Gather, Share, Build

Curated on Posted on by Stefaan Verhulst

Article by Nithya Ramanathan & Jim Fruchterman: “Recent milestones in generative AI have sent nonprofits, social enterprises, and funders alike scrambling to understand how these innovations can be harnessed for global good. Along with this enthusiasm, there is also warranted concern that AI will greatly increase the digital divide and fail to improve the lives of 90 percent of the people on our planet. The current focus on funding AI intelligently and strategically in the social sector is critical, and it will help ensure that money has the largest impact.

So how can the social sector meet the current moment?

AI is already good at a lot of things. Plenty of social impact organizations are using AI right now, with positive results. Great resources exist for developing a useful understanding of the current landscape and how existing AI tech can serve your mission, including this report from Stanford HAI and Project Evident and this AI Treasure Map for Nonprofits from Tech Matters.

While some tech-for-good companies are creating AI and thriving—Digital Green, Khan Academy, and Jacaranda Health, among many—most social sector companies are not ready to build AI solutions. But even organizations that don’t have AI on their radar need to be thinking about how to address one of the biggest challenges to harnessing AI to solve social sector problems: insufficient data…(More)”.

Why these scientists devote time to editing and updating Wikipedia

Curated on Posted on by Stefaan Verhulst

Article by Christine Ro: “…A 2018 survey of more than 4,000 Wikipedians (as the site’s editors are called) found that 12% had a doctorate. Scientists made up one-third of the Wikimedia Foundation’s 16 trustees, according to Doronina.

Although Wikipedia is the best-known project under the Wikimedia umbrella, there are other ways for scientists to contribute besides editing Wikipedia pages. For example, an entomologist could upload photos of little-known insect species to Wikimedia Commons, a collection of images and other media. A computer scientist could add a self-published book to the digital textbook site Wikibooks. Or a linguist could explain etymology on the collaborative dictionary Wiktionary. All of these are open access, a key part of Wikimedia’s mission.

Although Wikipedia’s structure might seem daunting for new editors, there are parallels with academic documents.

For instance, Jess Wade, a physicist at Imperial College London, who focuses on creating and improving biographies of female scientists and scientists from low- and middle-income countries, says that the talk page, which is the behind-the-scenes portion of a Wikipedia page on which editors discuss how to improve it, is almost like the peer-review file of an academic paper…However, scientists have their own biases about aspects such as how to classify certain topics. This matters, Harrison says, because “Wikipedia is intended to be a general-purpose encyclopaedia instead of a scientific encyclopaedia.”

One example is a long-standing battle over Wikipedia pages on cryptids and folklore creatures such as Bigfoot. Labels such as ‘pseudoscience’ have angered cryptid enthusiasts and raised questions about different types of knowledge. One suggestion is for the pages to feature a disclaimer that says that a topic is not accepted by mainstream science.

Wade raises a point about resourcing, saying it’s especially difficult for the platform to retain academics who might be enthusiastic about editing Wikipedia initially, but then drop off. One reason is time. For full-time researchers, Wikipedia editing could be an activity best left to evenings, weekends and holidays…(More)”.

AI Upgrades the Internet of Things

Curated on Posted on by Stefaan Verhulst

Article by R. Colin Johnson: “Artificial Intelligence (AI) is renovating the fast-growing Internet of Things (IoT) by migrating AI innovations, including deep neural networks, Generative AI, and large language models (LLMs) from power-hungry datacenters to the low-power Artificial Intelligence of Things (AIoT). Located at the network’s edge, there are already billions of connected devices today, plus a predicted trillion more connected devices by 2035 (according to Arm, which licenses many of their processors).

The emerging details of this AIoT development period got a boost from ACM Transactions on Sensor Networks, which recently accepted for publication “Artificial Intelligence of Things: A Survey,” a paper authored by Mi Zhang of Ohio State University and collaborators at Michigan State University, the University of Southern California, and the University of California, Los Angeles. The survey is an in-depth reference to the latest AIoT research…

The survey addresses the subject of AIoT with AI-empowered sensing modalities including motion, wireless, vision, acoustic, multi-modal, ear-bud, and GenAI-assisted sensing. The computing section covers on-device inference engines, on-device learning, methods of training by partitioning workloads among heterogeneous accelerators, offloading privacy functions, federated learning that distributes workloads while preserving anonymity, integration with LLMs, and AI-empowered agents. Connection technologies discussed include Internet over Wi-Fi and over cellular/mobile networks, visible light communication systems, LoRa (long-range chirp spread-spectrum connections), and wide-area networks.

A sampling of domain-specific AIoTs reviewed in the survey include AIoT systems for healthcare and well-being, for smart speakers, for video streaming, for video analytics, for autonomous driving, for drones, for satellites, for agriculture, for biology, and for artificial reality, virtual reality, and mixed reality…(More)”.

Figure for AIoT article

AI crawler wars threaten to make the web more closed for everyone

Curated on Posted on by Stefaan Verhulst

Article by Shayne Longpre: “We often take the internet for granted. It’s an ocean of information at our fingertips—and it simply works. But this system relies on swarms of “crawlers”—bots that roam the web, visit millions of websites every day, and report what they see. This is how Google powers its search engines, how Amazon sets competitive prices, and how Kayak aggregates travel listings. Beyond the world of commerce, crawlers are essential for monitoring web security, enabling accessibility tools, and preserving historical archives. Academics, journalists, and civil societies also rely on them to conduct crucial investigative research.  

Crawlers are endemic. Now representing half of all internet traffic, they will soon outpace human traffic. This unseen subway of the web ferries information from site to site, day and night. And as of late, they serve one more purpose: Companies such as OpenAI use web-crawled data to train their artificial intelligence systems, like ChatGPT. 

Understandably, websites are now fighting back for fear that this invasive species—AI crawlers—will help displace them. But there’s a problem: This pushback is also threatening the transparency and open borders of the web, that allow non-AI applications to flourish. Unless we are thoughtful about how we fix this, the web will increasingly be fortified with logins, paywalls, and access tolls that inhibit not just AI but the biodiversity of real users and useful crawlers…(More)”.

How Philanthropy Built, Lost, and Could Reclaim the A.I. Race

Curated on Posted on by Stefaan Verhulst

Article by Sara Herschander: “How do we know you won’t pull an OpenAI?”

It’s the question Stella Biderman has gotten used to answering when she seeks funding from major foundations for EleutherAI, her two-year-old nonprofit A.I. lab that has developed open-source artificial intelligence models.

The irony isn’t lost on her. Not long ago, she declined a deal dangled by one of Silicon Valley’s most prominent venture capitalists who, with the snap of his fingers, promised to raise $100 million for the fledgling nonprofit lab — over 30 times EleutherAI’s current annual budget — if only the lab’s leaders would agree to drop its 501(c)(3) status.

In today’s A.I. gold rush, where tech giants spend billions on increasingly powerful models and top researchers command seven-figure salaries, to be a nonprofit A.I. lab is to be caught in a Catch-22: defend your mission to increasingly wary philanthropic funders or give in to temptation and become a for-profit company.

Philanthropy once played an outsize role in building major A.I. research centers and nurturing influential theorists — by donating hundreds of millions of dollars, largely to university labs — yet today those dollars are dwarfed by the billions flowing from corporations and venture capitalists. For tech nonprofits and their philanthropic backers, this has meant embracing a new role: pioneering the research and safeguards the corporate world won’t touch.

“If making a lot of money was my goal, that would be easy,” said Biderman, whose employees have seen their pay packages triple or quadruple after being poached by companies like OpenAI, Anthropic, and Google.

But EleutherAI doesn’t want to join the race to build ever-larger models. Instead, backed by grants from Open Philanthropy, Omidyar Network, and A.I. companies Hugging Face and StabilityAI, the group has carved out a different niche: researching how A.I. systems make decisions, maintaining widely used training datasets, and shaping global policy around A.I. safety and transparency…(More)”.

Google-backed public interest AI partnership launches with $400M+ for open ecosystem building

Curated on Posted on by Stefaan Verhulst

Article by Natasha Lomas: “Make room for yet another partnership on AI. Current AI, a “public interest” initiative focused on fostering and steering development of artificial intelligence in societally beneficial directions, was announced at the French AI Action summit on Monday. It’s kicking off with an initial $400 million in pledges from backers and a plan to pull in $2.5 billion more over the next five years.

Such figures might are small beer when it comes to AI investment, with the French president fresh from trumpeting a private support package worth around $112 billion (which itself pales beside U.S. investments of $500 billion aiming to accelerate the tech). But the partnership is not focused on compute, so its backers believe such relatively modest sums will still be able to produce an impact in key areas where AI could make a critical difference to advancing the public interest in areas like healthcare and supporting climate goals.

The initial details are high level. Under the top-line focus on “the enabling environment for public interest AI,” the initiative has a number of stated aims — including pushing to widen access to “high quality” public and private datasets for AI training; support for open source infrastructure and tooling to boost AI transparency and security; and support for developing systems to measure AI’s social and environmental impact. 

Its founder, Martin Tisné, said the goal is to create a financial vehicle “to provide a North Star for public financing of critical efforts,” such as bringing AI to bear on combating cancers or coming up with treatments for long COVID.

“I think what’s happening is you’ve got a data bottleneck coming in artificial intelligence, because we’re running out of road with data on the web, effectively … and here, what we need is to really unlock innovations in how to make data accessible and available,” he told TechCrunch….(More)”

Trump’s shocking purge of public health data, explained

Curated on Posted on by Stefaan Verhulst

Article by Dylan Scott: “In the initial days of the Trump administration, officials scoured federal websites for any mention of what they deemed “DEI” keywords — terms as generic as “diverse” and “historically” and even “women.” They soon identified reams of some of the country’s most valuable public health data containing some of the targeted words, including language about LGBTQ+ people, and quickly took down much of it — from surveys on obesity and suicide rates to real-time reports on immediate infectious disease threats like bird flu.

The removal elicited a swift response from public health experts who warned that without this data, the country risked being in the dark about important health trends that shape life-and-death public health decisions made in communities across the country.

Some of this data was restored in a matter of days, but much of it was incomplete. In some cases, the raw data sheets were posted again, but the reference documents that would allow most people to decipher them were not. Meanwhile, health data continues to be taken down: The New York Times reported last week that data from the Centers for Disease Control and Prevention on bird flu transmission between humans and cats had been posted and then promptly removed…

It is difficult to capture the sheer breadth and importance of the public health data that has been affected. Here are a few illustrative examples of reports that have either been tampered with or removed completely, as compiled by KFF.

The Behavioral Risk Factor Surveillance System (BRFSS), which is “one of the most widely used national health surveys and has been ongoing for about 40 years,” per KFF, is an annual survey that contacts 400,000 Americans to ask people about everything from their own perception of their general health to exercise, diet, sexual activity, and alcohol and drug use.

That in turn allows experts to track important health trends, like the fluctuations in teen vaping use. One recent study that relied on BRFSS data warned that a recent ban on flavored e-cigarettes (also known as vapes) may be driving more young people to conventional smoking, five years after an earlier Yale study based on the same survey led to the ban being proposed in the first place. The Supreme Court and the Trump administration are currently revisiting the flavored vape ban, and the Yale study was cited in at least one amicus brief for the case.

This survey has also been of particular use in identifying health disparities among LGBTQ+ people, such as higher rates of uninsurance and reported poor health compared to the general population. Those findings have motivated policymakers at the federal, state and local levels to launch new initiatives aimed specifically at that at-risk population.

As of now, most of the BRFSS data has been restored, but the supplemental materials that make it legible to lay people still has not…(More)”.

How the System Works

Curated on Posted on by Stefaan Verhulst

Article by Charles C. Mann: “…We, too, do not have the luxury of ignorance. Our systems serve us well for the most part. But they will need to be revamped for and by the next generation — the generation of the young people at the rehearsal dinner — to accommodate our rising population, technological progress, increasing affluence, and climate change.

The great European cathedrals were built over generations by thousands of people and sustained entire communities. Similarly, the electric grid, the public-water supply, the food-distribution network, and the public-health system took the collective labor of thousands of people over many decades. They are the cathedrals of our secular era. They are high among the great accomplishments of our civilization. But they don’t inspire bestselling novels or blockbuster films. No poets celebrate the sewage treatment plants that prevent them from dying of dysentery. Like almost everyone else, they rarely note the existence of the systems around them, let alone understand how they work…(More)”.

Call to make tech firms report data centre energy use as AI booms

Curated on Posted on by Stefaan Verhulst

Article by Sandra Laville: “Tech companies should be required by law to report the energy and water consumption for their data centres, as the boom in AI risks causing irreparable damage to the environment, experts have said.

AI is growing at a rate unparalleled by other energy systems, bringing heightened environmental risk, a report by the National Engineering Policy Centre (NEPC) said.

The report calls for the UK government to make tech companies submit mandatory reports on their energy and water consumption and carbon emissions in order to set conditions in which data centres are designed to use fewer vital resources…(More)”.

Tech tycoons have got the economics of AI wrong

Curated on Posted on by Stefaan Verhulst

The Economist: “…The Jevons paradox—the idea that efficiency leads to more use of a resource, not less—has in recent days provided comfort to Silicon Valley titans worried about the impact of DeepSeek, the maker of a cheap and efficient Chinese chatbot, which threatens the more powerful but energy-guzzling American varieties. Satya Nadella, the boss of Microsoft, posted on X, a social-media platform, that “Jevons paradox strikes again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of,” along with a link to the Wikipedia page for the economic principle. Under this logic, DeepSeek’s progress will mean more demand for data centres, Nvidia chips and even the nuclear reactors that the hyperscalers were, prior to the unveiling of DeepSeek, paying to restart. Nothing to worry about if the price falls, Microsoft can make it up on volume.

The logic, however self-serving, has a ring of truth to it. Jevons’s paradox is real and observable in a range of other markets. Consider the example of lighting. William Nordhaus, a Nobel-prizewinning economist, has calculated that a Babylonian oil lamp, powered by sesame oil, produced about 0.06 lumens of light per watt of energy. That compares with up to 110 lumens for a modern light-emitting diode. The world has not responded to this dramatic improvement in energy efficiency by enjoying the same amount of light as a Babylonian at lower cost. Instead, it has banished darkness completely, whether through more bedroom lamps than could have been imagined in ancient Mesopotamia or the Las Vegas sphere, which provides passersby with the chance to see a 112-metre-tall incandescent emoji. Urban light is now so cheap and so abundant that many consider it to be a pollutant.

Likewise, more efficient chatbots could mean that AI finds new uses (some no doubt similarly obnoxious). The ability of DeepSeek’s model to perform about as well as more compute-hungry American AI shows that data centres are more productive than previously thought, rather than less. Expect, the logic goes, more investment in data centres and so on than you did before.

Although this idea should provide tech tycoons with some solace, they still ought to worry. The Jevons paradox is a form of a broader phenomenon known as “rebound effects”. These are typically not large enough to fully offset savings from improved efficiency….Basing the bull case for AI on the Jevons paradox is, therefore, a bet not on the efficiency of the technology but on the level of demand. If adoption is being held back by price then efficiency gains will indeed lead to greater use. If technological progress raises expectations rather than reduces costs, as is typical in health care, then chatbots will make up an ever larger proportion of spending. At the moment, that looks unlikely. America’s Census Bureau finds that only 5% of American firms currently use AI and 7% have plans to adopt it in the future. Many others find the tech difficult to use or irrelevant to their line of business…(More)”.