We've seen people across Africa do amazing things with the internet and technology—for themselves, their communities and the world. Over the past 10 years in which Google has had offices in Africa, we've been excited to be a part of that transformation. Ultimately 10 million Africans will benefit from our digital skills training program with 2 million people having already completed the course, and we’re supporting 100,000 developers and over 60 tech startups through our Launchpad Accelerator Africa. We’re also adapting our products to make it easy for people to discover the best of the internet, even on low-RAM smartphones or unstable network connections.
In recent years we've also witnessed an increasing interest in machine learning research across the continent. Events like Data Science Africa 2017 in Tanzania, the 2017 Deep Learning Indaba event in South Africa, and follow-on IndabaX events in 2018 in multiple countries have shown an exciting and continuing growth of the computer science research community in Africa.
Today, we’re announcing a Google AI research center in Africa, which will open later this year in Accra, Ghana. We’ll bring together top machine learning researchers and engineers in this new center dedicated to AI research and its applications.
We’re committed to collaborating with local universities and research centers, as well as working with policy makers on the potential uses of AI in Africa. On a personal note, both of the authors have ties to Africa—Jeff spent part of his childhood in Uganda and Somalia, and Moustapha grew up in Senegal. As such, we’re excited to combine our research interests in AI and machine learning and our experience in Africa to push the boundaries of AI while solving challenges in areas such as healthcare, agriculture, and education.
AI has great potential to positively impact the world, and more so if the world is well represented in the development of new AI technologies. So it makes sense to us that the world should be well represented in the development of AI. Our new AI center in Accra joins the list of other locations around the world where we focus on AI. If you’re a machine learning researcher interested in joining this new center, you can apply as a Research Scientist or a Research Software Engineer. You can also view all our open opportunities on our site.
Just about two years ago we introduced neural machine translation (NMT) to Google Translate, significantly improving accuracy of our online translations. Today, we’re bringing NMT technology offline—on device. This means that the technology will run in the Google Translate apps directly on your Android or iOS device, so that you can get high-quality translations even when you don't have access to an internet connection.
The neural system translates whole sentences at a time, rather than piece by piece. It uses broader context to help determine the most relevant translation, which it then rearranges and adjusts to sound more like a real person speaking with proper grammar. This makes translated paragraphs and articles a lot smoother and easier to read.
Offline translations can be useful when traveling to other countries without a local data plan, if you don’t have access to internet, or if you just don’t want to use cellular data. And since each language set is just 35-45MB, they won’t take too much storage space on your phone when you download them.
A comparison between our current phrase-based machine translation (PBMT), new offline neural machine translation (on-device), and online neural machine translation
To try NMT offline translations, go to your Translate app on Android or iOS. If you’ve used offline translations before, you’ll see a banner on your home screen which will take you to the right place to update your offline files. If not, go to your offline translation settings and tap the arrow next to the language name to download the package for that language. Now you’ll be ready to translate text whether you’re online or not.
We're rolling out this update in 59 languages over the next few days, so get out there and connect to the world around you!
Posted by Ekin Dogus Cubuk, Google AI Resident and Barret Zoph, Research Scientist, Google Brain Team
The success of deep learning in computer vision can be partially attributed to the availability of large amounts of labeled training data — a model’s performance typically improves as you increase the quality, diversity and the amount of training data. However, collecting enough quality data to train a model to perform well is often prohibitively difficult. One way around this is to hardcode image symmetries into neural network architectures so they perform better or have experts manually design data augmentation methods, like rotation and flipping, that are commonly used to train well-performing vision models. However, until recently, less attention has been paid to finding ways to automatically augment existing data using machine learning. Inspired by the results of our AutoML efforts to design neural network architectures and optimizers to replace components of systems that were previously human designed, we asked ourselves: can we also automate the procedure of data augmentation?
In “AutoAugment: Learning Augmentation Policies from Data”, we explore a reinforcement learning algorithm which increases both the amount and diversity of data in an existing training dataset. Intuitively, data augmentation is used to teach a model about image invariances in the data domain in a way that makes a neural network invariant to these important symmetries, thus improving its performance. Unlike previous state-of-the-art deep learning models that used hand-designed data augmentation policies, we used reinforcement learning to find the optimal image transformation policies from the data itself. The result improved performance of computer vision models without relying on the production of new and ever expanding datasets.
Augmenting Training Data The idea behind data augmentation is simple: images have many symmetries that don’t change the information present in the image. For example, the mirror reflection of a dog is still a dog. While some of these “invariances” are obvious to humans, many are not. For example, the mixup method augments data by placing images on top of each other during training, resulting in data which improves neural network performance.
Left: An original image from the ImageNet dataset. Right: The same image transformed by a commonly used data augmentation transformation, a horizontal flip about the center.
AutoAugment is an automatic way to design custom data augmentation policies for computer vision datasets, e.g., guiding the selection of basic image transformation operations, such as flipping an image horizontally/vertically, rotating an image, changing the color of an image, etc. AutoAugment not only predicts what image transformations to combine, but also the per-image probability and magnitude of the transformation used, so that the image is not always manipulated in the same way. AutoAugment is able to select an optimal policy from a search space of 2.9 x 1032 image transformation possibilities.
AutoAugment learns different transformations depending on what dataset it is run on. For example, for images involving street view of house numbers (SVHN) which include natural scene images of digits, AutoAugment focuses on geometric transforms like shearing and translation, which represent distortions commonly observed in this dataset. In addition, AutoAugment has learned to completely invert colors which naturally occur in the original SVHN dataset, given the diversity of different building and house numbers materials in the world.
Left: An original image from the SVHN dataset. Right: The same image transformed by AutoAugment. In this case, the optimal transformation was a result of shearing the image and inverting the colors of the pixels.
On CIFAR-10 and ImageNet, AutoAugment does not use shearing because these datasets generally do not include images of sheared objects, nor does it invert colors completely as these transformations would lead to unrealistic images. Instead, AutoAugment focuses on slightly adjusting the color and hue distribution, while preserving the general color properties. This suggests that the actual colors of objects in CIFAR-10 and ImageNet are important, whereas on SVHN only the relative colors are important.
Left: An original image from the ImageNet dataset. Right: The same image transformed by the AutoAugment policy. First, the image contrast is maximized, after which the image is rotated.
Results Our AutoAugment algorithm found augmentation policies for some of the most well-known computer vision datasets that, when incorporated into the training of the neural network, led to state-of-the-art accuracies. By augmenting ImageNet data we obtain a new state-of-the-art accuracy of 83.54% top1 accuracy and on CIFAR10 we achieve an error rate of 1.48%, which is a 0.83% improvement over the default data augmentation designed by scientists. On SVHN, we improved the state-of-the-art error from 1.30% to 1.02%. Importantly, AutoAugment policies are found to be transferable — the policy found for the ImageNet dataset could also be applied to other vision datasets (Stanford Cars, FGVC-Aircraft, etc.), which in turn improves neural network performance.
We are pleased to see that our AutoAugment algorithm achieved this level of performance on many different competitive computer vision datasets and look forward to seeing future applications of this technology across more computer vision tasks and even in other domains such as audio processing or language models. The policies with the best performance are included in the appendix of the paper, so that researchers can use them to improve their models on relevant vision tasks.
Acknowledgements Special thanks to the co-authors of the paper Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. We’d also like to thank Alok Aggarwal, Gabriel Bender, Yanping Huang, Pieter-Jan Kindermans, Simon Kornblith, Augustus Odena, Avital Oliver, and Colin Raffel for their help with this project.
Posted by Yonghui Wu, Principal Engineer, Google Brain Team
Last week at Google I/O, we introduced Smart Compose, a new feature in Gmail that uses machine learning to interactively offer sentence completion suggestions as you type, allowing you to draft emails faster. Building upon technology developed for Smart Reply, Smart Compose offers a new way to help you compose messages — whether you are responding to an incoming email or drafting a new one from scratch.
In developing Smart Compose, there were a number of key challenges to face, including:
Latency: Since Smart Compose provides predictions on a per-keystroke basis, it must respond ideally within 100ms for the user not to notice any delays. Balancing model complexity and inference speed was a critical issue.
Scale: Gmail is used by more than 1.4 billion diverse users. In order to provide auto completions that are useful for all Gmail users, the model has to have enough modeling capacity so that it is able to make tailored suggestions in subtly different contexts.
Fairness and Privacy: In developing Smart Compose, we needed to address sources of potential bias in the training process, and had to adhere to the same rigorous user privacy standards as Smart Reply, making sure that our models never expose user’s private information. Furthermore, researchers had no access to emails, which meant they had to develop and train a machine learning system to work on a dataset that they themselves cannot read.
Finding the Right Model Typical language generation models, such as ngram, neural bag-of-words (BoW) and RNN language (RNN-LM) models, learn to predict the next word conditioned on the prefix word sequence. In an email, however, the words a user has typed in the current email composing session is only one “signal” a model can use to predict the next word. In order to incorporate more context about what the user wants to say, our model is also conditioned on the email subject and the previous email body (if the user is replying to an incoming email).
One approach to include this additional context is to cast the problem as a sequence-to-sequence (seq2seq) machine translation task, where the source sequence is the concatenation of the subject and the previous email body (if there is one), and the target sequence is the current email the user is composing. While this approach worked well in terms of prediction quality, it failed to meet our strict latency constraints by orders of magnitude.
To improve on this, we combined a BoW model with an RNN-LM, which is faster than the seq2seq models with only a slight sacrifice to model prediction quality. In this hybrid approach, we encode the subject and previous email by averaging the word embeddings in each field. We then join those averaged embeddings, and feed them to the target sequence RNN-LM at every decoding step, as the model diagram below shows.
Smart Compose RNN-LM model architecture. Subject and previous email message are encoded by averaging the word embeddings in each field. The averaged embeddings are then fed to the RNN-LM at each decoding step.
Accelerated Model Training & Serving Of course, once we decided on this modeling approach we still had to tune various model hyperparameters and train the models over billions of examples, all of which can be very time-intensive. To speed things up, we used a full TPUv2 Pod to perform experiments. In doing so, we’re able to train a model to convergence in less than a day.
Even after training our faster hybrid model, our initial version of Smart Compose running on a standard CPU had an average serving latency of hundreds of milliseconds, which is still unacceptable for a feature that is trying to save users' time. Fortunately, TPUs can also be used at inference time to greatly speed up the user experience. By offloading the bulk of the computation onto TPUs, we improved the average latency to tens of milliseconds while also greatly increasing the number of requests that can be served by a single machine.
Fairness and Privacy Fairness in machine learning is very important, as language understanding models can reflect human cognitive biases resulting in unwanted word associations and sentence completions. As Caliskan et al. point out in their recent paper “Semantics derived automatically from language corpora contain human-like biases”, these associations are deeply entangled in natural language data, which presents a considerable challenge to building any language model. We are actively researching ways to continue to reduce potential biases in our training procedures. Also, since Smart Compose is trained on billions of phrases and sentences, similar to the way spam machine learning models are trained, we have done extensive testing to make sure that only common phrases used by multiple users are memorized by our model, using findings from this paper.
Future work We are constantly working on improving the suggestion quality of the language generation model by following state-of-the-art architectures (e.g., Transformer,RNMT+, etc.) and experimenting with most recent and advanced training techniques. We will deploy those more advanced models to production once our strict latency constraints can be met. We are also working on incorporating personal language models, designed to more accurately emulate an individual’s style of writing into our system.
Acknowledgements Smart Compose language generation model was developed by Benjamin Lee, Mia Chen, Gagan Bansal, Justin Lu, Jackie Tsay, Kaushik Roy, Tobias Bosch, Yinan Wang, Matthew Dierker, Katherine Evans, Thomas Jablin, Dehao Chen, Vinu Rajashekhar, Akshay Agrawal, Yuan Cao, Shuyuan Zhang, Xiaobing Liu, Noam Shazeer, Andrew Dai, Zhifeng Chen, Rami Al-Rfou, DK Choe, Yunhsuan Sung, Brian Strope, Timothy Sohn, Yonghui Wu, and many others.
Posted by Aseem Agarwala, Research Scientist, Clips Content Team Lead
To me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event as well as of a precise organization of forms which give that event its proper expression. — Henri Cartier-Bresson
The last few years have witnessed a Cambrian-like explosion in AI, with deep learning methods enabling computer vision algorithms to recognize many of the elements of a good photograph: people, smiles, pets, sunsets, famous landmarks and more. But, despite these recent advancements, automatic photography remains a very challenging problem. Can a camera capture a great moment automatically?
Recently, we released Google Clips, a new, hands-free camera that automatically captures interesting moments in your life. We designed Google Clips around three important principles:
We wanted all computations to be performed on-device. In addition to extending battery life and reducing latency, on-device processing means that none of your clips leave the device unless you decide to save or share them, which is a key privacy control.
We wanted the device to capture short videos, rather than single photographs. Moments with motion can be more poignant and true-to-memory, and it is often easier to shoot a video around a compelling moment than it is to capture a perfect, single instant in time.
We wanted to focus on capturing candid moments of people and pets, rather than the more abstract and subjective problem of capturing artistic images. That is, we did not attempt to teach Clips to think about composition, color balance, light, etc.; instead, Clips focuses on selecting ranges of time containing people and animals doing interesting activities.
Learning to Recognize Great Moments How could we train an algorithm to recognize interesting moments? As with most machine learning problems, we started with a dataset. We created a dataset of thousands of videos in diverse scenarios where we imagined Clips being used. We also made sure our dataset represented a wide range of ethnicities, genders, and ages. We then hired expert photographers and video editors to pore over this footage to select the best short video segments. These early curations gave us examples for our algorithms to emulate. However, it is challenging to train an algorithm solely from the subjective selection of the curators — one needs a smooth gradient of labels to teach an algorithm to recognize the quality of content, ranging from "perfect" to "terrible."
To address this problem, we took a second data-collection approach, with the goal of creating a continuous quality score across the length of a video. We split each video into short segments (similar to the content Clips captures), randomly selected pairs of segments, and asked human raters to select the one they prefer.
We took this pairwise comparison approach, instead of having raters score videos directly, because it is much easier to choose the better of a pair than it is to specify a number. We found that raters were very consistent in pairwise comparisons, and less so when scoring directly. Given enough pairwise comparisons for any given video, we were able to compute a continuous quality score over the entire length. In this process, we collected over 50,000,000 pairwise comparisons on clips sampled from over 1,000 videos. That’s a lot of human effort!
Training a Clips Quality Model Given this quality score training data, our next step was to train a neural network model to estimate the quality of any photograph captured by the device. We started with the basic assumption that knowing what’s in the photograph (e.g., people, dogs, trees, etc.) will help determine “interestingness”. If this assumption is correct, we could learn a function that uses the recognized content of the photograph to predict its quality score derived above from human comparisons.
To identify content labels in our training data, we leveraged the same Google machine learning technology that powers Google image search and Google Photos, which can recognize over 27,000 different labels describing objects, concepts, and actions. We certainly didn’t need all these labels, nor could we compute them all on device, so our expert photographers selected the few hundred labels they felt were most relevant to predicting the “interestingness” of a photograph. We also added the labels most highly correlated with the rater-derived quality scores.
Once we had this subset of labels, we then needed to design a compact, efficient model that could predict them for any given image, on-device, within strict power and thermal limits. This presented a challenge, as the deep learning techniques behind computer vision typically require strong desktop GPUs, and algorithms adapted to run on mobile devices lag far behind state-of-the-art techniques on desktop or cloud. To train this on-device model, we first took a large set of photographs and again used Google’s powerful, server-based recognition models to predict label confidence for each of the “interesting” labels described above. We then trained a MobileNet Image Content Model (ICM) to mimic the predictions of the server-based model. This compact model is capable of recognizing the most interesting elements of photographs, while ignoring non-relevant content.
The final step was to predict a single quality score for an input photograph from its content predicted by the ICM, using the 50M pairwise comparisons as training data. This score is computed with a piecewise linear regression model that combines the output of the ICM into a frame quality score. This frame quality score is averaged across the video segment to form a moment score. Given a pairwise comparison, our model should compute a moment score that is higher for the video segment preferred by humans. The model is trained so that its predictions match the human pairwise comparisons as well as possible.
Diagram of the training process for generating frame quality scores. Piecewise linear regression maps from an ICM embedding to a score which, when averaged across a video segment, yields a moment score. The moment score of the preferred segment should be higher.
This process allowed us to train a model that combines the power of Google image recognition technology with the wisdom of human raters–represented by 50 million opinions on what makes interesting content!
While this data-driven score does a great job of identifying interesting (and non-interesting) moments, we also added some bonuses to our overall quality score for phenomena that we know we want Clips to capture, including faces (especially recurring and thus “familiar” ones), smiles, and pets. In our most recent release, we added bonuses for certain activities that customers particularly want to capture, such as hugs, kisses, jumping, and dancing. Recognizing these activities required extensions to the ICM model.
Shot Control Given this powerful model for predicting the “interestingness” of a scene, the Clips camera can decide which moments to capture in real-time. Its shot control algorithms follow three main principles:
Respect Power & Thermals: We want the Clips battery to last roughly three hours, and we don’t want the device to overheat — the device can’t run at full throttle all the time. Clips spends much of its time in a low-power mode that captures one frame per second. If the quality of that frame exceeds a threshold set by how much Clips has recently shot, it moves into a high-power mode, capturing at 15 fps. Clips then saves a clip at the first quality peak encountered.
Avoid Redundancy: We don’t want Clips to capture all of its moments at once, and ignore the rest of a session. Our algorithms therefore cluster moments into visually similar groups, and limit the number of clips in each cluster.
The Benefit of Hindsight: It’s much easier to determine which clips are the best when you can examine the totality of clips captured. Clips therefore captures more moments than it intends to show to the user. When clips are ready to be transferred to the phone, the Clips device takes a second look at what it has shot, and only transfers the best and least redundant content.
Machine Learning Fairness In addition to making sure our video dataset represented a diverse population, we also constructed several other tests to assess the fairness of our algorithms. We created controlled datasets by sampling subjects from different genders and skin tones in a balanced manner, while keeping variables like content type, duration, and environmental conditions constant. We then used this dataset to test that our algorithms had similar performance when applied to different groups. To help detect any regressions in fairness that might occur as we improved our moment quality models, we added fairness tests to our automated system. Any change to our software was run across this battery of tests, and was required to pass. It is important to note that this methodology can’t guarantee fairness, as we can’t test for every possible scenario and outcome. However, we believe that these steps are an important part of our long-term work to achieve fairness in ML algorithms.
Conclusion Most machine learning algorithms are designed to estimate objective qualities – a photo contains a cat, or it doesn’t. In our case, we aim to capture a more elusive and subjective quality – whether a personal photograph is interesting, or not. We therefore combine the objective, semantic content of photographs with subjective human preferences to build the AI behind Google Clips. Also, Clips is designed to work alongside a person, rather than autonomously; to get good results, a person still needs to be conscious of framing, and make sure the camera is pointed at interesting content. We’re happy with how well Google Clips performs, and are excited to continue to improve our algorithms to capture that “perfect” moment!
Acknowledgements The algorithms described here were conceived and implemented by a large group of Google engineers, research scientists, and others. Figures were made by Lior Shapira. Thanks to Lior and Juston Payne for video content.
Posted by Yaniv Leviathan, Principal Engineer and Yossi Matias, Vice President, Engineering, Google
A long-standing goal of human-computer interaction has been to enable people to have a natural conversation with computers, as they would with each other. In recent years, we have witnessed a revolution in the ability of computers to understand and to generate natural speech, especially with the application of deep neural networks (e.g., Google voice search, WaveNet). Still, even with today’s state of the art systems, it is often frustrating having to talk to stilted computerized voices that don't understand natural language. In particular, automated phone systems are still struggling to recognize simple words and commands. They don’t engage in a conversation flow and force the caller to adjust to the system instead of the system adjusting to the caller.
Today we announce Google Duplex, a new technology for conducting natural conversations to carry out “real world” tasks over the phone. The technology is directed towards completing specific tasks, such as scheduling certain types of appointments. For such tasks, the system makes the conversational experience as natural as possible, allowing people to speak normally, like they would to another person, without having to adapt to a machine.
One of the key research insights was to constrain Duplex to closed domains, which are narrow enough to explore extensively. Duplex can only carry out natural conversations after being deeply trained in such domains. It cannot carry out general conversations.
Here are examples of Duplex making phone calls (using different voices):
Duplex scheduling a hair salon appointment:
Duplex calling a restaurant:
While sounding natural, these and other examples are conversations between a fully automatic computer system and real businesses.
The Google Duplex technology is built to sound natural, to make the conversation experience comfortable. It’s important to us that users and businesses have a good experience with this service, and transparency is a key part of that. We want to be clear about the intent of the call so businesses understand the context. We’ll be experimenting with the right approach over the coming months.
Conducting Natural Conversations There are several challenges in conducting natural conversations: natural language is hard to understand, natural behavior is tricky to model, latency expectations require fast processing, and generating natural sounding speech, with the appropriate intonations, is difficult.
When people talk to each other, they use more complex sentences than when talking to computers. They often correct themselves mid-sentence, are more verbose than necessary, or omit words and rely on context instead; they also express a wide range of intents, sometimes in the same sentence, e.g., “So umm Tuesday through Thursday we are open 11 to 2, and then reopen 4 to 9, and then Friday, Saturday, Sunday we... or Friday, Saturday we're open 11 to 9 and then Sunday we're open 1 to 9.”
Example of complex statement:
In natural spontaneous speech people talk faster and less clearly than they do when they speak to a machine, so speech recognition is harder and we see higher word error rates. The problem is aggravated during phone calls, which often have loud background noises and sound quality issues.
In longer conversations, the same sentence can have very different meanings depending on context. For example, when booking reservations “Ok for 4” can mean the time of the reservation or the number of people. Often the relevant context might be several sentences back, a problem that gets compounded by the increased word error rate in phone calls.
Deciding what to say is a function of both the task and the state of the conversation. In addition, there are some common practices in natural conversations — implicit protocols that include elaborations (“for next Friday” “for when?” “for Friday next week, the 18th.”), syncs (“can you hear me?”), interruptions (“the number is 212-” “sorry can you start over?”), and pauses (“can you hold? [pause] thank you!” different meaning for a pause of 1 second vs 2 minutes).
Enter Duplex Google Duplex’s conversations sound natural thanks to advances in understanding, interacting, timing, and speaking.
At the core of Duplex is a recurrent neural network (RNN) designed to cope with these challenges, built using TensorFlow Extended (TFX). To obtain its high precision, we trained Duplex’s RNN on a corpus of anonymized phone conversation data. The network uses the output of Google’s automatic speech recognition (ASR) technology, as well as features from the audio, the history of the conversation, the parameters of the conversation (e.g. the desired service for an appointment, or the current time of day) and more. We trained our understanding model separately for each task, but leveraged the shared corpus across tasks. Finally, we used hyperparameter optimization from TFX to further improve the model.
Incoming sound is processed through an ASR system. This produces text that is analyzed with context data and other inputs to produce a response text that is read aloud through the TTS system.
Duplex handling interruptions:
Duplex elaborating:
Duplex responding to a sync:
Sounding Natural We use a combination of a concatenative text to speech (TTS) engine and a synthesis TTS engine (using Tacotron and WaveNet) to control intonation depending on the circumstance.
The system also sounds more natural thanks to the incorporation of speech disfluencies (e.g. “hmm”s and “uh”s). These are added when combining widely differing sound units in the concatenative TTS or adding synthetic waits, which allows the system to signal in a natural way that it is still processing. (This is what people often do when they are gathering their thoughts.) In user studies, we found that conversations using these disfluencies sound more familiar and natural.
Also, it’s important for latency to match people’s expectations. For example, after people say something simple, e.g., “hello?”, they expect an instant response, and are more sensitive to latency. When we detect that low latency is required, we use faster, low-confidence models (e.g. speech recognition or endpointing). In extreme cases, we don’t even wait for our RNN, and instead use faster approximations (usually coupled with more hesitant responses, as a person would do if they didn’t fully understand their counterpart). This allows us to have less than 100ms of response latency in these situations. Interestingly, in some situations, we found it was actually helpful to introduce more latency to make the conversation feel more natural — for example, when replying to a really complex sentence.
System Operation The Google Duplex system is capable of carrying out sophisticated conversations and it completes the majority of its tasks fully autonomously, without human involvement. The system has a self-monitoring capability, which allows it to recognize the tasks it cannot complete autonomously (e.g., scheduling an unusually complex appointment). In these cases, it signals to a human operator, who can complete the task.
To train the system in a new domain, we use real-time supervised training. This is comparable to the training practices of many disciplines, where an instructor supervises a student as they are doing their job, providing guidance as needed, and making sure that the task is performed at the instructor’s level of quality. In the Duplex system, experienced operators act as the instructors. By monitoring the system as it makes phone calls in a new domain, they can affect the behavior of the system in real time as needed. This continues until the system performs at the desired quality level, at which point the supervision stops and the system can make calls autonomously.
Benefits for Businesses and Users Businesses that rely on appointment bookings supported by Duplex, and are not yet powered by online systems, can benefit from Duplex by allowing customers to book through the Google Assistant without having to change any day-to-day practices or train employees. Using Duplex could also reduce no-shows to appointments by reminding customers about their upcoming appointments in a way that allows easy cancellation or rescheduling.
Duplex calling a restaurant:
In another example, customers often call businesses to inquire about information that is not available online such as hours of operation during a holiday. Duplex can call the business to inquire about open hours and make the information available online with Google, reducing the number of such calls businesses receive, while at the same time, making the information more accessible to everyone. Businesses can operate as they always have, there’s no learning curve or changes to make to benefit from this technology.
Duplex asking for holiday hours:
For users, Google Duplex is making supported tasks easier. Instead of making a phone call, the user simply interacts with the Google Assistant, and the call happens completely in the background without any user involvement.
A user asks the Google Assistant for an appointment, which the Assistant then schedules by having Duplex call the business.
Another benefit for users is that Duplex enables delegated communication with service providers in an asynchronous way, e.g., requesting reservations during off-hours, or with limited connectivity. It can also help address accessibility and language barriers, e.g., allowing hearing-impaired users, or users who don’t speak the local language, to carry out tasks over the phone.
This summer, we’ll start testing the Duplex technology within the Google Assistant, to help users make restaurant reservations, schedule hair salon appointments, and get holiday hours over the phone.
Yaniv Leviathan, Google Duplex lead, and Matan Kalman, engineering manager on the project, enjoying a meal booked through a call from Duplex.
Duplex calling to book the above meal:
Allowing people to interact with technology as naturally as they interact with each other has been a long standing promise. Google Duplex takes a step in this direction, making interaction with technology via natural conversation a reality in specific scenarios. We hope that these technology advances will ultimately contribute to a meaningful improvement in people’s experience in day-to-day interactions with computers.
Posted by Christian Howard, Editor-in-Chief, Google AI Communications
Hmm, have I made a wrong turn? I was looking for Google Research…
For the past several years, we’ve pursued research that reflects our commitment to make AI available for everyone. From computer vision to healthcare research to AutoML, we have increasingly put emphasis on implementing machine learning techniques in nearly everything we do at Google. Our research has been core to the development and integration of these systems into Google products and platforms.
To better reflect this commitment, we’re unifying our efforts under “Google AI”, which encompasses all the state-of-the-art research happening across Google. As part of this, we have expanded the Google AI website, and are renaming our existing Google Research channels, including this blog and the affiliated Twitter and Google+ channels, to Google AI. And if you’re looking for information that existed on research.google.com or the affiliated social channels, don’t fret, it’s all still there. Any links to previous Google Research website content, blog posts or tweets will redirect appropriately.
The Google AI channels will continue to showcase the breadth of Google research, innovation and publications, in addition to a lot more new and exciting content to come. We encourage you to explore! We look forward to continuing to bring you the latest updates and results from Google, in AI and across many other areas of research.
Posted by Pankaj Gupta and Anand Rangarajan, Engineering Directors, Google India
Last month, Google Bangalore hosted the Workshop on Artificial Intelligence and Machine Learning, with the goal of fostering collaboration between the academic and industry research communities in India. This forum was designed to exchange current research and industry projects in AI & ML, and included faculty and researchers from Indian Institutes of Technology (IITs) and other leading universities in India, along with industry practitioners from Amazon, Delhivery, Flipkart, LinkedIn, Myntra, Microsoft, Ola and many more. Participants spoke on the ongoing research and work being undertaken in India in deep learning, computer vision, natural language processing, systems and generative models (you can access all the presentations from the workshop here).
Summary and Next Steps As in many countries around the world, we are seeing increased dialog on various aspects of AI and ML in multiple contexts in India. This workshop hosted 80 attendees representing 9 universities and 36 companies contributing 28 excellent talks, with many opportunities for discussing challenges and opportunities for AI/ML in India. Google will continue to foster this exchange of ideas across a diverse set of folks and applications. As part of this, we also announced the upcoming research awards round (applications due June 4) to support up to seven faculty members in India on their AI/ML research, and new work on an accelerator program for Indian entrepreneurs focused primarily on AI/ML technologies. Please keep an eye out for more information about these programs.
Posted by Pankaj Gupta and Anand Rangarajan, Engineering Directors, Google India
Last month, Google Bangalore hosted the Workshop on Artificial Intelligence and Machine Learning, with the goal of fostering collaboration between the academic and industry research communities in India. This forum was designed to exchange current research and industry projects in AI & ML, and included faculty and researchers from Indian Institutes of Technology (IITs) and other leading universities in India, along with industry practitioners from Amazon, Delhivery, Flipkart, LinkedIn, Myntra, Microsoft, Ola and many more. Participants spoke on the ongoing research and work being undertaken in India in deep learning, computer vision, natural language processing, systems and generative models (you can access all the presentations from the workshop here).
Summary and Next Steps As in many countries around the world, we are seeing increased dialog on various aspects of AI and ML in multiple contexts in India. This workshop hosted 80 attendees representing 9 universities and 36 companies contributing 28 excellent talks, with many opportunities for discussing challenges and opportunities for AI/ML in India. Google will continue to foster this exchange of ideas across a diverse set of folks and applications. As part of this, we also announced the upcoming research awards round (applications due June 4) to support up to seven faculty members in India on their AI/ML research, and new work on an accelerator program for Indian entrepreneurs focused primarily on AI/ML technologies. Please keep an eye out for more information about these programs.
Posted by Billy Rutledge, Director of AIY Projects
Last year, AIY Projects launched to give makers the power to build AI into their projects with two do-it-yourself kits. We're seeing continued demand for the kits, especially from the STEM audience where parents and teachers alike have found the products to be great tools for the classroom. The changing nature of work in the future means students may have jobs that haven't yet been imagined, and we know that computer science skills, like analytical thinking and creative problem solving, will be crucial.
We're taking the first of many steps to help educators integrate AIY into STEM lesson plans and help prepare students for the challenges of the future by launching a new version of our AIY kits. The Voice Kit lets you build a voice controlled speaker, while the Vision Kit lets you build a camera that learns to recognize people and objects (check it out here). The new kits make getting started a little easier with clearer instructions, a new app and all the parts in one box.
To make setup easier, both kits have been redesigned to work with the new Raspberry Pi Zero WH, which comes included in the box, along with the USB connector cable and pre-provisioned SD card. Now users no longer need to download the software image and can get running faster. The updated AIY Vision Kit v1.1 also includes the Raspberry Pi Camera v2.
AIY Voice Kit v2 includes Raspberry Pi Zero WH and pre-provisioned SD card
AIY Voice Kit v1.1 includes Raspberry Pi Zero WH, Raspberry Pi Cam 2 and pre-provisioned SD card
We're also introducing the AIY companion app for Android, available here in Google Play, to make wireless setup and configuration a snap. The kits still work with monitor, keyboard and mouse as an alternate path and we're working on iOS and Chrome companions which will be coming soon.
The AIY website has been refreshed with improved documentation, now easier for young makers to get started and learn as they build. It also includes a new AIY Models area, showcasing a collection of neural networks designed to work with AIY kits. While we've solved one barrier to entry for the STEM audience, we recognize that there are many other things that we can do to make our kits even more useful. We'll once again be at #MakerFaire events to gather feedback from our users and in June we'll be working with teachers from all over the world at the ISTE conference in Chicago.
The new AIY Voice Kit and Vision Kit have arrived at Target Stores and Target.com (US) this month and we're working to make them globally available through retailers worldwide. Sign up on our mailing list to be notified when our products become available.
We hope you'll pick up one of the new AIY kits and learn more about how to build your own smart devices. Be sure to share your recipes on Hackster.io and social media using #aiyprojects.
