to all the business news and analysis you need every morning after newsletter promotion 'It is how firms and managers choose to implement it that is so crucial in bringing benefit to their workforce and overall productivity,' he said.
