Google's fourth-generation tensor processing units (TPUs), the existence of which weren't publicly revealed until today, can complete AI and machine learning training workloads in close-to-record wall clock time. That's according to the latest set of metrics released by MLPerf, the consortium of over 70 companies and academic institutions behind the MLPerf suite for AI performance benchmarking. It shows clusters of fourth-gen TPUs surpassing the capabilities of third-generation TPUs -- and even those of Nvidia's recently released A100 -- on object detection, image classification, natural language processing, machine translation, and recommendation benchmarks. Google says its fourth-generation TPU offers more than double the matrix multiplication TFLOPs of a third-generation TPU, where a single TFLOP is equivalent to 1 trillion floating-point operations per second. It also offers a "significant" boost in memory bandwidth while benefiting from unspecified advances in interconnect technology.

Migrating a codebase from an archaic programming language such as COBOL to a modern alternative like Java or C is a difficult, resource-intensive task that requires expertise in both the source and target languages. COBOL, for example, is still widely used today in mainframe systems around the world, so companies, governments, and others often must choose whether to manually translate their code bases or commit to maintaining code written in a language that dates back to the 1950s. We've developed TransCoder, an entirely self-supervised neural transcompiler system that can make code migration far easier and more efficient. Our method is the first AI system able to translate code from one programming language to another without requiring parallel data for training. We've demonstrated that TransCoder can successfully translate functions between C, Java, and Python 3. TransCoder outperforms open source and commercial rule-based translation programs.

Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow. Because just in a few years' time span, natural language processing has evolved into something so powerful and impactful, which no one could have imagined.

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. After filtering out applications companies (e.g., companies that use ML to provide business analytics), tools that aren't being actively developed, and tools that nobody uses, I got 202 tools. Please let me know if there are tools you think I should include but aren't on the list yet! I categorize the tools based on which step of the workflow it supports. I don't include Project setup since it requires project management tools, not ML tools.

The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46 % in test loss and that of high-dimensional data was about 1625 % in perplexity with regard to the original seq2seq.

Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of EN -> ML of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.

Natural language processing, or NLP, is a type of artificial intelligence (AI) that specializes in analyzing human language. Have you ever used Apple's Siri and wondered how it understands (most of) what you're saying? This is an example of NLP in practice. NLP is becoming an essential part of our lives, and together with machine learning and deep learning, produces results that are far superior to what could be achieved just a few years ago. In this article we'll take a closer look at NLP, see how it's applied and learn how it works.

Machine translation is an automated way to translate text or speech from one language to another. It can take volumes of data and provide translations into a large number of supported languages. Although not intended to fully replace human translators, it can provide value when immediate translations are needed for a wide variety of languages. If you're looking to translate content on the web, you have several options. Many popular browsers offer translation capabilities, which are either built in (e.g.

Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another. Our observations about the challenges of human translation of tense and mood have important implications for multilingual NLP. Of particular importance is the challenge of modeling tense and mood in rule-based, phrase-based statistical and neural machine translation.

In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search. We start by applying a strong search algorithm (in particular, simulated annealing) towards a heuristically defined objective that (roughly) estimates the quality of sentences. Then, a conditional generative model learns from the search results, and meanwhile smooth out the noise of search. The alternation between search and learning can be repeated for performance bootstrapping. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase generation and text formalization. Our model significantly outperforms unsupervised baseline methods in both tasks. Especially, it achieves comparable performance with the state-of-the-art supervised methods in paraphrase generation.