Why This AI Moment May Be the Real Deal

Curated on Posted on by Stefaan Verhulst

Essay by Ari Schulman: “For many years, those in the know in the tech world have known that “artificial intelligence” is a scam. It’s been true for so long in Silicon Valley that it was true before there even was a Silicon Valley.

That’s not to say that AI hadn’t done impressive things, solved real problems, generated real wealth and worthy endowed professorships. But peek under the hood of Tesla’s “Autopilot” mode and you would find odd glitches, frustrated promise, and, well, still quite a lot of people hidden away in backrooms manually plugging gaps in the system, often in real time. Study Deep Blue’s 1997 defeat of world chess champion Garry Kasparov, and your excitement about how quickly this technology would take over other cognitive work would wane as you learned just how much brute human force went into fine-tuning the software specifically to beat Kasparov. Read press release after press release of Facebook, Twitter, and YouTube promising to use more machine learning to fight hate speech and save democracy — and then find out that the new thing was mostly a handmaid to armies of human grunts, and for many years relied on a technological paradigm that was decades old.

Call it AI’s man-behind-the-curtain effect: What appear at first to be dazzling new achievements in artificial intelligence routinely lose their luster and seem limited, one-off, jerry-rigged, with nothing all that impressive happening behind the scenes aside from sweat and tears, certainly nothing that deserves the name “intelligence” even by loose analogy.

So what’s different now? What follows in this essay is an attempt to contrast some of the most notable features of the new transformer paradigm (the T in ChatGPT) with what came before. It is an attempt to articulate why the new AIs that have garnered so much attention over the past year seem to defy some of the major lines of skepticism that have rightly applied to past eras — why this AI moment might, just might, be the real deal…(More)”.

Wikipedia’s Moment of Truth

Curated on Posted on by Stefaan Verhulst

Article by Jon Gertner at the New York Times: “In early 2021, a Wikipedia editor peered into the future and saw what looked like a funnel cloud on the horizon: the rise of GPT-3, a precursor to the new chatbots from OpenAI. When this editor — a prolific Wikipedian who goes by the handle Barkeep49 on the site — gave the new technology a try, he could see that it was untrustworthy. The bot would readily mix fictional elements (a false name, a false academic citation) into otherwise factual and coherent answers. But he had no doubts about its potential. “I think A.I.’s day of writing a high-quality encyclopedia is coming sooner rather than later,” he wrote in “Death of Wikipedia,” an essay that he posted under his handle on Wikipedia itself. He speculated that a computerized model could, in time, displace his beloved website and its human editors, just as Wikipedia had supplanted the Encyclopaedia Britannica, which in 2012 announced it was discontinuing its print publication.

Recently, when I asked this editor — he asked me to withhold his name because Wikipedia editors can be the targets of abuse — if he still worried about his encyclopedia’s fate, he told me that the newer versions made him more convinced that ChatGPT was a threat. “It wouldn’t surprise me if things are fine for the next three years,” he said of Wikipedia, “and then, all of a sudden, in Year 4 or 5, things drop off a cliff.”..(More)”.

‘Not for Machines to Harvest’: Data Revolts Break Out Against A.I.

Curated on Posted on by Stefaan Verhulst

Article by Sheera Frenkel, and Stuart A. Thompson: “Fan fiction writers are just one group now staging revolts against A.I. systems as a fever over the technology has gripped Silicon Valley and the world. In recent months, social media companies such as Reddit and Twitter, news organizations including The New York Times and NBC News, authors such as Paul Tremblay and the actress Sarah Silverman have all taken a position against A.I. sucking up their data without permission.

Their protests have taken different forms. Writers and artists are locking their files to protect their work or are boycotting certain websites that publish A.I.-generated content, while companies like Reddit want to charge for access to their data. At least 10 lawsuits have been filed this year against A.I. companies, accusing them of training their systems on artists’ creative work without consent. This past week, Ms. Silverman and the authors Christopher Golden and Richard Kadrey sued OpenAI, the maker of ChatGPT, and others over A.I.’s use of their work.

At the heart of the rebellions is a newfound understanding that online information — stories, artwork, news articles, message board posts and photos — may have significant untapped value.

The new wave of A.I. — known as “generative A.I.” for the text, images and other content it generates — is built atop complex systems such as large language models, which are capable of producing humanlike prose. These models are trained on hoards of all kinds of data so they can answer people’s questions, mimic writing styles or churn out comedy and poetry.

That has set off a hunt by tech companies for even more data to feed their A.I. systems. Google, Meta and OpenAI have essentially used information from all over the internet, including large databases of fan fiction, troves of news articles and collections of books, much of which was available free online. In tech industry parlance, this was known as “scraping” the internet…(More)”.

Russia Is Trying to Leave the Internet and Build Its Own

Curated on Posted on by Stefaan Verhulst

Article by Timmy Broderick: “Last week the Russian government tried to disconnect its Internet infrastructure from the larger global Web. This test of Russia’s “sovereign Internet” seemingly failed, causing outages that suggest the system is not ready for practical use.

“Sovereign Internet is not really a whole different Internet; it is more like a project that uses various tools,” says Natalia Krapiva, tech-legal counsel at the international digital-rights nonprofit Access Now. “It involves technology like deep packet inspection, which allows major filtering of the Internet and gives governments the ability to throttle certain connections and websites.” By cutting off access to sites such as Western social media platforms, the Russian government could restrict residents from viewing any source of information other than the country’s accepted channels of influence.

This method of curtailing digital freedom goes beyond Russia: other countries are also attempting to develop their own nationwide Internet. And if successful, these endeavors could fragment the World Wide Web. Scientific American talked with Krapiva over Zoom about the implications of this latest test, the motive behind Russia’s actions and the ways the push for a sovereign Internet affect the digital rights of all users…(More)”.

Digital divides are lower in Smart Cities

Curated on Posted on by Stefaan Verhulst

Paper by Andrea Caragliu and Chiara F. Del Bo: “Ever since the emergence of digital technologies in the early 1990s, the literature has discussed the potential pitfalls of an uneven distribution of e-skills under the umbrella of the digital divide. To provide a definition of the concept, “Lloyd Morrisett coined the term digital divide to mean “a discrepancy in access to technology resources between socioeconomic groups” (Robyler and Doering, 2014, p. 27)”

Despite digital divide being high on the policy agenda, statistics suggest the persisting relevance of this issue. For instance, focusing on Europe, according to EUROSTAT statistics, in 2021 about 90 per cent of people living in Zeeland, a NUTS2 region in the Netherlands, had ordered at least once in their life goods or services over the internet for private use, against a minimum in the EU27 of 15 per cent (in the region of Yugoiztochen, in Bulgaria). In the same year, while basically all (99 per cent) interviewees in the NUTS2 region of Northern and Western Ireland declared using the internet at least once a week, the same statistic drops to two thirds of the sample in the Bulgarian region of Severozapaden. While over time these territorial divides are converging, they can still significantly affect the potential positive impact of the diffusion of digital technologies.

Over the past three years, the digital divide has been made dramatically apparent by the COVID-19 pandemic outbreak. When, during the first waves of full lockdowns enacted in most Countries, tertiary and schooling activities were moved online, many economic outcomes showed significant worsening. Among these, learning outcomes in pupils and service sectors’ productivity were particularly affected.

A simultaneous development in the scientific literature has discussed the attractive features of planning and managing cities ‘smartly’. Smart Cities have been initially identified as urban areas with a tendency to invest and deploy ICTs. More recently, this notion also started to encompass the context characteristics that make a city capable of reaping the benefits of ICTs – social and human capital, soft and hard institutions.

While mounting empirical evidence suggests a superior economic performance of Cities ticking all these boxes, the Smart City movement did not come without critiques. The debate on urban smartness as an instrument for planning and managing more efficient cities has been recently positing that Smart Cities could be raising inequalities. This effect would be due to the role of driver of smart urban transformations played by multinational corporations, who, in a dystopic view, would influence local policymakers’ agendas.

Given these issues, and our own research on Smart Cities, we started asking ourselves whether the risks of increasing inequalities associated with the Smart City model were substantiated. To this end, we focused on empirically verifying whether cities moving forward along the smart city model were facing increases in income and digital inequalities. We answered the first question in Caragliu and Del Bo (2022), and found compelling evidence that smart city characteristics actually decrease income inequalities…(More)”.

How do we know how smart AI systems are?

Curated on Posted on by Stefaan Verhulst

Article by Melanie Mitchell: “In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: “Within a generation…the problem of creating ‘artificial intelligence’ will be substantially solved.” Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines?

Some leading AI researchers would answer that we are quite close. Earlier this year, deep-learning pioneer and Turing Award winner Geoffrey Hinton told Technology Review, “I have suddenly switched my views on whether these things are going to be more intelligent than us. I think they’re very close to it now and they will be much more intelligent than us in the future.” His fellow Turing Award winner Yoshua Bengio voiced a similar opinion in a recent blog post: “The recent advances suggest that even the future where we know how to build superintelligent AIs (smarter than humans across the board) is closer than most people expected just a year ago.”

These are extraordinary claims that, as the saying goes, require extraordinary evidence. However, it turns out that assessing the intelligence—or more concretely, the general capabilities—of AI systems is fraught with pitfalls. Anyone who has interacted with ChatGPT or other large language models knows that these systems can appear quite intelligent. They converse with us in fluent natural language, and in many cases seem to reason, to make analogies, and to grasp the motivations behind our questions. Despite their well-known unhumanlike failings, it’s hard to escape the impression that behind all that confident and articulate language there must be genuine understanding…(More)”.

To Save Society from Digital Tech, Enable Scrutiny of How Policies Are Implemented

Curated on Posted on by Stefaan Verhulst

Article by Ido Sivan-Sevilla: “…there is little discussion about how to create accountability when implementing tech policies. Decades of research exploring policy implementation across diverse areas consistently shows how successful implementation allows policies to be adapted and involves crucial bargaining. But this is rarely understood in the tech sector. For tech policies to work, those responsible for enforcement and compliance should be overseen and held to account. Otherwise, as history shows, tech policies will struggle to fulfill the intentions of their policymakers.

Scrutiny is required for three types of actors. First are regulators, who convert promising tech laws into enforcement practices but are often ill-equipped for their mission. My recent research found that across Europe, the rigor and methods of national privacy regulators tasked with enforcing the European Union’s GDPR vary greatly. The French data protection authority, for instance, proactively monitors for privacy violations and strictly sanctions companies that overstep; in contrast, Bulgarian authorities monitor passively and are hesitant to act. Reflecting on the first five years of the GDPR, Max Schrems, the chair of privacy watchdog NOYB, found authorities and courts reluctant to enforce the law, and companies free to take advantage: “It often feels like there is more energy spent in undermining the GDPR than in complying with it.” Variations in resources and technical expertise among regulators create regulatory arbitrage that the regulated eagerly exploit.

Tech companies are the second type of actor requiring scrutiny. Service providers such as Goolge, Meta, and Twitter, along with lesser-known technology companies, mediate digital services for billions around the world but enjoy considerable latitude on how and whether they comply with tech policies. Civil society groups, for instance, uncovered how Meta was trying to bypass the GDPR and use personal information for advertising…(More)”.

Meta Ran a Giant Experiment in Governance. Now It’s Turning to AI

Curated on Posted on by Stefaan Verhulst

Article by Aviv Ovadya: “Late last month, Meta quietly announced the results of an ambitious, near-global deliberative “democratic” process to inform decisions around the company’s responsibility for the metaverse it is creating. This was not an ordinary corporate exercise. It involved over 6,000 people who were chosen to be demographically representative across 32 countries and 19 languages. The participants spent many hours in conversation in small online group sessions and got to hear from non-Meta experts about the issues under discussion. Eighty-two percent of the participants said that they would recommend this format as a way for the company to make decisions in the future.

Meta has now publicly committed to running a similar process for generative AI, a move that aligns with the huge burst of interest in democratic innovation for governing or guiding AI systems. In doing so, Meta joins Google, DeepMind, OpenAI, Anthropic, and other organizations that are starting to explore approaches based on the kind of deliberative democracy that I and others have been advocating for. (Disclosure: I am on the application advisory committee for the OpenAI Democratic inputs to AI grant.) Having seen the inside of Meta’s process, I am excited about this as a valuable proof of concept for transnational democratic governance. But for such a process to truly be democratic, participants would need greater power and agency, and the process itself would need to be more public and transparent.

I first got to know several of the employees responsible for setting up Meta’s Community Forums (as these processes came to be called) in the spring of 2019 during a more traditional external consultation with the company to determine its policy on “manipulated media.” I had been writing and speaking about the potential risks of what is now called generative AI and was asked (alongside other experts) to provide input on the kind of policies Meta should develop to address issues such as misinformation that could be exacerbated by the technology.

At around the same time, I first learned about representative deliberations—an approach to democratic decisionmaking that has taken off like wildfire, with increasingly high-profile citizen assemblies and deliberative polls all over the world. The basic idea is that governments bring difficult policy questions back to the public to decide. Instead of a referendum or elections, a representative microcosm of the public is selected via lottery. That group is brought together for days or even weeks (with compensation) to learn from experts, stakeholders, and each other before coming to a final set of recommendations…(More)”.

AI tools are designing entirely new proteins that could transform medicine

Curated on Posted on by Stefaan Verhulst

Article by Ewen Callaway: “OK. Here we go.” David Juergens, a computational chemist at the University of Washington (UW) in Seattle, is about to design a protein that, in 3-billion-plus years of tinkering, evolution has never produced.

On a video call, Juergens opens a cloud-based version of an artificial intelligence (AI) tool he helped to develop, called RFdiffusion. This neural network, and others like it, are helping to bring the creation of custom proteins — until recently a highly technical and often unsuccessful pursuit — to mainstream science.

These proteins could form the basis for vaccines, therapeutics and biomaterials. “It’s been a completely transformative moment,” says Gevorg Grigoryan, the co-founder and chief technical officer of Generate Biomedicines in Somerville, Massachusetts, a biotechnology company applying protein design to drug development.

The tools are inspired by AI software that synthesizes realistic images, such as the Midjourney software that, this year, was famously used to produce a viral image of Pope Francis wearing a designer white puffer jacket. A similar conceptual approach, researchers have found, can churn out realistic protein shapes to criteria that designers specify — meaning, for instance, that it’s possible to speedily draw up new proteins that should bind tightly to another biomolecule. And early experiments show that when researchers manufacture these proteins, a useful fraction do perform as the software suggests.

The tools have revolutionized the process of designing proteins in the past year, researchers say. “It is an explosion in capabilities,” says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City, whose team has developed one such tool for protein design. “You can now create designs that have sought-after qualities.”

“You’re building a protein structure customized for a problem,” says David Baker, a computational biophysicist at UW whose group, which includes Juergens, developed RFdiffusion. The team released the software in March 2023, and a paper describing the neural network appears this week in Nature1. (A preprint version was released in late 2022, at around the same time that several other teams, including AlQuraishi’s2 and Grigoryan’s3, reported similar neural networks)…(More)”.

Weather Warning Inequity: Lack of Data Collection Stations Imperils Vulnerable People

Curated on Posted on by Stefaan Verhulst

Article by Chelsea Harvey: “Devastating floods and landslides triggered by extreme downpours killed hundreds of people in Rwanda and the Democratic Republic of Congo in May, when some areas saw more than 7 inches of rain in a day.

Climate change is intensifying rainstorms throughout much of the world, yet scientists haven’t been able to show that the event was influenced by warming.

That’s because they don’t have enough data to investigate it.

Weather stations are sparse across Africa, making it hard for researchers to collect daily information on rainfall and other weather variables. The data that does exist often isn’t publicly available.

“The main issue in some countries in Africa is funding,” said Izidine Pinto, a senior researcher on weather and climate at the Royal Netherlands Meteorological Institute. “The meteorological offices don’t have enough funding.”

There’s often too little money to build or maintain weather stations, and strapped-for-cash governments often choose to sell the data they do collect rather than make it free to researchers.

That’s a growing problem as the planet warms and extreme weather worsens. Reliable forecasts are needed for early warning systems that direct people to take shelter or evacuate before disasters strike. And long-term climate data is necessary for scientists to build computer models that help make predictions about the future.

The science consortium World Weather Attribution is the latest research group to run into problems. It investigates the links between climate change and individual extreme weather events all over the globe. In the last few months alone, the organization has demonstrated the influence of global warming on extreme heat in South Asia and the Mediterranean, floods in Italy, and drought in eastern Africa.

Most of its research finds that climate change is making weather events more likely to occur or more intense.

The group recently attempted to investigate the influence of climate change on the floods in Rwanda and Congo. But the study was quickly mired in challenges.

The team was able to acquire some weather station data, mainly in Rwanda, Joyce Kimutai, a research associate at Imperial College London and a co-author of the study, said at a press briefing announcing the findings Thursday. But only a few stations provided sufficient data, making it impossible to define the event or to be certain that climate model simulations were accurate…(More)”.