This post has been read 5342 times! KUWAIT CITY, Nov 17: The government agencies have begun implementing the action plan in the area of managing government facilities and assets, including roads, airports, residential, industrial and service cities, and government facilities in general, reports Al-Qabas daily. This is done through the latest technological means and communications, and the development of systems through artificial intelligence. A document, seen by the daily, revealed that the implementation period will range between 5 and 8 years, and that the government's approach aims to ensure that state facilities achieve the desired goals in terms of economic, financial and administrative aspects, in addition to managing them during disasters, through the use of modern performance measurement and monitoring systems. The document indicated that the program will go through four different phases including reviewing the current strategies (if any); updating and developing the infrastructure project assets strategy; developing the implementation mechanism for the asset management strategic plan and obtaining the necessary approvals to approve the asset management strategy for the concerned and related parties.

We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more accurately we can recover race or gender from the internal state of the network. Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task. After training the two-headed network, we enforce demographic parity by merging the two heads, creating a network with the same architecture as the original network. We establish a close relationship between existing approaches and our approach by showing (1) that the decisions of a fair classifier are well-approximated by our approach, and (2) that an unfair and optimally accurate classifier can be recovered from a fair classifier and our second head predicting the protected attribute. We use our explicit formulation to argue that the existing fairness approaches, just as ours, demonstrate disparate treatment and that they are likely to be unlawful in a wide range of scenarios under US law.

Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computationally expensive. This work proposes a context-aware multi-fidelity Monte Carlo method that optimally balances the costs of training low-fidelity models with the costs of Monte Carlo sampling. It generalizes the previously developed context-aware bi-fidelity Monte Carlo method to hierarchies of multiple models and to more general types of low-fidelity models. When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets. This is in stark contrast to traditional surrogate modeling and model reduction techniques that construct low-fidelity models with the primary goal of approximating well the high-fidelity model outputs and typically ignore the context in which the learned models will be used in upstream tasks. The proposed context-aware multi-fidelity Monte Carlo method applies to hierarchies of a wide range of types of low-fidelity models such as sparse-grid and deep-network models. Numerical experiments with the gyrokinetic simulation code \textsc{Gene} show speedups of up to two orders of magnitude compared to standard estimators when quantifying uncertainties in small-scale fluctuations in confined plasma in fusion reactors. This corresponds to a runtime reduction from 72 days to about four hours on one node of the Lonestar6 supercomputer at the Texas Advanced Computing Center.

In the study, we empirically compare the two recently proposed decoding methods, i.e. Contrastive Search (CS) and Contrastive Decoding (CD), for open-ended text generation. The automatic evaluation results suggest that, while CS performs worse than CD on the MAUVE metric, it substantially surpasses CD on the diversity and coherence metrics. More notably, extensive human evaluations across three different domains demonstrate that human annotators are universally more in favor of CS over CD with substantial margins. The contradicted results between MAUVE and human evaluations reveal that MAUVE does not accurately reflect human preferences. Therefore, we call upon the research community to develop better evaluation metrics for open-ended text generation. To ensure the reproducibility of our work, we have open-sourced all our code, evaluation results, as well as human annotations at https://github.com/yxuansu/Contrastive_Search_versus_Contrastive_Decoding.

We propose a new uniform framework for text classification and ranking that can automate the process of identifying check-worthy sentences in political debates and speech transcripts. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. In particular, we analyse the semantic meaning of each sentence using state-of-the-art neural language models such as BERT, ALBERT, and RoBERTa, while embeddings for entities are obtained from knowledge graph (KG) embedding models. Specifically, we instantiate our framework using five different language models, entity embeddings obtained from six different KG embedding models, as well as two combination methods leading to several Entity-Assisted neural language models. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF' 2019 & 2020 CheckThat! Labs. Our results show that the neural language models significantly outperform traditional TF.IDF and LSTM methods. In addition, we show that the ALBERT model is consistently the most effective model among all the tested neural language models. Our entity embeddings significantly outperform other existing approaches from the literature that are based on similarity and relatedness scores between the entities in a sentence, when used alongside a KG embedding.

Large language models (LLMs) offer a potential source of knowledge for agents that need to acquire new task competencies within a performance environment. We describe efforts toward a novel agent capability that can construct cues (or "prompts") that result in useful LLM responses for an agent learning a new task. Importantly, responses must not only be "reasonable" (a measure used commonly in research on knowledge extraction from LLMs) but also must be specific to the agent's task context and in a form that the agent can interpret given its native language capacities. We summarize a series of empirical investigations of agent prompting strategies and evaluate LLM responses against the goals of targeted and actionable responses for task learning. Our results demonstrate that actionable task knowledge can be obtained from LLMs in support of online agent task learning.

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., EleutherAI, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting

STR is seeking a Software Engineer to join our growing Autonomy team, with a focus on building User Interfaces (UIs) for controlling teams of autonomous robotic systems. As part of STR's Autonomy team, you will design, develop, and test software that ranges from early prototypes to fielded systems and will revolutionize the way that humans interact with robotic systems. You will be able to develop and test your software in simulation, live demonstration, and field experiments. This hybrid position is based in STR's state-of-the-art Autonomy lab, which is located steps from STR's Woburn, MA corporate headquarters. We are seeking candidates at all levels – from established technical leads with proven experience to talented recent graduates seeking impactful research and development opportunities.

The University of Maryland Strategic Partnership this week announced a transformative partnership to establish the University of Maryland 3 Institute for Health Computing, or UM-3-IHC. WHY IT MATTERS The effort is led by the University of Maryland, Baltimore, and the University of Maryland, College Park, in collaboration with the University of Maryland Medical System and Montgomery County, Maryland. The goal is to harness advances in artificial intelligence and machine learning to evaluate secure and deidentified and health data to diagnose, prevent and treat diseases in patients across the state of Maryland. The new institute will focus on algorithms to enable precision patient care for diabetes, high blood pressure, risk of opioid overdose and other health risks. "Scaling up research to address grand challenges in the life sciences has shifted from collecting data to using cutting-edge technology to discover meaningful patterns hidden in the data," said Darryll J. Pines, president of the University of Maryland, College Park, in a statement.

The hit BBC thriller The Capture has thrown the scale of camera surveillance into the spotlight. I ask Gus Hosein, director of Privacy International, just how advanced the technology is – and if it's possible to avoid it. You've got to watch The Capture, Gus. It's about a police officer who finds people hacking into the CCTV network, and it hit home how omnipresent cameras are. It started with stadiums and transport, because of football hooliganism, and was later expanded by Tony Blair as a solution to crime.