Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design OptiGuide -- a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes. Our framework does not forgo the state-of-the-art combinatorial optimization technology, but rather leverages it to quantitatively answer what-if scenarios (e.g., how would the cost change if we used supplier B instead of supplier A for a given demand?). Importantly, our design does not require sending proprietary data over to LLMs, which can be a privacy concern in some circumstances. We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain. Along the way, we develop a general evaluation benchmark, which can be used to evaluate the accuracy of the LLM output in other scenarios.

While summarization has been extensively researched in natural language processing (NLP), cross-lingual cross-temporal summarization (CLCTS) is a largely unexplored area that has the potential to improve cross-cultural accessibility and understanding. This paper comprehensively addresses the CLCTS task, including dataset creation, modeling, and evaluation. We build the first CLCTS corpus, leveraging historical fictive texts and Wikipedia summaries in English and German, and examine the effectiveness of popular transformer end-to-end models with different intermediate finetuning tasks. Additionally, we explore the potential of ChatGPT for CLCTS as a summarizer and an evaluator. Overall, we report evaluations from humans, ChatGPT, and several recent automatic evaluation metrics where we find that our intermediate task finetuned end-to-end models generate bad to moderate quality summaries; ChatGPT as a summarizer (without any finetuning) provides moderate to good quality outputs and as an evaluator correlates moderately with human evaluations but is prone to giving lower scores. ChatGPT also seems very adept at normalizing historical text and outperforms context-unaware spelling normalization tools such as Norma. We finally test ChatGPT in a scenario with adversarially attacked and unseen source documents and find that ChatGPT profits from its prior knowledge to a certain degree, with better performances for omission and entity swap than negation against its prior knowledge. This benefit inflates its assessed quality as ChatGPT performs slightly worse for unseen source documents compared to seen documents. We additionally introspect our models' performances to find that longer, older and more complex source texts (all of which are more characteristic for historical language variants) are harder to summarize for all models, indicating the difficulty of the CLCTS task.

The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to access clinical information efficiently. To tackle this challenge, biomedical text summarization (BTS) has been proposed as a solution to support clinical information retrieval and management. BTS aims at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, the rapid advancement of fundamental natural language processing (NLP) techniques, from pre-trained language models (PLMs) to large language models (LLMs), has greatly facilitated the progress of BTS. This growth has led to numerous proposed summarization methods, datasets, and evaluation metrics, raising the need for a comprehensive and up-to-date survey for BTS. In this paper, we present a systematic review of recent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to LLMs, to help understand the latest progress, challenges, and future directions. We begin by introducing the foundational concepts of BTS, PLMs and LLMs, followed by an in-depth review of available datasets, recent approaches, and evaluation metrics in BTS. We finally discuss existing challenges and promising future directions in the era of LLMs. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master. We believe that this survey will be a useful resource to researchers, allowing them to quickly track recent advancements and provide guidelines for future BTS research within the research community.

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs. The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available https://goattack.far.ai/.

Elon Musk wants to "understand the true nature of the universe." At least that's what his new AI company, xAI, said on its website as he announced its formation on Wednesday. Musk incorporated xAI in Nevada in March this year and reportedly purchased "roughly 10,000 graphics processing units"--hardware that is required to develop and run state-of-the-art AI systems. The company has not said how it is financed but the Financial Times reported in April that Musk was discussing getting funding from investors in SpaceX and Tesla, two companies he runs. The company has not shared much detail about its intentions, but said on its website that its team would be joining a Twitter Spaces call on July 14 to take questions.

In April, Elon Musk told right-wing commentator Tucker Carlson that he was starting a project to compete with ChatGPT and build "a maximum truth-seeking AI that tries to understand the nature of the universe." Today, Musk unveiled that new artificial intelligence venture. The company's spare landing page repeats that goal of understanding the universe and lists 11 AI researchers--seemingly all men--who have made significant contributions to the field of AI in recent years and worked at companies including Google, DeepMind, and OpenAI. The crew is an "all-star founding team," according to Linxi "Jim" Fan, an AI researcher at Nvidia. "I'm really impressed by the talent density--read too many papers by them to count," he writes in a LinkedIn post. This content can also be viewed on the site it originates from.

This copy is for your personal, non-commercial use only. For non-personal use or to order multiple copies, please contact Dow Jones Reprints at 1-800-843-0008 or visit www.djreprints.com.

Family doctors already have patients turning to'Dr Google' for a diagnosis. But Google has now developed AI which could perform as well as a doctor when answering questions about ailments. The tech giant reports in the journal, Nature, that its latest model, which processes language similarly to ChatGPT, can answer a range of medical questions with 92.6 per cent accuracy. That is on a par with the answers provided by nine doctors from the UK, US and India, who were asked to respond to the same 80 questions. Researchers at Google say the technology does not threaten the jobs of GPs. Google has now developed AI which could perform as well as a doctor when answering questions about ailments.

Elon Musk has officially joined the AI race with the launch of a new company named xAI - after years of claiming the tech will be the demise of humanity. The Twitter boss has not shared many details but revealed xAI was designed'to understand the true nature of the universe.' The xAI team includes members who previously worked at DeepMind, OpenAI, Google Research, Microsoft Research and Tesla. The company's sparse website notes include that more information will be shared in a live Twitter Space on Friday. Elon Musk announced Wednesday a new AI company he calls xAI.

Elon Musk has a new AI company. A website has appeared for xAI, which will embark on the self-described mission to "understand the true nature of the universe." The announcement comes after filing documents revealed the existence of a company called "X.AI Corp" earlier this year. Musk also said in an April interview that he wanted to start a venture for "maximum truth-seeking AI that tries to understand the nature of the universe" that "hopefully does more good than harm." Not much else is known yet about Musk's latest venture.