AI jobs are on the upswing, as are the capabilities of AI systems. The speed of deployments has also increased exponentially. It's now possible to train an image-processing algorithm in about a minute -- something that took hours just a couple of years ago. These are among the key metrics of AI tracked in the latest release of the AI Index, an annual data update from Stanford University's Human-Centered Artificial Intelligence Institute published in partnership with McKinsey Global Institute. The index tracks AI growth across a range of metrics, from papers published to patents granted to employment numbers.

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-side monolingual data. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and natural data. This improves standard back-translation and also enables the use of iterative back-translation on language pairs that underperformed using standard back-translation. This work presents a simplified approach of differentiating between the two data using pretraining and finetuning. The approach - tag-less back-translation - trains the model on the synthetic data and finetunes it on the natural data. Preliminary experiments have shown the approach to continuously outperform the tagging approach on low resource English-Vietnamese neural machine translation. While the need for tagging (noising) the dataset has been removed, the approach outperformed the tagged back-translation approach by an average of 0.4 BLEU.

Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow color hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.

Machine learning is progressing at an astounding rate, powered by complex models such as ensemble models and deep neural networks (DNNs). These models have a wide range of real-world applications, such as movie recommendations of Netflix, neural machine translation of Google, and speech recognition of Amazon Alexa. Despite the successes, machine learning has its own limitations and drawbacks. The most significant one is the lack of transparency behind their behaviors, which leaves users with little understanding of how particular decisions are made by these models. Consider, for instance, an advanced self-driving car equipped with various machine learning algorithms does not brake or decelerate when confronting a stopped firetruck. This unexpected behavior may frustrate and confuse users, making them wonder why. Even worse, the wrong decisions could cause severe consequences if the car is driving at highway speeds and might ultimately crash into the firetruck. The concerns about the black-box nature of complex models have hampered their further applications in our society, especially in those critical decision-making domains like self-driving cars. Interpretable machine learning would be an effective tool to mitigate these problems. It gives machine learning models the ability to explain or to present their behaviors in understandable terms to humans,10 which is called interpretability or explainability and we use them interchangeably in this article. Interpretability would be an indispensable part for machine learning models in order to better serve human beings and bring benefits to society. For end users, explanation will increase their trust and encourage them to adopt machine learning systems. From the perspective of machine learning system developers and researchers, the provided explanation can help them better understand the problem, the data and why a model might fail, and eventually increase the system safety. Thus, there is a growing interest among the academic and industrial community in interpreting machine learning models and gaining insights into their working mechanisms.

A new study co-authored by an MIT economist shows that improved translation software can significantly boost international trade online -- a notable case of machine learning having a clear impact on economic activity. The research finds that after eBay improved its automatic translation program in 2014, commerce shot up by 10.9 percent among pairs of countries where people could use the new system. To have it be so clear in such a short amount of time really says a lot about the power of this technology," says Erik Brynjolfsson, an MIT economist and co-author of a new paper detailing the results. To put the results in perspective, he adds, consider that physical distance is, by itself, also a significant barrier to global commerce. The 10.9 percent change generated by eBay's new translation software increases trade by the same amount as "making the world 26 percent smaller, in terms of its impact on the goods that we studied," he says. The paper, "Does Machine Translation Affect International Trade?

Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step. We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm. We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization. This simple, powerful technique operates strictly on the inputs and outputs of any translation model. We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models. Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "for" loop. Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.

Recent work has exhibited the surprising cross-lingual abi lities of multilingual BERT ( M-BERT) - surprising since it is trained without any cross-lingual objective and with no aligned data. In this work, we provide a compr ehensive study of the contribution of different components in M-BERT to its cross-lingual ability. The experimental study is done in the context of three typologically different languages - Spani sh, Hindi, and Russian - and using two conceptually different NLP tasks, textual en tailment and named entity recognition. Among our key conclusions is the fact th at the lexical overlap between languages plays a negligible role in the cross-ling ual success, while the depth of the network is an integral part of it. Embeddings of natural language text via unsupervised learn ing, coupled with sufficient supervised training data, have been ubiquitous in NLP in recent years an d have shown success in a wide range of monolingual NLP tasks, mostly in English. Training models f or other languages have been shown more difficult, and recent approaches relied on bilingual em beddings that allowed the transfer of supervision in high resource languages like English to mode ls in lower resource languages; however, inducing these bilingual embeddings required some level of supervision (Upadhyay et al., 2016). Not only the model is contextual, but its training also requires no supervisio n - no alignment between the languages is done. Nevertheless, and despite being trained with no exp licit cross-lingual objective, M-BERT produces a representation that seems to generalize well acr oss languages for a variety of downstream tasks (Wu & Dredze, 2019). In this work, we attempt to develop an understanding of the su ccess of M-BERT.

In January, Google announced a cool new feature that turns the Google Assistant into a two-way language interpreter, but it only worked visually on smart displays, which generally aren't used in the real world, when people are traveling. But now, just in time for the holidays, Google is finally making Interpreter Mode available on mobile Android and iOS phones. As always, Google is rolling the feature out and it could take up to a week for it to make it across the network. You start by asking Google to "be my Spanish translator," and then the Assistant takes over. You speak your phrase, and Google translates it, in audio and text and in real time, and the person on the other end can speak into your phone with the answer and keep the two-way conversation going.

Word translation is a problem in machine translation that seeks to build models that recover word level correspondence between languages. Recent approaches to this problem have shown that word translation models can learned with very small seeding dictionaries, and even without any starting supervision. In this paper we propose a method to jointly find translations between a pair of languages. Not only does our method learn translations in both directions but it improves accuracy of those translations over past methods.

Many of us are familiar with Google Translate, translation applications for travellers' smartphones and the instruction manuals of various devices and products. Professional translators also make use of machines. Training a computer to translate between two specific languages takes millions of sentences or billions of words worth of text. Maarit Koponen, a postdoctoral researcher at the University of Helsinki, is investigating which errors made by machines lead to misunderstandings and how those mistakes could be identified. The learning algorithms behind machine translation are called artificial intelligence, but machines are not intelligent in the way humans or the super AIs of science-fiction films are.