Author: torontoai
More ways to compete and win in the AWS DeepRacer League and two new champions!
It’s been a busy week for the AWS DeepRacer League. The world’s first global autonomous racing league allows machine learning developers of all skill levels to get hands-on with machine learning in a fun and exciting way.
On April 29 2019, the virtual circuit of the AWS DeepRacer League opened. The virtual circuit allows racers to compete from anywhere in the world by using the AWS DeepRacer console. Developers can put their skills to the test by competing in the Virtual Circuit World Tour, on virtual tracks inspired by famous raceways that will be revealed each month. They will race for prizes and glory, and a chance to win an expenses-paid trip to the AWS DeepRacer Championship Cup at re:Invent 2019. The first racetrack on the virtual world tour is inspired by the famous raceway in Silverstone, UK, named the London Loop. It’s open for racing until May 31, with developers from all parts of the globe already posting great lap times. Get racing today for a chance to win the AWS DeepRacer League Virtual Circuit!
Winners at the Sydney summit
In addition to the virtual circuit, race seven on the AWS Summit calendar took place in Sydney, Australia. The AWS Summit in Sydney was a two-day extravaganza, bringing together the cloud community down under, to learn and get hands-on with AWS services. The AWS DeepRacer League had three tracks for racers to compete on for more than 48 hours. It didn’t disappoint as hundreds of racers took to the tracks to compete for the champion’s spot on the podium.
Matt Kerrison (Matt@GJI) took first place, traveling to Sydney with three other teammates to learn how GJI Group, a Brisbane-based design and communications company, can continue to innovate with the help of AWS. They had no idea that they would walk away with the AWS DeepRacer trophy and two of the three of them in the top 10.
The Sydney winner, Matt Kerrison, started in the virtual league quickly after it launched on April 29th, and attended the AWS DeepRacer workshop on day two of the AWS Summit. He continued to tune his model overnight, which scored him a winning lap time of 8.29 seconds, just 1 hour and 45 minutes before racing finished.
Sydney Summit Champion Matt Kerrison
Matt is now on his way to AWS re:Invent 2019 in Las Vegas, Nevada, to compete for the championship cup. In preparation, he and his colleagues plan to host hackathons to continue experimenting with, and building knowledge of AI and machine learning, as well as participate in the virtual league.
Same week, different city
On to Atlanta, Georgia, which rounded out the week. More developers raced live on the track and attended workshops to learn about machine learning.
Our top three racers in Atlanta had sub 10-second lap times. Amelia Hough-Ross, a deputy chief technology officer, is the first female to stand on the podium and one of the most determined. Amelia had scored a third-place position during the morning hours of racing. However, she was moved down to tenth during the day. She went away and trained her model for several hours, and with only a couple of hours of racing left, she came from behind to clinch the third place finish. She’s excited to try out the virtual league, where she can also compete to win her place in the finals at re:Invent 2019. She also wants to see what improvements she can make to her model for the upcoming US summits in June and July. Amelia can score even more points for a chance to advance.
The Atlanta summit podium: Kevin Byuen (8.71 seconds) Steven Lucovsky (9.01 seconds) Amelia Hough-Ross (9.78 seconds)
Our Atlanta winner was Kevin Byuen, the only developer in Atlanta to beat the 9-second barrier. For his winning time of 8.71 seconds, he took to the track four times. Kevin prepared for the event for more than a week and learned from the AWS DeepRacer community in order to build the winning reinforcement learning model.
The AWS DeepRacer League is in full swing. In case you missed it, the AWS DeepRacer League now has a 21st stop on the schedule before re:Invent, at the inaugural re:MARS event. This event pairs the best of what’s possible today with perspectives on the future of machine learning, automation, robotics, and space travel. Developers of all skill levels can start competing today and in as many races as they like. Accumulate points throughout the season to earn more chances to win and advance to the AWS DeepRacer Championship at re:Invent 2019.
About the Author
Alexandra Bush is a Senior Product Marketing Manager for AWS AI. She is passionate about how technology impacts the world around us and enjoys being able to help make it accessible to all. Out of the office she loves to run, travel and stay active in the outdoors with family and friends.
[N] Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval
Google released a new landmark dataset yesterday. You can find the details in their blog post:
https://ai.googleblog.com/2019/05/announcing-google-landmarks-v2-improved.html
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[P] Implementing Billion-scale semi-supervised learning for image classification using Pytorch
Hi, reddit !! We are Myeongjun Kim and Taehun Kim. Our major is computer vision research using deep learning. Previously, we implemented the paper with RandWireNN pytorch version and tensorflow version. This time, we implemented a paper on “Billion-scale semi-supervised learning for image classification written by Facebook AI”(https://arxiv.org/abs/1905.00546). To briefly describe the paper, it is stated that the classification performance is improved by using unlabeled data. We realized that it was a simple and novel idea. So, we implemented it.
Due to the lack of GPU resources and unlabeled data, we are delaying the experiment on ImageNet and are experimenting with CIFAR-100 first. We would like to ask for your interest and feedback.
Thank you for reading long long story.
Github URL: https://github.com/leaderj1001/Billion-scale-semi-supervised-learning
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[N] OpenAI releasing the 345M model of GPT-2 and sharing the 1.5B model “with partners working on countermeasures”
OpenAI has decided to adopt a staged release approach to their GPT-2 language model.
Announcement on Twitter: https://twitter.com/OpenAI/status/1124440412679233536
The following quotes are from the update on their blog: https://openai.com/blog/better-language-models/#update
Staged Release
Staged release involves the gradual release of a family of models over time. The purpose of our staged release of GPT-2 is to give people time to assess the properties of these models, discuss their societal implications, and evaluate the impacts of release after each stage.
As the next step in our staged release strategy, we are releasing the 345M parameter version of GPT-2. This model features improved performance relative to the 117M version, though falls short of the 1.5B version with respect to the ease of generating coherent text. We have been excited to see so many positive uses of GPT-2-117M, and hope that 345M will yield still more benefits.
While the misuse risk of 345M is higher than that of 117M, we believe it is substantially lower than that of 1.5B, and we believe that training systems of similar capability to GPT-2-345M is well within the reach of many actors already; this evolving replication landscape has informed our decision-making about what is appropriate to release.
In making our 345M release decision, some of the factors we considered include: the ease of use (by various users) of different model sizes for generating coherent text, the role of humans in the text generation process, the likelihood and timing of future replication and publication by others, evidence of use in the wild and expert-informed inferences about unobservable uses, proofs of concept such as the review generator mentioned in the original blog post, the strength of demand for the models for beneficial purposes, and the input of stakeholders and experts. We remain uncertain about some of these variables and continue to welcome input on how to make appropriate language model publication decisions.
We hope that ongoing research on bias, detection, and misuse will give us the confidence to publish larger models in a timely manner, and at the six month mark we will share a fuller analysis of language models’ societal implications and our heuristics for release decisions.
Partnerships
Since releasing this blog post in February, we have had conversations with many external researchers, technology companies, and policymakers about our release strategy and the implications of increasingly large language models. We’ve also presented or discussed our work at events, including a dinner co-hosted with the Partnership on AI and a presentation to policymakers in Washington DC at the Global Engagement Center.
We are currently forming research partnerships with academic institutions, non-profits, and industry labs focused on increasing societal preparedness for large language models. In particular, we are sharing the 762M and 1.5B parameter versions of GPT-2 to facilitate research on language model output detection, language model bias analysis and mitigation, and analysis of misuse potential. In addition to observing the impacts of language models in the wild, engaging in dialogue with stakeholders, and conducting in-house analysis, these research partnerships will be a key input to our decision-making on larger models. See below for details on how to get involved.
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Machine Learning Scientist – TD Bank – Toronto, ON
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Full Stack Software Developer – AIH Technology – Toronto, ON
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