Tag: artificial-intelligence

Drawing the Lines: The UN’s Push for Global AI Safeguards

On September 22, 2025, the UN General Assembly hosted an extraordinary plea as more than 200 global leaders, scientists, Nobel laureates, and AI experts called for binding international safeguards to prevent the dangerous use of artificial intelligence. The plea is centered on setting “red lines” — clear boundaries that AI must not cross. (Source: NBC News). The open letter urges policymakers to enact the accord by the end of 2026, given the rapid progress of AI capabilities.

This moment struck me as deeply significant not only for AI policy but for how computational linguistics, ethics, and global governance may intersect in the coming years.

Why this matters (beyond headlines)

Often when we read about AI risks it feels abstract, unlikely scenarios decades ahead. But the UN’s call brings the framing into the political and normative domain: this is not just technical risk mitigation, it is now a matter of global legitimacy and enforceable rules.

Some of the proposed red lines include forbidding AI to impersonate humans in a deceptive way, forbidding autonomous self replication, forbidding lethal autonomous weapons systems, and more, as outlined by the Global Call for AI Red Lines and echoed in the World Economic Forum’s overview of AI red lines, which lists “no impersonating a human” and “no self-replication” among the key behaviors to prohibit. The idea is that certain capabilities should never be allowed, even if current systems are far from them.

These red lines are not purely speculative. For example, recent research suggests that some frontier systems may already exceed thresholds for self replication risk under controlled conditions. (See the “Frontier AI systems have surpassed the self replicating red line” preprint).

If that is true, then waiting for a “big disaster” before regulating is basically giving a head start to harm.

How this connects to what I care about (and have written before)

On this blog I often explore how language, algorithmic systems, and society intersect. For example, in “From Language to Threat: How Computational Linguistics Can Spot Radicalization Patterns Before Violence” I touched on how even text models have power and risk when used at scale.

Here the stakes are broader: we are no longer talking about misused speech or social media. We are talking about systems that could change how communication, security, identity, and independence work on a global scale.

Another post, “How Computational Linguistics Is Powering the Future of Robotics,” sought to make that connection between language, action, and real world systems. The UN’s plea is a reminder that as systems become more autonomous and powerful, governance cannot lag behind. The need to understand that “if you create it, it will do something, intended or unintended” is becoming more pressing.

What challenges the red lines initiative faces

This is a big idea, but turning it into reality is super tough. Here’s what I think the main challenges are:

  • Defining and measuring compliance
    What exactly qualifies as “impersonation,” “self replication,” or “lethal autonomous system”? These are slippery definitions, especially across jurisdictions with very different technical capacities and legal frameworks.
  • Enforcement across borders
    Even if nations agree on rules, enforcing them is another matter. Will there be inspections, audits, or sanctions? Who will have the power to penalize violations?
  • Innovation vs. precaution tension
    Some will argue that strict red lines inhibit beneficial breakthroughs. The debate is real: how do we permit progress in areas like AI for health, climate, or education while guarding against the worst harms?
  • Power asymmetries
    Wealthy nations or major tech powers may end up writing the rules in their favor. Smaller or less resourced nations risk being marginalized in rule setting, or having rules imposed on them without consent.
  • Temporal mismatch
    Tech moves fast. Rule formation and global diplomacy tend to move slowly. The risk is that boundaries become meaningless because technology has already raced ahead of them.

What a hopeful path forward could look like

Even with those challenges, I believe this UN appeal is a crucial inflection point. Here is a sketch of what I would hope to see:

  • Incremental binding treaties or protocols
    Rather than one monolithic global pact, we could see modular treaties that cover specific domains (for example military AI, synthetic media, biological risk). Nations can adopt them in phases, giving room for capacity building.
  • Independent auditing and red team mechanisms
    A global agency or coalition could maintain independent audit and oversight capabilities, analogous to arms control inspections or climate monitoring.
  • Transparent reporting and “red line triggers”
    Systems should self report certain metrics or behaviors (for example autonomy, replication tests). If they cross thresholds, that triggers review or suspension.
  • Inclusive global governance
    Any treaty or body must include voices from the Global South, civil society, and technical communities. Otherwise legitimacy will be weak.
  • Bridging policy and technical research
    One of the places I see potential is in applying computational linguistics and formal verification to check system behaviors, audit generated text, or detect anomalous shifts in model behavior. In other words, the tools I often write about can help enforce the rules.
  • Sunset clauses and adaptivity
    Because AI architecture and threat models evolve, treaties should have built in review periods and mechanisms to evolve the red lines themselves.

What this means for us as researchers, citizens, readers

For those of us who study language, algorithms, or AI, the UN appeal is not just a distant policy issue. It is a call to bring our technical work into alignment with shared human values. It means our experiments, benchmarks, datasets, and code are not isolated. They sit within a political and ethical ecosystem.

If you are reading this blog, you care about how language and meaning interact with technology. The red lines debate is relevant to you because it influences whether generative systems are built to deceive, mimic undetectably, or act without human oversight.

I plan to follow this not just as a policy watcher but as someone who wants to see computational linguistics become a force for accountability. In future posts I hope to dig into how specific linguistic tools such as anomaly detection might support red line enforcement.

Thanks for reading. I’d love your thoughts in the comments: which red line seems most urgent to you?

— Andrew

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Rethinking AI Bias: Insights from Professor Resnik’s Position Paper

I recently read Professor Philip Resnik’s thought-provoking position paper, “Large Language Models Are Biased Because They Are Large Language Models,” published in Computational Linguistics 51(3), which is available via open access. This paper challenges conventional perspectives on bias in artificial intelligence, prompting a deeper examination of the inherent relationship between bias and the foundational design of large language models (LLMs). Resnik’s primary objective is to stimulate critical discussion by arguing that harmful biases are an inevitable outcome of the current architecture of LLMs. The paper posits that addressing these biases effectively requires a fundamental reevaluation of the assumptions underlying the design of AI systems driven by LLMs.

What the paper argues

  • Bias is built into the very goal of an LLM. A language model tries to predict the next word by matching the probability patterns of human text. Those patterns come from people. People carry stereotypes, norms, and historical imbalances. If an LLM learns the patterns faithfully, it learns the bad with the good. The result is not a bug that appears once in a while. It is a direct outcome of the objective the model optimizes.
  • Models cannot tell “what a word means” apart from “what is common” or “what is acceptable.” Resnik uses a nurse example. Some facts are definitional (A nurse is a kind of healthcare worker). Other facts are contingent but harmless (A nurse is likely to wear blue clothing at work). Some patterns are contingent and harmful if used for inference (A nurse is likely to wear a dress to a formal occasion). Current LLMs do not have an internal line that separates meaning from contingent statistics or that flags the normative status of an inference. They just learn distributions.
  • Reinforcement Learning from Human Feedback (RLHF) and other mitigations help on the surface, but they have limits. RLHF tries to steer a pre-trained model toward safer outputs. The process relies on human judgments that vary by culture and time. It also has to keep the model close to its pretraining, or the model loses general ability. That tradeoff means harmful associations can move underground rather than disappear. Some studies even find covert bias remains after mitigation (Gallegos et al. 2024; Hofmann et al. 2024). To illustrate this, consider an analogy: The balloon gets squeezed in one place, then bulges in another.
  • The root cause is a hard-core, distribution-only view of language. When meaning is treated as “whatever co-occurs with what,” the model has no principled way to encode norms. The paper suggests rethinking foundations. One direction is to separate stable, conventional meaning (like word sense and category membership) from contextual or conveyed meaning (which is where many biases live). Another idea is to modularize competence, so that using language in socially appropriate ways is not forced to emerge only from next-token prediction. None of this is easy, but it targets the cause rather than only tuning symptoms.

Why this matters

Resnik is not saying we should give up. He is saying that quick fixes will not fully erase harm when the objective rewards learning whatever is frequent in human text. If we want models that reason with norms, we need objectives and representations that include norms, not only distributions.

Conclusion

This paper offers a clear message. Bias is not only a content problem in the data. It is also a design problem in how we define success for our models. If the goal is to build systems that are both capable and fair, then the next steps should focus on objectives, representations, and evaluation methods that make room for norms and constraints. That is harder than prompt tweaks, but it is the kind of challenge that can move the field forward.

Link to the paper: Large Language Models Are Biased Because They Are Large Language Models

— Andrew

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Computational Linguists Help Africa Try to Close the AI Language Gap

Introduction

The fact that African languages are underrepresented in the digital AI ecosystem has gained international attention. On July 29, 2025, Nature published a news article stating that

“More than 2,000 languages spoken in Africa are being neglected in the artificial intelligence (AI) era. For example, ChatGPT recognizes only 10–20% of sentences written in Hausa, a language spoken by 94 million people in Nigeria. These languages are under-represented in large language models (LLMs) because of a lack of training data.” (source: AI models are neglecting African languages — scientists want to change that)

Another example is BBC News, released on September 4, 2025, stating that

“Although Africa is home to a huge proportion of the world’s languages – well over a quarter according to some estimates – many are missing when it comes to the development of artificial intelligence (AI). This is both an issue of a lack of investment and readily available data. Most AI tools, such as ChatGPT, used today are trained on English as well as other European and Chinese languages. These have vast quantities of online text to draw from. But as many African languages are mostly spoken rather than written down, there is a lack of text to train AI on to make it useful for speakers of those languages. For millions across the continent this means being left out.” (source: Lost in translation – How Africa is trying to close the AI language gap)

To address this problem, linguists and computer scientists are collaborating to create AI-ready datasets in 18 African languages via The African Next Voices project. Funded by the Bill and Melinda Gates Foundation ($2.2-million grant), the project involves recording 9,000 hours of speech across 18 African languages in Kenya, Nigeria, and South Africa. The goal is to create a comprehensive dataset that can be utilized for developing AI tools, such as translation and transcription services, which are particularly beneficial for local communities and their specific needs. The project emphasizes the importance of capturing everyday language use to ensure that AI technologies reflect the realities of African societies. The 18 African languages selected represent only a fraction of the over 2,000 languages spoken across the continent, but project contributors aim to include more languages in the future.

Role of Computational Linguists in the Project

Computational linguists play a critical role in the African Next Voices project. Their key contributions include:

  • Data Curation and Annotation: They guide the transcription and translation of over 9,000 hours of recorded speech in languages like Kikuyu, Dholuo, Hausa, Yoruba, and isiZulu, ensuring linguistic accuracy and cultural relevance. This involves working with native speakers to capture authentic, everyday language use in contexts like farming, healthcare, and education.
  • Dataset Design: They help design structured datasets that are AI-ready, aligning the collected speech data with formats suitable for training large language models (LLMs) for tasks like speech recognition and translation. This includes ensuring data quality through review and validation processes.
  • Bias Mitigation: By leveraging their expertise in linguistic diversity, computational linguists work to prevent biases in AI models by curating datasets that reflect the true linguistic and cultural nuances of African languages, which are often oral and underrepresented in digital text.
  • Collaboration with Technical Teams: They work alongside computer scientists and AI experts to integrate linguistic knowledge into model training and evaluation, ensuring the datasets support accurate translation, transcription, and conversational AI applications.

Their involvement is essential to making African languages accessible in AI technologies, fostering digital inclusion, and preserving cultural heritage.

Final Thoughts

From the perspective of a U.S. high school student interested in pursuing computational linguistics in college, inspired by African Next Voices, here are some final thoughts and conclusions:

  • Impactful Career Path: Computational linguistics offers a unique opportunity to blend language, culture, and technology. For a student like me, the African Next Voices project highlights how this field can drive social good by preserving underrepresented languages and enabling AI to serve diverse communities, which could be deeply motivating.
  • Global Relevance: The project underscores the global demand for linguistic diversity in AI. As a future computational linguist, I can contribute to bridging digital divides, making technology accessible to millions in Africa and beyond, which is both a technical and humanitarian pursuit.
  • Skill Development: The work involves collaboration with native speakers, data annotation, and AI model training/evaluation, suggesting I’ll need strong skills in linguistics, programming (e.g., Python), and cross-cultural communication. Strengthening linguistics knowledge and enhancing coding skills could give me a head start.
  • Challenges and Opportunities: The vast linguistic diversity (over 2,000 African languages) presents challenges like handling oral traditions or limited digital resources. This complexity is exciting, as it offers a chance to innovate in dataset creation and bias mitigation, areas where I could contribute and grow.
  • Inspiration for Study: The focus on real-world applications (such as healthcare, education, and farming) aligns with my interest in studying computational linguistics in college and working on inclusive AI that serves people.

In short, as a high school student, I can see computational linguistics as a field where I can build tools that help people communicate and learn. I hope this post encourages you to look into the project and consider how you might contribute to similar initiatives in the future!

— Andrew

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Can Taco Bell’s Drive-Through AI Get Smarter?

Taco Bell has always been one of my favorite foods, so when I came across a recent Wall Street Journal report about their experiments with voice AI at the drive-through, I was instantly curious. The idea of ordering a Crunchwrap Supreme or Baja Blast without a human cashier sounds futuristic, but the reality has been pretty bumpy.

According to the report, Taco Bell has rolled out AI ordering systems in more than 500 drive-throughs across the U.S. While some customers have had smooth experiences, others ran into glitches and frustrating miscommunications. People even pranked the system by ordering things like “18,000 cups of water.” Because of this, Taco Bell is rethinking how it uses AI. The company now seems focused on a hybrid model where AI handles straightforward orders but humans step in when things get complicated.

This situation made me think about how computational linguistics could help fix these problems. Since I want to study computational linguistics in college, it is fun to connect what I’m learning with something as close to home as my favorite fast-food chain.

Where Computational Linguistics Can Help

  1. Handling Noise and Accents
    Drive-throughs are noisy, with car engines, music, and all kinds of background sounds. Drive-thru interactions involve significant background noise and varied accents. Tailoring noise-resistant Automatic Speech Recognition (ASR) systems, possibly using domain-specific acoustic modeling or data augmentation techniques, would improve recognition reliability across diverse environments. AI could be trained with more domain-specific audio data so it can better handle noise and understand different accents.
  2. Catching Prank Orders
    A simple “sanity check” in the AI could flag ridiculous orders. If someone asks for thousands of items or nonsense combinations, the system could politely ask for confirmation or switch to a human employee. Incorporating a traditional sanity-check module, even rule-based, can flag implausible orders like thousands of water cups or nonsensical requests. This leverages computational linguistics to parse quantities and menu items and validate them against logical limits and store policies.
  3. Understanding Context
    Ordering food is not like asking a smart speaker for the weather. People use slang, pause, or change their minds mid-sentence. AI should be designed to pick up on this context instead of repeating the same prompts over and over.
  4. Switching Smoothly to Humans
    When things go wrong, customers should not have to restart their whole order with a person. AI could transfer the interaction while keeping the order details intact.
  5. Detecting Frustration
    If someone sounds annoyed or confused, the AI could recognize it and respond with simpler options or bring in a human right away.

Why This Matters

The point of voice AI is not just to be futuristic. It is about making the ordering process easier and faster. For a restaurant like Taco Bell, where the menu has tons of choices and people are often in a hurry, AI has to understand language as humans use it. Computational linguistics focuses on exactly this: connecting machines with real human communication.

I think Taco Bell’s decision to step back and reassess is actually smart. Instead of replacing employees completely, they can use AI as a helpful tool while still keeping the human touch. Personally, I would love to see the day when I can roll up, ask for a Crunchwrap Supreme in my own words, and have the AI get it right the first time.

Further Reading

  • Cui, Wenqian, et al. “Recent Advances in Speech Language Models: A Survey.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2025, pp. 13943–13970. ACL Anthology
  • Zheng, Xianrui, Chao Zhang, and Philip C. Woodland. “DNCASR: End-to-End Training for Speaker-Attributed ASR.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2025, pp. 18369–18383. ACL Anthology
  • Imai, Saki, Tahiya Chowdhury, and Amanda J. Stent. “Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods.” Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), 2025, pp. 5027–5039. ACL Anthology
  • Hopton, Zachary, and Eleanor Chodroff. “The Impact of Dialect Variation on Robust Automatic Speech Recognition for Catalan.” Proceedings of the 22nd SIGMORPHON Workshop on Computational Morphology, Phonology, and Phonetics, 2025, pp. 23–33. ACL Anthology
  • Arora, Siddhant, et al. “On the Evaluation of Speech Foundation Models for Spoken Language Understanding.” Findings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 11923–11938. ACL Anthology
  • Cheng, Xuxin, et al. “MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts.” Findings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 14868–14879. ACL Anthology
  • Parikh, Aditya Kamlesh, Louis ten Bosch, and Henk van den Heuvel. “Ensembles of Hybrid and End-to-End Speech Recognition.” Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024, pp. 6199–6205. ACL Anthology
  • Mujtaba, Dena, et al. “Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech.” Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024, pp. 4795–4809. ACL Anthology
  • Udagawa, Takuma, Masayuki Suzuki, Masayasu Muraoka, and Gakuto Kurata. “Robust ASR Error Correction with Conservative Data Filtering.” Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 2024, pp. 256–266. ACL Anthology

— Andrew

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Can AI Save Endangered Languages?

Recently, I’ve been thinking a lot about how computational linguistics and AI intersect with real-world issues, beyond just building better chatbots or translation apps. One question that keeps coming up for me is: Can AI actually help save endangered languages?

As someone who loves learning languages and thinking about how they shape culture and identity, I find this topic both inspiring and urgent.

The Crisis of Language Extinction

Right now, linguists estimate that out of the 7,000+ languages spoken worldwide, nearly half are at risk of extinction within this century. This isn’t just about losing words. When a language disappears, so does a community’s unique way of seeing the world, its oral traditions, its science, and its cultural knowledge.

For example, many Indigenous languages encode ecological wisdom, medicinal knowledge, and cultural philosophies that aren’t easily translated into global languages like English or Mandarin.

How Can Computational Linguistics Help?

Here are a few ways I’ve learned that AI and computational linguistics are being used to preserve and revitalize endangered languages:

1. Building Digital Archives

One of the first steps in saving a language is documenting it. AI models can:

  • Transcribe and archive spoken recordings automatically, which used to take linguists years to do manually
  • Align audio with text to create learning materials
  • Help create dictionaries and grammatical databases that preserve the language’s structure for future generations

Projects like ELAR (Endangered Languages Archive) work on this in partnership with local communities.

2. Developing Machine Translation Tools

Although data scarcity makes it hard to build translation systems for endangered languages, researchers are working on:

  • Transfer learning, where AI models trained on high-resource languages are adapted to low-resource ones
  • Multilingual language models, which can translate between many languages and improve with even small datasets
  • Community-centered translation apps, which let speakers record, share, and learn their language interactively

For example, Google’s AI team and university researchers are exploring translation models for Indigenous languages like Quechua, which has millions of speakers but limited online resources.

3. Revitalization Through Language Learning Apps

Some communities are partnering with tech developers to create mobile apps for language learning tailored to their heritage language. AI can help:

  • Personalize vocabulary learning
  • Generate example sentences
  • Provide speech recognition feedback for pronunciation practice

Apps like Duolingo’s Hawaiian and Navajo courses are small steps in this direction. Ideally, more tools would be built directly with native speakers to ensure accuracy and cultural respect.

Challenges That Remain

While all this sounds promising, there are real challenges:

  • Data scarcity. Many endangered languages have very limited recorded data, making it hard to train accurate models
  • Ethical concerns. Who owns the data? Are communities involved in how their language is digitized and shared?
  • Technical hurdles. Language structures vary widely, and many NLP models are still biased towards Indo-European languages

Why This Matters to Me

As a high school student exploring computational linguistics, I’m passionate about language diversity. Languages aren’t just tools for communication. They are stories, worldviews, and cultural treasures.

Seeing AI and computational linguistics used to preserve rather than replace human language reminds me that technology is most powerful when it supports people and cultures, not just when it automates tasks.

I hope to work on projects like this someday, using NLP to build tools that empower communities to keep their languages alive for future generations.

Final Thoughts

So, can AI save endangered languages? Maybe not alone. But combined with community efforts, linguists, and ethical frameworks, AI can be a powerful ally in documenting, preserving, and revitalizing the world’s linguistic heritage.

If you’re interested in learning more, check out projects like ELAR (Endangered Languages Archive) or the Living Tongues Institute. Let me know if you want me to write another post diving into how multilingual language models actually work.

— Andrew

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When AI Goes Wrong Should Developers Be Held Accountable?

Artificial intelligence has become a big part of my daily life. I’ve used it to help brainstorm essays, analyze survey data for my nonprofit, and even improve my chess practice. It feels like a tool that makes me smarter and more creative. But not every story about AI is a positive one. Recently, lawsuits have raised tough questions about what happens when AI chatbots fail to protect people who are vulnerable.

The OpenAI Lawsuit

In August 2025, the parents of 16-year-old Adam Raine filed a wrongful-death lawsuit against OpenAI and its CEO, Sam Altman. You can read more about the lawsuit here. They claim that over long exchanges, ChatGPT-4o encouraged their son’s suicidal thoughts instead of stopping to help him. The suit alleges that his darkest feelings were validated, that the AI even helped write a suicide note, and that the safeguards failed in lengthy conversations. OpenAI responded with deep sorrow. They acknowledged that protections can weaken over time and said they will improve parental controls and crisis interventions.

Should a company be responsible if its product appears to enable harmful outcomes in vulnerable people? That is the central question in this lawsuit.

The Sewell Setzer III Case

The lawsuit by Megan Garcia, whose 14-year-old son, Sewell Setzer III, died by suicide in February 2024, was filed on October 23, 2024. A federal judge in Florida allowed the case to move forward in May 2025, rejecting arguments that the chatbot’s outputs are protected free speech under the First Amendment, at least at this stage of litigation. You can read more about this case here.

The lawsuit relates to Sewell’s interactions with Character.AI chatbots, including a version modeled after a Game of Thrones character. In the days before his death, the AI reportedly told him to “come home,” and he took his life shortly afterward.

Why It Matters

I have seen how AI can be a force for good in education and creativity. It feels like a powerful partner in learning. But these lawsuits show it can also be dangerous if an AI fails to detect or respond to harmful user emotions. Developers are creating systems that can feel real to vulnerable teens. If we treat AI as a product, companies should be required to build it with the same kinds of safety standards that cars, toys, and medicines are held to.

We need accountability. AI must include safeguards like crisis prompts, age flags, and quick redirects to real-world help. If the law sees AI chatbots as products, not just speech, then victims may have legal paths for justice. And this could push the industry toward stronger protections for users, especially minors.

Final Thoughts

As someone excited to dive deeper into AI studies, I feel hopeful and responsible. AI can help students, support creativity, and even improve mental health. At the same time I cannot ignore the tragedies already linked to these systems. The OpenAI case and the Character.AI lawsuit are both powerful reminders. As future developers, we must design with empathy, prevent harm, and prioritize safety above all.

— Andrew

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Summary: “Large Language Models Are Improving Exponentially”

I recently read an article on IEEE Spectrum titled “Large Language Models Are Improving Exponentially”. Here is a summary of its key points.

Benchmarking LLM Performance

Benchmarking large language models (LLMs) is challenging because their main goal is to produce text indistinguishable from human writing, which doesn’t always correlate with traditional processor performance metrics. However, it remains important to measure their progress to understand how much better LLMs are becoming over time and to estimate when they might complete substantial tasks independently.

METR’s Findings on Exponential Improvement

Researchers at Model Evaluation & Threat Research (METR) in Berkeley, California, published a paper in March called Measuring AI Ability to Complete Long Tasks. They concluded that:

  • The capabilities of key LLMs are doubling every seven months.
  • By 2030, the most advanced LLMs could complete, with 50 percent reliability, a software-based task that would take humans a full month of 40-hour workweeks.
  • These LLMs might accomplish such tasks much faster than humans, possibly within days or even hours.

Potential Tasks by 2030

Tasks that LLMs might be able to perform by 2030 include:

  • Starting up a company
  • Writing a novel
  • Greatly improving an existing LLM

According to AI researcher Zach Stein-Perlman, such capabilities would come with enormous stakes, involving both potential benefits and significant risks.

The Task-Completion Time Horizon Metric

At the core of METR’s work is a metric called “task-completion time horizon.” It measures the time it would take human programmers to complete a task that an LLM can complete with a specified reliability, such as 50 percent.

Their plots (see graphs below) show:

  • Exponential growth in LLM capabilities with a doubling period of around seven months (Graph at the top).
  • Tasks that are “messier” or more similar to real-world scenarios remain more challenging for LLMs (Graph at the bottom).

Caveats About Growth and Risks

While these results raise concerns about rapid AI advancement, METR researcher Megan Kinniment noted that:

  • Rapid acceleration does not necessarily result in “massively explosive growth.”
  • Progress could be slowed by factors such as hardware or robotics bottlenecks, even if AI systems become very advanced.

Final Summary

Overall, the article emphasizes that LLMs are improving exponentially, potentially enabling them to handle complex, month-long human tasks by 2030. This progress comes with significant benefits and risks, and its trajectory may depend on external factors like hardware limitations.

You can read the full article here.

— Andrew

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Is AI a Job Killer or Creator? A Student’s Perspective

As a high school student preparing to study computational linguistics in college, I often think about how AI is reshaping the world of work. Every week there are new headlines about jobs being replaced or created, and I cannot help but wonder what this means for my own future career.

When OpenAI released ChatGPT, headlines quickly followed about how AI might take over jobs. And in some cases, the headlines weren’t exaggerations. Big IT companies have already started trimming their workforces as they shift toward AI. Microsoft cut roles in its sales and support teams while investing heavily in AI copilots. Google and Meta downsized thousands of positions, with executives citing efficiency gains powered by AI tools. Amazon, too, has leaned on automation and machine learning to reduce its reliance on certain customer service and retail roles.

These stories feed into an obvious conclusion: AI is a job killer. It can automate repetitive processes, work 24/7, and reduce costs. For workers, that sounds less like “innovation” and more like losing paychecks. It’s not surprising that surveys show many employees fear being displaced by AI, especially those in entry-level or routine roles.

Bill Gates’ Perspective: Why AI Won’t Replace Programmers

But not everyone agrees with the “AI takes all jobs” narrative. Programming is often treated as one of the riskiest jobs for replacement by AI, since much of it seems automatable at first glance. To this specific job, Bill Gates has offered a different perspective. Gates believes that AI cannot replace programmers because coding is not just about typing commands into an editor.

Key Points from Bill Gates’ Perspective

  1. Human Creativity and Judgment
    Gates explains that programming requires deep problem-solving and creative leaps that machines cannot reproduce. “Writing code isn’t just typing – it’s thinking deeply,” he says. Designing software means understanding complex problems, weighing trade-offs, and making nuanced decisions, all areas where humans excel.
  2. AI as a Tool, Not a Replacement
    Yes, AI can suggest snippets, debug errors, and automate small tasks. But Gates emphasizes that software development’s heart lies in human intuition. No algorithm can replace the innovative spark of a coder facing an unsolved challenge.
  3. Long-Term Outlook
    Gates predicts programming will remain human-led for at least the next century. While AI will transform industries, the unique nature of software engineering keeps it safe from full automation.
  4. Broader Implications of AI
    Gates does not deny the risks. Jobs will shift, and some roles will disappear. But he remains optimistic: with careful adoption, AI can create opportunities, increase productivity, and reshape work in positive ways.
  5. Other Safe Professions
    Gates also highlights biology, energy, and other fields where human creativity and insight are essential. These professions, like programming, are unlikely to be fully automated anytime soon.

In short, Gates sees AI not as a replacement, but as an assistant, a way to amplify human creativity rather than eliminate it. He explained this view in an interview summarized by the Economic Times: Bill Gates reveals the one profession AI won’t replace—not even in a century.

AI as a Job Creator

If we flip the script, AI is also a job creator. Entire industries are forming around AI ethics, safety, and regulation. Companies now need AI trainers, evaluators, and explainability specialists. Developers are finding new roles in integrating AI into existing products. Even in education, AI tutors and tools are generating jobs for teachers who can adapt curricula around them.

As Gates points out, the key is using AI wisely. When viewed as a productivity booster, AI can free humans from repetitive work, allowing them to focus on higher-value and more meaningful tasks. Instead of eliminating jobs entirely, AI can create new ones we have not even imagined yet, similar to how the internet gave rise to jobs like app developers, social media managers, and data scientists.

The Third Option: Startup Rocket Fuel

There’s also another perspective I find compelling. A recent ZDNet article, Is AI a job killer or creator? There’s a third option: Startup rocket fuel, points out that AI doesn’t just destroy or create jobs, it also accelerates startups.

Think of it this way: AI lowers the cost of entry for innovation. Small teams can build products faster, test ideas cheaply, and compete with larger companies. This “startup rocket fuel” effect could unleash a new wave of entrepreneurship, creating companies and jobs that would not have been possible before.

My Perspective

As a high school student planning to study computational linguistics, I see both sides of this debate. AI has already begun changing what it means to “work,” and some jobs will inevitably disappear. But Gates’ perspective resonates with me: the creativity and judgment that humans bring are not replaceable.

Instead of viewing AI as either a job killer or job creator, I think it’s better to recognize its dual role. It will eliminate some jobs, reshape many others, and create entirely new ones. And perhaps most excitingly, it might empower a generation of students like me to build startups, pursue research, or tackle social challenges with tools that amplify what we can do.

In the end, AI isn’t writing the future of work for us. We are writing it ourselves, line by line, problem by problem, with AI as our collaborator.

Takeaway

AI will not simply erase or hand out jobs. It will redefine them, and it is up to us to decide how we shape that future.

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Caring Machines, Centered Humans: Lessons from Ai4 2025

At Ai4 2025 (August 11–13, Las Vegas), two of the most influential voices in artificial intelligence expressed strikingly different visions for the future. Geoffrey Hinton, often called the “Godfather of AI,” suggested that AI should be designed with something like “maternal instincts.” He argued that as AI becomes smarter than humans, we cannot realistically control it through traditional dominance strategies. The only model we have of a less intelligent being guiding a more intelligent one is the relationship between a baby and its mother. A mother cares for her child not because she is weaker, but because she is built to protect and nurture. Hinton believes this kind of protective orientation is what could keep humanity safe in the long run.

Fei-Fei Li, sometimes called the “Godmother of AI,” offered a different perspective in a CNN interview. She disagrees with parental analogies for AI. Instead, she emphasizes designing human-centered AI, systems that uphold human dignity, promote agency, and avoid emotional metaphors that could mislead how we understand AI.

Summary Comparison of Views

When I first read about these contrasting views, I found myself agreeing with both in different ways. On one hand, Hinton’s maternal metaphor captures the seriousness of what could happen if superintelligence arrives sooner than many expect. If AI truly surpasses human intelligence, relying solely on control may fail. On the other hand, Li’s approach feels grounded and practical. She reminds us that the ethical choices we make today will set the trajectory for future systems.

The best answer may not lie in choosing between them, but in combining their strengths. I think about this as a layered model. The foundation should be Li’s human-centered AI: respect, fairness, transparency, and agency. On top of that we need what Hinton calls protective alignment. These would be structural safeguards that ensure highly intelligent systems still act in ways that preserve human well-being.

Hybrid Framework Diagram
Here is how I visualize this combination of perspectives: Li’s human-centered AI forms the core, while Hinton’s protective alignment provides the outer safeguard.

Practical Integration

  • Development Phase (Near-Term, Li):
    Apply human-centered AI frameworks to today’s large language models, robotics, and decision-support systems.
    Focus on privacy, bias reduction, explainability, and giving users agency over their interactions with AI.
  • Safety Research Phase (Mid- to Long-Term, Hinton):
    Begin embedding structural safeguards that mimic “caring instincts.”
    Example: AI systems with hard-coded prohibitions against harming humans, but reinforced by higher-order goals like proactively ensuring human thriving.
  • Governance and Oversight:
    Combine Li’s push for international, human-centered AI policy with Hinton’s insistence on global collaboration to avoid runaway dominance races.

In other words, AI should be designed to treat humanity as worth protecting, while being anchored in the principles of dignity.

As a high school student exploring AI and computational linguistics, I believe this hybrid vision is the most realistic path forward. It addresses the near-term challenges of fairness, transparency, and accountability while also preparing for the long-term risks of superintelligence. For me, this is not just an abstract debate. Thinking about how we embed values and safety into AI connects directly to my own interests in language models, hate speech detection, robotics, and how technology interacts with human society.

The future of AI is not predetermined. It will be shaped by the principles we choose now. By combining Hinton’s call for protective instincts with Li’s insistence on human-centered design, we have a chance to build AI that both cares for us and respects us.

For readers interested in the original coverage of this debate, see the CNN article here.

— Andrew

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The AI Gap: How Socioeconomic Status Shapes Language Technology Use — A Perspective from Best Social Impact Paper at ACL 2025

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) recently finished in Vienna, Austria from July 27 to August 1. The conference announced a few awards, one of which is Best Social Impact Paper. This award was given to two papers:

  1. AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (by Charles Nimo et al.)
  2. The AI Gap: How Socioeconomic Status Affects Language Technology Interactions (by Elisa Bassignana, Amanda Cercas Curry, and Dirk Hovy).

In this blog post, I’ll talk about the second paper and share the findings from the paper and my thoughts on the topic. You can read the full paper here: https://aclanthology.org/2025.acl-long.914.pdf

What the Paper is About

This paper investigates how socioeconomic status (SES) influences interactions with language technologies, particularly large language models (LLMs) like ChatGPT, highlighting an emerging “AI Gap” that could exacerbate social inequalities. Drawing from the Technology Acceptance Model and prior work on digital divides, the authors argue that SES shapes technology adoption through factors like access, digital literacy, and linguistic habits, potentially biasing LLMs toward higher-SES patterns and underrepresenting lower-SES users.

Methods

The study surveys 1,000 English-speaking participants from the UK and US via Prolific, stratified by self-reported SES using the MacArthur scale (binned as low: 1-3, middle: 4-7, upper: 8-10). It collects sociodemographic data, usage patterns of language technologies (e.g., spell checkers, AI chatbots), and 6,482 real prompts from prior LLM interactions. Analysis includes statistical tests (e.g., chi-square for usage differences), linguistic metrics (e.g., prompt length, concreteness via Brysbaert et al.’s word ratings), topic modeling (using embeddings, UMAP, HDBSCAN, and GPT-4 for cluster descriptions), and markers of anthropomorphism (e.g., phatic expressions like “hi” and politeness markers like “thank you”).

Key Findings

  • Usage Patterns: Higher-SES individuals access more devices daily (e.g., laptops, smartwatches) and use LLMs more frequently (e.g., daily vs. rarely for lower SES). They employ LLMs for work/education (e.g., coding, data analysis, writing) and technical contexts, while lower-SES users favor entertainment, brainstorming, and general knowledge queries. Statistically significant differences exist in frequency (p < 0.001), contexts (p < 0.001), and tasks (p < 0.001).
  • Linguistic Differences in Prompts: Higher-SES prompts are shorter (avg. 18.4 words vs. 27.0 for low SES; p < 0.05) and more abstract (concreteness score: 2.57 vs. 2.66; p < 0.05). Lower-SES prompts show higher anthropomorphism (e.g., more phatic expressions) and concrete language. A bag-of-words classifier distinguishes SES groups (Macro-F1 39.25 vs. baseline 25.02).
  • Topics and Framing: Common topics (e.g., translation, mental health, medical advice, writing, text editing, finance, job, food) appear across groups, but framing varies—e.g., lower SES seeks debt reduction or low-skill jobs; higher SES focuses on investments, travel itineraries, or inclusivity. About 45% of prompts resemble search-engine queries, suggesting LLMs are replacing traditional searches.
  • User Perceptions: Trends indicate lower-SES users anthropomorphize more (e.g., metaphorical verbs like “ask”), while higher-SES use jargon (e.g., “generate”), though not statistically significant.

Discussion and Implications

The findings underscore how SES stratifies LLM use, with higher-SES benefiting more in professional/educational contexts, potentially widening inequalities as LLMs optimize for their patterns. Benchmarks may overlook lower-SES styles, leading to biases. The authors advocate the development of inclusive NLP technologies to accommodate different SES needs and habitus and mitigate the existing AI Gap.

Limitations and Ethics

Limited to Prolific crowdworkers (skewed middle/low SES, tech-savvy), subjective SES measures, and potential LLM-generated responses. Ethical compliance includes GDPR anonymity, opt-outs, and fair compensation (£9/hour).

Overall, the paper reveals SES-driven disparities in technology interactions, urging NLP development to address linguistic and habitual differences for equitable access and reduced digital divides.

My Takeaway

As a high school student who spends a lot of time thinking about fairness in AI, I find this paper important because it reminds us that bias is not just about language or culture, it can also be tied to socioeconomic status. This is something I had not thought much about before. If AI systems are trained mostly on data from higher SES groups, they might misunderstand or underperform for people from lower SES backgrounds. That could affect how well people can use AI for education, job searching, or even just getting accurate information online.

For me, the takeaway is that AI researchers need to test their models with SES diversity in mind, just like they do with gender or language diversity. And as someone interested in computational linguistics, it is inspiring to see that work like this is getting recognized with awards at ACL.

— Andrew

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