Recent works for Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank the passages with a separate reranker model and generate an answer using an another reader model. Despite performing related tasks, the models have separate parameters and are weakly-coupled during training. In this work, we propose casting the retriever and the reranker as hard-attention mechanisms applied sequentially within the transformer architecture and feeding the resulting computed representations to the reader. In this singular model architecture the hidden representations are progressively refined from the retriever to the reranker to the reader, which is more efficient use of model capacity and also leads to better gradient flow when we train it in an end-to-end manner. We also propose a pre-training methodology to effectively train this architecture. We evaluate our model on Natural Questions and TriviaQA open datasets and for a fixed parameter budget, our model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores.

A phrase like "It's cold today" would suggest a very different temperature if it were uttered in Nairobi or Montreal, while words like "troll" and "tweet" referred to totally different things just a generation ago. Although contemporary large-scale pretrained language models are very effective at learning linguistic representations, they are not as well equipped at capturing speaker/author-related temporal, geographical, social and other contextual aspects. In the new paper LMSOC: An Approach for Socially Sensitive Pretraining, a Twitter Cortex research team proposes LMSOC, a simple but effective approach for learning both linguistically contextualized and socially sensitive representations in large-scale language models. An implicit assumption in most pretrained language models (PLMs) is that language is independent of extra-linguistic contexts such as speaker/author identity and social settings. Despite the impressive achievements of PLMs, this remains a critical weakness, as there is strong evidence that socio-linguistics can significantly impact social context processing performance.

The writer is co-founder and head of research and development at Qure.ai, an AI developer for medical images In a medical artificial intelligence business, the quality of your algorithms -- and therefore the value of your company -- depends on your access to data. In this, the health tech sector is in some ways similar to advertising and internet search industries: it has quickly learnt that data is immensely valuable. However, on the internet, most user-generated data is used to train algorithms that encourage consumption, commerce or engagement. Health data is vastly different -- it can be used for the global public good. It can help us track epidemics and prevent their spread, discover new drugs and diagnostics, and advance medical research that can help us live healthier, longer lives.

DUBAI, 12th December, 2021 (WAM) -- A prominent line-up of researchers, practitioners and stakeholders from academia, industry and governments from 38 countries will gather tomorrow in Dubai for the IEEE Global Conference on Artificial Intelligence & Internet of Things (2021 IEEE GCAIoT) to share their latest research contributions, and exchange knowledge with the common goal of shaping the future of the interaction among Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), 5G, and related technologies to foster digital transformation and develop sustainable, smart cities. An introductory talk will focus on 50 Years of Architecting: How the UAE Became The Country For the Future? Organised by Institute of Electrical and Electronics Engineers, Inc. (IEEE) in partnership with Dubai University, the conference will include paper presentations, poster sessions, and project demonstrations, along with prominent keynote speakers and industry-focused workshops to address the unprecedented COVID-19 and future similar epidemics, life after COVID-19 and how to use AI and IoT to fight altogether and cope with consequences. Other events along with the conference will include IEEE 5G Summit, Women in Technology, Industry Summit, Africa AI and IoT Challenge, Triple Helix Exhibition, Poster Presentation, Workshops on IoT, Arab AIoT Challenge, Industry Exhibition and Student Poster Contest.

The classic win-win has a key flaw in that it cannot offer the parties the right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiations scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

Normalizing flows have become more popular within the last few years; however, they continue to have limitations compared to other generative models, more specifically that they are computationally expensive in terms of memory and time. Early implementations of Normalizing Flows were coupling layers (Dinh et al., 2014, 2017; Kingma and Dhariwal, 2018) and autoregressive flows (Papamakarios et al., 2017; Kingma et al., 2016). These have easy to compute log-likelihoods; however, coupling layers tend to need quite a few parameters to achieve strong performance and autoregressive flows are extremely expensive to sample from. The newer technique of residual flows (Chen et al., 2019) allows for models that are built on standard components and have inductive biases that favor simpler functions (Gopal, 2020); however, these have the problem of being expensive in terms of time for computing log-likelihoods and training, as well as require quite a few layers for strong performance. Since the introduction of these models, there have been many developments that have lead to improvement in parameter efficiency such as FFJORD (Grathwohl et al., 2019), a continuous normalizing flow, that has a dynamic number of layers. However, this too can have computational problems as having a few dynamics layers can lead to hundreds of implicit layers. Among the flows introduced, the ones with provable universal approximation capability are Affine Coupling Layers (Dinh et al., 2014, 2017; Teshima et al., 2020), Neural Autoregressive Flows (NAF, Huang et al. (2018)), Block NAFs (BNAF, Cao et al. (2019)), Sum-of-Squares Polynomial Flow (Jaini et al., 2019), and Convex Potential Flows (CP-Flow, Huang et al. (2021)). Though these have been shown to be universal approximators, they do not necessarily translate into faster, more efficient training, and some of the flows listed require the expensive sampling routine of autoregressive flows.

Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category information of each Place-of-Interest (POI) is a necessary step, and often the bottleneck, in designing an effective mobility prediction model. As opposed to the typical approach, we treat forecasting as a translation problem and propose a novel forecasting through a language generation pipeline. The paper aims to address the human mobility forecasting problem as a language translation task in a sequence-to-sequence manner. A mobility-to-language template is first introduced to describe the numerical mobility data as natural language sentences. The core intuition of the human mobility forecasting translation task is to convert the input mobility description sentences into a future mobility description from which the prediction target can be obtained. Under this pipeline, a two-branch network, SHIFT (Translating Human Mobility Forecasting), is designed. Specifically, it consists of one main branch for language generation and one auxiliary branch to directly learn mobility patterns. During the training, we develop a momentum mode for better connecting and training the two branches. Extensive experiments on three real-world datasets demonstrate that the proposed SHIFT is effective and presents a new revolutionary approach to forecasting human mobility.

Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.

Artificial Intelligence (AI), highly rated among the most disruptive technologies, is a great means for startups to achieve their growth targets. With a number of applications in big data, computer vision, natural language processing, etc, AI is revolutionizing businesses, industries and lives. AI, the ability of a digital computer or computer-controlled robot to perform tasks carried out by humans, is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Startups now attract the attention of several investors across the globe – In Europe, North America, Asia, and Africa. Some (co)founded by Nigerians include Cloud Minds, 4paradigm Intelia, Spear Lab, Ceretronics Technology, and Aide Solutions. Chopwork, the latest addition to Nigeria's e-market sector, is an AI-powered digital market platform created for sellers and buyers of digital products and services.

While discussions about AI often center around the technology's commercial potential, increasingly, researchers are investigating ways that AI can be harnessed to drive societal change. Among others, Facebook chief AI scientist Yann LeCun and Google Brain cofounder Andrew Ng have argued that mitigating climate change and promoting energy efficiency are preeminent challenges for AI researchers. Along this vein, researchers at the Montreal AI Ethics Institute have proposed a framework designed to quantify the social impact of AI through techniques like compute-efficient machine learning. An IBM project delivers farm cultivation recommendations from digital farm "twins" that simulate the future soil conditions of real-world crops. Other researchers are using AI-generated images to help visualize climate change, and nonprofits like WattTime are working to reduce households' carbon footprint by automating when electric vehicles, thermostats, and appliances are active based on where renewable energy is available.