From Answer-Giving to Question-Asking: Inverting the Socratic Method in the Age of AI

Curated on Posted on by Stefaan Verhulst

Blog by Anthea Roberts: “…If questioning is indeed becoming a premier cognitive skill in the AI age, how should education and professional development evolve? Here are some possibilities:

  1. Assessment Through Iterative Questioning: Rather than evaluating students solely on their answers, we might assess their ability to engage in sustained, productive questioning—their skill at probing, following up, identifying inconsistencies, and refining inquiries over multiple rounds. Can they navigate a complex problem through a series of well-crafted questions? Can they identify when an AI response contains subtle errors or omissions that require further exploration?
  2. Prompt Literacy as Core Curriculum: Just as reading and writing are foundational literacies, the ability to effectively prompt and question AI systems may become a basic skill taught from early education onward. This would include teaching students how to refine queries, test assumptions, and evaluate AI responses critically—recognizing that AI systems still hallucinate, contain biases from their training data, and have uneven performance across different domains.
  3. Socratic AI Interfaces: Future AI interfaces might be designed explicitly to encourage Socratic dialogue rather than one-sided Q&A. Instead of simply answering queries, these systems might respond with clarifying questions of their own: “It sounds like you’re asking about X—can you tell me more about your specific interest in this area?” This would model the kind of iterative exchange that characterizes productive human-human dialogue…(More)”.

How to Survive the A.I. Revolution

Curated on Posted on by Stefaan Verhulst

Essay by John Cassidy: “It isn’t clear where the term “Luddite” originated. Some accounts trace it to Ned Ludd, a textile worker who reportedly smashed a knitting frame in 1779. Others suggest that it may derive from folk memories of King Ludeca, a ninth-century Anglo-Saxon monarch who died in battle. Whatever the source, many machine breakers identified “General Ludd” as their leader. A couple of weeks after the Rawfolds attack, William Horsfall, another mill owner, was shot dead. A letter sent after Horsfall’s assassination—which hailed “the avenging of the death of the two brave youths who fell at the siege of Rawfolds”—began “By Order of General Ludd.”

The British government, at war with Napoleon, regarded the Luddites as Jacobin insurrectionists and responded with brutal suppression. But this reaction stemmed from a fundamental misinterpretation. Far from being revolutionary, Luddism was a defensive response to the industrial capitalism that was threatening skilled workers’ livelihoods. The Luddites weren’t mindless opponents of technology but had a clear logic to their actions—an essentially conservative one. Since they had no political representation—until 1867, the British voting franchise excluded the vast majority—they concluded that violent protest was their only option. “The burning of Factorys or setting fire to the property of People we know is not right, but Starvation forces Nature to do that which he would not,” one Yorkshire cropper wrote. “We have tried every effort to live by Pawning our Cloaths and Chattles, so we are now on the brink for the last struggle.”

As alarm about artificial intelligence has gone global, so has a fascination with the Luddites. The British podcast “The Ned Ludd Radio Hour” describes itself as “your weekly dose of tech skepticism, cynicism, and absurdism.” Kindred themes are explored in the podcast “This Machine Kills,” co-hosted by the social theorist Jathan Sadowski, whose new book, “The Mechanic and the Luddite,” argues that the fetishization of A.I. and other digital technologies obscures their role in disciplining labor and reinforcing a profit-driven system. “Luddites want technology—the future—to work for all of us,” he told the Guardian.The technology journalist Brian Merchant makes a similar case in “Blood in the Machine: The Origins of the Rebellion Against Big Tech” (2023). Blending a vivid account of the original Luddites with an indictment of contemporary tech giants like Amazon and Uber, Merchant portrays the current wave of automation as part of a centuries-long struggle over labor and power. “Working people are staring down entrepreneurs, tech monopolies, and venture capital firms that are hunting for new forms of labor-saving tech—be it AI, robotics, or software automation—to replace them,” Merchant writes. “They are again faced with losing their jobs to the machine.”..(More)”.

Mind the (Language) Gap: Mapping the Challenges of LLM Development in Low-Resource Language Contexts

Curated on Posted on by Stefaan Verhulst

White Paper by the Stanford Institute for Human-Centered AI (HAI), the Asia Foundation and the University of Pretoria: “…maps the LLM development landscape for low-resource languages, highlighting challenges, trade-offs, and strategies to increase investment; prioritize cross-disciplinary, community-driven development; and ensure fair data ownership…

  • Large language model (LLM) development suffers from a digital divide: Most major LLMs underperform for non-English—and especially low-resource—languages; are not attuned to relevant cultural contexts; and are not accessible in parts of the Global South.
  • Low-resource languages (such as Swahili or Burmese) face two crucial limitations: a scarcity of labeled and unlabeled language data and poor quality data that is not sufficiently representative of the languages and their sociocultural contexts.
  • To bridge these gaps, researchers and developers are exploring different technical approaches to developing LLMs that better perform for and represent low-resource languages but come with different trade-offs:
    • Massively multilingual models, developed primarily by large U.S.-based firms, aim to improve performance for more languages by including a wider range of (100-plus) languages in their training datasets.
    • Regional multilingual models, developed by academics, governments, and nonprofits in the Global South, use smaller training datasets made up of 10-20 low-resource languages to better cater to and represent a smaller group of languages and cultures.
    • Monolingual or monocultural models, developed by a variety of public and private actors, are trained on or fine-tuned for a single low-resource language and thus tailored to perform well for that language…(More)”

Artificial Intelligence and Big Data

Curated on Posted on by Stefaan Verhulst

Book edited by Frans L. Leeuw and Michael Bamberger: “…explores how Artificial Intelligence (AI) and Big Data contribute to the evaluation of the rule of law (covering legal arrangements, empirical legal research, law and technology, and international law), and social and economic development programs in both industrialized and developing countries. Issues of ethics and bias in the use of AI are also addressed and indicators of the growth of knowledge in the field are discussed.

Interdisciplinary and international in scope, and bringing together leading academics and practitioners from across the globe, the book explores the applications of AI and big data in Rule of Law and development evaluation, identifies differences in the approaches used in the two fields, and how each could learn from the approaches used in the other, as well as differences in the AI-related issues addressed in industrialized nations compared to those addressed in Africa and Asia.

Artificial Intelligence and Big Data is an essential read for researchers, academics and students working in the fields of Rule of Law and Development, and researchers in institutions working on new applications in AI will all benefit from the book’s practical insights…(More)”.

UAE set to use AI to write laws in world first

Curated on Posted on by Stefaan Verhulst

Article by Chloe Cornish: “The United Arab Emirates aims to use AI to help write new legislation and review and amend existing laws, in the Gulf state’s most radical attempt to harness a technology into which it has poured billions.

The plan for what state media called “AI-driven regulation” goes further than anything seen elsewhere, AI researchers said, while noting that details were scant. Other governments are trying to use AI to become more efficient, from summarising bills to improving public service delivery, but not to actively suggest changes to current laws by crunching government and legal data.

“This new legislative system, powered by artificial intelligence, will change how we create laws, making the process faster and more precise,” said Sheikh Mohammad bin Rashid Al Maktoum, the Dubai ruler and UAE vice-president, quoted by state media.

Ministers last week approved the creation of a new cabinet unit, the Regulatory Intelligence Office, to oversee the legislative AI push. 

Rony Medaglia, a professor at Copenhagen Business School, said the UAE appeared to have an “underlying ambition to basically turn AI into some sort of co-legislator”, and described the plan as “very bold”.

Abu Dhabi has bet heavily on AI and last year opened a dedicated investment vehicle, MGX, which has backed a $30bn BlackRock AI-infrastructure fund among other investments. MGX has also added an AI observer to its own board.

The UAE plans to use AI to track how laws affect the country’s population and economy by creating a massive database of federal and local laws, together with public sector data such as court judgments and government services.

The AI will “regularly suggest updates to our legislation,” Sheikh Mohammad said, according to state media. The government expects AI to speed up lawmaking by 70 per cent, according to the cabinet meeting readout…(More)”

AI models could help negotiators secure peace deals

Curated on Posted on by Stefaan Verhulst

The Economist: “In a messy age of grinding wars and multiplying tariffs, negotiators are as busy as the stakes are high. Alliances are shifting and political leaders are adjusting—if not reversing—positions. The resulting tumult is giving even seasoned negotiators trouble keeping up with their superiors back home. Artificial-intelligence (AI) models may be able to lend a hand.

Some such models are already under development. One of the most advanced projects, dubbed Strategic Headwinds, aims to help Western diplomats in talks on Ukraine. Work began during the Biden administration in America, with officials on the White House’s National Security Council (NSC) offering guidance to the Centre for Strategic and International Studies (CSIS), a think-tank in Washington that runs the project. With peace talks under way, CSIS has speeded up its effort. Other outfits are doing similar work.

The CSIS programme is led by a unit called the Futures Lab. This team developed an AI language model using software from Scale AI, a firm based in San Francisco, and unique training data. The lab designed a tabletop strategy game called “Hetman’s Shadow” in which Russia, Ukraine and their allies hammer out deals. Data from 45 experts who played the game were fed into the model. So were media analyses of issues at stake in the Russia-Ukraine war, as well as answers provided by specialists to a questionnaire about the relative values of potential negotiation trade-offs. A database of 374 peace agreements and ceasefires was also poured in.

Thus was born, in late February, the first iteration of the Ukraine-Russia Peace Agreement Simulator. Users enter preferences for outcomes grouped under four rubrics: territory and sovereignty; security arrangements; justice and accountability; and economic conditions. The AI model then cranks out a draft agreement. The software also scores, on a scale of one to ten, the likelihood that each of its components would be satisfactory, negotiable or unacceptable to Russia, Ukraine, America and Europe. The model was provided to government negotiators from those last three territories, but a limited “dashboard” version of the software can be run online by interested members of the public…(More)”.

AI Needs Your Data. That’s Where Social Media Comes In.

Curated on Posted on by Stefaan Verhulst

Article by Dave Lee: “Having scraped just about the entire sum of human knowledge, ChatGPT and other AI efforts are making the same rallying cry: Need input!

One solution is to create synthetic data and to train a model using that, though this comes with inherent challenges, particularly around perpetuating bias or introducing compounding inaccuracies.

The other is to find a great gushing spigot of new and fresh data, the more “human” the better. That’s where social networks come in, digital spaces where millions, even billions, of users willingly and constantly post reams of information. Photos, posts, news articles, comments — every interaction of interest to companies that are trying to build conversational and generative AI. Even better, this content is not riddled with the copyright violation risk that comes with using other sources.

Lately, top AI companies have moved more aggressively to own or harness social networks, trampling over the rights of users to dictate how their posts may be used to build these machines. Social network users have long been “the product,” as the famous saying goes. They’re now also a quasi-“product developer” through their posts.

Some companies had the benefit of a social network to begin with. Meta Platforms Inc., the biggest social networking company on the planet, used in-app notifications to inform users that it would be harnessing their posts and photos for its Llama AI models. Late last month, Elon Musk’s xAI acquired X, formerly Twitter, in what was primarily a financial sleight of hand but one that made ideal sense for Musk’s Grok AI. It has been able to gain a foothold in the chatbot market by harnessing timely tweets posted on the network as well as the huge archive of online chatter dating back almost two decades. Then there’s Microsoft Corp., which owns the professional network LinkedIn and has been pushing heavily for users (and journalists) to post more and more original content to the platform.

Microsoft doesn’t, however, share LinkedIn data with its close partner OpenAI, which may explain reports that the ChatGPT maker was in the early stages of building a social network of its own…(More)”

Artificial Intelligence and the Future of Work

Curated on Posted on by Stefaan Verhulst

Report by National Academies of Sciences, Engineering, and Medicine: “Advances in artificial intelligence (AI) promise to improve productivity significantly, but there are many questions about how AI could affect jobs and workers.

Recent technical innovations have driven the rapid development of generative AI systems, which produce text, images, or other content based on user requests – advances which have the potential to complement or replace human labor in specific tasks, and to reshape demand for certain types of expertise in the labor market.

Artificial Intelligence and the Future of Work evaluates recent advances in AI technology and their implications for economic productivity, the workforce, and education in the United States. The report notes that AI is a tool with the potential to enhance human labor and create new forms of valuable work – but this is not an inevitable outcome. Tracking progress in AI and its impacts on the workforce will be critical to helping inform and equip workers and policymakers to flexibly respond to AI developments…(More)”.

AI Is Evolving — And Changing Our Understanding Of Intelligence

Curated on Posted on by Stefaan Verhulst

Essay by Blaise Agüera y Arcas and James Manyika: “Dramatic advances in artificial intelligence today are compelling us to rethink our understanding of what intelligence truly is. Our new insights will enable us to build better AI and understand ourselves better.

In short, we are in paradigm-shifting territory.

Paradigm shifts are often fraught because it’s easier to adopt new ideas when they are compatible with one’s existing worldview but harder when they’re not. A classic example is the collapse of the geocentric paradigm, which dominated cosmological thought for roughly two millennia. In the geocentric model, the Earth stood still while the Sun, Moon, planets and stars revolved around us. The belief that we were at the center of the universe — bolstered by Ptolemy’s theory of epicycles, a major scientific achievement in its day — was both intuitive and compatible with religious traditions. Hence, Copernicus’s heliocentric paradigm wasn’t just a scientific advance but a hotly contested heresy and perhaps even, for some, as Benjamin Bratton notes, an existential trauma. So, today, artificial intelligence.

In this essay, we will describe five interrelated paradigm shifts informing our development of AI:

  1. Natural Computing — Computing existed in nature long before we built the first “artificial computers.” Understanding computing as a natural phenomenon will enable fundamental advances not only in computer science and AI but also in physics and biology.
  2. Neural Computing — Our brains are an exquisite instance of natural computing. Redesigning the computers that power AI so they work more like a brain will greatly increase AI’s energy efficiency — and its capabilities too.
  3. Predictive Intelligence — The success of large language models (LLMs) shows us something fundamental about the nature of intelligence: it involves statistical modeling of the future (including one’s own future actions) given evolving knowledge, observations and feedback from the past. This insight suggests that current distinctions between designing, training and running AI models are transitory; more sophisticated AI will evolve, grow and learn continuously and interactively, as we do.
  4. General Intelligence — Intelligence does not necessarily require biologically based computation. Although AI models will continue to improve, they are already broadly capable, tackling an increasing range of cognitive tasks with a skill level approaching and, in some cases, exceeding individual human capability. In this sense, “Artificial General Intelligence” (AGI) may already be here — we just keep shifting the goalposts.
  5. Collective Intelligence — Brains, AI agents and societies can all become more capable through increased scale. However, size alone is not enough. Intelligence is fundamentally social, powered by cooperation and the division of labor among many agents. In addition to causing us to rethink the nature of human (or “more than human”) intelligence, this insight suggests social aggregations of intelligences and multi-agent approaches to AI development that could reduce computational costs, increase AI heterogeneity and reframe AI safety debates.

But to understand our own “intelligence geocentrism,” we must begin by reassessing our assumptions about the nature of computing, since it is the foundation of both AI and, we will argue, intelligence in any form…(More)”.

Energy and AI

Curated on Posted on by Stefaan Verhulst

Report by the International Energy Agency (IEA): “The development and uptake of artificial intelligence (AI) has accelerated in recent years – elevating the question of what widespread deployment of the technology will mean for the energy sector. There is no AI without energy – specifically electricity for data centres. At the same time, AI could transform how the energy industry operates if it is adopted at scale. However, until now, policy makers and other stakeholders have often lacked the tools to analyse both sides of this issue due to a lack of comprehensive data. 

This report from the International Energy Agency (IEA) aims to fill this gap based on new global and regional modelling and datasets, as well as extensive consultation with governments and regulators, the tech sector, the energy industry and international experts. It includes projections for how much electricity AI could consume over the next decade, as well as which energy sources are set to help meet it. It also analyses what the uptake of AI could mean for energy security, emissions, innovation and affordability…(More)”.