Author: torontoai
AI Meets 5G at the Edge: The Innovation Center at MWC 2020 in Barcelona
AI is alive at the edge of the network, where it’s already transforming everything from car makers to supermarkets. And we’re just getting started.
NVIDIA’s AI Edge Innovation Center, a first for this year’s Mobile World Congress (MWC) in Barcelona, will put attendees at the intersection of AI, 5G and edge computing. There, they can hear about best practices for AI at the edge and get an update on how NVIDIA GPUs are paving the way to better, smarter 5G services.
It’s a story that’s moving fast.
AI was born in the cloud to process the vast amounts of data needed for jobs like recommending new products and optimizing news feeds. But most enterprises interact with their customers and products in the physical world at the edge of the network — in stores, warehouses and smart cities.
The need to sense, infer and act in real time as conditions change is driving the next wave of AI adoption at the edge. That’s why a growing list of forward-thinking companies are building their own AI capabilities using the NVIDIA EGX edge-computing platform.
Walmart, for example, built a smart supermarket it calls its Intelligent Retail Lab. Jakarta uses AI in a smart city application to manage its vehicle registration program. BMW and Procter & Gamble automate inspection of their products in smart factories. They all use NVIDIA EGX along with our Metropolis application framework for video and data analytics.
For conversational AI, the NVIDIA Jarvis developer kit enables voice assistants geared to run on embedded GPUs in smart cars or other systems. WeChat, the world’s most popular smartphone app, accelerates conversational AI using NVIDIA TensorRT software for inference.
All these software stacks ride on our CUDA-X libraries, tools, and technologies that run on an installed base of more than 500 million NVIDIA GPUs.
Carriers Make the Call
At MWC Los Angeles this year, NVIDIA founder and CEO Jensen Huang announced Aerial, software that rides on the EGX platform to let telecommunications companies harness the power of GPU acceleration.
With Aerial, carriers can both increase the spectral efficiency of their virtualized 5G radio-access networks and offer new AI services for smart cities, smart factories, cloud gaming and more — all on the same computing platform.
In Barcelona, NVIDIA and partners including Ericsson will give an update on how Aerial will reshape the mobile edge network.
Verizon is already using NVIDIA GPUs at the edge to deliver real-time ray tracing for AR/VR applications over 5G networks.
It’s one of several ways telecom applications can be taken to the next level with GPU acceleration. Imagine having the ability to process complex AI jobs on the nearest base station with the speed and ease of making a cellular call.
Your Dance Card for Barcelona
For a few days in February, we will turn our innovation center — located at Fira de Barcelona, Hall 4 — into a virtual university on AI with 5G at the edge. Attendees will get a world-class deep dive on this strategic technology mashup and how companies are leveraging it to monetize 5G.
Sessions start Monday morning, Feb. 24, and include AI customer case studies in retail, manufacturing and smart cities. Afternoon talks will explore consumer applications such as cloud gaming, 5G-enabled cloud AR/VR and AI in live sports.
We’ve partnered with the organizers of MWC on applied AI sessions on Tuesday, Feb. 25. These presentations will cover topics like federated learning, an emerging technique for collaborating on the development and training of AI models while protecting data privacy.
Wednesday’s schedule features three roundtables where attendees can meet executives working at the intersection of AI, 5G and edge computing. The week also includes two instructor-led sessions from the NVIDIA Deep Learning Institute that trains developers on best practices.
See Demos, Take a Meeting
For a hands-on experience, check out our lineup of demos based on the NVIDIA EGX platform. These will highlight applications such as object detection in a retail setting, ways to unsnarl traffic congestion in a smart city and our cloud-gaming service GeForce Now.
To learn more about the capabilities of AI, 5G and edge computing, check out the full agenda and book an appointment here.
The post AI Meets 5G at the Edge: The Innovation Center at MWC 2020 in Barcelona appeared first on The Official NVIDIA Blog.
[D] What’s your deep learning framework of choice?
I’m working on a project and it would really help me if i get to know what’s the majorly used deep learning framework. Thanks in advance. ❤️
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[R] Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
While searching for papers about inverse reinforcement learning (IRL, learning rewards instead of policies), I found this:
https://dsbrown1331.github.io/CoRL2019-DREX/ https://arxiv.org/pdf/1907.03976.pdf
It seems to be the only paper in which an agent manages to outperform the sub-optimal players it learns from.
I think this probably is an important step forward since eventually we would like to create programs that act in the real world and perform tasks better than us without having to code a reward function.
A short term application of this could be in self-driving cars, allowing them to learn from human drivers and be safer than them.
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[P] Dashcam Crash Classification
I am working on trying to classify dashcam videos of car crashes as Head-on, Rear-end by others, Rear-end by us, Sideswipe, T-Bone by others and T-Bone by us. We are only bothered about the videos where the dashcam car is involved in the accident. There are two types of accidents involved here
- Visible on dashcam footage – Head-on, Rear-end by us, etc
- Not visible on dashcam footage – Rear-end by others, T-Bone by others
Working with motion features seemed like a good idea to classify this. I have tried using the ideas from “Action Recognition with Improved Trajectories” to build a classifier. However, this has not proved fruitful. As a solution for the visible accident subcase, I am able to identify the other car involved in the accident and pass it through an image classification algorithm to obtain satisfactory results. I am currently exploring dynamic time warping to figure out a solution.
It seems like an interesting problem to solve. Any leads on how to approach this would be highly appreciated. 🙂
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[P] Memory Efficient Model-Agnostic Meta-Learning
PyTorch implementation of Model-Agnostic Meta-Learning with gradient checkpointing. This allows performing way (~10-100x) more MAML steps with the same GPU memory budget.
Code: https://github.com/dbaranchuk/memory-efficient-maml
Example: Open In Colab
Install: pip install torch_maml
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![[R] [RL] Dream to Control: Learning Behaviors by Latent Imagination](https://b.thumbs.redditmedia.com/M6z7lt3X9o9rhh5EoEYi5953Zr9tnBwXcvOZL5rPWzk.jpg "[R] [RL] Dream to Control: Learning Behaviors by Latent Imagination")
[D] Why is it difficult to sample from Energy Based Models?
I have very little experience with generative models, so apologies if that is a trivial question.
My understanding of an energy based model (EBM) is that it is an undiredted graph defining the joint distribution over the vector X as p(X)=exp(-E(X)) such that E(x) is a sum of potentials defined over clicks.
The well-known Deep learning book by Goodfellow et al. claims that sampling from an EBM is difficult:
To understand why drawing samples from an energy-based model (EBM) is difficult, consider the EBM over just two variables, defining a distribution p(a,b). In order to sample a, we must draw from p(a|b), and in order to sample b, we must draw it from p(b|a). It seems to be an intractable chicken-and-egg problem.
I really find that perplexing. We already know p(a,b), so why can’t we just compute the marginal p(a), sample from it, and then sample from p(b|a)?
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![[P] Simple Tensorflow implementation of "StarGAN v2: Diverse Image Synthesis for Multiple Domains"](https://b.thumbs.redditmedia.com/kz7k2jStTcxWymqYuXE3D_ZJzTgdR24WDdS-mHE-Qfo.jpg "[P] Simple Tensorflow implementation of \"StarGAN v2: Diverse Image Synthesis for Multiple Domains\"")
