Domain translation is the task of finding correspondence between two domains. Several Deep Neural Network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown remarkable successes on this task under the unsupervised setting---the mappings between the domains are learned from two independent sets of training data in both domains (without paired samples). However, those methods typically do not perform well on a significant proportion of test samples. In this paper, we hypothesize that many of such unsuccessful samples lie at the fringe---relatively low-density areas---of data distribution, where the DNN was not trained very well, and propose to perform Langevin dynamics to bring such fringe samples towards high density areas. We demonstrate qualitatively and quantitatively that our strategy, called Langevin Cooling (L-Cool), enhances state-of-the-art methods in image translation and language translation tasks.

Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them more difficult to analyse and interpret. In a run with $n$ time steps, a policy will decide $n$ times on an action to take, even when only a tiny subset of these decisions deliver value over selecting a simple default action. Given a pre-trained policy, we propose a black-box method based on statistical fault localisation that ranks the states of the environment according to the importance of decisions made in those states. We evaluate our ranking method by creating new, simpler policies by pruning decisions identified as unimportant, and measure the impact on performance. Our experimental results on a diverse set of standard benchmarks (gridworld, CartPole, Atari games) show that in some cases less than half of the decisions made contribute to the expected reward. We furthermore show that the decisions made in the most frequently visited states are not the most important for the expected reward.

Facebook continues to pour considerable resources into machine translation (MT); but, as evidenced by a recent Thai translation snafu, language technology remains a major challenge for the social media giant. In addition to improving quality estimation and various other initiatives, Facebook is currently working on two others that share information with the broader open source community, allowing developers to improve the technology. In a July 2020 blog post, Facebook AI made available CoVoST V2, a "massively multilingual" speech-to-text translation dataset. The original CoVoST was built on Mozilla's Common Voice, a database of crowdsourced voice recordings. This new version boasts 2,900 hours of speech, as well as speech translation data from 21 languages into English and from English into 15 languages.

Is the success of Google that of the algorithms or that of data? Today's fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Here, I argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms -- and that they forget the critical infrastructures that are needed to create and to manage data in the first place. There are now many companies that provide AI services. An attractive offer should affirm all of the above -- the sole expertise in analyses and algorithms is generally insufficient, as it does not necessarily address the data part of the equation.

All of us are aware of Google Translate. Today, we will provide examples of how we can use the googletrans which is a free and unlimited python library that implemented Google Translate API. This uses the Google Translate Ajax API to make calls to such methods as detect and translate . The first thing that we need to do is to install the googletrans library. I suggest to use the conda install command.

Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.

Many NLP models follow the embed-contextualize-predict paradigm, in which each sequence token is represented as a dense vector via an embedding matrix, and fed into a contextualization component that aggregates the information from the entire sequence in order to make a prediction. Could NLP models work without the embedding component? To that end, we omit the input and output embeddings from a standard machine translation model, and represent text as a sequence of bytes via UTF-8 encoding, using a constant 256-dimension one-hot representation for each byte. Experiments on 10 language pairs show that removing the embedding matrix consistently improves the performance of byte-to-byte models, often outperforms character-to-character models, and sometimes even produces better translations than standard subword models.

The ease with which you shop at Amazon and scroll though different products that are customised specific to your taste is the result of AI technology that analyses and predicts your shopping behavior. There are countless AI start-ups who are founded for the primary reason of using AI to make the world a better place. The challenges that the world faces today – primarily climate change – is a huge motivator for AI innovation. Every AI idea is bound to help us face such challenges. In 2018, venture funding for AI grew to about 9.3 billion dollars in U.S. alone.

Machine Learning, a sub-branch of Artificial Intelligence, has established itself as the new go-to technology for businesses worldwide. Whether it is e-commerce or healthcare, almost all the industries are using Machine Learning extensively to make futuristic solutions and products. Machine Learning depends heavily on programs and algorithms that help machines self-learn without having to be instructed explicitly. Machine Learning is pretty much dictating our daily lives- how, you wonder? Let's look at the top applications of Machine Learning to understand how it is shaping the digital economy.

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of "page quality", able to detect low quality content without any training. This enables fast bootstrapping of quality indicators in a low-resource setting. Secondly, curious to understand the prevalence and nature of low quality pages in the wild, we conduct extensive qualitative and quantitative analysis over 500 million web articles, making this the largest-scale study ever conducted on the topic.