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❶Interestingly, from thermodynamics arguments we would not expect the defects that we see to exhibit any dynamics at all. International Symposium on Experimental Robotics

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In this case, the confidence thresholds for the two groups were set automatically by the tool to optimize for equal opportunity. A multiclass classification model , which predicts plant type from four measurements of a flower from the plant. The tool is helpful in showing the decision boundary of the model and what causes misclassifications. This model is trained with the UCI iris dataset. Assessing fairness in binary classification models: The image classification model for smile detection mentioned above.

The tool is helpful in assessing algorithmic fairness across different subgroups. The model was purposefully trained without providing any examples from a specific subset of the population, in order to show how the tool can help uncover such biases in models. Assessing fairness requires careful consideration of the overall context — but this is a useful quantitative starting point.

Investigating model performance across different subgroups: The tool is helpful in showing relative performance of the model across subgroups and how the different features individually affect the prediction. This model is trained with the UCI census dataset.

One team quickly found that their model was incorrectly ignoring an entire feature of their dataset, leading them to fix a previously-undiscovered code bug. Another team used it to visually organize their examples from best to worst performance, leading them to discover patterns about the types of examples their model was underperforming on. We look forward to people inside and outside of Google using this tool to better understand ML models and to begin assessing fairness.

And as the code is open-source, we welcome contributions to the tool. We would like to thank the Google teams that piloted the tool and provided valuable feedback and the TensorBoard team for all their help. Machine Learning , TensorBoard , Visualization. One Down, to Go Friday, September 7, Posted by Alexander Gutkin, Software Engineer, Google AI This is the fourth episode in the series of posts reporting on the work we are doing to build text-to-speech TTS systems for low resource languages.

In the first episode , we described the crowdsourced acoustic data collection effort for Project Unison. In the second episode , we described how we built parametric voices based on that data. In the third episode , we described the compilation of a pronunciation lexicon for a TTS system. In this episode, we describe how to make a single TTS system speak many languages. Developing TTS systems for any given language is a significant challenge, and requires large amounts of high quality acoustic recordings and linguistic annotations.

Because of this, these systems are only available for a tiny fraction of the world's languages. A natural question that arises in this situation is, instead of attempting to build a high quality voice for a single language using monolingual data from multiple speakers, as we described in the previous three episodes, can we somehow combine the limited monolingual data from multiple speakers of multiple languages to build a single multilingual voice that can speak any language?

Building upon an initial investigation into creating a multilingual TTS system that can synthesize speech in multiple languages from a single model, we developed a new model that uses uniform phonological representation for all languages — the International Phonetic Alphabet IPA. The model trained using this representation can synthesize both the languages seen in the training data as well as languages not observed in training.

This has two main benefits: First, pooling training data from related languages increases phonemic coverage which results in improved synthesis quality of the languages observed in training. Exploring the Closely Related Languages of Indonesia We applied this multilingual approach first to languages of Indonesia , where Standard Indonesian is the official national language, and is spoken natively or as a second language by more than million people.

Javanese , with roughly 90 million native speakers, and Sundanese , with approximately 40 million native speakers, constitute the two largest regional languages of Indonesia. Unlike Indonesian, which received a lot of attention by the computational linguists and speech scientists over the years, both Javanese and Sundanese are currently low-resourced due to the lack of openly available high-quality corpora.

We collaborated with universities in Indonesia to collect crowd-sourced Javanese and Sundanese recordings. To test this, we first proceeded to analyze the similarities and crucial differences between the phonologies of these three languages shown below and used this information to design the phonological representation that allows maximum degree of sharing between the languages while preserving their crucial differences.

The resulting Javanese and Sundanese voices trained jointly with Standard Indonesian strongly outperformed our corresponding monolingual multispeaker voices that we used as a baseline. Unlike the languages of Indonesia described above, these languages are much more diverse. In particular, they have significantly smaller overlap in their phonologies. These languages show considerable variation within each group, but also such similarities across groups. In this work, we leveraged the unified phonological representation mentioned above to make the most of the data we have and eliminate scarcity of data for certain phonemes.

This was accomplished by conflating similar phonemes into a single representative phoneme in the multilingual phoneme inventory. Where possible, we use the same inventory for phonologically close languages. For example we have an identical phoneme inventory for Telugu and Kannada , and another one for West Bengali and Odia. For other language pairs like Gujarati and Marathi , we copied over the inventory of one language to another, but made a few changes to reflect the differences in their phonemic inventories.

For all languages in these experiments we retained a common underlying representation, mapping similar phonemes across different inventories, so that we could still use the data from one language in training the others. In addition, we made sure our representation is driven by the phonology in use, rather than the orthography.

For example, although there are distinct letters for long and short vowels in Marathi, they are not contrastive in a linguistic sense, so we used a single representation for them, increasing the robustness of our training data. Similarly, if two languages use one character that was historically related to the same Sanskrit letter to represent different sounds or different letters for a similar sound, our mapping reflected the phonological closeness rather than the historical or orthographic representation.

Describing all the features of the unified phoneme inventory is outside the scope of this post, the details can be found in our recent paper. Diagram illustrating our multilingual text-to-speech approach. The input text queries are processed by language-specific linguistic front-ends to generate pronunciations in a shared phonemic representation serving as input to the language-agnostic acoustic model.

The model then generates audio for the respective queries. For most of these languages, apart from Bengali and Marathi, the recording data and the transcriptions were crowd-sourced.

For each of these languages we constructed a multilingual acoustic model that used all the data available. In addition, the acoustic model included the previously crowd-sourced Nepali and Sinhala data, as well as Hindi and Bangladeshi Bengali. The results were encouraging: We performed a further experiment with the Odia language, for which we had no training data, by attempting to synthesize it using the South Asian multilingual model.

Subjective listening tests revealed that the native speakers of Odia judged the resulting audio to be acceptable and intelligible. Using crowd-sourcing in data collections was interesting from a research point of view and rewarding in terms of establishing fruitful collaborations with the native speaker communities.

Our experiments with the Malayo-Polynesian, Indo-Aryan and Dravidian language families have shown that in most instances carefully sharing the data across multiple languages in a single multilingual acoustic model using deep learning techniques alleviates some of the severe data scarcity issues plaguing the low-resource languages and results in good quality voices used in Google products.

To contribute to the research community we have open sourced corpora for Nepali , Sinhala , Bengali , Khmer , Javanese and Sundanese as we return from SLTU and Interspeech conferences, where we have been discussing this work with other researchers. We are planning on continuing to release additional datasets for other languages in our projects in the future.

Posted by Tulsee Doshi, Product Manager, Google AI The release of large, publicly available image datasets, such as ImageNet , Open Images and Conceptual Captions , has been one of the factors driving the tremendous progress in the field of computer vision.

While these datasets are a necessary and critical part of developing useful machine learning ML models, some open source data sets have been found to be geographically skewed based on how they were collected. Because the shape of a dataset informs what an ML model learns, such skew may cause the research community to inadvertently develop models that may perform less well on images drawn from geographical regions under-represented in those data sets. Wedding photographs donated by Googlers , labeled by a classifier trained on the Open Images dataset.

While Google is focusing on building even more representative datasets, we also want to encourage additional research in the field around ways that machine learning methods can be more robust and inclusive when learning from imperfect data sources. This is an important research challenge, and one that pushes the boundaries of ways that machine learning models are currently created. In support of this effort and to spur further progress in developing inclusive ML models, we are happy to announce the Inclusive Images Competition on Kaggle.

Developed in partnership with the Conference on Neural Information Processing Systems Competition Track , this competition challenges you to use Open Images , a large, multilabel, publicly-available image classification dataset that is majority-sampled from North America and Europe, to train a model that will be evaluated on images collected from a different set of geographic regions across the globe.

The three geographical distributions of data in this competition. Competitors will train their models on Open Images, a widely used publicly available benchmark dataset for image classification which happens to be drawn mostly from North America and Western Europe.

Models are then evaluated first on Challenge Stage 1 and finally on Challenge Stage 2, each with different un-revealed geographical distributions.

In this way, models are stress-tested for their ability to operate inclusively beyond their training data. For model evaluation, we have created two Challenge datasets via our Crowdsource project , where we asked our volunteers from across the globe to participate in contributing photos of their surroundings.

We also plan to release a larger set of images at the end of the competition to further encourage inclusive development, with more inclusive data.

Examples of labeled images from the challenge dataset. The deadline for submitting your results will be Monday, November 5th , and the test set will be released on Tuesday, November 6th. It is remarkable how some of the fundamental problems Google grapples with are also some of the hardest research problems in the academic community.

At Google, this research translates direction into practice, influencing how production systems are designed and used. Our Education Innovation research area includes publications on: Google's highest leverage is in transforming scientific research itself. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning including deep learning. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.

The machinery that powers many of our interactions today — Web search, social networking, email, online video, shopping, game playing — is made of the smallest and the most massive computers.

The smallest part is your smartphone, a machine that is over ten times faster than the iconic Cray-1 supercomputer. The capabilities of these remarkable mobile devices are amplified by orders of magnitude through their connection to Web services running on building-sized computing systems that we call Warehouse-scale computers WSCs.

The tight collaboration among software, hardware, mechanical, electrical, environmental, thermal and civil engineers result in some of the most impressive and efficient computers in the world.

HCI researchers at Google have enormous potential to impact the experience of Google users as well as conduct innovative research.

We declare success only when we positively impact our users and user communities, often through new and improved Google products.

We are engaged in a variety of HCI disciplines such as predictive and intelligent user interface technologies and software, mobile and ubiquitous computing, social and collaborative computing, interactive visualization and visual analytics.

Many projects heavily incorporate machine learning with HCI, and current projects include predictive user interfaces; recommenders for content, apps, and activities; smart input and prediction of text on mobile devices; user engagement analytics; user interface development tools; and interactive visualization of complex data. The science surrounding search engines is commonly referred to as information retrieval, in which algorithmic principles are developed to match user interests to the best information about those interests.

Google started as a result of our founders' attempt to find the best matching between the user queries and Web documents, and do it really fast. During the process, they uncovered a few basic principles: Theories were developed to exploit these principles to optimize the task of retrieving the best documents for a user query.

Search and Information Retrieval on the Web has advanced significantly from those early days: Through our research, we are continuing to enhance and refine the world's foremost search engine by aiming to scientifically understand the implications of those changes and address new challenges that they bring.

Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence.

In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize. Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals.

Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.

Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of Google Photos.

The ability to mine meaningful information from multimedia is broadly applied throughout Google. Machine Translation is an excellent example of how cutting-edge research and world-class infrastructure come together at Google. We focus our research efforts on developing statistical translation techniques that improve with more data and generalize well to new languages.

Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality. This research backs the translations served at translate. Deployed within a wide range of Google services like GMail , Books , Android and web search , Google Translate is a high-impact, research-driven product that bridges language barriers and makes it possible to explore the multilingual web in 90 languages.

Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages. Mobile devices are the prevalent computing device in many parts of the world, and over the coming years it is expected that mobile Internet usage will outpace desktop usage worldwide. Google is committed to realizing the potential of the mobile web to transform how people interact with computing technology.

Google engineers and researchers work on a wide range of problems in mobile computing and networking, including new operating systems and programming platforms such as Android and ChromeOS ; new interaction paradigms between people and devices; advanced wireless communications; and optimizing the web for mobile settings.

We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware. Natural Language Processing NLP research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more. To do this, we use novel and intuitive mutation operators that navigate large search spa Deep learning yields great results across many fields, from speech recognition, image classification, to translation.

But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning COCO dataset , a speech recognition corpus, and an English parsing task.

Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an atte Lukasz Kaiser , Aidan N. Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products. Researchers across Google are innovating across many domains. We challenge conventions and reimagine technology so that everyone can benefit.

Heart attacks, strokes and other cardiovascular CV diseases continue to be among the top public health issues. Assessing this risk is critical first step toward reducing the likelihood that a patient suffers a CV event in the future. Learn more about PAIR, an initiative using human-centered research and design to make AI partnerships productive, enjoyable, and fair.

The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. We generate human-like speech from text using neural networks trained using only speech examples and corresponding text transcripts. With motion photos, a new camera feature available on the Pixel 2 and Pixel 2 XL phones, you no longer have to choose between a photo and a video so every photo you take captures more of the moment.

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Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products. Sep 13,  · Me when i realised my 4, word essay is due in less than 2 weeks. essay bataan death march a raisin in the sun research paper notes. Reminder to attend dissertation defense essay on prevention of water pollution essay essay on domestic violence xanax. a method for writing essays about literature festival essay on funfair in gujarati?.