Author: torontoai
[D] Data-poisoning and Trojan attacks at training time. Is it a real threat?
I would like to know anyone’s opinion on this.
Recent work has identified that classification models implemented as neural networks are vulnerable to data-poisoning and Trojan attacks at training time.
Source: Attacks on Deep Reinforcement Learning Agents : https://arxiv.org/abs/1903.06638
- Is it a real threat?
- How the risk can be identified from someone that just uses the model without access to its source or training data (i.e. prepare a set of tests)?
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[D] Artificial Life for AI People
AI asks fundamental questions about the nature of “intelligence”, but what about understanding life itself? Sina gives an overview of Artificial Life for AI people.
https://thegradient.pub/an-introduction-to-artificial-life-for-people-who-like-ai/
submitted by /u/weiqiplayer
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[R][P] Talking Head Anime from a Single Image
![[R][P] Talking Head Anime from a Single Image](https://b.thumbs.redditmedia.com/JVJulLpW6JNqUySh5DikSBJNeMUtUle3H-jlXCHj6Bc.jpg "[R][P] Talking Head Anime from a Single Image") |
I trained a network to animate faces of anime characters. The input is an image of the character looking straight at the viewer and a pose, specified by 6 numbers. The output is another image of the character with the face posed accordingly. What the network can do in a nutshell. I created two tools with this network.
Using a face tracker, I could transfer human face movements from existing videos to anime characters. Here are some characters impersonating President Obama: https://reddit.com/link/e1k092/video/jqb6eziwgv041/player The approach I took is to combine two previous works. The first is the Pumarola et al.’s 2018 GANimation paper, which I use to change the facial features (closing eyes and mouth, in particular). The second is Zhou et al.’s 2016 object rotation by appearance flow paper, which I use to rotate the face. I generated a new dataset by rendering 8,000 downloadable 3D models of anime characters. You can find out more about the project at https://pkhungurn.github.io/talking-head-anime/. submitted by /u/pramook |
[Discussion][D] Gradient norm tracking
Are there any best practices on how one should track gradient norms during training? Surprisingly, I haven’t been able to find much reliable information on it, except the classical Glorot’s paper.
My current approach is to track 2-norm of weights raw gradients. However, I don’t have any practical intuition on which values should make me worried. Tracking the actual weight updates (e.g adjusted by Adam) makes make much more sense, but I haven’t seen anyone doing so.
A few words why am I concerned: I’m working on some exotic NN architecture for 3D, where different architecture choices implicate gradient behavior drastically, up to blow up.
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[D] “Deep” Machine Learning
So, I’m a big fan of Lex Fridman’s deep learning podcast. A big ago I watched one he did with Ian Goodfellow.
At the start of the interview Goodfellow describes how deep learning methods are distinguished by the fact that it involves a bunch of computations done in sequence rather than in parallel. (You can watch the video to get a better idea of what he was talking about).
Does anyone have any other examples of machine learning techniques that you feel fit his description of being deep? Just curious about this.
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[P] Webpage Data Extraction using Image Classification and Object Detection
I am working on creating something that can detect and ideally extract information from a job posting.
I have some questions around the data I am using. I currently crawl websites and take screenshots of their career pages. These screenshots vary in dimensions due to the length of the website.
Disclaimer, I am not a ML Pro. I am self taught everything and currently using Google’s AutoML Services for training my model.
My Questions:
- Should I use these long/large images? Or is it better to cut them in half and then feed it to the AI. With the large images when I zoom in I can see everything fine for labeling. When not zoomed in, it can be hard to make things out.
- How small should labels be? Google allows the smallest to be 8 pixels by 8 pixels. If they can be big I can use the large images and just zoom in?
- Is there a way to give context to the classifier/object detector? I realized when I evaluate a job posting I get context from the url and other words on the page that it doesn’t get since it only sees a screenshot.
- Should I try to label every element on the page? if yes, In a high level way or granular?
- Any other hints or tips I should think about to solve this problem?
My Attempts/Approaches
Attempt 1: Object Detection
My first attempt was to perform object detection on screen shots that were cut down to ~2,000 pixels. I then labeled most of the content on the page with labels like: Header, Footer, Section, Heading, SubHeading, Job Title, Job Posting, Paragraph, Section Heading, Section SubHeading.
Results :
Total images: 183
Test items: 17
Total objects: 244
Object to image avg: 14.35
Precision: 91.43% (Using a score threshold of 0.508)
Recall: 13.11% (Using a score threshold of 0.508)
Average precision: 0.171 at 0.5 IoU
Conclusion: Object detection needs many more images, also the labels I provided were not concrete enough. Looking back I found the definitions for certain things to be vague. For example I was using the label heading, subheading and job title. Well sometimes the heading is also a job title, but I would only mark it as job title. Thinking about it from the computers perspective how will it know a heading from a job title? There is not much there visually for it to grab onto. This lead me to cut the images down to a height of 2,000 pixels so I could see each element more clearly.
The problem here is do I try to label every HTML element?
Attempt 2 Object Classification
My second try was to use image classification to determine if I was on a job posting page, then if true use another model to extract the data.
My first model1 results
Total images: 85
Test items: 9
Precision: 77.78%
Recall: 77.78%
My second model2 results
Total images: 484
Test items:55
Precision: 90.7%
Recall: 70.91%
These results were more in-line with what I had thought. When looking at the overall page there over and over there becomes a familiar pattern with what a job posting looks like.
Final Attempt – Object Detection:
I am now trying again with an object detection model, that is trained only on job posting’s, I think this will do better as it only has 3 labels, Job Title, Job Location and Apply Button. I wanted to include a label for: Responsibilities, Qualifications, skills, bonus, ect… but came back to the fact that there is not much for it to grab onto…as I find these in the posting by reading.
Model currently in training…
Final Notes
I believe the correct way for me to do this problem would be to train the AI on the html code, but I am using google’s automl services so I dont know how/if that is possible. I was thinking about using/combining different types of data/techniques since there is information in the URL and code that I’m not leveraging. Perhaps apply NLP to the URLS?
Thanks for checking out my project any thoughts are appreciated.
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Announcing Amazon Rekognition Custom Labels
Today, Amazon Web Services (AWS) announced Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own specialized machine learning (ML) based image analysis capabilities to detect unique objects and scenes integral to their specific use case. For example, customers using Amazon Rekognition to detect machine parts from images can now train a model with a small set of labeled images to detect “turbochargers” and “torque converters” without needing any ML expertise. Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs.
To better understand Amazon Rekognition Custom Labels, let’s walk through an example of how you can use this new feature of the service.
An auto repair shop uses Amazon Rekognition Label detection (objects and scenes) to analyze and sort machine parts in their inventory. For all these images, Amazon Rekognition successfully returns “machine parts”.
Using Amazon Rekognition Custom Labels, the customer can train their own custom model to identify specific machine parts, such as turbocharger, torque converter, etc. To start, the customer collects as few as 10 sample images for each specific machine part that they would like to identify.
Using the service console, customer can upload and label these images.
No machine learning expertise is required at this stage. Customers are guided through each step of the process within the console.
Once the dataset is ready and fully labeled, customers can put Amazon Rekognition Custom Labels to work with just one click. Amazon Rekognition automatically chooses the most effective machine learning techniques for each use case.
On completion of training, customers can access visualizations to see how each model is performing and get suggestions of how to further improve their model.
In our example, the auto repair shop can now start analyzing images to detect specific machine parts by their names, automating inventory management, by using a fully managed easy-to-use API built for large-scale image processing.
Amazon Rekognition Object and Scene detection returns “Machine Parts”, while Amazon Rekognition Custom Labels trained with a few labeled images returns “Turbocharger”, “Torque Converter”, and “Crankshaft”.
Now let’s look at how customers like the NFL and Vidmob are using Amazon Custom Labels.
- NFL Media, part of the National Football League, manages an exponentially-growing library of videos and images that is difficult to search for relevant content such team logos, pylons, or foam fingers with traditional methods. Amazon Rekognition Custom Labels makes that easier, says Brad Boim, NFL Senior Director of Post Production and Asset Management.
“By using the new feature in Amazon Rekognition, Custom Labels, we are able to automatically generate metadata tags tailored to specific use cases for our business and provide searchable facets for our content creation teams. This significantly improves the speed in which we can search for content and, more importantly, it enables us to automatically tag elements that required manual efforts before. These tools allow our production teams to leverage this data directly and provides enhanced products to our customers across all of our media platforms.”
- VidMob is a marketing creative platform that provides an end-to-end technology solution for all of a brand’s creative needs with a single integrated platform combining first-of-a-kind creative analytics with best-in-class creative production to transform marketing effectiveness. Alex Collmer, VidMob CEO says,
“With the introduction of Amazon Rekognition Custom Labels, marketers will be equipped with advanced capabilities within our Agile Creative Studio, enabling them to build and train the specific products (custom labels) that they care about within their ads, at scale, within minutes. Using VidMob’s integration of Amazon Rekognition, customers have historically been able to identify common objects but now the new ability for custom labels will make our platform even more targeted for every business. With a lift of 150% in creative performance and 30% reduction in human analyst time, this will extend their ability to measure their creative performance using VidMob’s Agile Creative Studio.”
AWS customers can now easily train high-quality custom vision models with a reasonably small set of labeled images. Doing this requires no ML experience, and with only a few lines of code customers can access Amazon Rekognition’s easy-to-use fully managed Custom Labels API that can process tens of thousands of images stored in Amazon S3 in an hour.
Amazon Rekognition Custom Labels will be generally available on December 3, 2019. Click here to be notified when the service becomes available. To learn more, visit https://aws.amazon.com/rekognition/custom-labels-features/.
About the author
Anushri Mainthia is the Senior Product Manager on the Amazon Rekognition team and product lead for Amazon Rekognition Custom Labels. Outside of work, Anushri loves to cook, explore Seattle and video-chat with her nephew.
Read ‘em and Reap: 6 Success Factors for AI Startups
Now that data is the new oil, AI software startups are sprouting across the tech terrain like pumpjacks in Texas. A whopping $80 billion in venture capital is fueling as many as 12,000 new companies.
Only a few will tap a gusher. Those who do, experts say, will practice six key success factors.
- Master your domain
- Gather big data fast
- See (a little) ahead of the market
- Make a better screwdriver
- Scale across the clouds
- Stay flexible
Some of the biggest wins will come from startups with AI apps that “turn an existing provider on its head by figuring out a new approach for call centers, healthcare or whatever it is,” said Rajeev Madhavan who manages a $300 million fund at Clear Ventures, nurturing nine AI startups.
1. Master Your Domain
Madhavan sold his electronic design automation startup Magma Design in 2012 to Synopsys for $523 million. His first stop on the way to becoming a VC was to take Andrew Ng’s Stanford course in AI.
“For a brief period in Silicon Valley every startup’s pitch would just throw in jargon on AI, but most of them were just doing collaborative filtering,” he said. “The app companies we look for have to be heavy on AI, but success comes down to how good a startup is in its domain space,” he added.
Chris Rowen agrees. The veteran entrepreneur who in 2013 sold his startup Tensilica to Cadence Design for $380 million considers domain expertise the top criteria for an AI software startup’s success.
Rowen’s latest startup, BabbleLabs, uses AI to filter noise from speech in real time. “At the root of it, I’m doing something analogous to what I’ve done in much of my career — work on really hard real-time computing problems that apply to mass markets,” Rowen said.
Overall, “deep learning is still at the stage where people are having challenges understanding which problems can be handled with this technique. The companies that recognize a vertical-market need and deliver a solution for it have a bigger chance of getting early traction. Over time, there will be more broad, horizontal opportunities,” he added.
Jeff Herbst nurtures more than 5,000 AI startups under the NVIDIA Inception program that fuels entrepreneurs with access to its technology and market connections. But the AI tag is just shorthand.
In a way, it’s like a rerun of The Invasion of the DotComs. “We call them AI companies today, but they are all in specialized markets — in the not-so-distant future, every company will be an AI company,” said Herbst, vice president of business development at NVIDIA.
Today’s AI software landscape looks like a barbell to Herbst. Lots of activity by a handful of cloud-computing giants at one end and a bazillion startups at the other.
2. Get Big Data Fast
Collecting enough bits to fill a data lake is perhaps the hardest challenge for an AI startup.
Among NVIDIA’s Inception startups, Zebra Medical Vision uses AI on medical images to make faster, smarter diagnoses. To get the data it needed, it partnered both with Israel’s largest healthcare provider as well as Intermountain Healthcare, which manages 215 clinics and 24 hospitals in the U.S.
“We understood data was the most important asset we needed to secure, so we invested a lot in the first two years of the startup not only in data but also in developing all kinds of algorithms in parallel,” said Eyal Toledano, co-founder and CTO of Zebra. “To find one good clinical solution, you have to go through many candidates.”
Getting access to 20 years of digital data from top drawer healthcare organizations “took a lot of convincing” both from Zebra’s chief executive and Toledano.
“My contribution was showing how security, compliance and anonymity could be done. There was a lot of education and co-development so they would release the data and we could do research that could contribute back to their patient population in return,” he added.
It’s working. To date Zebra has raised $50 million, received FDA approvals on three products with two more pending “and a few other submissions are on the way,” he said.
Toledano also gave kudos to NVIDIA’s Inception program.
“We had many opportunities to examine new technologies before they became widely used. We saw the difference in applying new GPUs to current processes, and looked at inference in the hospital with GPUs to improve the user experience, especially in time-critical applications,” he said.
“We also got some good know-how and ideas to improve our own infrastructure with training and infrastructure libraries to build projects. We tried quite a lot of the NVIDIA technologies and some were really amazing and fruitful, and we adopted a DGX server and decreased our development and training time substantially in many evaluations,” he added.
Six Steps to AI Startup Gold
| Success Factor | Call to Action | Startups Using It |
|---|---|---|
| Master your domain | Have deep expertise in your target application | BabbleLabs |
| Gather big data fast | Tap partners, customers to gather data and refine models | Zebra Medical Vision, Scale |
| See (a little) ahead of the market | Find solutions to customer pain points before rivals see them | FASTDATA.io, Netflix |
| Make a better screwdriver | Create tools that simplify the work of data scientists | Scale, Dataiku |
| Scale across the clouds | Support private and multiple public cloud services | Robin.io |
| Stay flexible | Follow changing customer pain points to novel solutions | Keyhole Corp. |
Another Inception startup, Scale, which provides training and validation data for self-driving cars and other platforms, got on board with Toyota and Lyft. “Working with more people makes your algorithms smarter, and then more people want to work with you — you get into a cycle of success,” said Herbst.
Reflektion, one of Madhavan’s startups, now has a database of 200 million unique shoppers, the third largest retail database after Amazon and Walmart. It started with zero. Getting big took three years and a few great partners.
Rowen’s BabbleLabs applied a little creativity and elbow grease to get a lot of data cheaply and fast. It siphoned speech data from free sources as diverse as YouTube and the Library of Congress. When it needed specialized data, it activated a network of global contractors “quite economically,” he said.
“You can find low-cost, low-quality data sources, then use algorithms to filter and curate the data. Controlling the amount of noise associated with the speech helped simplify training.” he added.
“In AI, access to data no one else has is the big win,” said Herbst. “The world has a lot of open source frameworks and tools, but a lot of the differentiation comes from proprietary access to the data that does the programming,” he added.
When seeking data-rich customers and partners “the fastest way to get in the door is knowing what their pain points are,” said Alen Capalik, founder of FASTDATA.io.
Work in high-frequency trading on Wall Street taught Capalik the value of GPUs. When he came up with an idea for using them to ingest real-time data fast for any application, he sought out Herbst at NVIDIA in 2017.
“He almost immediately wrote me a check for $1.5 million,” Capalik said.
3. See (a Little) Ahead of the Market
Today, FASTDATA.io is poised for a Series A financing round to fuel its recently released PlasmaENGINE, which already has two customers and over 20 more in the pipeline. “I think we are 12-18 months ahead of the market, which is a great spot to be in,” said Capalik, whose product can process as much data as 100 Spark instances.
That wasn’t the position Capalik found himself in his last time out. His cybersecurity startup — GoSecure, formerly CounterTack — pioneered the idea of end-point threat detection as much as six years before it caught on.
“People told me I was crazy. Palo Alto Networks and FireEye were doing perimeter security, and users thought they’d never install agents again because they slowed systems down. So, we struggled for a while and had to educate the market a lot,” he said.
Education and awareness are the kinds of jobs established corporations tackle. For startups, being visionary is like Steve Jobs unveiling an iPhone — “show them what they didn’t know they wanted,” he said.
“Netflix went after video streaming before there was enough bandwidth or end points — they skated to where the puck was going,” said Herbst.
4. Make a Better Screwdriver
AI holds opportunities for arms dealers, too — the kind who sell the software tools data scientists use to tighten down the screws on their neural networks.
The current Swiss Army knife of AI is the workbench. It’s a software platform for developing and deploying machine-learning models in today’s DevOps IT environment.
Jupyter notebooks could be seen as a sort of two-blade model you get for free as open source. Giants such as AWS, IBM and Microsoft and dozens of startups such as H20.ai and Dataiku are rolling out versions with more forks, corkscrews and toothpicks.
Despite all the players and a fast-moving market, there are still opportunities here, said James Kobielus, a lead analyst for AI and data science at Wikibon. Start as a plug-in for a popular workbench, he suggested.
Startups can write modules to support emerging frameworks and languages, or a mod to help a workbench tap into the AI goodness embedded in the latest smartphones. Alternatively, you can automate streaming operations or render logic automatically into code, the former IBM data-science evangelist advised.
If workbenches aren’t for you, try robotic process automation, another emerging category trying to make AI easier for more people to use. “You can clean up if you can democratize RPA for makers and kids — that’s exciting,” Kobielus said.
There’s a wide-open opportunity for tools that cram neural nets into the kilobytes of memory on devices such as smart speakers, appliances and even thermostats, BabbleLabs’ Rowen said. His company aims to run its speech models on some of the world’s smallest microcontrollers.
“We need compilers that take trained models and do quantization, model compression and optimized model generation to fit into the skinny memory of embedded systems — nothing solves this problem yet,” he said.
5. Expand Across the Clouds
The playing field is very competitive with more startups than ever because it’s easier than ever to start a company, said Herbst, who worked closely with entrepreneurs as a corporate and IP attorney even before he joined NVIDIA 18 years ago.
All you need to get started today is an idea, a laptop, a cup of coffee and a cloud-computing account. “All the infrastructure is a service now,” he said.
But if you get lucky and scale, that one cloud-computing account can become a bottleneck and your biggest cost after payroll.
“That’s a good problem to have, but to hit breakeven and make it easier for customers, you need your software running on any cloud,” said Madhavan.
The need is so striking, he wound up funding a startup to address it. Robin.io is an expert in stateful and stateless workloads, helping companies become cloud-agnostic. “We have been extremely successful with 5G telcos going cloud native and embracing containers,” he said.
6. Stay Flexible as a Yogi
Few startups wind up where they thought they were going. Apple planned to make desktop computers, Amazon aimed to sell books online.
Over time “they pivot one way or another. They go in with a problem to solve, but as they talk to customers the smart ones learn from those interactions how to re-target or tailor themselves,” said Herbst, who gives an example from his pre-AI days
Keyhole Corp. wanted to provide 3D mapping services initially for real estate agents and other professionals. Its first product was distributed on CDs
As a veteran of early search startup AltaVista, “I thought this startup belonged more to a Yahoo! or some other internet company. I realized it was not a professional but a major consumer app,” said Herbst, who was happy to fund them as one of NVIDIA’s first investments outside gaming.
In time, Google agreed with Herbst and acquired the company. Keyhole’s technology became part of the underpinnings of Google Maps and Google Earth.
“They had a nice exit, their people went on to have rock-star careers at Google, and I believe were among the original creators of Pokemon Go,” he said.
The lesson is simple: Follow good directions — like the six success factors for AI software startups — and there’s no telling where you may end up.
The post Read ‘em and Reap: 6 Success Factors for AI Startups appeared first on The Official NVIDIA Blog.
[D] how do you expect ML to transition over to safety critical systems?
First off I am not an ML engineer. I am am embedded software engineer working mostly in safety critical systems. So if there are some dumb assumptions here don’t crucify me. One of the biggest things that strikes me about ML is it’s black box nature. We can’t ask the machine how it made a descision, in fact I’ve heard claims that we shouldn’t because it would inject human bias into the system. For things like data scraping and image recognition that seems fine, but I can’t imagine having a conversation at my work go like this:
“X failed, people died. Go figure out how and fix it.
Sorry boss I can retrain the model with this new outcome but I can’t tell you why it broke or guarantee to any degree of certainty it won’t happen again”
That just wouldn’t fly. Is there something I’m missing?
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