Lifeboat Foundation

Meta Platforms will begin detecting and labeling images generated by other companies’ artificial intelligence services in the coming months, using a set of invisible markers built into the files, its top policy executive said on Tuesday.

Meta is promising to roll out auto-labeling for AI-generated images — as soon as it figures out how, that is.

Nick Clegg, Meta’s president of global affairs, said in a policy update that the company is currently working with “industry partners” to formulate criteria that will help identify AI content. Once those criteria are determined, Meta will begin automatically labeling posts featuring any AI-generated images, video, or audio “in the coming months.”

“This approach represents the cutting edge of what’s technically possible right now. But it’s not yet possible to identify all AI-generated content, and there are ways that people can strip out invisible markers,” Clegg wrote. “So we’re pursuing a range of options. We’re working hard to develop classifiers that can help us to automatically detect AI-generated content, even if the content lacks invisible markers.”

The recently published tech policy document by the Ministry of Industry and Information Technology reflects their dedication to fostering innovation and development in future industries. The roadmap emphasizes the importance of forward-looking planning, policy guidance, and cultivating new quality productive forces to support the country’s aspirations for global technological leadership.

The race for supremacy in brain-computer interfaces intensifies as the world watches China’s technological journey unfold. With Neuralink marking its milestones, China’s bold ambitions signal a new era of competition in the ever-evolving landscape of cutting-edge technologies.

The question now is not just about who will lead the race but what groundbreaking innovations lie ahead for humanity.

Should all patients with COPD exacerbations receive oral steroids, or only those with eosinophilia?

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Given the value of the vaccine, it’s mindboggling that some in the US would choose not to protect their children. And yet, vaccine rates among US kindergartners fell for the second consecutive year in 2022, a situation the Centers for Disease Control and Prevention said left some 250,000 kids vulnerable to measles. While some of those missed shots were potentially due to challenges accessing timely health care during the pandemic, there’s reason to worry that growing hesitancy about vaccination is also at play.

It does not help that some states are making it easier to forgo routine childhood vaccines. Mississippi, for example, previously led the nation in vaccination coverage for kindergarteners, with more than 98.6% of kids receiving both doses of their MMR shots in the 2021–2022 school year. But anti-vaccine activists succeeded in loosening the state’s childhood vaccination policy, and last year families could for the first time seek religious exemptions for basic shots like MMR, tetanus, polio and others. According to a report from NBC, the state granted more than 2,200 exemptions in the first five months they were allowed.

The shift seemingly reflects a new partisan divide. A recent Pew Research Center poll found a steep drop in the number of Republicans and people who lean Republican who don’t believe vaccines should be required for attending public school.

From blanket bans to specific prohibitions

Previously, OpenAI had a strict ban on using its technology for any “activity that has high risk of physical harm, including” “weapons development” and “military and warfare.” This would prevent any government or military agency from using OpenAI’s services for defense or security purposes. However, the new policy has removed the general ban on “military and warfare” use. Instead, it has listed some specific examples of prohibited use cases, such as “develop or use weapons” or “harm yourself or others.”

On top of this, the use of quantification has significantly increased over the last decades with the inflation of metrics, indicators, and scores to rank and benchmark options (Muller, 2018). The case of energy policy making in the European Union is again an effective example. The European Union’s recent energy strategy has been underpinned by the Clean Energy for all Europeans packages, which are in turn supported by a number of individual directives, each one characterized by a series of quantitative goals (European Commission, 2023). The quantification of the impact (impact assessment) is customarily required to successfully promote new political measures (European Commission, 2015a) and is in turn based on quantification, often from mathematical models (Saltelli et al., 2023). The emphasis on producing exact figures to assess the contribution of a new technology, political or economic measure has put many models and their users into contexts of decision-making that at times extends beyond their original intent (Saltelli, Bammer et al., 2020). At the same time, the efforts to retrospectively assess the performance of energy models have been extremely limited, one example being the Energy Modeling Forum in the United States (Huntington et al., 1982). In spite of this, retrospective assessments can be very helpful in understanding the sources of mismatch between a forecast and the actual figures reported a posteriori (Koomey et al., 2003). For example, long-range forecast models are typically based on the assumption of gradual structural changes, which are at stake with the disruptive events and discontinuities occurring in the real world (Craig et al., 2002). This dimension is especially important in terms of the nature and pace of technology change (Bistline et al., 2023 ; Weyant & Olavson, 1999). A further critical element in this approach is the cognitive bias in scenario analysis that naturally leads to overconfidence in the option being explored and results in an underestimate of the ranges of possible outcomes (Morgan & Keith, 2008).

Additionally, in their quest for capturing the features of the energy systems represented, models have increased their complicatedness and/or complexity. In this context, the need to appraise model uncertainty has become of paramount importance, especially considering the uncertainty due to propagation errors caused by model complexification (Puy et al., 2022). In ecology, this is known as the O’Neil conjecture, which posits a principle of decreasing returns for model complexity when uncertainties come to dominate the output (O’Neill, 1989 ; Turner & Gardner, 2015). Capturing and apportioning uncertainty is crucial for a healthy interaction at the science–policy interface, including energy policy making, because it promotes better informed decision-making. Yet Yue et al. (2018) found that only about 5% of the studies covering energy system optimization models have included some form of assessment of stochastic uncertainty, which is the part of uncertainty that can be fully quantified (Walker et al., 2003). When it comes to adequately apportioning this uncertainty onto the input parameters and hypotheses through sensitivity analysis, the situation is even more critical: Only very few papers in the energy field have made the use of state-of-the-art approaches (Lo Piano & Benini, 2022 ; Saltelli et al., 2019). Further to that, the epistemic part of uncertainty, the one that arises due to imperfect knowledge and problem framing, has been largely ignored in the energy modeling literature (Pye et al., 2018). For instance, important sources of uncertainties associated with regulatory lag and public acceptance have typically been overlooked. 1

In this contribution, we discuss three approaches to deal with the challenges of non-neutrality and uncertainty in models: The numerical unit spread assessment pedigree (NUSAP) method, diagnostic diagrams, and sensitivity auditing (SAUD). These challenges are especially critical when only one (set of) model(s) has been selected to contribute to decision-making. One practical case is used to showcase in retrospective the relevance of the issue and the associated problems: the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s.

Valve has changed its policy and will now allow games made by AI, or that use AI generated content, to be sold on Steam.

Back in June, we shared that while our goal continues to be shipping as many games as possible on Steam, we needed some time to learn about the fast-moving and legally murky space of AI technology, especially given Steam’s worldwide reach. Today, after spending the last few months learning more about this space and talking with game developers, we are making changes to how we handle games that use AI technology. This will enable us to release the vast majority of games that use it.

The coming year is going to see the first sweeping AI laws enter into force, with global efforts to hold tech companies accountable.

In 2023, AI policy and regulation went from a niche, nerdy topic to front-page news.