The images pop up in Mophat Okinyi's mind when he's alone, or when he's about to sleep. Okinyi, a former content moderator for Open AI's ChatGPT in Nairobi, Kenya, is one of four people in that role who have filed a petition to the Kenyan government calling for an investigation into what they describe as exploitative conditions for contractors reviewing the content that powers artificial intelligence programs. "It has really damaged my mental health," said Okinyi. The 27-year-old said he would would view up to 700 text passages a day, many depicting graphic sexual violence. He recalls he started avoiding people after having read texts about rapists and found himself projecting paranoid narratives on to people around him.

Kuhn's framework of scientific progress (Kuhn, 1962) provides a useful framing of the paradigm shifts that have occurred in Artificial Intelligence over the last 60 years. The framework is also useful in understanding what is arguably a new paradigm shift in AI, signaled by the emergence of large pre-trained systems such as GPT-3, on which conversational agents such as ChatGPT are based. Such systems make intelligence a commoditized general purpose technology that is configurable to applications. In this paper, I summarize the forces that led to the rise and fall of each paradigm, and discuss the pressing issues and risks associated with the current paradigm shift in AI.

Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. However, the use of such systems raises significant ethical and safety concerns, including privacy violations, misinformation and intellectual property theft. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. To mitigate these risks, there is an urgent need of policies and regulations responsible and ethical development in the field of generative AI. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity, lack of interoperability across lines of jurisdictions, restricting innovation, and hindering free market competition. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative. However, they have clear deficiencies in terms of lack of adequacy of oversight and difficulty of enforcement of ethical and safety standards, and are thus not enough by themselves as a regulation mechanism. We propose a marriage of these two strategies via a framework we call Dual Governance. This framework proposes a cooperative synergy between centralized government regulations in a U.S. specific context and safety mechanisms developed by the community to protect stakeholders from the harms of generative AI. By implementing the Dual Governance framework, we posit that innovation and creativity can be promoted while ensuring safe and ethical deployment of generative AI.

Large language models have been used as the foundation of highly sophisticated artificial intelligences, capable of delivering human-like responses to probes about legal and moral issues. However, these models are unreliable guides to their own inner workings, and even the engineering teams behind their creation are unable to explain exactly how they came to develop all of the capabilities they currently have. The emerging field of machine psychology seeks to gain insight into the processes and concepts that these models possess. In this paper, we employ the methods of psychology to probe into GPT-4's moral and legal reasoning. More specifically, we investigate the similarities and differences between GPT-4 and humans when it comes to intentionality ascriptions, judgments about causation, the morality of deception, moral foundations, the impact of moral luck on legal judgments, the concept of consent, and rule violation judgments. We find high correlations between human and AI responses, but also several significant systematic differences between them. We conclude with a discussion of the philosophical implications of our findings.

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.

The backup Uber driver for a self-driving vehicle that killed a pedestrian in suburban Phoenix in 2018 pleaded guilty Friday to endangerment in the first deadly crash involving a fully autonomous car. Arizona state judge David Garbarino, who accepted the plea agreement, sentenced Rafaela Vasquez to three years of supervised probation for the crash that killed 49-year-old Elaine Herzberg. Vasquez, 49, told police that Herzberg "came out of nowhere" and that she didn't see Herzberg before hitting her on a darkened Tempe street on 18 March 2018. Vasquez had been charged with felony negligent homicide. The charge to which she pleaded could be reclassified as a misdemeanor if she completes probation. Authorities say Vasquez was streaming the television show The Voice on a phone and looking down in the moments before Uber's Volvo XC-90 SUV struck Herzberg, who was crossing with her bicycle.

German semiconductor maker Infineon Technologies announced it's making printed circuit boards (PCBs) that dissolve in water. Sourced from UK startup Jiva Materials, the plant-based Soluboard could provide a new way for the tech industry to reduce electronic waste. Jiva's biodegradable PCB is made of natural fibers and a halogen-free polymer with a much lower carbon footprint. A team at the University of Washington College of Engineering and Microsoft Research created a mouse using a Soluboard PCB as its core. The researchers found the Soluboard dissolved in hot water in under six minutes.

The UK intelligence agencies are lobbying the government to weaken surveillance laws they argue place a "burdensome" limit on their ability to train artificial intelligence models with large amounts of personal data. The proposals would make it easier for GCHQ, MI6 and MI5 to use certain types of data, by relaxing safeguards designed to protect people's privacy and prevent the misuse of sensitive information. Privacy experts and civil liberties groups have expressed alarm at the move, which would unwind some of the legal protection introduced in 2016 after disclosures by Edward Snowden about intrusive state surveillance. The UK's spy agencies are increasingly using AI-based systems to help analyse the vast and growing quantities of data they hold. Privacy campaigners argue rapidly advancing AI capabilities require stronger rather than weaker regulation.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' Republicans and Democrats in the Artificial Intelligence Caucus are proposing the creation of a public research center that will give people and organizations access to the tools they need to create their own AI systems, even if they don't have access to billions of dollars in research funding. Lawmakers proposed the "Creating Resources for Every American To Experiment with Artificial Intelligence Act," or the CREATE AI Act, a bill that would establish the National Artificial Intelligence Research Resource (NAIRR). In January, a federal task force called for the creation of this body and estimated it would need about $440 million per year to get off the ground. The CREATE AI Act doesn't authorize that specific level of funding, but the bill signals that both parties are interested in establishing the NAIRR in order to ensure entities other than the billion- and trillion-dollar AI developers aren't the only ones developing this new technology.

Abstract-- We present a novel approach for fast and reliable policy selection for navigation in partial maps. Leveraging the recent learning-augmented model-based Learning over Subgoals Planning (LSP) abstraction to plan, our robot reuses data collected during navigation to evaluate how well other alternative policies could have performed via a procedure we call offline alt-policy replay. Costs from offline alt-policy replay constrain policy selection among the LSP-based policies during deployment, allowing for improvements in convergence speed, cumulative regret and average navigation cost. With only limited prior knowledge about the nature of unseen environments, we achieve at least 67% and as much as 96% improvements on cumulative regret over the baseline bandit approach in our experiments in simulated maze and office-like environments.