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Amazon Rekognition adds support for six new content moderation categories

Amazon Rekognition content moderation is a deep learning-based service that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy of moderation categories, such as Explicit Nudity, Suggestive, Violence, and Visually Disturbing. You can now detect six new categories: […]

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Amazon Rekognition content moderation is a deep learning-based service that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy of moderation categories, such as Explicit Nudity, Suggestive, Violence, and Visually Disturbing.

You can now detect six new categories: Drugs, Tobacco, Alcohol, Gambling, Rude Gestures, and Hate Symbols. In addition, you get improved detection rates for already supported categories.

In this post, we learn about the details of the content moderation service, how to use the APIs, and how you can perform comprehensive moderation using AWS machine learning (ML) services. Lastly, we see how customers in social media, broadcast media, advertising, and ecommerce create better user experiences, provide brand safety assurances to advertisers, and comply with local and global regulations.

Challenges with content moderation

The daily volume of user-generated content (UGC) and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and photo sharing. You may want to review this content to ensure that your end-users aren’t exposed to potentially inappropriate or offensive material, such as nudity, violence, drug use, adult products, or disturbing images. In addition, broadcast and video-on-demand (VOD) media companies may be required to ensure that the content they create or license carries appropriate ratings as per compliance guidelines for various geographies or target audiences.

Many companies employ teams of human moderators to review content, while others simply react to user complaints to take down offensive images, ads, or videos. However, human moderators alone can’t scale to meet these needs at sufficient quality or speed, which leads to poor user experience, prohibitive costs to achieve scale, or even loss of brand reputation.

Amazon Rekognition content moderation enables you to streamline or automate your image and video moderation workflows using ML. You can use fully managed image and video moderation APIs to proactively detect inappropriate, unwanted, or offensive content containing nudity, suggestiveness, violence, and other such categories. Amazon Rekognition returns a hierarchical taxonomy of moderation-related labels that make it easy to define granular business rules as per your own standards and practices, user safety, or compliance guidelines—without requiring any ML experience. You can then use machine predictions to automate certain moderation tasks completely or significantly reduce the review workload of trained human moderators, so they can focus on higher-value work.

In addition, Amazon Rekognition allows you to quickly review millions of images or thousands of videos using ML, and flag only a small subset of assets for further action. This makes sure that you get comprehensive but cost-effective moderation coverage for all your content as your business scales, and your moderators can reduce the burden of looking at large volumes of disturbing content.

Granular moderation using a hierarchical taxonomy

Different use cases need different business rules for content review. For example, you may want to just flag content with blood, or detect violence with weapons in addition to blood. Content moderation solutions that only provide broad categorizations like violence don’t provide you with enough information to create granular rules. To address this, Amazon Rekognition designed a hierarchical taxonomy with 4 top-level moderation categories (Explicit Nudity, Suggestive, Violence, and Visually Disturbing) and 18 subcategories, which allow you to build nuanced rules for different scenarios.

We have now added 6 new top-level categories (Drugs, Hate Symbols, Tobacco, Alcohol, Gambling, and Rude Gestures), and 17 new subcategories to provide enhanced coverage for a variety of use cases in domains such as social media, photo sharing, broadcast media, gaming, marketing, and ecommerce. The full taxonomy is provided in the following table.

Top-level Category Second-level Category
Explicit Nudity Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Illustrated Explicit Nudity
Adult Toys
Suggestive Female Swimwear Or Underwear
Male Swimwear Or Underwear
Partial Nudity
Barechested Male
Revealing Clothes
Sexual Situations
Violence Graphic Violence Or Gore
Physical Violence
Weapon Violence
Weapons
Self Injury
Visually Disturbing Emaciated Bodies
Corpses
Hanging
Air Crash
Explosions and Blasts
Rude Gestures Middle Finger
Drugs Drug Products
Drug Use
Pills
Drug Paraphernalia
Tobacco Tobacco Products
Smoking
Alcohol Drinking
Alcoholic Beverages
Gambling Gambling
Hate Symbols Nazi Party
White Supremacy
Extremist

How it works

For analyzing images, you can use the DetectModerationLabels API to pass in the Amazon Simple Storage Service (Amazon S3) location of your stored images, or even use raw image bytes in the request itself. You can also specify a minimum prediction confidence. Amazon Rekognition automatically filters out results that have confidence scores below this threshold.

The following code is an image request:

{ "Image": { "S3Object": { "Bucket": "bucket", "Name": "input.jpg" } }, "MinConfidence": 60
}

You get back a JSON response with detected labels, the prediction confidence, and information about the taxonomy in the form of a ParentName field:

{ "ModerationLabels": [ { "Confidence": 99.24723052978516, "ParentName": "", "Name": "Explicit Nudity" }, { "Confidence": 99.24723052978516, "ParentName": "Explicit Nudity", "Name": "Sexual Activity" },
]
}

For more information and a code sample, see Content Moderation documentation. To experiment with your own images, you can use the Amazon Rekognition console.

In the following screenshot, one of our new categories (Smoking) was detected (image sourced from Pexels.com).

For analyzing videos, Amazon Rekognition provides a set of asynchronous APIs. To start detecting moderation categories on your video that is stored in Amazon S3, you can call StartContentModeration. Amazon Rekognition publishes the completion status of the video analysis to an Amazon Simple Notification Service (Amazon SNS) topic. If the video analysis is successful, you call GetContentModeration to get the analysis results. For more information about starting video analysis and getting the results, see Calling Amazon Rekognition Video Operations. For each detected moderation label, you also get its timestamp. For more information and a code sample, see Detecting Inappropriate Stored Videos.

For nuanced situations or scenarios where Amazon Rekognition returns low-confidence predictions, content moderation workflows still require human reviewers to audit results and make final judgements. You can use Amazon Augmented AI (Amazon A2I) to easily implement a human review and improve the confidence of predictions. Amazon A2I is directly integrated with Amazon Rekognition moderation APIs. Amazon A2I allows you to use in-house, private, or even third-party vendor workforces with a user-defined web interface that has instructions and tools to carry out review tasks. For more information about using Amazon A2I with Amazon Rekognition, see Build alerting and human review for images using Amazon Rekognition and Amazon A2I.

Audio, text, and customized moderation

You can use Amazon Rekognition text detection for images and videos to read text, and then check it against your own list of prohibited words or phrases. To detect profanities or hate speech in videos, you can use Amazon Transcribe to convert speech to text, and then check it against a similar list. If you want to further analyze text using natural language processing (NLP), you can use Amazon Comprehend.

If you have very specific or fast-changing moderation needs and access to your own training data, Amazon Rekognition offers Custom Labels to easily train and deploy your own moderation models with a few clicks or API calls. For example, if your ecommerce platform needs to take action on a new product carrying an offensive or politically sensitive message, or your broadcast network needs to detect and blur the logo of a specific brand for legal reasons, you can quickly create and operationalize new models with custom labels to address these scenarios.

Use cases

In this section, we discuss three potential use cases for expanded content moderation labels, depending on your industry.

Social media and photo-sharing platforms

Social media and photo-sharing platforms work with very large amounts of user-generated photos and videos daily. To make sure that uploaded content doesn’t violate community guidelines and societal standards, you can use Amazon Rekognition to flag and remove such content at scale even with small teams of human moderators. Detailed moderation labels also allow for creating a more granular set of user filters. For example, you might find images containing drinking or alcoholic beverages to be acceptable in a liquor ad, but want to avoid ones showing drug products and drug use under any circumstances.

Broadcast and VOD media companies

As a broadcast or VOD media company, you may have to ensure that you comply with the regulations of the markets and geographies in which you operate. For example, content that shows smoking needs to carry an onscreen health advisory warning in countries like India. Furthermore, brands and advertisers want to prevent unsuitable associations when placing their ads in a video. For example, a toy brand for children may not want their ad to appear next to content showing consumption of alcoholic beverages. Media companies can now use the comprehensive set of categories available in Amazon Rekognition to flag the portions of a movie or TV show that require further action from editors or ad traffic teams. This saves valuable time, improves brand safety for advertisers, and helps prevent costly compliance fines from regulators.

Ecommerce and online classified platforms

Ecommerce and online classified platforms that allow third-party or user product listings want to promptly detect and delist illegal, offensive, or controversial products such as items displaying hate symbols, adult products, or weapons. The new moderation categories in Amazon Rekognition help streamline this process significantly by flagging potentially problematic listings for further review or action.

Customer stories

We now look at some examples of how customers are deriving value from using Amazon Rekognition content moderation:

SmugMug operates two very large online photo platforms, SmugMug and Flickr, enabling more than 100M members to safely store, search, share, and sell tens of billions of photos. Flickr is the world’s largest photographer-focused community, empowering photographers around the world to find their inspiration, connect with each other, and share their passion with the world.

As a large, global platform, unwanted content is extremely risky to the health of our community and can alienate photographers. We use Amazon Rekognition’s content moderation feature to find and properly flag unwanted content, enabling a safe and welcoming experience for our community. At Flickr’s huge scale, doing this without Amazon Rekognition is nearly impossible. Now, thanks to content moderation with Amazon Rekognition, our platform can automatically discover and highlight amazing photography that more closely matches our members’ expectations, enabling our mission to inspire, connect, and share.”

– Don MacAskill, Co-founder, CEO & Chief Geek

Mobisocial is a leading mobile software company, focused on building social networking and gaming apps. The company develops Omlet Arcade, a global community where tens of millions of mobile gaming live-streamers and esports players gather to share gameplay and meet new friends.

“To ensure that our gaming community is a safe environment to socialize and share entertaining content, we used machine learning to identify content that doesn’t comply with our community standards. We created a workflow, leveraging Amazon Rekognition, to flag uploaded image and video content that contains non-compliant content. Amazon Rekognition’s content moderation API helps us achieve the accuracy and scale to manage a community of millions of gaming creators worldwide. Since implementing Amazon Rekognition, we’ve reduced the amount of content manually reviewed by our operations team by 95%, while freeing up engineering resources to focus on our core business. We’re looking forward to the latest Rekognition content moderation model update, which will improve accuracy and add new classes for moderation.”

-Zehong, Senior Architect at Mobisocial

Conclusion

In this post, we learned about the six new categories of inappropriate or offensive content now available in the Amazon Rekognition hierarchical taxonomy for content moderation, which contains 10 top-level categories and 35 subcategories overall. We also saw how Amazon Rekognition moderation APIs work, and how customers in different domains are using them to streamline their review workflows.

For more information about the latest version of content moderation APIs, see Content Moderation. You can also try out your own images on the Amazon Rekognition console. If you want to test visual and audio moderation with your own videos, check out the Media Insights Engine (MIE)—a serverless framework to easily generate insights and develop applications for your video, audio, text, and image resources, using AWS ML and media services. You can easily spin up your own MIE instance using the provided AWS CloudFormation template, and then use the sample application.


About the Author

Venkatesh Bagaria is a Principal Product Manager for Amazon Rekognition. He focuses on building powerful but easy-to-use deep learning-based image and video analysis services for AWS customers. In his spare time, you’ll find him watching way too many stand-up comedy specials and movies, cooking spicy Indian food, and pretending that he can play the guitar.

Source: https://aws.amazon.com/blogs/machine-learning/amazon-rekognition-adds-support-for-six-new-content-moderation-categories/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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