In the vision and language navigation task, the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. We propose an interactive learning framework to endow the agent with the ability to ask for users' help in such situations. As part of this framework, we investigate multiple learning approaches for the agent with different levels of complexity. The simplest model-confusion-based method lets the agent ask questions based on its confusion, relying on the predefined confidence threshold of a next action prediction model. To build on this confusion-based method, the agent is expected to demonstrate more sophisticated reasoning such that it discovers the timing and locations to interact with a human. We achieve this goal using reinforcement learning (RL) with a proposed reward shaping term, which enables the agent to ask questions only when necessary. The success rate can be boosted by at least 15% with only one question asked on average during the navigation. Furthermore, we show that the RL agent is capable of adjusting dynamically to noisy human responses. Finally, we design a continual learning strategy, which can be viewed as a data augmentation method, for the agent to improve further utilizing its interaction history with a human. We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.

In the past year, the field of Natural Language Processing (NLP) has seen the rise of pretrained language models such as as ELMo (Peters et al., 2018), ULMFiT (Howard and Ruder, 2018) and BERT (Devlin et al., 2019). These approaches train a deep - learning language model on large volumes of unlabeled text, which is subsequently fine - tuned for particular NLP tasks. Applying these models to th e General Language Understanding Evaluation (GLUE) benchmark introduced by Wang et al. (2018) has achieved the best performance to date on tasks ranging from sentiment classification to question answering (Devlin et al., 2019). The benefit of these models has also been demonstrated in specialized NLP domains. BioBERT (Lee et al., 2019), a version of BER T trained exclusively on biomedical text, was able to significantly increase performance on biomedical named entity recognition. Further refining this model on clinical text produced an increase in performance in medical natural language inference (Alsentz er et al. 2019). While large pretrained models offer significantly increased performance, they come with their own constraints, as the number of parameters in the classic BERT - base model exceeds 100 million. As such, their computational cost can thus be p rohibitively high at both training and prediction time (Devlin et al., 2019). More recent work has addressed this challenge by'distilling' the models, training smaller versions of BERT which reduce the number of parameters to train by 40% while retaining more than 95% of the full model performance and even outperforming it on two out of eleven GLUE tasks (Sanh et al., 2019).

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From Siri, the virtual assistant in Apple mobile devices, to self-driving cars, artificial intelligence (AI) is progressing rapidly, outperforming humans at some tasks. As with the majority of the changes happening globally, there will be positive and negative impacts as AI continues to shape the world we live in. Every single one of us will have to reckon with our ability to balance the human way of life and the transition to the AI cosmos. According to a report by the technology research group IDC, spending on AI is expected to reach US$46 billion by 2020 with no signs of slowing down. AI is definitely on the rise in both business and life in general. The question is, will humans eventually lose control as machines become super-intelligent?

What is the difference between humans and robots? What are the dangers of artificial intelligence? I'll discuss when AI began and how it relates to art. Author Jeff Krimmel states, "AI began with the calendar and abacus." The word'automata' (pleural for automaton) is from the Greek word meaning'self-acting'.

New Delhi, Nov 30 (PTI) There is a need for degree courses and professional training programmes in Artificial Intelligence (AI) with the changing technology landscape, according to industry and academic experts. While the Central Board of Secondary Education (CBSE) has already introduced AI as an optional subject in schools, no full fledged degree courses are available in the area in the country besides few short term courses. 'In the digital era and rapidly-evolving business landscape, AI is influencing a range of industries and altering the job roles. The world is looking at AI for its widespread applications in almost every industry and is considered to be the next big technological shift in industrial and smartphone revolution. The need of the hour is to make AI education more focused and easily available,' said Varun Dhamija, Vice President, Pearson Professional Programs (PPP). 'According to our recent survey, 60 pc Indians believe that the world is shifting to a model where people participate in education over a lifetime which makes it age agnostic.

How will technological change affect the quantity and quality of future jobs? It is difficult to know. The issue of whether there will be enough jobs in the future receives substantial attention, mainly because of the well-publicized experiences of manufacturing workers whose jobs have been displaced by the introduction of robotics. It also reflects a long-held societal anxiety that machines will replace us. The consensus among most scholars, however, is that jobs are not going away.

Note from the editor of this site: In the United States, there are over 700 colleges with less than 3K (3000) students. Many of these colleges are totally overlooked in the college search process. This website is devoted to these wonderful small colleges that totally transform the lives of their graduates. I recently retired from 42 years in the College Admission profession. All 42 years were at small, private, colleges.

Note from the editor of this site: In the United States, there are over 700 colleges with less than 3K (3000) students. Many of these colleges are totally overlooked in the college search process. This website is devoted to these wonderful small colleges that totally transform the lives of their graduates. I recently retired from 42 years in the College Admission profession. All 42 years were at small, private, colleges.

Over the last couple of years, powerful deep learning methods have emerged to build industrial scale natural language understanding applications. The first wave of deep learning models employed pre-trained word embeddings (word2vec or GloVe) to initialize the first layer of a neural network followed by a task specific model trained using labelled data. The next wave of deep learning architectures (ELMo, ULMFiT, BERT) showed how to learn contextual word embeddings from massive amounts of unlabelled text data and then transfer this information to a wide variety of downstream tasks such as sentiment analysis, question answering etc. with limited amounts of labelled data. This approach is quite relevant for industrial settings where obtaining large amounts of labelled data is expensive. In this hands on tutorial, we will cover the important concepts behind recent developments such as word embeddings, sequence to sequence models, attention mechanism, contextual word embeddings, transfer learning and probing embeddings.