It runs factories, controls transportation networks, and defines the way we interact with personal devices. It is estimated that somewhere in the neighborhood of 2.8 trillion lines of code have been written over the last two decades alone.a Yet it is easy to overlook a basic fact: people have to write software--and that is often a long, tedious, and error-prone process. Although low-code and no-code environments have simplified things--and even allowed non-data scientists to build software through drag-and-drop interfaces--they still require considerable time and effort. Over the last several years, various systems and frameworks have appeared that can automate code generation.

America's Federal Trade Commission has warned it may crack down on companies that not only use generative AI tools to scam folks, but also those making the software in the first place, even if those applications were not created with that fraud in mind. Now the US government agency is wagging its finger at those using generative machine-learning tools to hoodwink victims into parting with their cash and suchlike as well as the people who made the code to begin with. Commercial software and cloud services, as well as open source tools, can be used to churn out fake images, text, videos, and voices on an industrial scale, which is all perfect for cheating marks. Picture adverts for stuff featuring convincing but faked endorsements by celebrities; that kind of thing is on the FTC's radar. And to be clear, there are no new rules or regulations at play here: it's just the FTC doing its usual thing of reminding people that today's tech fads are still covered by consumer protection laws, in the US at least.

The deceleration of global poverty reduction in the last decades suggests that traditional redistribution policies are losing their effectiveness. Alternative ways to work towards the #1 United Nations Sustainable Development Goal (poverty eradication) are required. NGOs have insistingly denounced the criminalization of poverty, and the social science literature suggests that discrimination against the poor (a phenomenon known as aporophobia) could constitute a brake to the fight against poverty. This paper describes a proposal for an agent-based model to examine the impact that aporophobia at the institutional level has on poverty levels. This aporophobia agent-based model (AABM) will first be applied to a case study in the city of Barcelona. The regulatory environment is central to the model, since aporophobia has been identified in the legal framework. The AABM presented in this paper constitutes a cornerstone to obtain empirical evidence, in a non-invasive way, on the causal relationship between aporophobia and poverty levels. The simulations that will be generated based on the AABM have the potential to inform a new generation of poverty reduction policies, which act not only on the redistribution of wealth but also on the discrimination of the poor.

Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we investigate the problems of learning summarizers with only few examples and propose corresponding methods for improvements. First, typical transfer learning methods are prone to be affected by data properties and learning objectives in the pretext tasks. Therefore, based on pretrained language models, we further present a meta learning framework to transfer few-shot learning processes from source corpora to the target corpus. Second, previous methods learn from training examples without decomposing the content and preference. The generated summaries could therefore be constrained by the preference bias in the training set, especially under low-resource settings. As such, we propose decomposing the contents and preferences during learning through the parameter modulation, which enables control over preferences during inference. Third, given a target application, specifying required preferences could be non-trivial because the preferences may be difficult to derive through observations. Therefore, we propose a novel decoding method to automatically estimate suitable preferences and generate corresponding summary candidates from the few training examples. Extensive experiments demonstrate that our methods achieve state-of-the-art performance on six diverse corpora with 30.11%/33.95%/27.51% and 26.74%/31.14%/24.48% average improvements on ROUGE-1/2/L under 10- and 100-example settings.

Congress has never been the quickest off the mark when it comes to making laws dealing with technology. Now, even as AI takes over creative writing and art, Congress continues to sit idle. As legislators endeavor to comprehend generative AI programs such as Microsoft Bing, ChatGPT and Google Bard, some of the more technology-oriented lawmakers are apprehensive about a repeat of Congress's unpreparedness in responding to the previous major tech wave -- social media. Worries, however, don't appear to be leading to action. True, there's a backlash now for letting tech companies keep Washington at arm's length with promises of "self-regulation" on critical issues such as privacy protection, child safety, disinformation, cryptocurrency, and data portability.

Artificial Intelligence (AI) has become ubiquitous in the past several years. There is not a part of our businesses, cultures, governments and consumer markets. The continuous research and innovation directed by tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing, staffing and education. Technology has always been an essential element for these industries, but artificial intelligence has brought technology to the center of organizations. For instance, from self-driving vehicles to crucial life-saving medical gear, AI is being infused virtually into every apparatus and program.

At the beginning of February 2023, the Council of Europe Committee on Artificial Intelligence published the "zero" draft Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law. The document is dated 6 January, 2023, and is the Council of Europe's preliminary proposal for the AI future regulatory framework. The Council of Europe began work on the draft in September 2019, and was tasked with examining the feasibility of creating a legal framework to ensure that the design, development and application of AI systems would adhere to standards on human rights, the functioning of democracy and the observance of rule of law . At the beginning of 2022, the Council of Europe set up the Committee on Artificial Intelligence (CAI), which published the revised "zero" draft [Framework] Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law, dated 6 January 2023, in February 2023[1]. The Convention is being drafted to establish only a general framework on application of AI systems. Upon examination, even though it is a preliminary and working document, the proposal reveals that the envisaged Convention will not provide for requirements or bans directly applicable to private and legal persons.

The Supreme Court decided that a photograph is not just a mechanical process, but an authored work based on the photographer's decisions in curating the backdrop and subject's clothing. In the realm of generative works, the Office asks applicants if the included AI elements are the result of "mechanical reproduction" or of an author's "own original mental conception, to which [the author] gave visible form."

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America's Federal Trade Commission has warned it may crack down on companies that not only use generative AI tools to scam folks, but also those making the software in the first place, even if those applications were not created with that fraud in mind. Now the US government agency is wagging its finger at those using generative machine-learning tools to hoodwink victims into parting with their cash and suchlike as well as the people who made the code to begin with. Commercial software and cloud services, as well as open source tools, can be used to churn out fake images, text, videos, and voices on an industrial scale, which is all perfect for cheating marks. Picture adverts for stuff featuring convincing but faked endorsements by celebrities; that kind of thing is on the FTC's radar. "Evidence already exists that fraudsters can use these tools to generate realistic but fake content quickly and cheaply, disseminating it to large groups or targeting certain communities or specific individuals," Michael Atleson, an attorney for the FTC's division of advertising practices, wrote in a memo this week.