Hi everyone! We've just released Chrome Stable 107 (107.0.5304.66) for iOS; it'll become available on App Store in the next few hours.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
Erhu Eakpobaro
Google Chrome
Hi everyone! We've just released Chrome Stable 107 (107.0.5304.66) for iOS; it'll become available on App Store in the next few hours.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
Erhu Eakpobaro
Google Chrome
Hi everyone! We've just released Chrome Stable 107 (107.0.5304.66) for iOS; it'll become available on App Store in the next few hours.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
Erhu Eakpobaro
Google Chrome
This week, UNESCO’s Media Literacy Week is focused on nurturing trust in media and information. There’s no better time to educate and empower people to be confident consumers of media than at school, which is why Squiz Kids has partnered with the Google News Initiative to roll out its media literacy program ‘Newshounds’ to primary schools across New Zealand.
Squiz Kids, a daily news podcast for 8-12yos, has developed Newshounds by Squiz Kids as a plug-and-play media literacy teaching resource comprising eight x 10 minute podcasts and accompanying in-classroom activities, packaged up in an engaging board-game style format.
Squiz-E the Newshound takes primary-aged kids on a media literacy journey, teaching them to understand the myriad forms of media to which they’re exposed every day and recognise the multiple agendas that drive them. Underpinning it all are exercises that give kids the skills to identify misinformation and disinformation.
“Kids today have more information coming at them on a daily basis than at any other time in history,” said Squiz Kids Director Bryce Corbett. “We created Newshounds to make kids critical consumers of media - to teach them to stop, think and check before believing everything they come across on the internet. Teachers and parents alike know it’s important to teach their children media literacy, but few know where to start. By partnering with Google, it’s hoped that Newshounds starts conversations with adults that help kids recognise online fact from fiction.”
The partnership with Google will allow classrooms across New Zealand to access the Newshounds media literacy program for free from this week.
The Manaiakalani Education schools in Tāmaki Makaurau have been running a pilot of the programme in their classrooms over the past few months, and they’ve found students were engaged by the content, and most importantly, were transferring these concepts to other areas of learning when they were online.
Listeners, readers and viewers are incredibly powerful in the fight against misinformation - the more they demand quality information, the higher chance facts have to win the battle. But those audiences need support.
Understanding the many complex elements that go into deciding what is fact and what is a falsehood starts at an early age, which is why we’re so proud to work with Squiz Kids to launch Newshounds in New Zealand schools. This partnership builds on our efforts to build a sustainable, diverse and innovative news ecosystem.
Posted by Yasmine Evjen, Community Lead, Android Developer Relations
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Google is proud to be a Platinum Sponsor of the European Conference on Computer Vision (ECCV 2022), a premier forum for the dissemination of research in computer vision and machine learning (ML). This year, ECCV 2022 will be held as a hybrid event, in person in Tel Aviv, Israel with virtual attendance as an option. Google has a strong presence at this year’s conference with over 60 accepted publications and active involvement in a number of workshops and tutorials. We look forward to sharing some of our extensive research and expanding our partnership with the broader ML research community.
Registered for ECCV 2022? We hope you’ll visit our on-site or virtual booths to learn more about the research we’re presenting at ECCV 2022, including several demos and opportunities to connect with our researchers. Learn more about Google's research being presented at ECCV 2022 below (Google affiliations in bold).
Program Chairs include: Moustapha Cissé
Awards Paper Committee: Todd Zickler
Area Chairs include: Ayan Chakrabarti, Tali Dekel, Alireza Fathi, Vittorio Ferrari, David Fleet, Dilip Krishnan, Michael Rubinstein, Cordelia Schmid, Deqing Sun, Federico Tombari, Jasper Uijlings, Ming-Hsuan Yang, Todd Zickler
NeuMesh: Learning Disentangled Neural Mesh-Based Implicit Field for Geometry and Texture Editing
Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, Guofeng Zhang
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks
Zihang Zou, Boqing Gong, Liqiang Wang
Exploiting Unlabeled Data with Vision and Language Models for Object Detection
Shiyu Zhao, Zhixing Zhang, Samuel Schulter, Long Zhao, Vijay Kumar B G, Anastasis Stathopoulos, Manmohan Chandraker, Dimitris N. Metaxas
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
Jieru Mei, Alex Zhu, Xinchen Yan, Hang Yan, Siyuan Qiao, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar
PRIF: Primary Ray-Based Implicit Function
Brandon Yushan Feng, Yinda Zhang, Danhang Tang, Ruofei Du, Amitabh Varshney
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling
Boyan Jiang, Xinlin Ren, Mingsong Dou, Xiangyang Xue, Yanwei Fu, Yinda Zhang
k-Means Mask Transformer (see blog post)
Qihang Yu*, Siyuan Qiao, Maxwell D Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
MaxViT: Multi-Axis Vision Transformer (see blog post)
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li
E-Graph: Minimal Solution for Rigid Rotation with Extensibility Graphs
Yanyan Li, Federico Tombari
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Ruida Zhang, Yan Di, Zhiqiang Lou, Fabian Manhardt, Federico Tombari, Xiangyang Ji
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
Huseyin Coskun, Alireza Zareian, Joshua L Moore, Federico Tombari, Chen Wang
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
Golnaz Ghiasi, Xiuye Gu, Yin Cui, Tsung-Yi Lin*
Adaptive Transformers for Robust Few-Shot Cross-Domain Face Anti-spoofing
Hsin-Ping Huang, Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Taihong Xiao, Jinwei Yuan, Hartwig Adam, Ming-Hsuan Yang
DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning
Zifeng Wang*, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister
BLT: Bidirectional Layout Transformer for Controllable Layout Generation
Xiang Kong, Lu Jiang, Huiwen Chang, Han Zhang, Yuan Hao, Haifeng Gong, Irfan Essa
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
Runsheng Xu, Hao Xiang, Zhengzhong Tu, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma
Learning Visibility for Robust Dense Human Body Estimation
Chun-Han Yao, Jimei Yang, Duygu Ceylan, Yi Zhou, Yang Zhou, Ming-Hsuan Yang
Are Vision Transformers Robust to Patch Perturbations?
Jindong Gu, Volker Tresp, Yao Qin
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Zhaoqi Leng, Shuyang Cheng, Ben Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov
Structure and Motion from Casual Videos
Zhoutong Zhang, Forrester Cole, Zhengqi Li, Noah Snavely, Michael Rubinstein, William T. Freeman
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Chenfeng Xu, Tian Li, Chen Tang, Lingfeng Sun, Kurt Keutzer, Masayoshi Tomizuka, Alireza Fathi, Wei Zhan
Novel Class Discovery Without Forgetting
Joseph K J, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Yuxiao Chen, Long Zhao, Jianbo Yuan, Yu Tian, Zhaoyang Xia, Shijie Geng, Ligong Han, Dimitris N. Metaxas
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks
Nan Ding, Xi Chen, Tomer Levinboim, Soravit Changpinyo, Radu Soricut
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
Zhengqi Li, Qianqian Wang*, Noah Snavely, Angjoo Kanazawa*
Generalizable Patch-Based Neural Rendering (see blog post)
Mohammed Suhail*, Carlos Esteves, Leonid Sigal, Ameesh Makadia
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su, Dragomir Anguelov
The Missing Link: Finding Label Relations Across Datasets
Jasper Uijlings, Thomas Mensink, Vittorio Ferrari
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, Jia-Bin Huang
Learning Audio-Video Modalities from Image Captions
Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold, Anja Hauth, Santiago Manen, Chen Sun, Cordelia Schmid
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
Medhini Narasimhan*, Arsha Nagrani, Chen Sun, Michael Rubinstein, Trevor Darrell, Anna Rohrbach, Cordelia Schmid
On Label Granularity and Object Localization
Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge Belongie, Andrew Howard, Oisin Mac Aodha
Disentangling Architecture and Training for Optical Flow
Deqing Sun, Charles Herrmann, Fitsum Reda, Michael Rubinstein, David J. Fleet, William T. Freeman
NewsStories: Illustrating Articles with Visual Summaries
Reuben Tan, Bryan Plummer, Kate Saenko, J.P. Lewis, Avneesh Sud, Thomas Leung
Improving GANs for Long-Tailed Data Through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali, Varun Jampani, Venkatesh Babu Radhakrishnan
Planes vs. Chairs: Category-Guided 3D Shape Learning Without Any 3D Cues
Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James Rehg
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch
Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang, James Hays
Learned Monocular Depth Priors in Visual-Inertial Initialization
Yunwen Zhou, Abhishek Kar, Eric L. Turner, Adarsh Kowdle, Chao Guo, Ryan DuToit, Konstantine Tsotsos
How Stable are Transferability Metrics Evaluations?
Andrea Agostinelli, Michal Pandy, Jasper Uijlings, Thomas Mensink, Vittorio Ferrari
Data-Free Neural Architecture Search via Recursive Label Calibration
Zechun Liu*, Zhiqiang Shen, Yun Long, Eric Xing, Kwang-Ting Cheng, Chas H. Leichner
Fast and High Quality Image Denoising via Malleable Convolution
Yifan Jiang*, Bartlomiej Wronski, Ben Mildenhall, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay R Kulkarni, Hiran Sarkar,
Varun Jampani, Venkatesh Babu Radhakrishnan
Learning Online Multi-Sensor Depth Fusion
Erik Sandström, Martin R. Oswald, Suryansh Kumar, Silvan Weder, Fisher Yu, Cristian Sminchisescu, Luc Van Gool
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh K Singh, Venkatesh Babu Radhakrishnan
RayTran: 3D Pose Estimation and Shape Reconstruction of Multiple Objects from Videos with Ray-Traced Transformers
Michał J Tyszkiewicz, Kevis-Kokitsi Maninis, Stefan Popov, Vittorio Ferrari
Neural Video Compression Using GANs for Detail Synthesis and Propagation
Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
Grant Van Horn, Rui Qian, Kimberly Wilber, Hartwig Adam, Oisin Mac Aodha, Serge Belongie
Implicit Neural Representations for Image Compression
Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, Federico Tombari
3D Compositional Zero-Shot Learning with DeCompositional Consensus
Muhammad Ferjad Naeem, Evin Pınar Örnek, Yongqin Xian, Luc Van Gool, Federico Tombari
FindIt: Generalized Localization with Natural Language Queries (see blog post)
Weicheng Kuo, Fred Bertsch, Wei Li, AJ Piergiovanni, Mohammad Saffar, Anelia Angelova
A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation
Wuyang Chen*, Xianzhi Du, Fan Yang, Lucas Beyer, Xiaohua Zhai, Tsung-Yi Lin, Huizhong Chen, Jing Li, Xiaodan Song, Zhangyang Wang, Denny Zhou
Improved Masked Image Generation with Token-Critic
Jose Lezama, Huiwen Chang, Lu Jiang, Irfan Essa
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, Ming-Hsuan Yang
AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation
Efthymios Tzinis*, Scott Wisdom, Tal Remez, John Hershey
Simple Open-Vocabulary Object Detection with Vision Transformers
Matthias Minderer, Alexey Gritsenko, Austin C Stone, Maxim Neumann, Dirk Weißenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Honglu Zhou, Asim Kadav, Aviv Shamsian, Shijie Geng, Farley Lai, Long Zhao, Ting Liu, Mubbasir Kapadia, Hans Peter Graf
Video Question Answering with Iterative Video-Text Co-tokenization (see blog post)
AJ Piergiovanni, Kairo Morton*, Weicheng Kuo, Michael S. Ryoo, Anelia Angelova
Class-Agnostic Object Detection with Multi-modal Transformer
Muhammad Maaz, Hanoona Abdul Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
FILM: Frame Interpolation for Large Motion (see blog post)
Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, Brian Curless
Compositional Human-Scene Interaction Synthesis with Semantic Control
Kaifeng Zhao, Shaofei Wang, Yan Zhang, Thabo Beeler, Siyu Tang
LatinX in AI
Mentors include: José Lezama
Keynote Speakers include: Andre Araujo
AI for Creative Video Editing and Understanding
Keynote Speakers include: Tali Dekel, Negar Rostamzadeh
Learning With Limited and Imperfect Data (L2ID)
Invited Speakers include: Xiuye Gu
Organizing Committee includes: Sadeep Jayasumana
International Challenge on Compositional and Multimodal Perception (CAMP)
Program Committee includes: Edward Vendrow
Self-Supervised Learning: What is Next?
Invited Speakers include: Mathilde Caron, Arsha Nagrani
Organizers include: Andrew Zisserman
3rd Workshop on Adversarial Robustness In the Real World
Invited Speakers include: Ekin Dogus Cubuk
Organizers include: Xinyun Chen, Alexander Robey, Nataniel Ruiz, Yutong Bai
AV4D: Visual Learning of Sounds in Spaces
Invited Speakers include: John Hershey
Challenge on Mobile Intelligent Photography and Imaging (MIPI)
Invited Speakers include: Peyman Milanfar
Robust Vision Challenge 2022
Organizing Committee includes: Alina Kuznetsova
Computer Vision in the Wild
Challenge Organizers include: Yi-Ting Chen, Ye Xia
Invited Speakers include: Yin Cui, Yongqin Xian, Neil Houlsby
Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving (SSLAD)
Organizers include: Fisher Yu
Responsible Computer Vision
Organizing Committee includes: Been Kim
Invited Speakers include: Emily Denton
Cross-Modal Human-Robot Interaction
Invited Speakers include: Peter Anderson
ISIC Skin Image Analysis
Organizing Committee includes: Yuan Liu
Steering Committee includes: Yuan Liu, Dale Webster
Invited Speakers include: Yuan Liu
Observing and Understanding Hands in Action
Sponsored by Google
Autonomous Vehicle Vision (AVVision)
Speakers include: Fisher Yu
Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark
Organizers include: Edward Vendrow
Language for 3D Scenes
Invited Speakers include: Jason Baldridge
Organizers include: Leonidas Guibas
Designing and Evaluating Computer Perception Systems (CoPe)
Organizers include: Andrew Zisserman
Learning To Generate 3D Shapes and Scenes
Panelists include: Pete Florence
Advances in Image Manipulation
Program Committee includes: George Toderici, Ming-Hsuan Yang
TiE: Text in Everything
Challenge Organizers include: Shangbang Long, Siyang Qin
Invited Speakers include: Tali Dekel, Aishwarya Agrawal
Instance-Level Recognition
Organizing Committee: Andre Araujo, Bingyi Cao, Tobias Weyand
Invited Speakers include: Mathilde Caron
What Is Motion For?
Organizing Committee: Deqing Sun, Fitsum Reda, Charles Herrmann
Invited Speakers include: Tali Dekel
Neural Geometry and Rendering: Advances and the Common Objects in 3D Challenge
Invited Speakers include: Ben Mildenhall
Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications
Invited Speakers include: Klaus Greff, Thomas Kipf
Organizing Committee includes: Leonidas Guibas
Vision with Biased or Scarce Data (VBSD)
Program Committee includes: Yizhou Wang
Multiple Object Tracking and Segmentation in Complex Environments
Invited Speakers include: Xingyi Zhou, Fisher Yu
3rd Visual Inductive Priors for Data-Efficient Deep Learning Workshop
Organizing Committee includes: Ekin Dogus Cubuk
DeeperAction: Detailed Video Action Understanding and Anomaly Recognition
Advisors include: Rahul Sukthankar
Sign Language Understanding Workshop and Sign Language Recognition, Translation & Production Challenge
Organizing Committee includes: Andrew Zisserman
Speakers include: Andrew Zisserman
Ego4D: First-Person Multi-Modal Video Understanding
Invited Speakers include: Michal Irani
AI-Enabled Medical Image Analysis: Digital Pathology & Radiology/COVID19
Program Chairs include: Po-Hsuan Cameron Chen
Workshop Partner: Google Health
Visual Object Tracking Challenge (VOT 2022)
Technical Committee includes: Christoph Mayer
Assistive Computer Vision and Robotics
Technical Committee includes: Maja Mataric
Human Body, Hands, and Activities from Egocentric and Multi-View Cameras
Organizers include: Francis Engelmann
Frontiers of Monocular 3D Perception: Implicit x Explicit
Panelists include: Pete Florence
Self-Supervised Representation Learning in Computer Vision
Invited Speakers include: Ting Chen
Neural Volumetric Rendering for Computer Vision
Organizers include: Ben Mildenhall, Pratul Srinivasan, Jon Barron
Presenters include: Ben Mildenhall, Pratul Srinivasan
New Frontiers in Efficient Neural Architecture Search!
Speakers include: Ruochen Wang
In 2020, there were 2.3 million people diagnosed with breast cancer and 685,000 deaths globally. Early cancer detection is key to better health outcomes. But screenings are work intensive, and patients often find getting mammogramsand waiting for results stressful.
In response to these challenges, Google Health and Northwestern Medicine partnered in 2021 on a clinical research study to explore whether artificial intelligence (AI) models can reduce the time to diagnosis during the screening process, narrowing the assessment gap and improving the patient experience. This work is among the first prospective randomized controlled studies for AI in breast cancer screening, and the results will be published in early 2023.
Behind this work, are scientists and researchers united in the fight against breast cancer. We spoke with Dr. Sunny Jansen, a technical program manager at Google, and Sally Friedewald, MD, the division chief of Breast and Women's Imaging at Northwestern University’s Feinberg School of Medicine, on how they hope this work will help screening providers catch cancer earlier and improve the patient experience.
Dr. Jansen:Like so many of us, I know how breast cancer can impact families and communities, and how critical early detection can be. The experiences of so many around me have influenced my work in this area. I hope that AI can make the future of breast cancer screening easier, faster, more accurate — and, ultimately, more accessible for women globally.
So we sought to understand how AI can reduce diagnostic delays and help patients receive diagnoses as soon as possible by streamlining care into a single visit. For patients with abnormal findings at screening, the diagnostic delay to get additional imaging tests is typically a couple of weeks in the U.S.Often, the results are normal after the additional imaging tests, but that waiting period can be nerve-racking. Additionally, it can be harder for some patients to come back to get additional imaging tests, which exacerbates delays and leads to disparities in the timeliness of care.
Dr. Friedewald:I anticipate an increase in the demand for screenings and challenges in having enough providers with the necessary specialized training. Using AI, we can identify patients who need additional imaging when they are still in the clinic. We can expedite their care, and, in many cases, eliminate the need for return visits. Patients who aren’t flagged still receive the care they need as well. This translates into operational efficiencies and ultimately leads to patients getting a breast cancer diagnosis faster. We already know the earlier treatment starts, the better.
Dr. Jansen: Most existing publications about AI and breast cancer analyze AI performance retrospectively by reviewing historical datasets. While retrospective studies have a lot of value, they don’t necessarily represent how AI works in the real world. Sally decided early on that it would be important to do a prospective study, incorporating AI into real-world clinical workflows and measuring the impact. I wasn’t sure what to expect!
Dr. Friedewald:Computer-aided detection (CAD), which was developed a few decades ago to help radiologists identify cancers via mammogram, has proven to be helpful in some environments. Overall, in the U.S., CAD has not resulted in increased cancer detection. I was concerned that AI would be similar to CAD in efficacy. However, AI gathers data in a fundamentally different way. I am hopeful that with this new information we can identify cancers earlier with the ultimate goal of saving lives.
Dr. Jansen:The patients who consented to participate in the study inspired me. Clinicians and scientists must conduct quality real-world research so that the best ideas can be identified and moved forward, and we need patients as equal partners in our research.
Dr. Friedewald:Agreed! There’s an appetite to improve our processes and make screening easier and less anxiety-provoking. I truly believe that if we can streamline care for our patients, we will decrease the stress associated with screening and hopefully improve access for those who need it.
Additionally, AI has the potential to go beyond the prioritization of patients who need care. By prospectively identifying patients who are at higher risk of developing breast cancer, AI could help us determine patients that might need a more rigorous screening regimen. I am looking forward to collaborating with Google on this topic and others that could ultimately improve cancer survival.
As New York emerges from the COVID-19 pandemic, the tech sector continues to play a critical role in the city’s economic recovery. While hiring has slowed in many of the city’s industries, tech is still among the fastest areas of job growth. In fact, there were more openings for tech positions during the pandemic than in any other industry.
We believe the city’s good-paying tech jobs should be within reach of all New Yorkers. That’s why earlier this year we announced the Google NYC Tech Opportunity Fund — a $4 million commitment to computer science (CS) education, career development and job-preparedness to make sure every New Yorker, today and in the future, has the chance to get into tech.
With over 680,000 good-paying tech jobs, New York has more tech workers than any other U.S. city. That means for every one Googler in New York, there are over 50 additional tech jobs here. So we’ve extended our support for tech in New York beyond our own hiring to the city’s overall tech employment pipeline — starting from the classroom all the way to the office.
We’ve had some early success: We’ve trained 1,200 New York City high school students through our CS education programs like Code Next and the Computer Science Summer Institute (CSSI). Meanwhile, Grow with Google has partnered with over 530 organizations to train more than 430,000 New Yorkers on digital skills with the help of organizations like public libraries and chambers of commerce. We also launched an apprenticeship program where over 90% of participants nationally landed quality jobs in tech, including at Google, within six months of completing the program. And we’re supporting New York-based startups through Google’s Black Founders Fund and Latino Founders Fund.
With the Google NYC Tech Opportunity Fund, we’re going a step further. We’ve identified key areas we believe Google can help address larger systemic issues and where we’ll focus our investments.
P-12 students with access to CS classes in school are nearly three times more likely to aspire to have a job in the field. But to offer these courses, schools need teachers who are trained in computational skills. After supporting a CS teacher training program at Hunter College in 2021, we committed an additional $1.5 million to The City University of New York (CUNY) and Hunter College to help them train more CS teachers and incorporate computational thinking into their curricula.
New York City's public libraries are essential learning environments for many, especially in under-resourced communities. Thousands of teens use the city’s three library systems annually to get college and career mentoring, build digital literacy, borrow books and more. So we granted a total of $1.5 million to Brooklyn Public Library, The New York Public Library and Queens Public Library to help them create special teen centers. These spaces will offer access to technology, resources and programs teens need to develop essential career skills for the future.
We’re also providing a $1 million Google.org grant to the New York City Employment and Training Coalition (NYCETC) to assemble a consortium of leaders in tech education and workforce development, and to seed a grant fund for organizations that support BIPOC job seekers in NYC.
As part of this effort, we also offer free Google Career Certificates for community colleges, such as The State University of New York’s (SUNY) online center. Over 10,000 New Yorkers have already completed a Google Career certificate and built up their qualifications for high-demand tech jobs.
By taking steps to support students and those already in the workforce, we can help ensure all New Yorkers have access to career opportunities so the tech sector in New York really looks like New York.
