However, existing benchmarks focus on outdated content and limited fields, facing difficulties in real-time updating and leaving new terms unexplored. To address this problem, we propose an adaptive benchmark, NewTerm, for real-time evaluation of new terms. We design a highly automated construction method to ensure high-quality benchmark construction with minimal human effort, allowing flexible updates for real-time information. Empirical results on various LLMs demonstrate over 20% performance reduction caused by new terms. Additionally, while updates to the knowledge cutoff of LLMs can cover some of the new terms, they are unable to generalize to more distant new terms. We also analyze which types of terms are more challenging and why LLMs struggle with new terms, paving the way for future research. Finally, we construct NewTerm 2022 and 2023 to evaluate the new terms updated each year and will continue updating annually. The benchmark and codes can be found at https://anonymous.4open.science/r/NewTerms.

However, existing benchmarks focus on outdated content and limited fields, facing difficulties in real-time updating and leaving new terms unexplored. To address this problem, we propose an adaptive benchmark, NewTerm, for real-time evaluation of new terms. We design a highly automated construction method to ensure high-quality benchmark construction with minimal human effort, allowing flexible updates for real-time information. Empirical results on various LLMs demonstrate over 20% performance reduction caused by new terms. Additionally, while updates to the knowledge cutoff of LLMs can cover some of the new terms, they are unable to generalize to more distant new terms.

Google is partnering with The Associated Press to bring real-time information from the news agency to its Gemini app, the search giant announced on Wednesday. The financial terms of the agreement were not disclosed. The deal builds on an existing partnership Google had with The Associated Press to source real-time information for its search engine. "This will be particularly helpful to [Gemini app] users looking for up-to-date information," Google says of the deal. "AP and Google's longstanding relationship is based on working together to provide timely, accurate news and information to global audiences," said Kristin Heitmann, The Associated Press senior vice president and chief revenue officer.

Earlier today, Google presented a new vision for its flagship search engine, one that is uniquely tailored to the generative-AI moment. With advanced technology at its disposal, "Google will do the Googling for you," Liz Reid, the company's head of search, declared onstage at the company's annual software conference. Googling something rarely yields an immediate, definitive answer. You enter a query, confront a wall of blue links, open a zillion tabs, and wade through them to find the most relevant information. If that doesn't work, you refine the search and start again.

When I first reviewed the Ray-Ban Meta smart glasses, I wrote that some of the most intriguing features were the ones I couldn't try out yet. Of these, the most interesting is what Meta calls "multimodal AI," the ability for the glasses to respond to queries based on what you're looking at. For example, you can look at text and ask for a translation, or ask it to identify a plant or landmark. The other major update I was waiting for was the addition of real-time information to the Meta AI assistant. Last fall, the assistant had a "knowledge cutoff" of December 2022, which significantly limited the types of questions it could answer. But Meta has started to make both of these features available (multimodal search is in an "early access" period").

ChatGPT can now browse the internet to provide users with current information, its parent company OpenAI has announced. The chatbot was previously trained to use data up to September 2021 and was unable to provide real-time information. On Wednesday, Microsoft-backed OpenAI announced on X, formerly Twitter, that the new update allows it to move past the September 2021 cutoff and access current information on the internet. ChatGPT can now browse the internet to provide you with current and authoritative information, complete with direct links to sources. It is no longer limited to data before September 2021.

A kriging-random forest hybrid model is developed for real-time ground property prediction ahead of the earth pressure balanced shield by integrating Kriging extrapolation and random forest, which can guide shield operating parameter selection thereby mitigate construction risks. The proposed KRF algorithm synergizes two types of information: prior information and real-time information. The previously predicted ground properties with EPB operating parameters are extrapolated via the Kriging algorithm to provide prior information for the prediction of currently being excavated ground properties. The real-time information refers to the real-time operating parameters of the EPB shield, which are input into random forest to provide a real-time prediction of ground properties. The integration of these two predictions is achieved by assigning weights to each prediction according to their uncertainties, ensuring the prediction of KRF with minimum uncertainty. The performance of the KRF algorithm is assessed via a case study of the Changsha Metro Line 4 project. It reveals that the proposed KRF algorithm can predict ground properties with an accuracy of 93%, overperforming the existing algorithms of LightGBM, AdaBoost-CART, and DNN by 29%, 8%, and 12%, respectively. Another dataset from Shenzhen Metro Line 13 project is utilized to further evaluate the model generalization performance, revealing that the model can transfer its learned knowledge from one region to another with an accuracy of 89%.

This study investigates the potential of an ambulatory device that incorporates Large Language Models (LLMs) in cadence with other specialized ML models to assess anemia severity in sickle cell patients in real time. The device would rely on sensor data that measures angiogenic material levels to assess anemia severity, providing real-time information to patients and clinicians to reduce the frequency of vaso-occlusive crises because of the early detection of anemia severity, allowing for timely interventions and potentially reducing the likelihood of serious complications. The main challenges in developing such a device are the creation of a reliable non-invasive tool for angiogenic level assessment, a biophysics model and the practical consideration of an LLM communicating with emergency personnel on behalf of an incapacitated patient. A possible system is proposed, and the limitations of this approach are discussed.

Artificial Intelligence (AI) and Machine Learning (ML) are two types of intelligent software solutions that aim to design futuristic technology with human-like qualities. At its crux, AI is a technology system whose objective is to emulate human faculties and perform a task while simultaneously justifying its actions based on factual information. On the contrary, ML is a subset of AI. It revolves around building software programs that facilitate more'sentient' or'sound' data-backed decision-making for computers. The history of AI can be traced back to the 1950s when computational techniques and abilities began to be infused in machines. The goal was simple: to transcend the current usage of computers and condition them for feasible decision-making.

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep reinforcement learning (DRL) to learn the best policy for the agent in such an environment without any need to store the huge number of state representations for GSG-I. However, the decision-making process of DRL methods is largely opaque, which results in a lack of trust in their predictions. To tackle this issue, we present an interpretable DRL method for GSG-I that generates visualization to explain the decisions taken by the DRL algorithm. We also show that this approach performs better and works well with a simpler training regimen compared to the existing method.