Latest news with #computerScience
Bloomberg
16-07-2025
- Business
- Bloomberg
How Much Should a Tsinghua Graduate Be Making?
It's post-graduation season in China. Tiger Moms are naturally comparing notes on the salaries of fresh alumni from top universities. What kind of return might they expect, after spending years — and sometimes a fortune — demanding academic excellence from their offspring? A doctor friend told me recently that her son got a job at Huawei Technologies Co., considered one of the most prestigious employers in China. The young man studied computer science at Tsinghua University and then Brown University in the US. Huawei is starting him at 400,000 yuan ($55,689) a year, the parent beamed.
Geek Wire
08-07-2025
- Business
- Geek Wire
This Seattle tech giant is gobbling up computer science grads from the University of Washington
The graduation ceremony last month in Seattle for the University of Washington Paul G. Allen School of Computer Science & Engineering. (UW Photo / Matt Hagen) Amazon doesn't have to go far to find fresh tech talent. The Seattle tech giant is hiring more than 100 engineers from the latest graduating class at the University of Washington Paul G. Allen School of Computer Science & Engineering — an all-time high, according to university data shared with GeekWire. The record-setting move underscores Amazon's close ties to the UW's top-ranked computer science program, even as the broader tech job market shifts amid the rise of generative AI. Microsoft, headquartered in nearby Redmond, Wash., along with Meta and Google — which have major engineering hubs in the Seattle region — are each hiring more than 20 UW CSE grads. Those four corporations are taking a sizable chunk of the graduating class. There were about 650 students who graduated this year from the Allen School, not including PhD grads. About 17% of that group is headed to some type of graduate school. The hiring trend reflects a strong talent pipeline between the UW and Seattle's tech industry. The data is also notable given that computer science grads face a tougher job market than in years past, with companies laying off workers and pulling back on headcount growth — a stark contrast to the post-pandemic tech hiring boom. Amazon and Microsoft are aiming to streamline management layers and rely more heavily on AI for efficiency. Meanwhile, entry-level roles — jobs that new graduates often fill — appear most susceptible to automation. Computer science schools are already adjusting their approach to keep pace with generative AI tools, including those capable of writing code. Amazon has a longtime relationship with the Allen School. It donated $10 million in 2016 to help the school construct its second building (Microsoft also donated $10 million). The company in 2022 helped launch the 'UW+Amazon Science Hub' that supports various engineering-related programs. Amazon has also funded endowed professorships in machine learning. In response to a GeekWire inquiry, Amazon said it works with university partners to foster the next generation of tech talent. Previously: Founders, recruiters, professors share advice for grads on how to land a tech job in the AI era
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.
New York Times
30-06-2025
- Science
- New York Times
How Do You Teach Computer Science in the A.I. Era?
Carnegie Mellon University has a well-earned reputation as one of the nation's top schools for computer science. Its graduates go on to work at big tech companies, start-ups and research labs worldwide. Still, for all its past success, the department's faculty is planning a retreat this summer to rethink what the school should be teaching to adapt to the rapid advancement of generative artificial intelligence. The technology has 'really shaken computer science education,' said Thomas Cortina, a professor and an associate dean for the university's undergraduate programs. Computer science, more than any other field of study, is being challenged by generative A.I. The A.I. technology behind chatbots like ChatGPT, which can write essays and answer questions with humanlike fluency, is making inroads across academia. But A.I. is coming fastest and most forcefully to computer science, which emphasizes writing code, the language of computers. Big tech companies and start-ups have introduced A.I. assistants that can generate code and are rapidly becoming more capable. And in January, Mark Zuckerberg, Meta's chief executive, predicted that A.I. technology would effectively match the performance of a midlevel software engineer sometime this year. Computer science programs at universities across the country are now scrambling to understand the implications of the technological transformation, grappling with what to keep teaching in the A.I. era. Ideas range from less emphasis on mastering programming languages to focusing on hybrid courses designed to inject computing into every profession, as educators ponder what the tech jobs of the future will look like in an A.I. economy. Want all of The Times? Subscribe.
