In the fall of 2018, the University of California tasked a team of researchers with identifying projects from which it could confidently purchase carbon offsets that would reliably cancel out greenhouse gas emissions across its campuses. They found next to nothing. The findings helped prompt the entire university system to radically rethink its sustainability plans. Now the researchers are sharing the lessons they learned over the course of the project, in the hopes of helping other universities and organizations consider what role, if any, offsets should play in sustainability strategies, MIT Technology Review can report. The project's leaders have three main takeaways for what others should do.

The idea of a robot taking your job might sound like science fiction. But a new study suggests it could soon become a reality for many Britons. The study, by the Department for Education, has revealed the jobs most likely to be taken by robots. However, there's sports players, roofers, and steel erectors can all rest easy, with the study suggesting these professions are the safest from the advance of AI technology. The idea of a robot taking your job might sound like science fiction.

Fox News correspondent Gillian Turner has the latest on the presidents focus amid calls for an impeachment inquiry on Special Report. The U.S. and U.K. joined more than a dozen countries to unveil a new artifical intelligence agreement aimed at preventing rogue actors from abusing the technology, though not all experts are sold on how useful the pact will be. "This is really more of an agreement of intent than actual substance," Phil Siegel, founder of the Center for Advanced Preparedness and Threat Response Simulation, told Fox News Digital. Siegel's comments come after what a U.S. official described as the first ever detailed agreement on AI safety was unveiled Sunday, according to a report from Reuters, putting measures in place that are meant to create AI systems that are "secure by design." Vice President Kamala Harris watches President Biden sign an executive order during an AI event at the White House on Oct. 30, 2023.

NASA and IBM have teamed up to build an AI foundation model for weather and climate applications. They're combining their respective knowledge and skills in the Earth science and AI fields, respectively, for the model, which they say should offer "significant advantages over existing technology." Current AI models such as GraphCast and Fourcastnet are already generating weather forecasts more quickly than traditional meteorological models. However, IBM notes those are AI emulators rather than foundation models. As the name suggests, foundation models are the base technologies that power generative AI applications.

Elon Musk, the billionaire owner of X, says the advertisers that have stopped spending on the platform due to his endorsement of an antisemitic post can "f----" themselves. "What it's going to do is it's going to kill the company, and the whole world will know the advertisers killed the company," Musk said at the New York Times DealBook conference on Wednesday. The post was the "worst and dumbest I've ever done," said Musk, the chief executive officer of Tesla Inc. Still, if advertisers leave the company, its failure will be their fault, not his -- saying they were trying to "blackmail me with money," he said. "I won't tap dance" to prove trustworthy, he said.

Power demand forecasting is a crucial and challenging task for new power system and integrated energy system. However, as public feature databases and the theoretical mechanism of power demand changes are unavailable, the known features of power demand fluctuation are much limited. Recently, multimodal learning approaches have shown great vitality in machine learning and AIGC. In this paper, we interact two modal data and propose a textual-knowledge-guided numerical feature discovery (TKNFD) method for short-term power demand forecasting. TKNFD extensively accumulates qualitative textual knowledge, expands it into a candidate feature-type set, collects numerical data of these features, and eventually builds four-dimensional multivariate source-tracking databases (4DM-STDs). Next, TKNFD presents a two-level quantitative feature identification strategy independent of forecasting models, finds 43-48 features, and systematically analyses feature contribution and dependency correlation. Benchmark experiments in two different regions around the world demonstrate that the forecasting accuracy of TKNFD-discovered features reliably outperforms that of SoTA feature schemes by 16.84% to 36.36% MAPE. In particular, TKNFD reveals many unknown features, especially several dominant features in the unknown energy and astronomical dimensions, which extend the knowledge on the origin of strong randomness and non-linearity in power demand fluctuation. Besides, 4DM-STDs can serve as public baseline databases.

Our work presents a novel approach to shape optimization, with the twofold objective to improve the efficiency of global optimization algorithms while promoting the generation of high-quality designs during the optimization process free of geometrical anomalies. This is accomplished by reducing the number of the original design variables defining a new reduced subspace where the geometrical variance is maximized and modeling the underlying generative process of the data via probabilistic linear latent variable models such as factor analysis and probabilistic principal component analysis. We show that the data follows approximately a Gaussian distribution when the shape modification method is linear and the design variables are sampled uniformly at random, due to the direct application of the central limit theorem. The degree of anomalousness is measured in terms of Mahalanobis distance, and the paper demonstrates that abnormal designs tend to exhibit a high value of this metric. This enables the definition of a new optimization model where anomalous geometries are penalized and consequently avoided during the optimization loop. The procedure is demonstrated for hull shape optimization of the DTMB 5415 model, extensively used as an international benchmark for shape optimization problems. The global optimization routine is carried out using Bayesian optimization and the DIRECT algorithm. From the numerical results, the new framework improves the convergence of global optimization algorithms, while only designs with high-quality geometrical features are generated through the optimization routine thereby avoiding the wastage of precious computationally expensive simulations.

Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Ni\~no and La Ni\~na with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

The volume of data is continuously increasing and the ability and demand to efficiently analyze the data has become even more crucial. Machine learning and data mining are suitable techniques and tools to efficiently process and analyze the data. Complementary to both techniques is process mining [Aa16]. Process mining is a promising approach to find additional patterns (e.g., in terms of causal effects or bottlenecks) in data and in that way to give new insights into the data that could not be directly found with techniques like machine learning or data mining. The insights from processes are given by means of events that have been tracked by information systems. Then, this event data that is structured within a log (i.e., an event log), is used as input to any process mining algorithm. Process mining allows both an analysis based solely on event logs as well as a comparison between (manually generated or as-is) process models and an event log reflecting the to-be processes.

Generative artificial intelligence (GenAI), exemplified by ChatGPT, Midjourney, and other state-of-the-art large language models and diffusion models, holds significant potential for transforming education and enhancing human productivity. While the prevalence of GenAI in education has motivated numerous research initiatives, integrating these technologies within the learning analytics (LA) cycle and their implications for practical interventions remain underexplored. This paper delves into the prospective opportunities and challenges GenAI poses for advancing LA. We present a concise overview of the current GenAI landscape and contextualise its potential roles within Clow's generic framework of the LA cycle. We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions. As the lines blur between learners and GenAI tools, a renewed understanding of learners is needed. Future research can delve deep into frameworks and methodologies that advocate for human-AI collaboration. The LA community can play a pivotal role in capturing data about human and AI contributions and exploring how they can collaborate most effectively. As LA advances, it is essential to consider the pedagogical implications and broader socioeconomic impact of GenAI for ensuring an inclusive future.