Technology has made significant advancements and has already solved many of the supply chain challenges affecting companies today. However, we can't claim that all the challenges have decreased when compared to previous years. On the contrary, globalisation, trade sanctions, Brexit, an eCommerce revolution and finally a global pandemic are just some of the factors that are complicating an overcomplicated supply chain – especially for companies that might lack the resources of bigger corporations. Developments in AI have assisted in the planning and development of operations across the supply chain. And if the pandemic has taught us one thing, it is the importance of forward planning and anticipating supply chain challenges.

Despite the important and ever-increasing role of artificial intelligence in many parts of modern society, there is very little policy or regulation governing the development and use of AI systems in the United States. Tech companies have largely been left to regulate themselves in this arena, potentially leading to decisions and situations that have garnered criticism. Google fired an employee who publicly raised concerns over how a certain type of AI can contribute to environmental and social problems. Other AI companies have developed products that are used by organizations like the Los Angeles Police Department where they have been shown to bolster existing racially biased policies. There are some government recommendations and guidance regarding AI use.

In the 36th week of war in Ukraine, Russia backed out of a United Nations-sponsored agreement guaranteeing the safe passage of grain ships through the Black Sea, only to rejoin it three days later. Moscow's withdrawal over the weekend renewed fears of a global food crisis – concerns that have not been completely quelled since it rejoined because its return came with conditions. President Vladimir Putin said he reserved the right to back out again if Kyiv used the humanitarian corridor for attacks, the reason Russia gave for the initial pullout. The Kremlin has also warned that it has not yet decided whether to extend the grain deal, which expires in two weeks. Officials in Moscow had said that grain ships may have acted as a cloak for an attack on its naval base on Saturday at Sevastopol on the Crimean Peninsula.

What does AI bring to interventional surgical robots? Interventional surgical robots remove the physician from X-ray hazards, enable surgeries and stenting without compromising safety, and allow increased precision. Image navigation is the eye and brain of interventional robots, playing a crucial role in both diagnoses and as the primary guidance tool during interventions. Fortunately, powerful artificial intelligence (AI) technology is penetrating the medical imaging arena, holding significant promise for creating an'eye-hand-brain' collaborative system for interventional robots and optimizing fluoroscopic interventional procedures. From preoperative treatment plans to intraoperative imaging navigation and postoperative imaging follow-ups, AI can help realize image-guided precision medical visualization and provide physicians with additional information not available through conventional approaches.

The Department of Transportation is soliciting applicants to integrate artificial intelligence and environmental analytics into highway …

The use of AI (artificial intelligence) technologies is transforming industries from manufacturing to healthcare, retail, agriculture, transportation, and beyond. Precedence Research estimates the global market for AI will reach nearly $1.6 trillion by 2030, up from about $87 billion in 2021. A new AI and machine learning-powered project funded by the NSF (National Science Foundation) will leverage these powerful technologies to transform the way scientists analyze ocean imagery, adding yet one more way AI is changing the way humans interact with everything--from other humans to machines and even data from the depths of the sea. Every day, new information from Earth's oceans is being collected by research crews and ROVs (remotely operated vehicles) equipped with cameras, video cameras, and instruments that measure parameters from the ROV's surroundings, such as water temperature. This equipment allows research vehicles to collect massive amounts of imagery and other data about the ocean.

Even as the economic pressures that drove millions of white working-class voters to the right are moderating, the hostility this key segment of the electorate feels toward the Democratic Party has deepened and is less and less amenable to change. "You cannot really understand the working-class rightward shift without discussing what the Democratic Party is doing," Daron Acemoglu, an economist at M.I.T., wrote by email: Many of the trends that negatively impacted workers, especially non-college workers, including rapid automation and trade with China, were advocated and supported by Democratic politicians. Perhaps worse from a political point of view, when these politicians were advocating such policies, they were also viewed as adopting a tone of indifference to the plight of non-college workers. Poll data suggest that Democratic struggles with the white working class are worsening. In "Elections and Demography: Democrats Lose Ground, Need Strong Turnout," an Oct. 22 American Enterprise institute report by Ruy Teixeira, Karlyn Bowman and Nate Moore write: The gap between non-college and college whites continues to grow. For the first time this cycle, the difference in margin between the two has surpassed an astounding 40 points, well above the 33-point gap in 2020's presidential contest.

The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new event extraction dataset from WikiPpedia including 142 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin. Source code and dataset are available on our GitHub repository: https://github.com/yzjiao/RolePred

TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure and transparent research whilst protecting data confidentiality. There is an increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These complex AI models require more information to describe and reproduce, increasing the possibility that sensitive personal data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. The development of these recommendations has been funded by the GRAIMATTER UKRI DARE UK sprint research project. This version of our recommendations was published at the end of the project in September 2022. During the course of the project, we have identified many areas for future investigations to expand and test these recommendations in practice. Therefore, we expect that this document will evolve over time.

This paper proposes a possible method using natural language processing that might assist in the FDA medical device marketing process. Actual device descriptions are taken and matched with the device description in FDA Title 21 of CFR to determine their corresponding device type. Both pre-trained word embeddings such as FastText and large pre-trained sentence embedding models such as sentence transformers are evaluated on their accuracy in characterizing a piece of device description. An experiment is also done to test whether these models can identify the devices wrongly classified in the FDA database. The result shows that sentence transformer with T5 and MPNet and GPT-3 semantic search embedding show high accuracy in identifying the correct classification by narrowing down the correct label to be contained in the first 15 most likely results, as compared to 2585 types of device descriptions that must be manually searched through. On the other hand, all methods demonstrate high accuracy in identifying completely incorrectly labeled devices, but all fail to identify false device classifications that are wrong but closely related to the true label.