"'We were there operating the machines, we were obeying orders, we were applying logic to do what we were told to do, and we were doing so efficiently and intelligently, but we didn't design the machines for decoding.

The robot revolution began long ago, and so did the killing. One day in 1979, a robot at a Ford Motor Company casting plant malfunctioned--human workers determined that it was not going fast enough. And so 25-year-old Robert Williams was asked to climb into a storage rack to help move things along. The one-ton robot continued to work silently, smashing into Williams's head and instantly killing him. This was reportedly the first incident in which a robot killed a human; many more would follow.

As I reported last week, Baidu became the first Chinese tech company to roll out its large language model--called Ernie Bot--to the general public, following a regulatory approval from the Chinese government. Previously, access required an application or was limited to corporate clients. You can read more about the news here. I have to admit the Chinese public has reacted more passionately than I had expected. According to Baidu, the Ernie Bot mobile app reached 1 million users in the 19 hours following the announcement, and the model responded to more than 33.42 million user questions in 24 hours, averaging 23,000 questions per minute.

Almost six billion tonnes of sand and other sediment are extracted from the world's seas and oceans every year on average, according to the United Nations. The UN Environment Programme (UNEP) warned on Tuesday of the devastating toll on biodiversity and coastal communities, adding that the scale of dredging was growing, with dire consequences. "The scale of environmental impacts of shallow sea mining activities and dredging is alarming," said Pascal Peduzzi, who heads UNEP's analytics centre GRID-Geneva. He pointed to the effects on biodiversity, as well as on water turbidity, and noise effects on marine mammals. The UNEP launched a global data platform on sediment extraction in marine environments, Marine Sand Watch, which uses artificial intelligence to track and monitor dredging activities of sand, clay, silt, gravel and rock in the world's marine environment.

Fox News correspondent David Spunt has the latest on questions over whether the former president can hold office again on Special Report. A liberal reporter added fuel to online fire that a conservative news outlet was duped by a former President Trump impersonator, or even artificial intelligence – resulting in an embarrassing editor's note. Last week, Trump called into right-wing channel Real America's Voice for an interview that resulted in online speculation that the outlet had spoken with an impostor. Audio was shaky, and speculation erupted that Trump either had a cold, poor service or something more malicious, such as someone impersonating the 45th president, or modern technology generating the interview with old clips of Trump. Zachary Petrizzo, a politics reporter for the left-wing Daily Beast, took things a step further and reported that Real America's Voice owner Robert Sigg told him that the company would investigate whether the call was some sort of prank.

One person was killed in Russian drone attacks on a port district in Ukraine's southwestern Odesa region while Ukrainian air defence successfully shot down a barrage of Russian missiles fired at targets in the capital Kyiv, officials said. The night-time attacks on Odesa's Izmail district lasted three hours, regional Governor Oleg Kiper said on the Telegram messaging app early on Wednesday. "Unfortunately, one person died," said Kiper, adding that it was an agricultural worker who had been seriously injured and died in hospital. "Destruction and fires were recorded in several settlements," he added, saying that port and agricultural infrastructure had been damaged, including administrative buildings. The Danube river port of Izmail, which borders NATO member Romania, has become a main export route for Ukrainian agricultural produce since Russia's withdrawal from a United Nations-brokered grain deal in July.

The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum's capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders' ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception.

This paper addresses a critical challenge in cybersecurity: the gap between vulnerability information represented by Common Vulnerabilities and Exposures (CVEs) and the resulting cyberattack actions. CVEs provide insights into vulnerabilities, but often lack details on potential threat actions (tactics, techniques, and procedures, or TTPs) within the ATT&CK framework. This gap hinders accurate CVE categorization and proactive countermeasure initiation. The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation. TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions. It initially extracts threat actions from unstructured cyber threat reports using Semantic Role Labeling (SRL) techniques. These actions, along with their contextual attributes, are correlated with MITRE's attack functionality classes. This automated correlation facilitates the creation of labeled data, essential for categorizing novel threat actions into threat functionality classes and TTPs. The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques. TTPpredictor outperforms state-of-the-art language model tools like ChatGPT. Overall, this paper offers a robust solution for linking CVEs to potential attack techniques, enhancing cybersecurity practitioners' ability to proactively identify and mitigate threats.

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations.

Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider. Previous research on secure reasoning using multi-party computation (MPC) has proven to be impractical for LLM applications due to its time-consuming and communication-intensive nature. While lightweight anonymization techniques can protect private information in prompts through substitution or masking, they fail to recover sensitive data replaced in the LLM-generated results. In this paper, we expand the application scenarios of anonymization techniques by training a small local model to de-anonymize the LLM's returned results with minimal computational overhead. We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization, respectively. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models. Furthermore, we conduct experiments to evaluate HaS's usability in translation and classification tasks. The experimental findings demonstrate that the HaS framework achieves an optimal balance between privacy protection and utility.