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)”

For sale: Data on US servicemembers — and lots of it

Curated on Posted on by Stefaan Verhulst

Article by Alfred Ng: “Active-duty members of the U.S. military are vulnerable to having their personal information collected, packaged and sold to overseas companies without any vetting, according to a new report funded by the U.S. Military Academy at West Point.

The report highlights a significant American security risk, according to military officials, lawmakers and the experts who conducted the research, and who say the data available on servicemembers exposes them to blackmail based on their jobs and habits.

It also casts a spotlight on the practices of data brokers, a set of firms that specialize in scraping and packaging people’s digital records such as health conditions and credit ratings.

“It’s really a case of being able to target people based on specific vulnerabilities,” said Maj. Jessica Dawson, a research scientist at the Army Cyber Institute at West Point who initiated the study.

Data brokers gather government files, publicly available information and financial records into packages they can sell to marketers and other interested companies. As the practice has grown into a $214 billion industry, it has raised privacy concerns and come under scrutiny from lawmakers in Congress and state capitals.

Worried it could also present a risk to national security, the U.S. Military Academy at West Point funded the study from Duke University to see how servicemembers’ information might be packaged and sold.

Posing as buyers in the U.S. and Singapore, Duke researchers contacted multiple data-broker firms who listed datasets about active-duty servicemembers for sale. Three agreed and sold datasets to the researchers while two declined, saying the requests came from companies that didn’t meet their verification standards.

In total, the datasets contained information on nearly 30,000 active-duty military personnel. They also purchased a dataset on an additional 5,000 friends and family members of military personnel…(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)”.

To Understand Global Migration, You Have to See It First

Curated on Posted on by Stefaan Verhulst

Data visualization by The New York Times: “In the maps below, Times Opinion can provide the clearest picture to date of how people move across the globe: a record of permanent migration to and from 181 countries based on a single, consistent source of information, for every month from the beginning of 2019 through the end of 2022. These estimates are drawn not from government records but from the location data of three billion anonymized Facebook users all over the world.

The analysis — the result of new research published on Wednesday from Meta, the University of Hong Kong and Harvard University — reveals migration’s true global sweep. And yes, it excludes business travelers and tourists: Only people who remain in their destination country for more than a year are counted as migrants here.

The data comes with some limitations. Migration to and from certain countries that have banned or restricted the use of Facebook, including China, Iran and Cuba, is not included in this data set, and it’s impossible to know each migrant’s legal status. Nevertheless, this is the first time that estimates of global migration flows have been made publicly available at this scale. The researchers found that from 2019 to 2022, an annual average of 30 million people — approximately one-third of a percent of the world’s population — migrated each year.

If you would like to see the data behind this analysis for yourself, we made an interactive tool that you can use to explore the full data set…(More)”

Inside arXiv—the Most Transformative Platform in All of Science

Curated on Posted on by Stefaan Verhulst

Article by Sheon Han: “Nearly 35 years ago, Ginsparg created arXiv, a digital repository where researchers could share their latest findings—before those findings had been systematically reviewed or verified. Visit arXiv.org today (it’s pronounced like “archive”) and you’ll still see its old-school Web 1.0 design, featuring a red banner and the seal of Cornell University, the platform’s institutional home. But arXiv’s unassuming facade belies the tectonic reconfiguration it set off in the scientific community. If arXiv were to stop functioning, scientists from every corner of the planet would suffer an immediate and profound disruption. “Everybody in math and physics uses it,” Scott Aaronson, a computer scientist at the University of Texas at Austin, told me. “I scan it every night.”

Every industry has certain problems universally acknowledged as broken: insurance in health care, licensing in music, standardized testing in education, tipping in the restaurant business. In academia, it’s publishing. Academic publishing is dominated by for-profit giants like Elsevier and Springer. Calling their practice a form of thuggery isn’t so much an insult as an economic observation. Imagine if a book publisher demanded that authors write books for free and, instead of employing in-house editors, relied on other authors to edit those books, also for free. And not only that: The final product was then sold at prohibitively expensive prices to ordinary readers, and institutions were forced to pay exorbitant fees for access…(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)”

DOGE’s Growing Reach into Personal Data: What it Means for Human Rights

Curated on Posted on by Stefaan Verhulst

Article by Deborah Brown: “Expansive interagency sharing of personal data could fuel abuses against vulnerable people and communities who are already being targeted by Trump administration policies, like immigrants, lesbian, gay, bisexual, and transgender (LGBT) people, and student protesters. The personal data held by the government reveals deeply sensitive information, such as people’s immigration status, race, gender identity, sexual orientation, and economic status.

A massive centralized government database could easily be used for a range of abusive purposes, like to discriminate against current federal employees and future job applicants on the basis of their sexual orientation or gender identity, or to facilitate the deportation of immigrants. It could result in people forgoing public services out of fear that their data will be weaponized against them by another federal agency.

But the danger doesn’t stop with those already in the administration’s crosshairs. The removal of barriers keeping private data siloed could allow the government or DOGE to deny federal loans for education or Medicaid benefits based on unrelated or even inaccurate data. It could also facilitate the creation of profiles containing all of the information various agencies hold on every person in the country. Such profiles, combined with social media activity, could facilitate the identification and targeting of people for political reasons, including in the context of elections.

Information silos exist for a reason. Personal data should be collected for a determined, specific, and legitimate purpose, and not used for another purpose without notice or justification, according to the key internationally recognized data protection principle, “purpose limitation.” Sharing data seamlessly across federal or even state agencies in the name of an undefined and unmeasurable goal of efficiency is incompatible with this core data protection principle…(More)”.

Can We Measure the Impact of a Database?

Curated on Posted on by Stefaan Verhulst

Article by Peter Buneman, Dennis Dosso, Matteo Lissandrini, Gianmaria Silvello, and He Sun: “Databases publish data. This is undoubtedly the case for scientific and statistical databases, which have largely replaced traditional reference works. Database and Web technologies have led to an explosion in the number of databases that support scientific research, for obvious reasons: Databases provide faster communication of knowledge, hold larger volumes of data, are more easily searched, and are both human- and machine-readable. Moreover, they can be developed rapidly and collaboratively by a mixture of researchers and curators. For example, more than 1,500 curated databases are relevant to molecular biology alone. The value of these databases lies not only in the data they present but also in how they organize that data.

In the case of an author or journal, most bibliometric measures are obtained from citations to an associated set of publications. There are typically many ways of decomposing a database into publications, so we might use its organization to guide our choice of decompositions. We will show that when the database has a hierarchical structure, there is a natural extension of the h-index that works on this hierarchy…(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)”.

‘We are flying blind’: RFK Jr.’s cuts halt data collection on abortion, cancer, HIV and more

Curated on Posted on by Stefaan Verhulst

Article by Alice Miranda Ollstein: “The federal teams that count public health problems are disappearing — putting efforts to solve those problems in jeopardy.

Health Secretary Robert F. Kennedy Jr.’s purge of tens of thousands of federal workers has halted efforts to collect data on everything from cancer rates in firefighters to mother-to-baby transmission of HIV and syphilis to outbreaks of drug-resistant gonorrhea to cases of carbon monoxide poisoning.

The cuts threaten to obscure the severity of pressing health threats and whether they’re getting better or worse, leaving officials clueless on how to respond. They could also make it difficult, if not impossible, to assess the impact of the administration’s spending and policies. Both outside experts and impacted employees argue the layoffs will cost the government more money in the long run by eliminating information on whether programs are effective or wasteful, and by allowing preventable problems to fester.

“Surveillance capabilities are crucial for identifying emerging health issues, directing resources efficiently, and evaluating the effectiveness of existing policies,” said Jerome Adams, who served as surgeon general in the first Trump’s administration. “Without robust data and surveillance systems, we cannot accurately assess whether we are truly making America healthier.”..(More)”.