Your guide to AI and ML at AWS re:Invent 2021

It’s almost here! Only 9 days until AWS re:Invent 2021, and we’re very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders’ sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge. There are over 200 AI/ML sessions, including breakout sessions with customers such as Aon Corporation, Qualtrics, Shutterstock, and Bloomberg.

To help you plan your agenda for this year’s re:Invent, here are some highlights of the AI/ML track. You can also get the scoop from some of our AI/ML Community Heroes. So buckle up, and start registering for your favorite sessions.

Swami Sivasubramanian keynote

Wednesday, December 1, 8:30 am PT

Join Swami Sivasubramanian, Vice President, Machine Learning, AWS on an exploration of what it takes to put data in action with an end-to-end data strategy including the latest news on databases, analytics, and ML.

AI/ML leadership session with Bratin Saha

Wednesday, December 1, 4:00 pm PT

With the rise in compute power and data proliferation, ML has moved from the peripheral to being a core part of businesses and organizations across industries. AWS customers use ML and AI services to make accurate predictions, get deeper insights from their data, reduce operational overhead, improve customer experiences, and create entirely new lines of business. In this session, hear from Bratin Saha, Vice President, Machine Leaning, AWS and explore how AWS services can help you move from idea to production with ML.

AI/ML session preview

Here’s a preview of some of the different sessions we’re offering this year by session type. You can always log in to the event portal to favorite or register for any of these sessions, or search the catalog for over 200 other sessions available.

Breakout sessions

Prepare data for ML with ease, speed, and accuracy (AIM319)

Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data. Walk through a complete data-preparation workflow, including how to label training datasets using SageMaker Ground Truth, as well as how to extract data from multiple data sources, transform it using the prebuilt visualization templates in SageMaker Data Wrangler, and create model features. Also, learn how to improve efficiency by using SageMaker Feature Store to create a repository to store, retrieve, and share features.

Achieve high performance and cost-effective model deployment (AIM408)

To maximize your ML investments, high performance and cost-effective techniques are needed to scale model deployments. In this session, learn about the deployment options available in Amazon SageMaker, including optimized infrastructure choices; real-time, asynchronous, and batch inferences; multi-container endpoints; multi-model endpoints; auto scaling; model monitoring; and CI/CD integration for your ML workloads. Discover how to choose a better inference option for your ML use case. Then, hear from Goldman Sachs about how they use SageMaker for fast, low-latency, and scalable deployments to provide relevant research content recommendations for their clients.

Implementing MLOps practices with Amazon SageMaker, featuring Vanguard (AIM320)

Implementing MLOps practices helps data scientists and operations engineers collaborate to prepare, build, train, deploy, and manage models at scale. During this session, explore the breadth of MLOps features in Amazon SageMaker that help you provision consistent model development environments, automate ML workflows, implement CI/CD pipelines for ML, monitor models in production, and standardize model governance capabilities. Then, hear from Vanguard as they share their journey enabling MLOps to achieve ML at scale for their polyglot model development platforms using SageMaker features, including SageMaker projects, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor.

Enhancing the customer experience with Amazon Personalize (AIM204)

Personalizing content for a customer online is key to breaking through the noise. Yet, brands face challenges that often prevent them from providing these seamless, relevant experiences. Learn how easy it is to use Amazon Personalize to tailor product and content recommendations to ensure that your users are getting the content they want, leading to increased engagement and retention.

AI/ML for sustainability innovation: Insight at the edge (AIM207)

As climate change, wildlife conservation, public health, racial and economic equity, and new energy solutions become increasingly interdependent, scalable solutions are needed for actionable analysis at the intersection of these fields. In this session, learn how the power of AI/ML and IoT can be brought as close as possible to the challenging edge environments that provide data to create these insights. Also learn how AWS puts AI/ML in the hands of the largest-scale fisheries on the planet, and how organizations can leverage data to support more sustainable, resilient supply chains.

Get started with AWS computer vision services (AIM202)

This session provides an overview of AWS computer vision services and demonstrates how these pretrained and customizable ML capabilities can help you get started quickly—no ML expertise required. Learn how to deploy these models onto the device of your choice to run an inference locally or use cloud APIs for your specific computing needs. Learn first-hand how Shutterstock uses AWS computer vision services to create performance at scale for media analysis, content moderation, and quality inspection use cases.

Chalk talk sessions

Build an ML-powered demand planning system using Amazon Forecast (AIM310)

This chalk talk explores how you can use Amazon Forecast to build an ML-powered, fully automated demand planning system for your business or your multi-tenant SaaS platform without needing any ML expertise. Forecast automatically generates highly accurate forecasts using ML, explains the drivers behind those forecasts, and keeps your ML models always up to date to capture new trends.

Hello, is it conversational AI you’re looking for? (AIM305)

Customers calling in for support expect a personalized experience and a quick resolution to their issue. With chatbots, you can provide automated and human-like conversational experiences for your customers. In this chalk talk, discuss strategies to design personalized experiences using Amazon Lex and Amazon Polly. Explore how to design conversation paths, customize responses, integrate with your applications, and enable self-service use cases to scale your customer support functions.

Harness the power of ML to protect your business with Amazon Fraud Detector (AIM308)

How does more than 20 years of Amazon experience fighting fraud translate into an AI service that can help companies detect more online fraud faster? In this session, learn how Amazon Fraud Detector transforms raw data into highly accurate ML-based fraud detection models. Then, discover how the service does data preparation and validation, feature engineering, data enrichment, and model training and tuning. Finally, with actual customer examples across a wide range of industries and fraud use cases, find out how the service makes deployment easy.

Deep learning applications with PyTorch (AIM404)

By using PyTorch in Amazon SageMaker, you have a flexible deep learning framework combined with a fully managed ML solution that allows you to transition seamlessly from research prototyping to production deployment. In this session, hear from the PyTorch team on the latest features and library releases. Also, learn how to develop with PyTorch using SageMaker for key use cases, such as using a BERT model for natural language processing (NLP) and instance segmentation for fine-grained computer vision with distributed training and model parallelism.

Explore, analyze, and process data using Jupyter notebooks (AIM324)

Before using a dataset to train a model, you need to explore, analyze, and preprocess it. During this chalk talk, learn how to use Amazon SageMaker to complete these tasks in a Jupyter notebook environment.

Machine learning at the edge with Amazon SageMaker (AIM410)

More ML models are being deployed on edge devices such as robots and smart cameras. In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more. Learn how to operate and monitor multiple models across a fleet of devices. Also walk through the process to build and train CV models with Amazon SageMaker and how to package, deploy, and manage them with SageMaker Edge Manager. The chalk talk also covers edge device setup and MLOps lifecycle with over-the-air model updates and data capture to the cloud.

Builders’ sessions

Build and deploy a custom computer vision model in 60 minutes (AIM314)

Amazon Rekognition Custom Labels is an automated ML feature that enables customers to quickly train their own custom models for detecting business-specific objects and scenes from images—no ML expertise is required. In this builders’ session, learn how to use Amazon Rekognition Custom Labels to build and deploy your own computer vision model and push it to an application to showcase inference on images from a camera feed. Bring your laptop and an AWS account.

Easily label training data for machine learning at scale (AIM406)

Join this session to learn how to create high-quality labels while also reducing your data labeling costs by up to 70%. This builders’ session walks through the different workflow options in Amazon SageMaker Ground Truth, such as automatic labeling and assistive labeling features like auto-segmentation and image label verification. It also details how to build highly accurate training datasets for company brand logos, so you can build an ML model for company brand protection.

Workshop sessions

Develop your ML project with Amazon SageMaker (AIM402)

In this workshop, learn how to develop a full ML project end to end with Amazon SageMaker. Start with data exploration and analysis, data cleansing, and feature engineering with SageMaker Data Wrangler. Then, store features in SageMaker Feature Store, extract features for training with SageMaker Processing, train a model with SageMaker training, and then deploy it with SageMaker hosting. Also, learn how to use SageMaker Studio as an IDE and SageMaker Pipelines for orchestrating the ML workflow.

End-to-end 3D machine learning on Amazon SageMaker (AIM414)

As lidar sensors become more accessible and cost-effective, customers increasingly use point cloud data in new spaces like autonomous driving, robotics, and augmented reality. The growing availability of lidar sensors has increased use of point cloud data for ML tasks like 3D object detection, segmentation, object synthesis, and reconstruction. This workshop features Amazon SageMaker Ground Truth and explains how to ingest raw 3D point cloud data, label it, train a 3D object detection model, and deploy the model. The model in this session will be trained on an autonomous vehicle dataset.

AI workflow automation for document processing (AIM316)

Mortgage packets have hundreds of documents in various layouts and formats. With ML, you can set up a document-processing pipeline to automate mortgage application workflows like extracting text from W2s, paystubs, and deeds; classifying documents; or using custom entity recognition to pull out specific data points. In this workshop, learn various ways to use optical character recognition (OCR), NLP, and human-in-the-loop services to build a document-processing pipeline to automate mortgage applications—saving time, reducing manual effort, and improving ROI for your organization.

Boost the value of your media content with ML-powered search (AIM315)

Consumers rely on content not only to entertain but also to educate and facilitate purchasing decisions. To meet this demand, media content production is exploding. However, the process of producing, distributing, and monetizing this content is often complex, expensive, and time-consuming. Applying artificial intelligence and ML capabilities like image and video analysis, audio transcription, machine translation, and text analytics can solve many of these problems. In this workshop, utilize ML to extract detailed metadata from content and make it available for search, discovery, and editing use cases.

Instantly detect and diagnose anomalies within your business data (AIM302)

Anomalies in business data often indicate potential issues or even opportunities. ML can help you detect anomalies and then act on them proactively. In this workshop, learn how Amazon Lookout for Metrics automatically detects anomalies across thousands of metrics in near-real time and reduces false alarms.

Join the first annual AWS BugBust re:Invent Challenge and help set a Guinness record

The largest code fixing challenge is here! Python and Java developers of all skill levels can compete to fix software bugs, earn points, and win an array of prizes including Amazon Echo Dots, hoodies, and the grand prize of $1,500 USD. As you bust bugs, you also become part of an attempt to set the record for the largest bug fixing challenge with the Guinness World Records. All registered participants who fix even one bug will receive exclusive prizes and a certificate from AWS and Guinness to commemorate their contribution. Let the bug busting begin! You can join the challenge virtually or in-person at the AWS BugBust Hub in the main expo. Register now for free.

AWS DeepRacer: The fastest way to get rolling with machine learning

Developers of all skill levels from beginners to experts can get hands-on with ML by using AWS DeepRacer to train models in a cloud-based 3D racing simulator. Racers from virtually anywhere in the world can compete in the AWS DeepRacer League, the first global autonomous racing league driven by reinforcement learning. The race is on now! Sign in to AWS DeepRacer and compete in the AWS re:Invent Open for prizes and glory now through December 31, 2021. Tune in to the AWS DeepRacer League Championships on Twitch November 19 and 22 to see the 40 fastest developers of the 2021 season compete live. Learn from the best as they vie for a chance to advance to the Championship Cup Finale during Swami Sivasubramanian’s keynote on December 1, where they will race for their shot at $20,000 USD in cash prizes and the right to hoist the Championship Cup!

For those attending re:Invent in Las Vegas, don’t miss out on the opportunity to take your model from Sim2Real (simulation to reality) on the AWS DeepRacer Speedway inside the content hub at Caesar’s Forum. Upload your model and race a 1/18th scale autonomous RC car on a physical track. Stop by Tuesday afternoon to participate in the livestreamed wildcard race for a chance to win a trip back for re:Invent 2022. No model? No problem! The all-new AWS DeepRacer Arcade is available in the expo, where you can get literally get in the driver’s seat and take the wheel in this educational racing game. Take a spin on the virtual track and then compete against a featured AWS DeepRacer autonomous model in this arcade racing experience, with prizes and giveaways galore. Shift into the fast lane on your ML learning journey with AWS DeepRacer.

Head over to the re:Invent portal to build your schedule so you’re ready to hit the ground running. Be sure to stop by and talk to our experts at the AI/ML booth, or chat with the speakers after sessions. We can’t wait to see you in Las Vegas!

About the Authors

Andrea Youmans is a Product Marketing Manager on the AI Services team at AWS. Over the past 10 years she has worked in the technology and telecommunications industries, focused on developer storytelling and marketing campaigns. In her spare time, she enjoys heading to the lake with her husband and Aussie dog Oakley, tasting wine and enjoying a movie from time to time.

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AWS AI/ML Community attendee guides to AWS re:Invent 2021

The AWS AI/ML Community has compiled a series of session guides to AWS re:Invent 2021 to help you get the most out of re:Invent this year. They covered four distinct categories relevant to AI/ML. With a number of our guide authors attending re:Invent virtually, you will find a balance between virtually accessible sessions and sessions available in-person.

The AWS AI/ML Community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and machine learning (ML) concepts, contribute with real-world experiences, and collaborate on building projects together.

Community guides for developers new to machine learning

From AWS ML Hero Mike Chambers AWS reInvent 2021: How To, tips, and my session selection (video). In this video—which should be required viewing for anyone new to re:Invent—Mike dives deep, beyond simply recommending sessions, with loads of tips and advice for how to make the most of your re:Invent experience—in-person or virtual.

AWS ML Hero Cyrus Wong’s top five AL/ML newbies should attend! For folks new to ML on AWS, spend your time leaning and making use of Amazon AI/ML services with Cyrus’s top five re:Invent sessions.

AWS re:Invent 2021: How to maximize your in-person learning experience as a new Machine Learning practitioner, from AWS ML Community Builder Martin Paradesi. For those attending re:Invent in-person this year, check out Martin’s guide for five sessions curated for new ML practitioners.

From our new Egypt-based AWS ML Hero Salah Elhossiny: Top 5 AWS ML Sessions to Attend at AWS re:Invent 2021. For those new to AWS ML, spend your time learning and using Amazon SageMaker with the best five AWS re:Invent sessions to help you get started quickly!

Community guides for AI/ML developers

AWS ML Hero Juv Chan’s top five recommendations for AI/ML builders and architects. Juv, a Sr. Cloud AI Engineer/Architect, ML Hero, and re:Invent Championship Cup 2019 finalist, shares his top five session picks and can’t miss photos from re:Invent 2019.

Top 5 Sessions for AI/ML Developers at AWS re:Invent 2021, from AWS ML Community Builder Brooke Jamieson. For those attending re:Invent virtually this year, check out Brooke’s guide.

AWS ML Hero Tomasz Ptak’s AWS re:Invent 2021 schedule. Tomasz shares his session picks plus tips and advice for making the most of your re:Invent experience.

Production-grade ML re:Invent 2021 sessions guide, from AWS ML Community Builder Kyle Gallatin. Builder Kyle Gallatin shares five ML talks skewed towards his interests in scalable, production-grade ML.

Community guides for MLOps developers

AWS ML Hero Rustem Feyzkhanov’s top MLOps breakout sessions to look forward to at re:Invent 2021. Rustem shares seven sessions to help you stay in the loop of MLOps in the AWS Cloud.

AWS ML Community Builder Phil Basford’s must-see sessions. For those interested in MLOps, ML architecture, edge computing, or data analytics, see Phil’s guide and his tips on how to have fun in Vegas and at home for those attending virtually.

Community guides for ML data scientists

AWS ML Hero’s Philipp Schmid’s remote guide for your virtual re:Invent 2021, focused on NLP and machine learning. Attending remote from Germany, Hugging Face ML engineer and AWS ML Hero Philipp Schmid offers an in-depth guide.

AWS ML Community Builder Pier Paolo Ippolito’s top five suggestions for ML data scientists. Pier, a data scientist at SAS and editor at Towards Data Science, shares his top five picks curated for technical ML builders.

Other AWS ML Community guides worth exploring

AWS ML Hero Kesha Williams’s Machine Learning Attendee Guide 2021. The official AWS Hero guide from Kesha dives deep across all session categories. Check this guide out for a full walkthrough of how to build your schedule, and the ultimate deep dive into Kesha’s ML session picks.

Lastly, we have a unique in-depth guide from AWS ML Community Builder Janos Tolgyesi. Learn how to fight climate change with ML skills and make the Earth a better place with ML at re:Invent 2021. Janos shares his sessions picks and a bonus session suggestion for those interested in beer, plus personalized recommendations!

Whether you’re attending in-person or virtually this year, we hope these recommendations and advice from the AWS ML Community help you make the most of your re:Invent experience. Have a great re:Invent!

About the Author

Paxton Hall is a Marketing Program Manager for the AWS AI/ML Community on the AI/ML Education team at AWS. He has worked in retail and experiential marketing for the past 7 years, focused on developing communities and marketing campaigns. Out of the office, he’s passionate about public lands access and conservation, and enjoys backcountry skiing, climbing, biking, and hiking throughout Washington’s Cascade mountains.

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Decisiveness in Imitation Learning for Robots

Posted by Pete Florence, Research Scientist and Corey Lynch, Research Engineer, Robotics at Google

Despite considerable progress in robot learning over the past several years, some policies for robotic agents can still struggle to decisively choose actions when trying to imitate precise or complex behaviors. Consider a task in which a robot tries to slide a block across a table to precisely position it into a slot. There are many possible ways to solve this task, each requiring precise movements and corrections. The robot must commit to just one of these options, but must also be capable of changing plans each time the block ends up sliding farther than expected. Although one might expect such a task to be easy, that is often not the case for modern learning-based robots, which often learn behavior that expert observers describe as indecisive or imprecise.

Example of a baseline explicit behavior cloning model struggling on a task where the robot needs to slide a block across a table and then precisely insert it into a fixture.

To encourage robots to be more decisive, researchers often utilize a discretized action space, which forces the robot to choose option A or option B, without oscillating between options. For example, discretization was a key element of our recent Transporter Networks architecture, and is also inherent in many notable achievements by game-playing agents, such as AlphaGo, AlphaStar, and OpenAI’s Dota bot. But discretization brings its own limitations — for robots that operate in the spatially continuous real world, there are at least two downsides to discretization: (i) it limits precision, and (ii) it triggers the curse of dimensionality, since considering discretizations along many different dimensions can dramatically increase memory and compute requirements. Related to this, in 3D computer vision much recent progress has been powered by continuous, rather than discretized, representations.

With the goal of learning decisive policies without the drawbacks of discretization, today we announce our open source implementation of Implicit Behavioral Cloning (Implicit BC), which is a new, simple approach to imitation learning and was presented last week at CoRL 2021. We found that Implicit BC achieves strong results on both simulated benchmark tasks and on real-world robotic tasks that demand precise and decisive behavior. This includes achieving state-of-the-art (SOTA) results on human-expert tasks from our team’s recent benchmark for offline reinforcement learning, D4RL. On six out of seven of these tasks, Implicit BC outperforms the best previous method for offline RL, Conservative Q Learning. Interestingly, Implicit BC achieves these results without requiring any reward information, i.e., it can use relatively simple supervised learning rather than more-complex reinforcement learning.

Implicit Behavioral Cloning
Our approach is a type of behavior cloning, which is arguably the simplest way for robots to learn new skills from demonstrations. In behavior cloning, an agent learns how to mimic an expert’s behavior using standard supervised learning. Traditionally, behavior cloning involves training an explicit neural network (shown below, left), which takes in observations and outputs expert actions.

The key idea behind Implicit BC is to instead train a neural network to take in both observations and actions, and output a single number that is low for expert actions and high for non-expert actions (below, right), turning behavioral cloning into an energy-based modeling problem. After training, the Implicit BC policy generates actions by finding the action input that has the lowest score for a given observation.

Depiction of the difference between explicit (left) and implicit (right) policies. In the implicit policy, the “argmin” means the action that, when paired with a particular observation, minimizes the value of the energy function.

To train Implicit BC models, we use an InfoNCE loss, which trains the network to output low energy for expert actions in the dataset, and high energy for all others (see below). It is interesting to note that this idea of using models that take in both observations and actions is common in reinforcement learning, but not so in supervised policy learning.

Animation of how implicit models can fit discontinuities — in this case, training an implicit model to fit a step (Heaviside) function. Left: 2D plot fitting the black (X) training points — the colors represent the values of the energies (blue is low, brown is high). Middle: 3D plot of the energy model during training. Right: Training loss curve.

Once trained, we find that implicit models are particularly good at precisely modeling discontinuities (above) on which prior explicit models struggle (as in the first figure of this post), resulting in policies that are newly capable of switching decisively between different behaviors.

But why do conventional explicit models struggle? Modern neural networks almost always use continuous activation functions — for example, Tensorflow, Jax, and PyTorch all only ship with continuous activation functions. In attempting to fit discontinuous data, explicit networks built with these activation functions cannot represent discontinuities, so must draw continuous curves between data points. A key aspect of implicit models is that they gain the ability to represent sharp discontinuities, even though the network itself is composed only of continuous layers.

We also establish theoretical foundations for this aspect, specifically a notion of universal approximation. This proves the class of functions that implicit neural networks can represent, which can help justify and guide future research.

Examples of fitting discontinuous functions, for implicit models (top) compared to explicit models (bottom). The red highlighted insets show that implicit models represent discontinuities (a) and (b) while the explicit models must draw continuous lines (c) and (d) in between the discontinuities.

One challenge faced by our initial attempts at this approach was “high action dimensionality”, which means that a robot must decide how to coordinate many motors all at the same time. To scale to high action dimensionality, we use either autoregressive models or Langevin dynamics.

Highlights
In our experiments, we found Implicit BC does particularly well in the real world, including an order of magnitude (10x) better on the 1mm-precision slide-then-insert task compared to a baseline explicit BC model. On this task the implicit model does several consecutive precise adjustments (below) before sliding the block into place. This task demands multiple elements of decisiveness: there are many different possible solutions due to the symmetry of the block and the arbitrary ordering of push maneuvers, and the robot needs to discontinuously decide when the block has been pushed far “enough” before switching to slide it in a different direction. This is in contrast to the indecisiveness that is often associated with continuous-controlled robots.

Example task of sliding a block across a table and precisely inserting it into a slot. These are autonomous behaviors of our Implicit BC policies, using only images (from the shown camera) as input.

A diverse set of different strategies for accomplishing this task. These are autonomous behaviors from our Implicit BC policies, using only images as input.

In another challenging task, the robot needs to sort blocks by color, which presents a large number of possible solutions due to the arbitrary ordering of sorting. On this task the explicit models are customarily indecisive, while implicit models perform considerably better.

Comparison of implicit (left) and explicit (right) BC models on a challenging continuous multi-item sorting task. (4x speed)

In our testing, implicit BC models can also exhibit robust reactive behavior, even when we try to interfere with the robot, despite the model never seeing human hands.

Robust behavior of the implicit BC model despite interfering with the robot.

Overall, we find that Implicit BC policies can achieve strong results compared to state of the art offline reinforcement learning methods across several different task domains. These results include tasks that, challengingly, have either a low number of demonstrations (as few as 19), high observation dimensionality with image-based observations, and/or high action dimensionality up to 30 — which is a large number of actuators to have on a robot.

Policy learning results of Implicit BC compared to baselines across several domains.

Conclusion
Despite its limitations, behavioral cloning with supervised learning remains one of the simplest ways for robots to learn from examples of human behaviors. As we showed here, replacing explicit policies with implicit policies when doing behavioral cloning allows robots to overcome the “struggle of decisiveness”, enabling them to imitate much more complex and precise behaviors. While the focus of our results here was on robot learning, the ability of implicit functions to model sharp discontinuities and multimodal labels may have broader interest in other application domains of machine learning as well.

Acknowledgements
Pete and Corey summarized research performed together with other co-authors: Andy Zeng, Oscar Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson. The authors would also like to thank Vikas Sindwhani for project direction advice; Steve Xu, Robert Baruch, Arnab Bose for robot software infrastructure; Jake Varley, Alexa Greenberg for ML infrastructure; and Kamyar Ghasemipour, Jon Barron, Eric Jang, Stephen Tu, Sumeet Singh, Jean-Jacques Slotine, Anirudha Majumdar, Vincent Vanhoucke for helpful feedback and discussions.

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An Elevated Experience: Xpeng G9 Takes EV Innovation Higher with NVIDIA DRIVE Orin

You don’t need a private plane to be at the forefront of personal travel.

Electric automaker Xpeng took the wraps off the G9 SUV this week at the international Auto Guangzhou show in China. The intelligent, software-defined vehicle is built on the high-performance compute of NVIDIA DRIVE Orin and delivers AI capabilities that are continuously upgraded with each over-the-air update.

The new flagship SUV debuts Xpeng’s centralized electronic and electrical architecture and Xpilot 4.0 advanced driver assistance system for a seamless driving experience. The G9 is also compatible with the next-generation “X-Power” superchargers for charging up to 124 miles in 5 minutes.

The Xpeng G9 and its fellow EVs are elevating the driving experience with intelligent features that are always at the cutting edge.

Intelligence at the Edge

The G9 is intelligently designed from the inside out.

The SUV is the first to be equipped with Xpilot 4.0, an AI-assisted driving system capable of address-to-address automated driving, including valet parking.

Xpilot 4.0 is built on two NVIDIA DRIVE Orin systems-on-a-chip (SoC), achieving 508 trillion operations per second (TOPS). It uses an 8-million-pixel front-view camera and 2.9-million-pixel side-view cameras that cover front, rear, left and right views, as well as a highly integrated and expandable domain controller.

This technology is incorporated into a centralized compute architecture for a streamlined design, powerful performance and seamless upgrades.

Charging Ahead

The G9 is designed for the international market, bringing software-defined innovation to roads around the world.

It incorporates new signature details, such as daytime running lights designed to make a sharp-eyed impression. Four daytime running lights at the top and bottom of the headlights form the Xpeng logo. These headlights also include discrete lidar sensors, merging cutting-edge technology with an elegant exterior.

In addition to fast charging, the electric SUV meets global sustainability requirements as well as NCAP five-star safety standards. The G9 is scheduled to officially launch in China in the third quarter of 2022, with plans to expand to global markets soon after.

The intelligent EV joined a growing lineup of software-defined vehicles powered by NVIDIA DRIVE that are transforming the way the world moves.Also on the Auto Guangzhou showfloor until the event closes on Nov. 28 are the Human Horizons HiPhi Z Digital-GT, NIO ET7 and SAIC’s IM Motors all-electric lineup, displaying the depth and diversity of the NVIDIA DRIVE Orin ecosystem.

The post An Elevated Experience: Xpeng G9 Takes EV Innovation Higher with NVIDIA DRIVE Orin appeared first on The Official NVIDIA Blog.

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Design’s new frontier

In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

“CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

Computational design

Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

“The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

“When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

“With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

Personal fabrication

Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

“We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

“This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

“It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

Computational fluid dynamics and participatory design

Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

“As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

“My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

“We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

Designing intelligent systems

When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

“Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

“This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

“These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

“This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

“MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD.

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Understand drivers that influence your forecasts with explainability impact scores in Amazon Forecast

We’re excited to launch explainability impact scores in Amazon Forecast, which help you understand the factors that impact your forecasts for specific items and time durations of interest. Forecast is a managed service for developers that uses machine learning (ML) to generate more accurate demand forecasts, without requiring any ML experience. To increase forecast model accuracy, you can add additional information or attributes such as price, promotion, category details, holidays, or weather information to your forecasting model, but you may not know how each attribute influences your forecast. With today’s launch, you can now understand how each attribute impacts your forecasted values using the explainability feature, which we discuss in this post.

ML-based forecasting models, which are more accurate than heuristic rules or human judgment, can drive significant improvement in revenue and customer experience. However, business leaders often lose trust in technology when they see forecasted numbers drastically differing from their intuition, and may find it hard to trust ML systems. Because demand planning decisions have a high impact on the business, business leaders may end up overriding forecasts because they may believe that they have to take the forecast model predictions at face value to make critical business decisions, without understanding why those forecasts were generated and what factors are influencing forecasts to be higher or lower. This can lead to compromising forecast accuracy, and you may lose the benefit of ML forecasting.

Amazon Forecast now provides explainability, which gives you item-level insights across your preferred time duration. Having a certain level of understanding on why a particular forecast value is high or low at a particular time is helpful for decision-making and building trust and confidence in your ML solutions. Explainability reports include impact scores, which help you understand how each attribute in your training data contributes to either increasing or decreasing your forecasted values for specific items. In addition, you can choose to understand explainability for your entire forecast horizon or for specific time durations. Explainability removes the need of running multiple manual analyses to understand past sales and external variable trends to explain forecast results.

How to interpret explainability impact scores

Explainability helps you better understand how the attributes, such as price, category, or holidays, in your datasets impact your forecast values. Forecast uses a metric called impact scores to quantify the relative impact of each attribute and determine whether they generally increase or decrease forecast values.

Impact scores measure the relative impact attributes have on forecast values. For example, if the price attribute has an impact score that is twice as large as the brand_id attribute, you can conclude that the price of an item has twice the impact on forecast values than the product brand. Impact scores also provide information on whether an attribute increases or decreases the forecasted value. A negative impact score reflects that the attribute tends to decrease the value of the forecast.

Impact scores measure the relative impact of attributes to each other, not the absolute impact. If an attribute has a low impact score, that doesn’t necessarily mean that it has a low impact on forecast values; it means that it has a lower impact on forecast values than other attributes used by the predictor. If you change attributes in your predictor, the impact scores may differ, and the attribute with the low impact score may have a higher score relative to other attributes. Also, you can’t use impact scores to determine whether particular attributes improve the model accuracy or not. You should use accuracy metrics such as weighted quantile loss and others provided by Forecast to access predictor accuracy.

In the following graph, we take an example of an explainability report graph that shows the relative impact of different attributes on the forecasted value of item_d 1 across all the time points in the forecast horizon. We see that the relative impact is in the following order: Price has the highest impact, followed by StoreLocation, then Promo and Holiday_US. Price has the highest influence item_id 1 and tends to increase the forecast value. StoreLocation has the second highest impact on item_id 1 but tends to decrease the forecast value. Because Promo is close to 0.2 impact score, Price has five times more impact than Promo on the forecasted value of item_id 1, and both attributes tend to increase the forecast value. Holiday_US has an impact score of 0, which means that this attribute doesn’t increase or decrease the forecast value for item_id 1 relative to other attributes.

The following image shows an example of the explainability report export file with the impact scores for specific time series and time points as well as aggregated scores across those time series and time points.

Generate explainability impact scores

In this section, we walk through how to generate explainability impact scores for your forecasts using the Forecast console. To use the new CreateExplainability API, refer to the notebook in our GitHub repo or review Forecast Explainability.

  1. On the Forecast console, create a dataset group. Upload your historical demand dataset as target time series followed by related time series or item metadata that you want to use for more accurate forecasting and for which you’re interested in seeing explainability impact scores.

  1. In the navigation pane, under your dataset, choose Predictors.
  2. Choose Train new predictor.

Forecast defaults to AutoPredictor as the default training option. No further action is needed from you, but remember that only forecasts generated from a model that has been trained with AutoPredictor are eligible for later generating explainability impact scores for specific forecasts.

  1. Now that your model is trained, choose Forecasts in the navigation pane.
  2. Choose Create a forecast.
  3. Select your trained predictor to create a forecast.
  4. Choose Insights in the navigation pane.
  5. Choose Create explainability.

  1. Choose the forecast that you want to generate explainability impact scores for.
  2. Choose if you want to see impact scores for all the time points in the forecast horizon or only for a specific time duration.

You can specify up to 500 consecutive time points per explainability report.

  1. Upload the list of specific time series for which you want to see explainability impact scores.

A time series is a unique combination of item ID and dimension. You can specify up to 50 time series per Forecast explainability.

  1. Specify the schema of the CSV file that you have uploaded.
  2. Choose Create explainability.

It takes less than an hour to generate the explainability impact scores.

  1. When the job status is active, choose the explainability job to view the impact score.

Here you can review the explainability impact score graph. You can use the controls at the top of the graph to drill down to specific time series or time points or view at an aggregated level.

  1. To export all the impact scores, choose Create explainability export in the Explainability exports
  2. Provide the export details and choose Create explainability export.

The export is saved in an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

  1. When the export is complete, navigate to your S3 bucket to review the explainability report CSV file.

The following is an example of your explainability export CSV file. Depending on how large your dataset is, multiple files may be exported.

Aggregate explainability impact scores for category level analysis

You may want to review explainability for a group of items together, which can have more than 50 items. For example, a grocery retailer might be interested in understanding what is driving the forecasts for all their fruits and vegetables, and this category may consist of more than 50 SKUs in their data. However, Forecast lets you specify up to 50 time series per Forecast explainability job. If you have more than 50 time series, you need to run the explainability job multiple times with different items in each job and then combine them.

The explainability export file provides two type of impact scores: normalized impact scores and raw impact scores. Raw impact scores are based on Shapley values and aren’t scaled or bounded. Normalized impact scores scale the raw scores to a value between -1 and 1. Raw impact scores are useful for combining and comparing scores across different explainability resources. Use the raw impact scores of all the time series across multiple explainability jobs to aggregate, then compare it to find the relative influence of each attribute. You can view an example on how to do so by following the notebook in our GitHub repo.

Conclusion

Forecast now provides explainability for specific items and time durations of interest. With the explainability feature, you can understand how each attribute impacts your forecasted values. To learn more, review Forecast Explainability and the notebook in our GitHub repo. If you are interested in aggregated explainability for all your items at the predictor level, review our blog on using the CreateAutoPredictor API here. Explainability is available in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.

About the Authors

Namita Das is a Sr. Product Manager for Amazon Forecast. Her current focus is to democratize machine learning by building no-code/low-code ML services. On the side, she frequently advises startups and loves training her dog with new tricks.

Dima Fayyad is a Software Development Engineer on the Amazon Forecast team. She is passionate about machine learning and AI and is currently working on large-scale distributed systems in the forecasting space. In her free time, she enjoys exploring different cuisines, traveling, and skiing.

Youngsuk Park is a Machine Learning Scientist at AWS AI and Amazon Forecast. His research lies in the interplay between machine learning, optimization, and decision-making, with over 10 publications in top-notch ML/AI venues. Before joining AWS, he obtained a PhD from Stanford University.

Shannon Killingsworth is a UX Designer for Amazon Forecast. His current work is creating console experiences that are usable by anyone, and integrating new features into the console experience. In his spare time, he is a fitness and automobile enthusiast.

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New Amazon Forecast API that creates up to 40% more accurate forecasts and provides explainability

We’re excited to announce a new forecasting API for Amazon Forecast that generates up to 40% more accurate forecasts and helps you understand which factors, such as price, holidays, weather, or item category, are most influencing your forecasts. Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any ML experience. Forecast brings the same technology used at Amazon to developers as a fully managed service, removing the need to manage resources.

With today’s launch, Forecast can now forecast up to 40% more accurate results by using a combination of ML algorithms that are best suited for your data. In many scenarios, ML experts train separate models for different parts of their dataset to improve forecasting accuracy. This process of segmenting your data and applying different algorithms can be very challenging for non-ML experts. Forecast uses ML to learn not only the best algorithm for each item, but the best ensemble of algorithms for each item, leading to up to 40% better accuracy on forecasts.

To further increase forecast model accuracy, you can add additional information or attributes such as price, promotion, category details, holidays, or weather information, but you may not know how each attribute influences your forecast. Forecasting is mission critical, and therefore having a certain level of attribute explainability is helpful for decision-making. With today’s launch, Forecast now helps you understand and explain how your forecasting model is making predictions by providing explainability reports after your model has been trained. Explainability reports include impact scores, so you can understand how each attribute in your training data contributes to either increasing or decreasing your forecasted values. By understanding how your model makes predictions, you can make more informed business decisions. For example, you can verify that your model is behaving as expected by confirming that attributes with a high impact score represent a valid signal for predictions in your business problem.

You can bring in your recent data to use the latest insights before forecasting for the next period. However, in doing so, you have to train your entire forecasting model again, which is a time-consuming process. Most Forecast customers deploy their forecasting workflow within their operations such as an inventory management solution and run their operations at a set cadence. Because retraining on the entire data can be time-consuming, customer operations may get delayed. With today’s launch, you can save up to 50% of retraining time by selecting to incrementally retrain your models with the new information that you have added.

To get more accurate forecasts, faster retraining, and explainability, use the new experience through the AWS Management Console or the CreateAutoPredictor API. This launch is accompanied with new pricing, which you can review at Amazon Forecast pricing.

Interpreting model explainability

Explainability helps you better understand how the attributes in your datasets, such as price, category, or holidays, impact your forecast values. Forecast uses a metric called impact scores to quantify the relative impact of each attribute and determine whether they generally increase or decrease forecast values.

Impact scores measure the relative impact attributes have on forecast values. For example, if the price attribute has an impact score that is twice as large as the brand_id attribute, you can conclude that the price of an item has twice the impact on forecast values than the product brand. Impact scores also provide information on whether an attribute increases or decreases the forecasted value. A negative impact score reflects that the attribute tends to decrease the value of the forecast.

Impact scores measure the relative impact of attributes to each other, not the absolute impact. If an attribute has a low impact score, that doesn’t necessarily mean that it has a low impact on forecast values; it means that it has a lower impact on forecast values than other attributes used by the predictor. If you change attributes in your predictor, the impact scores may differ, and the attribute with the low impact score may have a higher score relative to other attributes. Also, you can’t use impact scores to determine whether particular attributes improve the model accuracy or not. You should use accuracy metrics such as weighted quantile loss and others provided by Forecast to access predictor accuracy.

In the following graph, we take an example of a predictor where the relative impact of attributes is as follows: US holidays, promos, weather, price, and category. US holidays has the highest impact on the forecast values. US holidays tend to increase the forecasted value. Category has the lowest impact on the forecast values, and this attribute tends to decrease the forecast value.

Train a new predictor with the new Forecast API

In this section, we walk through how to train a new predictor using the newly launched forecasting API through the console. To use the new CreateAutoPredictor API directly, refer to the notebook in our GitHub repo or review Training Predictors.

  1. On the Forecast console, create a dataset group and upload your historical demand dataset as target time series followed by any related time series or item metadata that you want to use for more accurate forecasting.
  2. In the navigation pane, under your dataset, choose Predictors.
  3. Choose Train new predictor.
  4. In the Predictor settings section, enter a name for your predictor, how long in the future you want to forecast with the forecasting frequency, and the number of quantiles you want to forecast for.
  5. AutoPredictor is enabled by default; no further action is needed from you.
  6. For Optimization metric, you can choose an optimization metric to optimize AutoPredictor to tune a model for a specific accuracy metric of your choice. We leave this as default for our walkthrough.
  7. To get the predictor explainability report, select Enable predictor explainability.
  8. Under the input data configuration, you can add local weather information and national holidays for more accurate demand forecasts.
  9. In the Attribute configuration section, you can choose filling options for missing values.
  10. Choose Start to start training your predictor.
  11. After your predictor is trained, choose your predictor on the Predictors page.

On the predictor’s details page, you can view the overall predictor accuracy metrics and the explainability impact score.

  1. Now that your model is trained, choose Forecasts in the navigation pane.
  2. Choose Create a forecast.
  3. For Predictor, choose your trained predictor to create a forecast.

Retrain your predictor with new data

We now walk through how to use the Forecast console to retrain your predictor when you have new data for the same forecasting problem. You can also follow the notebook in our GitHub repo to learn how to use the CreateAutoPredictor API for retraining your predictor.

Before you retrain your predictor, you have to re-import your dataset with the latest available historical observations.

  1. On the Forecast console, under your dataset group in the navigation pane, choose Datasets.

In our example, we only update the target time series data. You can follow the same steps to update the related time series data as well.

  1. Choose the dataset name to view the details.
  2. In the Dataset imports section, choose Create dataset import.
  3. Provide the Amazon Simple Storage Service (Amazon S3) location of your dataset and complete importing your data.
  4. After your dataset has been imported, choose Predictors in the navigation pane.
  5. Select the predictor for which AutoPredictor enabled is True.

Only predictors with AutoPredictor enabled are eligible to be retrained.

  1. On the Predictor actions menu, choose Retrain.
  2. Enter a new name for the retrained predictor and choose Retrain predictor.

All the predictor configuration from the source predictor is automatically copied over to the new predictor that you retrain.

You’re redirected to the predictor details page where you can review the predictor settings.

  1. Now that your model is trained, choose Forecasts in the navigation pane.
  2. Choose Create a forecast.
  3. Choose your trained predictor to create a forecast.

Upgrade your existing legacy predictor to AutoPredictor

You can easily move your existing predictors to AutoPredictor to take advantage of more accurate forecasts by using a predictor that selects the best ensemble of algorithms for each item, faster retraining, and predictor explainability. Forecast takes the old predictor as a reference and creates a new AutoPredictor. You can follow the notebook in our GitHub repo to do the same through the CreateAutoPredictor API.

  1. On the Forecast console, choose a dataset group for which you have previously trained a predictor.
  2. In the navigation pane, under your dataset, choose Predictors.

An Upgrade link is next to any legacy predictor for which AutoPredictor is False.

  1. Select your predictor and on the Predictor actions menu, choose Upgrade.
  2. Enter the name of the new predictor.

All the predictor configurations from the old predictor are automatically copied over to train the new AutoPredictor.

You’re redirected to the predictor details page where you can review the predictor settings.

  1. Now that your model is trained, choose Forecasts in the navigation pane.
  2. Choose Create a forecast.
  3. Choose your trained predictor to create a forecast.

Conclusion

To get more accurate forecasts, faster retraining, and explainability, you can follow the steps mentioned in this post or follow the notebook in our GitHub repo. If you want to upgrade your existing forecasting models to the new CreateAutoPredictor API, you can do so with one click either on through console or as shown in the notebook in our GitHub repo. To learn more, review Training Predictors. We recommend reviewing the pricing for using these new features. All these new capabilities are available in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.

About the Authors

Namita Das is a Sr. Product Manager for Amazon Forecast. Her current focus is to democratize machine learning by building no-code/low-code ML services. On the side, she frequently advises startups and loves training her dog with new tricks.

Jitendra Bangani is an Engineering Manager at AWS, leading a growing team of curious and driven engineers for Amazon Forecast. He started his career at Amazon as an intern in 2013; since then he has helped build engaging shopping experiences, hyperscale distributed systems, and autonomous AI services that delight Amazon and AWS customers.

Hilaf Hasson is a Machine Learning Scientist at AWS, and currently leads the R&D team of scientists working on Amazon Forecast. Before joining AWS, he held multiple faculty positions, including as an Assistant Professor of Mathematics at Stanford University.

 Adarsh Singh works as a Software Development Engineer in the Amazon Forecast team. In his current role, he focuses on engineering problems and building scalable distributed systems that provide the most value to end users. In his spare time, he enjoys watching anime and playing video games.

Chinmay Bapat is a Sr. Software Development Engineer in the Amazon Forecast team. His interests lie in the applications of machine learning and building scalable distributed systems. Outside of work, he enjoys playing board games and cooking.

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In Pursuit of Smart City Vision, Startup Two-i Keeps an AI on Worker Safety

When Julien Trombini and Guillaume Cazenave founded video-analytics startup Two-i four years ago, they had an ambitious goal: improving the quality of urban life by one day being able to monitor a city’s roads, garbage collection and other public services.

Along the way, the pair found a wholly different niche. Today, the company’s technology — which combines computer vision, data science and deep learning — is helping to prevent deadly accidents in the oil and gas industry, one of the world’s most dangerous sectors.

Initially, Trombini and Cazenave envisioned a system that would enable civic leaders to see what improvements were needed across a municipality.

“It would be like having a weather map of the city, only one that measures efficiency,” said Trombini, who serves as chairman of Two-i, an NVIDIA Metropolis partner based in Metz, a historic city in northeast France.

That proved a tall order, so the two refocused on specific facilities, such as stadiums, retirement homes and transit stations, where its tech helps with security and incident detection. For instance, it can alert the right people when a retirement home resident falls in a corridor. Or when a transit rider using a wheelchair can’t get on a train because of a broken lift.

Two-i founders Julien Trombini and Guillaume Cazenave.
Two-i founders Julien Trombini (left) and Guillaume Cazenave.

More recently, the company was approached by ExxonMobil to help with a potentially deadly issue: improving worker safety around open oil tanks.

Together with the energy giant, Two-i has created an AI-enabled video analytics application to detect when individuals near a danger zone and risk falling and immediately alert others to take quick action. In its initial months of operation, the vision AI system prevented two accidents from occurring.

While this use case is highly specific, the company’s AI architecture is designed to flexibly support many different algorithms and functions.

“The algorithms are exactly the same as what we’re using for different clients,” said Trombini. “It’s the same technology, but it’s packaged in a different way.”

Making the Most of Vision AI

Two-i’s flexibility stems from its reliance on using the NVIDIA Metropolis platform for AI-enabled video analytics applications, leveraging advanced tools and adopting a full-stack approach.

To do so, it relies on a variety of NVIDIA-Certified Systems, using the latest workstation and data center GPUs based on the high-performance NVIDIA Ampere architecture, for both training and inference. To shorten training times further, Two-i is looking to test its huge image dataset on the powerful NVIDIA A100 GPU.

The company looks to frequently upgrade its GPUs to ensure it’s offering customers the fastest possible solution, no matter how many cameras are feeding data into its system.

“The time we can save there is crucial, and the better the hardware, the more accurate the results and faster we get to market,” said Trombini.

Two-i taps the CUDA 11.1 toolkit and cuDNN 8.1 library to support its deep learning process, and NVIDIA TensorRT to accelerate inference throughput.

Trombini says one of the most compelling pieces of NVIDIA tech is the NVIDIA TAO Toolkit, which helps the company keep costs down as it tinkers with its algorithms.

“The heavier the algorithm, the more expensive,” he said. “We use the TAO toolkit to prune algorithms and make them more tailored to the task.”

For example, training that initially took up to two weeks has been slashed to three days using the NVIDIA TAO Toolkit, a CLI- and Jupyter Notebook-based version of the NVDIA train, adapt and optimize framework.

Two-i has also started benchmarking NVIDIA’s pretrained models against its algorithms and begun using the NVIDIA DeepStream SDK to enhance its video analytics pipeline.

Two-i Video Analytics

Building on Success

Two-i sees its ability to solve complicated problems in a variety of settings, such as for ExxonMobil, as a springboard to swinging back around to its original smart city aspirations.

Already, it’s monitoring all roads in eight European cities, analyzing traffic flows and understanding where cars are coming from and going to.

Trombini recognizes that Two-i has to keep its focus on delivering one benefit after another to achieve the company’s long-term goals.

“It’s coming slowly,” he said, “but we are starting to implement our vision.”

The post In Pursuit of Smart City Vision, Startup Two-i Keeps an AI on Worker Safety appeared first on The Official NVIDIA Blog.

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Sequence Modeling Solutions for Reinforcement Learning Problems

Sequence Modeling Solutions for Reinforcement Learning Problems




Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model.

Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data.
This is particularly clear from recent advances in sequence modeling, where simply increasing the size of a stable architecture and its training set leads to qualitatively different capabilities.1

Meanwhile, the situation in reinforcement learning has proven more complicated.
While it has been possible to apply reinforcement learning algorithms to large–scale problems, generally there has been much more friction in doing so.
In this post, we explore whether we can alleviate these difficulties by tackling the reinforcement learning problem with the toolbox of sequence modeling.
The end result is a generative model of trajectories that looks like a large language model and a planning algorithm that looks like beam search.
Code for the approach can be found here.