Machine learning is a trendy topic in this age of Artificial Intelligence. The fields of computer vision and Natural Language Processing (NLP) are making breakthroughs that no one could've predicted. We see both of them in our lives more and more, facial recognition in your smartphones, language translation software, self-driving cars and so on. What might seem sci-fi is becoming a reality, and it is only a matter of time before we attain Artificial General Intelligence. In this article, I will be covering Jeff Dean's keynote on the advancements of computer vision and language models and how ML will progress towards the future from the perspective of model building.

Any high school student would guess there is a cosine involved when they see an integral of a sine. Regardless of whether the person understands the thought process behind these functions, it does the job for them. This intuition behind calculus is rarely explored. Though Newton and Leibnitz developed advanced mathematics to solve real-world problems, today most of the schools teach differential equations through semantics. The linguistic appeal of mathematics might get grades in high school, but in the world of research, this is hysterical.

Does anyone know of scientific literature that shows that, even in cases in which we have enough parallel data (English-French), use of monolingual data can be beneficial? To me it seems reasonable that if we, for instance, added monolingual data to the decoder, it would be better at scoring candidate predictions in terms of fluency. That being said, I cannot find peer-reviewed articles that show this.

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

Google's I/O 2018 asserts one thing – the next wave of smartphones will run on a generous amount of Artificial Intelligence. Even the recent Mobile World Congress (MWC) also had conversations that were largely revolving around Artificial Intelligence. Major smartphones makers, led by Apple, Google, Samsung, and many others are creating operating systems, mobile apps and even smartphone that have Artificial Intelligence at their core. McKinsey Global Institute estimates that the investments in Artificial Intelligence R&D made by tech giants by Google and Baidu to be in the range of $20 Billion to $30 Billion. In fact, Ai is ranked to be one among the 5 disruptive Technologies that are shaping up our future digital landscape.

Note: The animations below are videos. Touch or hover on them (if you're using a mouse) to get play controls so you can pause if needed. Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016. These models are explained in the two pioneering papers (Sutskever et al., 2014, Cho et al., 2014).

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed to the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 Transformer matrix multiplications to INT8, leaving 48 out of 133 of them in FP32 because of unacceptable accuracy loss, we convert them all to INT8 without compromising accuracy. Tested on the new-stest2014 English-to-German translation task, our INT8 Transformer Base and Transformer Big models yield BLEU scores that are 99.3% to 100% relative to those of the corresponding FP32 models. Our approach converts all matrix-multiplication tensors from an existing FP32 model into INT8 tensors by automatically making range-precision tradeoffs during training. To demonstrate the robustness of this approach, we also include results from INT6 Transformer models. 1 Introduction We report a method for training accurate yet compact Transformer machine-translation models [ V aswaniet al., 2017 ] . Specifically, we aim these models at hardware with 8-bit integer (INT8) matrix multipliers. Compared to single-precision floating-point (FP32) matrix multiplications, INT8 matrix multiplications not only reduce both storage and bandwidth four times, but they also consume 15 times less energy [ Horowitz, 2014 ] .

Many of the biggest tech companies like Google, Facebook and Amazon have realized the value of creating their own AI platforms for both internal and customer-facing services. Facebook's FBLearner Flow helps the social media site filter out offensive posts, while Uber's Michelangelo gives users time predictions for food deliveries. To keep up with the competition, eBay has unveiled its AI platform, Krylov, which has given the company a wide range of new capabilities from improved language translation services to searching with images. In a blog post, eBay's Sanjeev Katariya, vice president and chief architect of the eBay AI and platforms, and Ashok Ramani, director of product management, computer vision, natural and language processing, discussed the creation of Krylov and how it has changed things both inside eBay and for users of the site. "With computer vision powered by eBay's modern AI platform, the technology helps you find items based on the click of your camera or an image. Users can go onto the eBay app and take a photo of what they are looking for and within milliseconds, the platform surfaces items that match the image," Katariya and Ramani wrote in December.

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?