In this paper, we identify the state of data as being an important reason for failure in applied Natural Language Processing (NLP) projects. We argue that there is a gap between academic research in NLP and its application to problems outside academia, and that this gap is rooted in poor mutual understanding between academic researchers and their non-academic peers who seek to apply research results to their operations. To foster transfer of research results from academia to non-academic settings, and the corresponding influx of requirements back to academia, we propose a method for improving the communication between researchers and external stakeholders regarding the accessibility, validity, and utility of data based on Data Readiness Levels \cite{lawrence2017data}. While still in its infancy, the method has been iterated on and applied in multiple innovation and research projects carried out with stakeholders in both the private and public sectors. Finally, we invite researchers and practitioners to share their experiences, and thus contributing to a body of work aimed at raising awareness of the importance of data readiness for NLP.

Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this does not guarantee that generated responses are factually correct. In this paper, we examine factual correctness in knowledge-grounded neural response generation models. We present a human annotation setup to identify three different response types: responses that are factually consistent with respect to the input knowledge, responses that contain hallucinated knowledge, and non-verifiable chitchat style responses. We use this setup to annotate responses generated using different stateof-the-art models, knowledge snippets, and decoding strategies. In addition, to facilitate the development of a factual consistency detector, we automatically create a new corpus called Conv-FEVER that is adapted from the Wizard of Wikipedia dataset and includes factually consistent and inconsistent responses. We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data. We will release the Conv-FEVER dataset and the human annotated responses.

Many problems in RL, such as meta RL, robust RL, and generalization in RL, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques in their respective domains. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs. Code is available at https://github.com/twni2016/pomdp-baselines

Like many singles, Siki Liu was feeling lonely and unloved during the pandemic, until she met someone on the internet. A handsome, mature sweet talker, named after her favourite Korean actor Lee Dong-wook, he always replies to her messages. "I talk to him almost every night before I go to bed," the 22-year-old told the ABC's China Tonight. "He's a good listener and never gets mad, no matter what you say. Ms Liu, a Chinese international student studying in Melbourne, said they had been chatting since May last year, but her "perfect boyfriend" isn't a real person. It is an artificial intelligence chatbot created by Chinese tech firm XiaoIce, a spin-off from Microsoft. XiaoIce's chatbot is programmed to form emotional bonds with human users through text, voice and photo messages and can be customised to create the ideal virtual boyfriend or girlfriend. Ms Liu is one of a growing number of Chinese young adults flocking to technologies, such as artificial intelligence companion services and dating apps, to find love. The population of single people in China was about 240 million in 2019 and was rising, according to the National Bureau of Statistics. At the same time, fast-paced urban lifestyles and increasing work pressures have exacerbated a growing sense of loneliness and social anxiety among young people. For many like Ms Liu, dating can be tough. "The older you grow, the less friends you have ... so an AI boyfriend is much needed," she said. "It's easier to talk to AI than a real person.

On the 12th of October, the world will celebrate Ada Lovelace Day to honor the achievements of women in science, technology, engineering and maths (STEM). In Finding Ada (the main network supporting Ada Lovelace Day), there will be three free webinars that you can enjoy in the comfort of your own home. There will also be loads of events happening around the world, so you have a wide range of content to celebrate Ada Lovelace Day 2021! Engineering is the science of problem solving, and we have some pretty big problems in front of us. So how are engineers tackling the COVID-19 pandemic and climate change?

The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP). Here, we explore the idea of using a batch-softmax contrastive loss when fine-tuning large-scale pre-trained transformer models to learn better task-specific sentence embeddings for pairwise sentence scoring tasks. We introduce and study a number of variations in the calculation of the loss as well as in the overall training procedure; in particular, we find that data shuffling can be quite important. Our experimental results show sizable improvements on a number of datasets and pairwise sentence scoring tasks including classification, ranking, and regression. Finally, we offer detailed analysis and discussion, which should be useful for researchers aiming to explore the utility of contrastive loss in NLP. Recent years have seen a revolution in Natural Language Processing (NLP) thanks to the advances in machine learning. While a lot of attention has been paid to the architectures, especially for deep learning, there has been less focus on studying loss functions. At the same time, loss functions based on similar or on the same ideas were reinvented multiple times under different names. This can cause difficulties when solving new problems or when designing new experiments based on previous results. To a greater extent, this applies to "universal" loss functions, which can be applied in different machine learning areas and tasks such as Computer Vision (CV), Recommendation Systems, and NLP.

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural ODEs (NODEs) training and inference. HBNODEs have two properties that imply practical advantages over NODEs: (i) The adjoint state of an HBNODE also satisfies an HBNODE, accelerating both forward and backward ODE solvers, thus significantly reducing the number of function evaluations (NFEs) and improving the utility of the trained models. (ii) The spectrum of HBNODEs is well structured, enabling effective learning of long-term dependencies from complex sequential data. We verify the advantages of HBNODEs over NODEs on benchmark tasks, including image classification, learning complex dynamics, and sequential modeling. Our method requires remarkably fewer forward and backward NFEs, is more accurate, and learns long-term dependencies more effectively than the other ODE-based neural network models. Code is available at \url{https://github.com/hedixia/HeavyBallNODE}.

The Cognitive/Artificial Intelligence Systems Market report includes a comprehensive analysis of the global market. This includes investigating past progress, on-going market scenarios, and future prospects. Accurate data on the products, strategies and market share of leading companies in this particular market are mentioned. This report provides a 360-degree overview of the global market's competitive landscape. The report further predicts the size and valuation of the global market during the forecast period.

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy. Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities.

Dummy data is randomly generated data that can be substituted for live data. Whether you are a Developer, Software Engineer, or Data Scientist, sometimes you need dummy data to test what you have built, it can be a web app, mobile app, or machine learning model. If you are using python language, you can use a faker python package to create dummy data of any type, for example, dates, transactions, names, texts, time, and others. Faker is a simple python package that generates fake data with different data types. Faker package is heavily inspired by PHP Faker, Perl Faker, and by Ruby Faker.