This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. The GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we could identify hidden patterns in the geothermal field data needed to discover blind systems efficiently. Crucial geothermal signatures often overlooked in traditional PFA are extracted using the GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology to various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work). The results provide valuable insights into resource types within those regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

Language models have demonstrated the ability to generate highly fluent text; however, it remains unclear whether their output retains coherent high-level structure (e.g., story progression). Here, we propose to apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of the generated text. Model criticism compares the distributions between real and generated data in a latent space obtained according to an assumptive generative process. Different generative processes identify specific failure modes of the underlying model. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality -- and find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.

Analyzing both temporal and spatial patterns for an accurate forecasting model for financial time series forecasting is a challenge due to the complex nature of temporal-spatial dynamics: time series from different locations often have distinct patterns; and for the same time series, patterns may vary as time goes by. Inspired by the successful applications of deep learning, we propose a new model to resolve the issues of forecasting household leverage in China. Our solution consists of multiple RNN-based layers and an attention layer: each RNN-based layer automatically learns the temporal pattern of a specific series with multivariate exogenous series, and then the attention layer learns the spatial correlative weight and obtains the global representations simultaneously. The results show that the new approach can capture the temporal-spatial dynamics of household leverage well and get more accurate and solid predictive results. More, the simulation also studies show that clustering and choosing correlative series are necessary to obtain accurate forecasting results.

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE. Experimental results show that ConReader makes the prediction more interpretable and achieves new state-of-the-art on two CCE tasks in both conventional and zero-shot settings.

Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; and 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models are released as open-source.

At my institution we take great pride in remaining innovative and staying apace with technologies lessons. The past couple years have been rife with meetings, research, and developments about new technology that most American consumers are aware of and know are coming, though don't yet have a deep understanding about. Robotics and artificial intelligence are two sometimes intertwined examples. We have been building and activating degrees and courses in these fascinating, cutting-edge areas of tech. They are both in relatively infant stages, from a historical perspective.

Professor Tshilidzi Marwala is the outgoing vice-chancellor and principal of the University of Johannesburg, and on 1 March 2023, he will be the Rector of the United Nations (UN) University and UN under-secretary-general. He is the author of the upcoming book, 'Heal Our World'. He is on Twitter at @txm1971. I recently gave a talk in New York on the role of artificial intelligence (AI) technology in Africa's development and was reminded of Kwame Nkrumah, the former Ghanaian president who said "we shall accumulate machinery and establish steel works, iron foundries and factories… it is within the possibility of science and technology to make even the Sahara bloom into a vast field with verdant vegetation for agricultural and industrial developments." More than 60 years after Nkrumah made this speech, we are still battling to economically develop Africa.

Financial regulation is forever running to catch up with evolving technology. There are many examples of this: the Second Markets in Financial Instruments Directive (MiFID II) sought to make up ground on the increased electronification of markets since the introduction of MiFID I; policymakers in both the EU and the UK are at this very moment defining the regulatory perimeter around cryptoassets, more than a decade after the initial launch of bitcoin; and regulators first took action against runaway algorithms long before restrictions on algorithmic trading made it into regulatory rulebooks. Continuing this trend, on 11 October 2022, the Bank of England (BoE) and the UK Financial Conduct Authority (FCA) launched a joint discussion paper on how the UK regulators should approach the "safe and responsible" adoption of AI in financial services (FCA DP22/4 and BoE DP5/22) (the AI Discussion Paper), which is now open for responses. This follows the UK Government's Command Paper published in July 2022, announcing a "pro-innovation" approach to regulating AI (CP 728) across different sectors. One strong theme that comes out of the AI Discussion Paper is that, notwithstanding the potential benefits of AI in fostering innovation and reducing costs in financial services, the human factor is key to ensure that AI is governed and overseen responsibly and that potential negative impacts on clients and other stakeholders are mitigated appropriately.

Politicians are often accused of giving robotic answers when facing questions - but politicians in the UK may just have been shown how it's really done. The android Ai-Da, which is claimed to be the world's first ultra-realistic AI robot artist, was questioned by a committee in the British parliament on Tuesday. The politicians from the Communications and Digital Committee in the House of Lords asked the robot - named after the 19th century computer pioneer Ada Lovelace - about the relationship between artificial intelligence, robots, and the arts. "I do not have subjective experiences despite being able to talk about where I am and depend on computer programmes and algorithms who are very not alive. I can still create art," said the robot.

… Administration for an advanced spinal cord stimulator (SCS) that uses artificial intelligence to individualize treatment for each patient.