Trump says Musk has restored access to Ukraine's Starlink network To play this video you need to enable JavaScript in your browser. Figure caption, Watch: Trump says Musk has restored Ukraine's Starlink internet access after row Elon Musk has restored Ukraine's Starlink internet access, US President Donald Trump has said after restrictions hampered Kyiv's armed forces. Trump made the comments after Ukrainian President Volodymyr Zelensky said connectivity to the military-grade Starshield network, which is provided by Musk's satellite-based Starlink system, had yet to be fully restored. Musk previously denied he was behind the slowdown, which affects Ukraine's ability to use drones to attack Russia and protect itself from attacks. Trump refused to be drawn on a report that he had called Musk to ask him to lift restrictions to the service, but when asked about it he said: Bottom line, he put it back.

US actor Tom Skerritt, who starred in the TV series Picket Fences, and the films Alien and Top Gun, has died aged 93, his family says. Skerritt died on Wednesday of natural causes, his family said: While the world knew Tom through his remarkable acting career, we knew him for his humor, curiosity, wisdom, generosity, and the love he brought into our lives. In a six-decade career, he took on film and television roles, appearing in the film Mash, A River Runs Through It and Ted. He also co-founded TheFilmSchool in Seattle to support next-generation filmmakers and storytellers. His family said he will be remembered for his belief that stories can help people heal, find purpose, and create change.

Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.

Operational disruptions can significantly impact companies performance. Ford, with its 37 plants globally, uses 17 billion parts annually to manufacture six million cars and trucks. With up to ten tiers of suppliers between the company and raw materials, any extended disruption in this supply chain can cause substantial financial losses. Therefore, the ability to forecast and identify such disruptions early is crucial for maintaining seamless operations. In this study, we demonstrate how we construct a dataset consisting of many multivariate time series to forecast first-tier supply chain disruptions, utilizing features related to capacity, inventory, utilization, and processing, as outlined in the classical Factory Physics framework. This dataset is technically challenging due to its vast scale of over five hundred thousand time series. Furthermore, these time series, while exhibiting certain similarities, also display heterogeneity within specific subgroups. To address these challenges, we propose a novel methodology that integrates an enhanced Attention Sequence to Sequence Deep Learning architecture, using Neural Network Embeddings to model group effects, with a Survival Analysis model. This model is designed to learn intricate heterogeneous data patterns related to operational disruptions. Our model has demonstrated a strong performance, achieving 0.85 precision and 0.8 recall during the Quality Assurance (QA) phase across Ford's five North American plants. Additionally, to address the common criticism of Machine Learning models as black boxes, we show how the SHAP framework can be used to generate feature importance from the model predictions. It offers valuable insights that can lead to actionable strategies and highlights the potential of advanced machine learning for managing and mitigating supply chain risks in the automotive industry.

This study tackles the complexities of global supply chains, which are increasingly vulnerable to disruptions caused by port congestion, material shortages, and inflation. To address these challenges, we explore the application of machine learning methods, which excel in predicting and optimizing solutions based on large datasets. Our focus is on enhancing supply chain security through fraud detection, maintenance prediction, and material backorder forecasting. We introduce an automated machine learning framework that streamlines data analysis, model construction, and hyperparameter optimization for these tasks. By automating these processes, our framework improves the efficiency and effectiveness of supply chain security measures. Our research identifies key factors that influence machine learning performance, including sampling methods, categorical encoding, feature selection, and hyperparameter optimization. We demonstrate the importance of considering these factors when applying machine learning to supply chain challenges. Traditional mathematical programming models often struggle to cope with the complexity of large-scale supply chain problems. Our study shows that machine learning methods can provide a viable alternative, particularly when dealing with extensive datasets and complex patterns. The automated machine learning framework presented in this study offers a novel approach to supply chain security, contributing to the existing body of knowledge in the field. Its comprehensive automation of machine learning processes makes it a valuable contribution to the domain of supply chain management.

Large language models (LLMs) are increasingly used across society, including in domains like business, engineering, and medicine. These fields often grapple with decision-making under uncertainty, a critical yet challenging task. In this paper, we show that directly prompting LLMs on these types of decision-making problems yields poor results, especially as the problem complexity increases. To overcome this limitation, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step scaffolding procedure, drawing upon principles from decision theory and utility theory, to provide an optimal and human-auditable decision-making process. We validate our framework on decision-making environments involving real agriculture and finance data. Our results show that DeLLMa can significantly improve LLM decision-making performance, achieving up to a 40% increase in accuracy over competing methods.

To meet these rising expectations, many procurement teams are turning to advanced analytics, AI, and machine learning (ML) to transform the way they make smart business buying decisions and create value for the organization. AI and ML tools have long helped procurement teams automate mundane and manual procurement processes, allowing them to focus on more strategic initiatives. But recent advances in natural language processing (NLP), pattern recognition, cognitive analytics, and large language models (LLMs) are "opening up opportunities to make procurement more efficient and effective," says Julie Scully, director of software development at Amazon Business. The good news is procurement teams are already well-positioned to capitalize on these technological advances. Their access to rich data sources, ranging from contracts to invoices, enables AI/ML solutions that can illuminate the insights contained within this data.

Check out all the on-demand sessions from the Intelligent Security Summit here. Inflation and extended supply chain disruptions, among other geopolitical factors, will further complicate the world of supplier sourcing going into 2023. Now more than ever, it's critical that procurement leaders base their spend management and sourcing strategies on highly accurate data. Doing so is paramount to finding new, lower-cost or diverse suppliers. When implemented correctly, a diverse set of suppliers improves agility, helps navigate supply chain disruptions, improves brand reputation, facilitates innovation and increases competition.

As 2023 kicks off, the industrial transformation continues to accelerate with automation, artificial intelligence and edge-cloud platforms at the core. From production and supply chains to fleet and logistics, companies are leveraging technology to become data-driven. On January 17, Samsara -- a US-based industrial IoT cloud operations provider with over 15,000 core customers -- brought together a group of experts for a virtual panel. The panel discussed 2023 IIoT predictions, C-suite executives' priorities and tech investments. Experts agreed that AI and automation would be big disruptors, increasing safety, efficiency and sustainability.

In recent years, we've seen how rising costs, disrupted supply chains, and lockdowns can adversely affect businesses of any size. But there are some solutions that, if followed, can reduce your risk and help make turbulent times a little easier. This week on Let's Talk, our experts share their tips that will help you address the risks and prepare your business for any supply chain shocks. "There are several tactics that Australian business leaders can adopt to prepare for and address the aftershocks of shipment delays and stock unavailability. "Rather than relying on the just in time approach, which can be risky when there are supply shortages or shipping delays, the just in case approach is recommended. This approach focuses on forecasting demand to proactively secure sufficient supplies ahead of time. For this to work, a robust business management solution which grants to timely data which provides insight into incoming orders versus available stock is a key requirement. The just in case approach can boost profitability, while preventing wastage. "Having up-to-date industry data like procurement lead times, stock levels and order volumes can allow business owners to manage potential vulnerabilities in the supply chain and optimise efficiencies within. Finance teams can leverage this data allowing them to create more accurate financial forecasting models to save on supply chain costs and inventory management."