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The AI Dividend

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/the-ai-dividend.html

For four decades, Alaskans have opened their mailboxes to find checks waiting for them, their cut of the black gold beneath their feet. This is Alaska’s Permanent Fund, funded by the state’s oil revenues and paid to every Alaskan each year. We’re now in a different sort of resource rush, with companies peddling bits instead of oil: generative AI.

Everyone is talking about these new AI technologies—like ChatGPT—and AI companies are touting their awesome power. But they aren’t talking about how that power comes from all of us. Without all of our writings and photos that AI companies are using to train their models, they would have nothing to sell. Big Tech companies are currently taking the work of the American people, without our knowledge and consent, without licensing it, and are pocketing the proceeds.

You are owed profits for your data that powers today’s AI, and we have a way to make that happen. We call it the AI Dividend.

Our proposal is simple, and harkens back to the Alaskan plan. When Big Tech companies produce output from generative AI that was trained on public data, they would pay a tiny licensing fee, by the word or pixel or relevant unit of data. Those fees would go into the AI Dividend fund. Every few months, the Commerce Department would send out the entirety of the fund, split equally, to every resident nationwide. That’s it.

There’s no reason to complicate it further. Generative AI needs a wide variety of data, which means all of us are valuable—not just those of us who write professionally, or prolifically, or well. Figuring out who contributed to which words the AIs output would be both challenging and invasive, given that even the companies themselves don’t quite know how their models work. Paying the dividend to people in proportion to the words or images they create would just incentivize them to create endless drivel, or worse, use AI to create that drivel. The bottom line for Big Tech is that if their AI model was created using public data, they have to pay into the fund. If you’re an American, you get paid from the fund.

Under this plan, hobbyists and American small businesses would be exempt from fees. Only Big Tech companies—those with substantial revenue—would be required to pay into the fund. And they would pay at the point of generative AI output, such as from ChatGPT, Bing, Bard, or their embedded use in third-party services via Application Programming Interfaces.

Our proposal also includes a compulsory licensing plan. By agreeing to pay into this fund, AI companies will receive a license that allows them to use public data when training their AI. This won’t supersede normal copyright law, of course. If a model starts producing copyright material beyond fair use, that’s a separate issue.

Using today’s numbers, here’s what it would look like. The licensing fee could be small, starting at $0.001 per word generated by AI. A similar type of fee would be applied to other categories of generative AI outputs, such as images. That’s not a lot, but it adds up. Since most of Big Tech has started integrating generative AI into products, these fees would mean an annual dividend payment of a couple hundred dollars per person.

The idea of paying you for your data isn’t new, and some companies have tried to do it themselves for users who opted in. And the idea of the public being repaid for use of their resources goes back to well before Alaska’s oil fund. But generative AI is different: It uses data from all of us whether we like it or not, it’s ubiquitous, and it’s potentially immensely valuable. It would cost Big Tech companies a fortune to create a synthetic equivalent to our data from scratch, and synthetic data would almost certainly result in worse output. They can’t create good AI without us.

Our plan would apply to generative AI used in the US. It also only issues a dividend to Americans. Other countries can create their own versions, applying a similar fee to AI used within their borders. Just like an American company collects VAT for services sold in Europe, but not here, each country can independently manage their AI policy.

Don’t get us wrong; this isn’t an attempt to strangle this nascent technology. Generative AI has interesting, valuable, and possibly transformative uses, and this policy is aligned with that future. Even with the fees of the AI Dividend, generative AI will be cheap and will only get cheaper as technology improves. There are also risks—both every day and esoteric—posed by AI, and the government may need to develop policies to remedy any harms that arise.

Our plan can’t make sure there are no downsides to the development of AI, but it would ensure that all Americans will share in the upsides—particularly since this new technology isn’t possible without our contribution.

This essay was written with Barath Raghavan, and previously appeared on Politico.com.

Building Generative AI into Marketing Strategies: A Primer

Post Syndicated from nnatri original https://aws.amazon.com/blogs/messaging-and-targeting/building-generative-ai-into-marketing-strategies-a-primer/

Introduction

Artificial Intelligence has undoubtedly shaped many industries and is poised to be one of the most transformative technologies in the 21st century. Among these is the field of marketing where the application of generative AI promises to transform the landscape. This blog post explores how generative AI can revolutionize marketing strategies, offering innovative solutions and opportunities.

According to Harvard Business Review, marketing’s core activities, such as understanding customer needs, matching them to products and services, and persuading people to buy, can be dramatically enhanced by AI. A 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value. The ability to leverage AI can not only help automate and streamline processes but also deliver personalized, engaging content to customers. It enhances the ability of marketers to target the right audience, predict consumer behavior, and provide personalized customer experiences. AI allows marketers to process and interpret massive amounts of data, converting it into actionable insights and strategies, thereby redefining the way businesses interact with customers.

Generating content is just one part of the equation. AI-generated content, no matter how good, is useless if it does not arrive at the intended audience at the right point of time. Integrating the generated content into an automated marketing pipeline that not only understands the customer profile but also delivers a personalized experience at the right point of interaction is also crucial to getting the intended action from the customer.

Amazon Web Services (AWS) provides a robust platform for implementing generative AI in marketing strategies. AWS offers a range of AI and machine learning services that can be leveraged for various marketing use cases, from content creation to customer segmentation and personalized recommendations. Two services that are instrumental to delivering customer contents and can be easily integrated with other generative AI services are Amazon Pinpoint and Amazon Simple Email Service. By integrating generative AI with Amazon Pinpoint and Amazon SES, marketers can automate the creation of personalized messages for their customers, enhancing the effectiveness of their campaigns. This combination allows for a seamless blend of AI-powered content generation and targeted, data-driven customer engagement.

As we delve deeper into this blog post, we’ll explore the mechanics of generative AI, its benefits and how AWS services can facilitate its integration into marketing communications.

What is Generative AI?

Generative AI is a subset of artificial intelligence that leverages machine learning techniques to generate new data instances that resemble your training data. It works by learning the underlying patterns and structures of the input data, and then uses this understanding to generate new, similar data. This is achieved through the use of models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.

What do Generative AI buzzwords mean?

In the world of AI, buzzwords are abundant. Terms like “deep learning”, “neural networks”, “machine learning”, “generative AI”, and “large language models” are often used interchangeably, but they each have distinct meanings. Understanding these terms is crucial for appreciating the capabilities and limitations of different AI technologies.

Machine Learning (ML) is a subset of AI that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. These algorithms can be ‘trained’ on a dataset and then used to predict or classify new data. Machine learning models can be broadly categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns. These layers of neurons process different features, and their outputs are combined to produce a final result. Deep learning models can handle large amounts of data and are particularly good at processing images, speech, and text.

Generative AI refers specifically to AI models that can generate new data that mimic the data they were trained on. This is achieved through the use of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Generative AI can create anything from written content to visual designs, and even music, making it a versatile tool in the hands of marketers.

Large Language Models (LLMs) are a type of generative AI that are trained on a large corpus of text data and can generate human-like text. They predict the probability of a word given the previous words used in the text. They are particularly useful in applications like text completion, translation, summarization, and more. While they are a type of generative AI, they are specifically designed for handling text data.

Simply put, you can understand that Large Language Model is a subset of Generative AI, which is then a subset of Machine Learning and they ultimately falls under the umbrella term of Artificial Intelligence.

What are the problems with generative AI and marketing?

While generative AI holds immense potential for transforming marketing strategies, it’s important to be aware of its limitations and potential pitfalls, especially when it comes to content generation and customer engagement. Here are some common challenges that marketers should be aware of:

Bias in Generative AI Generative AI models learn from the data they are trained on. If the training data is biased, the AI model will likely reproduce these biases in its output. For example, if a model is trained primarily on data from one demographic, it may not accurately represent other demographics, leading to marketing campaigns that are ineffective or offensive. Imagine if you are trying to generate an image for a campaign targeting females, a generative AI model might not generate images of females in jobs like doctors, lawyers or judges, leading your campaign to suffer from bias and uninclusiveness.

Insensitivity to Cultural Nuances Generative AI models may not fully understand cultural nuances or sensitive topics, which can lead to content that is insensitive or even harmful. For instance, a generative AI model used to create social media posts for a global brand may inadvertently generate content that is seen as disrespectful or offensive by certain cultures or communities.

Potential for Inappropriate or Offensive Content Generative AI models can sometimes generate content that is inappropriate or offensive. This is often because the models do not fully understand the context in which certain words or phrases should be used. It’s important to have safeguards in place to review and approve content before it’s published. A common problem with LLMs is hallucination: whereby the model speaks false knowledge as if it is accurate. A marketing team might mistakenly publish a auto-generated promotional content that contains a 20% discount on an item when no such promotions were approved. This could have disastrous effect if safeguards are not in place and erodes customers’ trust.

Intellectual Property and Legal Concerns Generative AI models can create new content, such as images, music, videos, and text, which raises questions of ownership and potential copyright infringement. Being a relatively new field, legal discussions are still ongoing to discuss legal implications of using Generative AI, e.g. who should own generated AI content, and copyright infringement.

Not a Replacement for Human Creativity Finally, while generative AI can automate certain aspects of marketing campaigns, it cannot replace the creativity or emotional connections that marketers use in crafting compelling campaigns. The most successful marketing campaigns touch the hearts of the customers, and while Generative AI is very capable of replicating human content, it still lacks in mimicking that “human touch”.

In conclusion, while generative AI offers exciting possibilities for marketing, it’s important to approach its use with a clear understanding of its limitations and potential pitfalls. By doing so, marketers can leverage the benefits of generative AI while mitigating risks.

How can I use generative AI in marketing communications?

Amazon Web Services (AWS) provides a comprehensive suite of services that facilitate the use of generative AI in marketing. These services are designed to handle a variety of tasks, from data processing and storage to machine learning and analytics, making it easier for marketers to implement and benefit from generative AI technologies.

Overview of Relevant AWS Services

AWS offers several services that are particularly relevant for generative AI in marketing:

  • Amazon Bedrock: This service makes FMs accessible via an API. Bedrock offers the ability to access a range of powerful FMs for text and images, including Amazon’s Titan FMs. With Bedrock’s serverless experience, customers can easily find the right model for what they’re trying to get done, get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS tools and capabilities they are familiar with.
  • Amazon Titan Models: These are two new large language models (LLMs) that AWS is announcing. The first is a generative LLM for tasks such as summarization, text generation, classification, open-ended Q&A, and information extraction. The second is an embeddings LLM that translates text inputs into numerical representations (known as embeddings) that contain the semantic meaning of the text. In response to the pitfalls mentioned above around Generative AI hallucinations and inaccurate information, AWS is actively working on improving accuracy and ensuring its Titan models produce high-quality responses, said Bratin Saha, an AWS vice president.
  • Amazon SageMaker: This fully managed service enables data scientists and developers to build, train, and deploy machine learning models quickly. SageMaker includes modules that can be used for generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
  • Amazon Pinpoint: This flexible and scalable outbound and inbound marketing communications service enables businesses to engage with customers across multiple messaging channels. Amazon Pinpoint is designed to scale with your business, allowing you to send messages to a large number of users in a short amount of time. It integrates with AWS’s generative AI services to enable personalized, AI-driven marketing campaigns.
  • Amazon Simple Email Service (SES): This cost-effective, flexible, and scalable email service enables marketers to send transactional emails, marketing messages, and other types of high-quality content to their customers. SES integrates with other AWS services, making it easy to send emails from applications being hosted on services such as Amazon EC2. SES also works seamlessly with Amazon Pinpoint, allowing for the creation of customer engagement communications that drive user activity and engagement.

How to build Generative AI into marketing communications

Dynamic Audience Targeting and Segmentation: Generative AI can help marketers to dynamically target and segment their audience. It can analyze customer data and behavior to identify patterns and trends, which can then be used to create more targeted marketing campaigns. Using Amazon Sagemaker or the soon-to-be-available Amazon Bedrock and Amazon Titan Models, Generative AI can suggest labels for customers based on unstructured data. According to McKinsey, generative AI can analyze data and identify consumer behavior patterns to help marketers create appealing content that resonates with their audience.

Personalized Marketing: Generative AI can be used to automate the creation of marketing content. This includes generating text for blogs, social media posts, and emails, as well as creating images and videos. This can save marketers a significant amount of time and effort, allowing them to focus on other aspects of their marketing strategy. Where it really shines is the ability to productionize marketing content creation, reducing the needs for marketers to create multiple copies for different customer segments. Previously, marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food). Generative AI can automate this process, providing the opportunities to dynamically create these contents programmatically and automatically send out to the most relevant segments via Amazon Pinpoint or Amazon SES.

Marketing Automation: Generative AI can automate various aspects of marketing, such as email marketing, social media marketing, and search engine marketing. This includes automating the creation and distribution of marketing content, as well as analyzing the performance of marketing campaigns. Amazon Pinpoint currently automates customer communications using journeys which is a customized, multi-step engagement experience. Generative AI could create a Pinpoint journey based on customer engagement data, engagement parameters and a prompt. This enables GenAI to not only personalize the content but create a personalized omnichannel experience that can extend throughout a period of time. It then becomes possible that journeys are created dynamically by generative AI and A/B tested on the fly to achieve an optimal pre-defined Key Performance Indicator (KPI).

A Sample Generative AI Use Case in Marketing Communications

AWS services are designed to work together, making it easy to implement generative AI in your marketing strategies. For instance, you can use Amazon SageMaker to build and train your generative AI models which assist with automating marketing content creation, and Amazon Pinpoint or Amazon SES to deliver the content to your customers.

Companies using AWS can theoretically supplement their existing workloads with generative AI capabilities without the needs for migration. The following reference architecture outlines a sample use case and showcases how Generative AI can be integrated into your customer journeys built on the AWS cloud. An e-commerce company can potentially receive many complaints emails a day. Companies spend a lot of money to acquire customers, it’s therefore important to think about how to turn that negative experience into a positive one.

GenAIMarketingSolutionArchitecture

When an email is received via Amazon SES (1), its content can be passed through to generative AI models using GANs to help with sentiment analysis (2). An article published by Amazon Science utilizes GANs for sentiment analysis for cases where a lack of data is a problem. Alternatively, one can also use Amazon Comprehend at this step and run A/B tests between the two models. The limitations with Amazon Comprehend would be the limited customizations you can perform to the model to fit your business needs.

Once the email’s sentiment is determined, the sentiment event is logged into Pinpoint (3), which then triggers an automatic winback journey (4).

Generative AI (e.g. HuggingFace’s Bloom Text Generation Models) can again be used here to dynamically create the content without needing to wait for the marketer’s input (5). Whereas marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food), generative AI provides the opportunities to dynamically create these contents on the fly given the above inputs.

Once the campaign content has been generated, the model pumps the template backs into Amazon Pinpoint (6), which then sends the personalized copy to the customer (7).

Result: Another customer is saved from attrition!

Conclusion

The landscape of generative AI is vast and ever-evolving, offering a plethora of opportunities for marketers to enhance their strategies and deliver more personalized, engaging content. AWS plays a pivotal role in this landscape, providing a comprehensive suite of services that facilitate the implementation of generative AI in marketing. From building and training AI models with Amazon SageMaker to delivering personalized messages with Amazon Pinpoint and Amazon SES, AWS provides the tools and infrastructure needed to harness the power of generative AI.

The potential of generative AI in relation to the marketer is immense. It offers the ability to automate content creation, personalize customer interactions, and derive valuable insights from data, among other benefits. However, it’s important to remember that while generative AI can automate certain aspects of marketing, it is not a replacement for human creativity and intuition. Instead, it should be viewed as a tool that can augment human capabilities and free up time for marketers to focus on strategy and creative direction.

Get started with Generative AI in marketing communications

As we conclude this exploration of generative AI and its applications in marketing, we encourage you to:

  • Brainstorm potential Generative AI use cases for your business. Consider how you can leverage generative AI to enhance your marketing strategies. This could involve automating content creation, personalizing customer interactions, or deriving insights from data.
  • Start leveraging generative AI in your marketing strategies with AWS today. AWS provides a comprehensive suite of services that make it easy to implement generative AI in your marketing strategies. By integrating these services into your workflows, you can enhance personalization, improve customer engagement, and drive better results from your campaigns.
  • Watch out for the next part in the series of integrating Generative AI into Amazon Pinpoint and SES. We will delve deeper into how you can leverage Amazon Pinpoint and SES together with generative AI to enhance your marketing campaigns. Stay tuned!

The journey into the world of generative AI is just beginning. As technology continues to evolve, so too will the opportunities for marketers to leverage AI to enhance their strategies and deliver more personalized, engaging content. We look forward to exploring this exciting frontier with you.

About the Author

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen is an Amazon Pinpoint and Amazon Simple Email Service Specialist Solutions Architect at AWS. At work, he specializes in technical implementation of communications services in enterprise systems and architecture/solutions design. In his spare time, he enjoys chess, rock climbing, hiking and triathlon.

Belgian Tax Hack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/belgian-tax-hack.html

Here’s a fascinating tax hack from Belgium (listen to the details here, episode #484 of “No Such Thing as a Fish,” at 28:00).

Basically, it’s about a music festival on the border between Belgium and Holland. The stage was in Holland, but the crowd was in Belgium. When the copyright collector came around, they argued that they didn’t have to pay any tax because the audience was in a different country. Supposedly it worked.

AWS Application Migration Service Major Updates: Global View, Import and Export from Local Disk, and Additional Post-launch Actions

Post Syndicated from Irshad Buchh original https://aws.amazon.com/blogs/aws/aws-application-migration-service-major-updates-global-view-import-and-export-from-local-disk-and-additional-post-launch-actions/

AWS Application Migration Service simplifies, expedites, and reduces the cost of migrating your applications to AWS. It allows you to lift and shift many physical, virtual, or cloud servers without compatibility issues, performance disruption, or long cutover windows. You can minimize time-intensive, error-prone manual processes by automating replication and conversion of your source servers from physical, virtual, or cloud infrastructure to run natively on AWS by using Application Migration Service for migration. Earlier this year, we introduced major improvements, such as a server migration metrics dashboard, import and export, and additional post-launch modernization actions.

Today, I’m pleased to announce three major updates to Application Migration Service. Here’s the quick summary for each feature release:

  • Global View – You can manage large-scale migrations across multiple accounts. This feature provides you both visibility and the ability to perform specific actions on source servers, apps, and waves in different AWS accounts.
  • Import and Export from Local Disk – You can now use Application Migration Service to import your source environment inventory list to the service from a CSV file on your local disk. You can also export your source server inventory list from the service to a CSV file and download it to your local disk. You can continue leveraging the previously launched import and export functionality to and from an S3 bucket.
  • Additional Post-launch Actions – In this update, Application Migration Service added four additional predefined post-launch actions. These actions are applied to your migrated applications when you launch them on AWS.

Let me share how you can use these features for your migration.

Global View
Global View provides you the visibility and the ability to perform specific actions on source servers, applications, and waves in different AWS accounts. Global view uses AWS Organizations to structure a management account (which has access to source servers in multiple member accounts) and member accounts (which only have access to their own source servers).

To use this feature, you need to have an AWS account in which AWS Application Migration Service is initialized. This account must be an admin in AWS Organizations or a delegated admin for AWS Application Migration Service. You can view the Global View page on the Application Migration Service page in the AWS Management Console by selecting Global View in the left navigation menu.

You can use the global view feature to see source servers, applications and waves across multiple managed accounts and perform various actions, including:

  • Launching test and cutover instances across accounts
  • Monitoring migration execution progress across accounts

The main Global View page provides an overview of your account and this information changes depending on whether you have a management account or a member account.

In a management account, you can see the AWS organizations permissions, the count of linked accounts, and the total number of source servers, applications, and waves under Account information. The Linked accounts section displays the relevant information for your linked accounts. It shows all the linked accounts this account has access to, including the account you’re logged into (the management account) and the member accounts that are linked to it. If the management account has access to two additional member accounts, the Linked accounts section will show three accounts. It’s the total number of accounts that are visible through this management account (including itself). For member accounts, this page only displays the account information that includes the AWS organizations permissions and the number of source servers, applications, and waves in the specific account.

Global view

In your management account, you can access and review source servers, applications and waves within your account and across all member accounts. As a manager, you can choose between All accounts and My account from the drop-down menu, which allows you to change you view of presented source servers, applications or waves.

Waves

Import and Export from Local Disk
A comprehensive data center inventory forms the foundation of any successful migration endeavor. This inventory encompasses a comprehensive list of servers and applications managed by customers on premises. The inventory is categorized into migration waves to facilitate efficient migration planning.

Typically, this inventory is compiled using discovery tools or created manually by IT administrators. Perhaps you maintain your data center inventory in Excel spreadsheets. With Application Migration Service, we offer seamless support for importing your inventory list from a CSV file, which follows a format similar to the one used by Cloud Migration Factory.

In the previous release, Application Migration Service supported the option to import a file from Amazon S3 and export a file to Amazon S3. In this latest release, Application Migration Service supports the option to import a file from local disk and export a file to local disk. This makes it easy for you to manage large scale-migrations and ingest your inventory of source servers, applications and waves, including their attributes such as EC2 instance type, subnet and tags. These attributes are the parameters used to populate the EC2 launch template.

Import and Export

To start using the import feature, you need to identify your servers and application inventory. You can do this manually or using discovery tools. The next thing you need to do is download the import template, which you can access from the console.

Import Local

After you download the import template, you can start mapping your inventory list onto this template. While mapping your inventory, you can group related servers into applications and waves. You can also perform configurations, such as defining Amazon Elastic Compute Cloud (Amazon EC2) launch template settings and specifying tags for each wave.

The following screenshot is an example of the results of my import template.

Inventory

On the Application Migration Service page in the AWS Management Console, select Import on the left-side navigation menu (under Import and Export). Under the Import inventory tab, select Import from local disk. Select Choose file and choose the local file containing your inventory list. Select Import, and the inventory file is imported into Application Migration Service. When the import process is complete, the details of the import results appear.

Now, you can view all your inventory inside the Source servers, Applications, and Waves pages on the Application Migration Service console.

To export your inventory to a local file, select Export on the left-side navigation menu of the Application Migration Service page. Under Export inventory tab, choose Export to local disk. Specify the name of the file to download under Destination filename. Choose Export, and the inventory file downloads to your local disk. Application Migration Service uses an S3 bucket within your account for the import and export operations, even when using local disk. You must have the required permissions to perform this action. You can modify the exported inventory file and reimport it to perform bulk configuration updates across your inventory. When the global view feature is activated upon reimport, configuration changes are applied also across accounts.

Export Local

Additional Post-launch Actions
Post-launch actions allow you to control and automate actions performed after your servers have been launched in AWS. You can use predefined or custom post-launch actions.

Application Migration Service now has four additional predefined post-launch actions to run in your Amazon EC2 instances on top of the existing predefined post-launch actions. These additional post-launch actions provide you with flexibility to maximize your migration experience.

Post Launch template

The new four additional predefined post-launch actions are as follows:

  • Configure Time Sync – You can use the Time Sync feature to set the time for your Linux instance using ATSS.
  • Validate disk space – You can use the disk space validation feature to obtain visibility into the disk space and to ensure that you have enough available disk space on your target server.
  • Verify HTTP(S) response – You can use the Verify HTTP(S) response feature to conduct HTTP(S) connectivity checks to a predefined list of URLs. The feature verifies the connectivity to the launched target instance.
  • Enable Amazon Inspector – The Enable Amazon Inspector feature allows you to run security scans on your Amazon EC2 resources, including the target instances launched by Application Migration Service. The Amazon Inspector service is enabled at the account level. This action uses the Enable, BatchGetAccountStatus, and CreateServiceLinkedRole APIs.

Now Available
The Global View, Import and Export Feature from Local, and Additional Post-launch Actions are available now, and you can start using them today in all Regions where AWS Application Migration Service is supported. Visit the Application Migration Service User Guide to dive deeper into these exciting features and you can refer to the Getting started with AWS Application Migration Service to kickstart your workload migration to AWS.

—Irshad

Class-Action Lawsuit for Scraping Data without Permission

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/class-action-lawsuit-for-scraping-data-without-permission.html

I have mixed feelings about this class-action lawsuit against OpenAI and Microsoft, claiming that it “scraped 300 billion words from the internet” without either registering as a data broker or obtaining consent. On the one hand, I want this to be a protected fair use of public data. On the other hand, I want us all to be compensated for our uniquely human ability to generate language.

There’s an interesting wrinkle on this. A recent paper showed that using AI generated text to train another AI invariably “causes irreversible defects.” From a summary:

The tails of the original content distribution disappear. Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions. We call this effect model collapse.

Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale. Indeed, we already see AI startups hammering the Internet Archive for training data.

This is the same idea that Ted Chiang wrote about: that ChatGPT is a “blurry JPEG of all the text on the Web.” But the paper includes the math that proves the claim.

What this means is that text from before last year—text that is known human-generated—will become increasingly valuable.

AWS Week in Review – Generative AI with LLM Hands-on Course, Amazon SageMaker Data Wrangler Updates, and More – July 3, 2023

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-week-in-review-generative-ai-with-llm-hands-on-course-amazon-sagemaker-data-wrangler-updates-and-more-july-3-2023/

In last week’s AWS Week in Review post, Danilo mentioned that it’s summer in London. Well, I’m based in Singapore, and it’s mostly summer here. But, June is a special month here as it marks the start of durian season.

Starting next week, I’ll be travelling to Thailand, Malaysia, and the Philippines. But before I go, I want to share some interesting updates from last week for you.

Let’s get started.

Last Week’s Launches
Here are some launches that caught my attention:

New Hands-on Course: Generative AI with Large Language Models – Generative AI has been a technology highlight for the past few months. If you are on your journey to learn large language models (LLM), then you can try the new hands-on course Generative AI with LLMs at Coursera. Antje wrote a post to announce this collaboration course between DeepLearning.AI and AWS. This course is designed to prepare data scientists and engineers to become experts in selecting, training, fine-tuning, and deploying LLMs for real-world applications.

Generative AI with large language models

Amazon SageMaker Data Wrangler direct connection to Snowflake – With this announcement, you can now browse databases, tables, schemas, and query data from Snowflake in SageMaker Data Wrangler. This unlocks the possibility for you to join your data with other popular data sources, such as S3, Amazon Athena, Amazon Redshift, Amazon EMR and over 50 SaaS applications to create the right data set for machine learning.

Amazon SageMaker Role Manager now provides CDK library to create fine-grained permissions — The CDK support for Amazon SageMaker Role Manager lets you define permissions with fine-grained access for SageMaker users, jobs, and SageMaker pipelines programmatically. This will reduce manual efforts and consistent permissions management. For example, the following code grants permissions with a set of related machine learning activities specific to a persona.


export class myCDKStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const persona = new Persona(this, 'example-persona-id', {
        activities: [
            Activity.runStudioAppsV2(this, 'example-id1', {}), 
            Activity.accessS3Buckets(this, 'example-id2', {s3buckets: [s3.S3Bucket.fromBucketName('DOC-EXAMPLE-BUCKET')]}) 
            Activity.accessAwsServices(this, 'example-id3', {})
        ]
    });

    const role = persona.createRole(this, 'example-IAM-role-id', 'example-IAM-role-name');
    
    }
}

AWS SDK for SAP ABAP – Great news for SAP ABAP developers! We just recently announced the general availability of the AWS SDK for SAP ABAP. With this, ABAP developers can use simple, secure and configurable connections between ABAP environments and 200+ supported AWS services in all AWS Regions, including AWS GovCloud (US) Regions. This AWS SDK helps ABAP developers to modernize their business processes with AWS services.

Amazon OpenSearch Ingestion now supports ingesting events from Amazon Security Lake – Amazon OpenSearch Ingestion now lets you bring data in the Apache Parquet format. As Amazon Security Lake also uses Open Cybersecurity Schema Framework (OCSF) in Apache Parquet format, it means you can easily ingest data from Amazon Security Lake.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

AWS Open-Source Updates
As always, my colleague Ricardo has curated the latest updates for open-source news at AWS. Here are some of the highlights.

lightsail-miab-installer – This handy command-line tool developed by my colleague Rio Astamal was designed to simplify the process of setting up Mail-in-a-Box on Amazon Lightsail. With lightsail-miab-installer, you can effortlessly streamline the installation and configuration of Mail-in-a-Box, making it even more accessible and user-friendly.

rdsconn – This amazing tool, created by AWS Hero Aidan Steele, simplifies the process of connecting to an AWS RDS instance within a VPC directly from your laptop. Using the recently launched EC2 Instance Connect, rdsconn eliminates the need for cumbersome SSH tunnels.

cdk-appflow – If you’re using AWS CDK to build your applications and Amazon AppFlow to create bidirectional data transfer integrations between various SaaS applications and AWS, then you’re going to love cdk-appflow, a new AWS CDK construct for Amazon AppFlow. It’s currently in technical preview, but you’re more than welcome to try it and provide us with your feedback.

Upcoming AWS Events
There are also upcoming events that you can join to learn. Let’s start with AWS Summit events:

And, let’s learn from our fellow builders and join AWS Community Days:

Open for Registration for AWS re:Invent
Before I end this post, AWS re:Invent registration is now open!

This learning conference hosted by AWS for the global cloud computing community will be held from Nov 27 to Dec 1, 2023 in Las Vegas.

Pro-tip: You can use information on the Justify Your Trip page to prove the value of your trip to AWS re:Invent.

That’s all for this week. Check back next Monday for another Week in Review.

Happy building.
— Donnie

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Self-Driving Cars Are Surveillance Cameras on Wheels

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/self-driving-cars-are-surveillance-cameras-on-wheels.html

Police are already using self-driving car footage as video evidence:

While security cameras are commonplace in American cities, self-driving cars represent a new level of access for law enforcement ­ and a new method for encroachment on privacy, advocates say. Crisscrossing the city on their routes, self-driving cars capture a wider swath of footage. And it’s easier for law enforcement to turn to one company with a large repository of videos and a dedicated response team than to reach out to all the businesses in a neighborhood with security systems.

“We’ve known for a long time that they are essentially surveillance cameras on wheels,” said Chris Gilliard, a fellow at the Social Science Research Council. “We’re supposed to be able to go about our business in our day-to-day lives without being surveilled unless we are suspected of a crime, and each little bit of this technology strips away that ability.”

[…]

While self-driving services like Waymo and Cruise have yet to achieve the same level of market penetration as Ring, the wide range of video they capture while completing their routes presents other opportunities. In addition to the San Francisco homicide, Bloomberg’s review of court documents shows police have sought footage from Waymo and Cruise to help solve hit-and-runs, burglaries, aggravated assaults, a fatal collision and an attempted kidnapping.

In all cases reviewed by Bloomberg, court records show that police collected footage from Cruise and Waymo shortly after obtaining a warrant. In several cases, Bloomberg could not determine whether the recordings had been used in the resulting prosecutions; in a few of the cases, law enforcement and attorneys said the footage had not played a part, or was only a formality. However, video evidence has become a lynchpin of criminal cases, meaning it’s likely only a matter of time.

Предпазване срещу радиоактивен йод

Post Syndicated from original http://www.gatchev.info/blog/?p=2592

Вече от поне две седмици от различни източници постъпва информация, че руската армия е минирала АЕЦ Запорижя на практика напълно. Повечето от източниците (напр. украинското разузнаване) са пристрастни и е разумно да не бъдат приемани с абсолютно безрезервно доверие. Има обаче източници, непублични и с ограничен достъп, които не са твърде пристрастни, а твърдят същото. Така че е разумно да се приеме, че е възможно (ако и не абсолютно сигурно) да е вярно.

А от няколко дни постъпват новини, че руската армия се изтегля тихомълком от АЕЦ-а. Досега тя го държеше под строг контрол – в централата по всяко време имаше сериозни по размер руски военни части, включително тежко въоръжение (танкове, ракетни установки…). От военна точка изтегляне не се налага, пунктът все още е далече от бойната линия. Едно от вероятните предположения е, че има опасност централата да стане източник на радиоактивно заразяване – поради взривяване на мините или по друга причина.

В тази връзка може би ще е полезно да знаем какво да правим, ако този най-лош сценарий се сбъдне. Ако пък не, знаенето няма да ни навреди.

Общи думи

Адекватните мерки при радиоактивно заразяване са различни, в зависимост от типа и силата на заразяването. Те пък зависят от вида на източника, разстоянието до него и други фактори.

Когато източникът е единичен (ядрен взрив, авария в АЕЦ…) и разстоянието е много стотици или хиляди километри, почти единствената опасност е от отделения радиоактивен йод (осноно изотоп 131). Той се натрупва в щитовидната жлеза на хората и животните и увеличава вероятността от рак в нея. Другите опасности от него или други радионуклиди в този случай обикновено са минимални. (При много лоши стечения на обстоятелствата могат да бъдат реални. Това описание не ги покрива.)

Опасността от йод-131 зависи от количеството му около нас. То – от общото отделено количество йод-131 и дали и колко от него е пренесено от мястото на инцидента към нас. При разстояние като това от България до АЕЦ Запорижя пренасянето зависи от атмосферните условия около и след инцидента.

Количеството отделен йод би зависело от какви и колко обекти в АЕЦ Запорижя са пострадали, и в каква степен. АЕЦ-ът има 6 реактора ВВЕР-1000, плюс сериозни като размер охладителни басейни за първично съхранение на отработеното ядрено гориво. Ако всички те пострадат сериозно, количеството отделен йод-131 може да е десетки пъти над отделеното при авариите в АЕЦ Чернобил и Фукушима, и хиляди пъти над отделеното при употреба на един или няколко тактически ядрени заряда. (Това не означава, че другите последствия непременно също ще бъдат толкова по-големи, или дори същите.)

Размерът на уврежданията при потенциален инцидент е невъзможно да се предскаже отсега, атмосферните условия – също. Затова давам съвет как да се действа срещу радиоактивния йод при най-лош вероятен вариант. (Ако се появи реална опасност и от други радионуклиди, това ще е тема за отделен запис.)

За щастие Енергодар е на много стотици километри от нас. Ако там се случи нещо, дори ако вятърът духа оттам насам (което е рядко), ще имаме време да научим. Ще имаме време и да разберем дали е отделена радиация, и ако да – дали е количество, което заслужава предпазни мерки. След това ще имаме време и да проверим дали вятърът го носи към нас или надругаде. И след това, ако се налага, ще имаме предостатъчно време да вземем предпазни мерки, без да сме закъснели. Дебело подчертавам – не взимайте предпазните мерки преди да е потвърдено, че се налага!

Защита срещу радиоактивен йод

Радиоактивният йод е опасен с това, че щитовидната ни жлеза го поема и натрупва точно както обикновения йод. (И с това се излага на лъчението от атомите му.) Ако тя бъде наситена с достатъчно йод преди радиоактивният да стигне до нас, тя няма да поема повече и съответно няма да поеме от радиоактивния.

Предпочитаното за насищане с йод средство е калиев йодид. Той е активното вещество във всички йодни хапчета. Химическият калиев йодид е точно същото – ако нямате йодни хапчета, но имате химически калиев йодид, можете да отмерите от него правилната доза. (Горчив е на вкус – можете да го приемете с ядене или някакъв подсладител, не му пречат. Може да подразни стомаха – добре е да се приема с храна, или поне с прясно или кисело мляко. Храната не пречи на резорбцията му.)

Ако нямате други варианти, йод може да се набави и от йодна тинктура. Тинктурата е разтвор на йод (често и калиев йодид) във вода, спирт или комбинация от двете. Какво съдържа и точното му количество трябва да са посочени на опаковката – ако не са, не я използвайте! Ако съдържа само или основно чист йод, занижете малко милиграмите (100 мг чист йод са приблизително еквивалентни на 126 мг калиев йодид). Вкусът ѝ също не е приятен.

Тинктурата може да бъде предпочитана при хора, които не могат да приемат йод през стомаха (напр. тежки стомашни проблеми или силно чувствителен стомах). При намазване по кожата в областта на корема обикновено се резорбира около 80% от йода в нея. (Вместо да пиете таблетка от 130 мг, можете да намажете колкото тинктура съдържа 160 мг. калиев йодид или 125 мг. йод. Типичната за България йодна тинктура съдържа 5% йод и 10% калиев йодид – 0.8 до 1 куб. см от нея е подходяща доза за 72 кг. тегло.) Не е страшно да намажете повече от необходимото – опасно за здравето количество няма да се резорбира. (Но по-чувствителна кожа може да се раздразни – внимавайте пък за това!)

Може да ви предложат в аптеката Йодасепт или друга търговска марка на повидон-йодин. За дезинфекция на рани е прекрасен, за насищане с йод – не. Йодът в него е 1%, 15 пъти по-малко, отколкото в йодна тинктура. При намазване върху кожата не е известно какъв процент се резорбира, възможно е да е близък до нулата. При вътрешно поглъщане може да отдели атомарен йод, който е силен дразнител, по-висока доза може да ви изгори стомаха. Не го пийте!

При опасност като описаната, правилната стратегия би била да се приема ежедневно доза йод до намаляване на опасността от радиоактивния йод до пренебрежима. Последното става, когато радиацията от него спадне до нива, сравними или по-ниски от тези на естествения радиационен фон. Периодът на полуразпад на йод-131 е 8 дни – тоест, на всеки 8 дни радиацията и опасността от него спадат наполовина. След 16 дни те ще са 1/4 от първоначалните, след 24 дни – 1/8, след 32 дни – 1/16 и т.н.

След десетия ден можете да намалите дозата двойно или да я приемате през ден – защитата остава на практика същата.

Добре е да започнете приема на йод поне няколко часа преди радоактивният да достигне до вас. Ако закъснеете с няколко часа или дори дни, пак си струва – по-малко предпазване е по-добре от никакво. Безсмислено е да се започва преди информация, че имаме авария с изхвърляне на радиоактивен йод – дори в идеални метеорологични условия той надали би достигнал от АЕЦ Запорижя до България за по-малко от денонощие. В типичния случай биха му трябвали 2 денонощия, а ако вятърът не го носи насам, не е изключено дори при сериозно изхвърляне дотук да не достигне опасно количество.

Оптималната доза калиев йодид зависи от теглото на индивида. Зависимостта обаче не е линейна: по-ниското тегло изисква повече калиев йодид на килограм. Ето таблица за приблизителните оптимални дози:

72 кг. – 130 мг. на ден

40-45 кг. – 65 мг. на ден.

20-30 кг. – 32-45 мг. на ден

10 кг. – 25 мг. на ден

5 кг. – 20 мг. на ден

3 кг. – 15 мг. на ден

Приемането на доза до 2 пъти оптималната обикновено е безопасно за организма – не се притеснявайте, ако не можете да я отмерите абсолютно точно. Изключение са алергичните на йод и йодни препарати – те е добре да се консултират с лекар по темата.

При болести на щитовидната жлеза тези дози може да не са адекватни. Например при болест на Хашимото може да са необходими по-ниски. Каква доза е подходяща за конкретния болен зависи от болестта и стадия ѝ – може да е от няколко пъти дадената до нулева. Задължително се консултирайте с лекаря си по въпроса!

Йодните хапчета са обикновено 2 грамажа – 130 мг. и 65 мг. Смятайте според какви имате.

Ако се налага да делите хапче (или отмерена доза калиев йодид), най-добре ги разтворете във вода. Сложете в чаша такъв брой лъжици, малки чашки или кубически сантиметри вода (ако имате с какво да ги мерите), какъвто после ще ви е най-лесно да разделите на нужните ви дози. Сипете калиевия йодид или смачкайте добре хапчето във водата с лъжичка и разбърквайте до пълно разтваряне. Дръжте разтвора в хладилник, най-добре захлупен, за да не се изпарява лесно (увеличава концентрацията на калиевия йодид в остатъка).

Ако се налага да отмерите калиев йодид на прах, най-добре намерете точна везна за малки тегла. Аптеки които изпълняват микстури и места които ремонтират бижута най-често имат. Заложни къщи, които приемат злато и сребро, често също имат (може да са по-неточни, но отклонението няма да е фатално). Ако не можете да намерите иначе, попитайте в Интернет кой би ви отмерил. (А ако имате с какво да мерите, не се колебайте да помагате на нуждаещи се.)

Ако не можете да отмерите точно малка доза, отмерете най-малкото количество, което можете да отмерите точно. Количества от няколко грама могат да бъдат внимателно доразделени на 2 или 3 равни части върху стъкло с нож; после делете отново до достигане до желаната доза или близо до нея. При делене отведнъж на повече части е по-трудно да ги направите еднакви. Количества от грам и по-малко често е най-добре да бъдат разтворени във вода и тя да бъде делена. (Спринцовки от аптеката – една 10-20 мл и една 1-2 мл – могат да са много полезни. Ако малката е за инсулин, внимавайте с деленията – може да са в инсулинови единици, а не в милилитри: 40 инсулинови единици са 1 милилитър.)

Добра идея е да дадете калиев йодид и на домашните си любимци – и те заслужават предпазване. Най-добре става с нещо, което обичат (калиевият йодид няма мирис, но има горчив вкус). Оптималните дози на единица тегло за бозайници са на практика същите като за хора. За други домашни любимци първо се консултирайте с ветеринарен лекар.

The US Is Spying on the UN Secretary General

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/the-us-is-spying-on-the-un-secretary-general.html

The Washington Post is reporting that the US is spying on the UN Secretary General.

The reports on Guterres appear to contain the secretary general’s personal conversations with aides regarding diplomatic encounters. They indicate that the United States relied on spying powers granted under the Foreign Intelligence Surveillance Act (FISA) to gather the intercepts.

Lots of details about different conversations in the article, which are based on classified documents leaked on Discord by Jack Teixeira.

There will probably a lot of faux outrage at this, but spying on foreign leaders is a perfectly legitimate use of the NSA’s capabilities and authorities. (If the NSA didn’t spy on the UN Secretary General, we should fire it and replace it with a more competent NSA.) It’s the bulk surveillance of whole populations that should outrage us.

Redacting Documents with a Black Sharpie Doesn’t Work

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/redacting-documents-with-a-black-sharpie-doesnt-work.html

We have learned this lesson again:

As part of the FTC v. Microsoft hearing, Sony supplied a document from PlayStation chief Jim Ryan that includes redacted details on the margins Sony shares with publishers, its Call of Duty revenues, and even the cost of developing some of its games.

It looks like someone redacted the documents with a black Sharpie ­ but when you scan them in, it’s easy to see some of the redactions. Oops.

I don’t particularly care about the redacted information, but it’s there in the article.

How to verify an email address in SES which does not have an inbox

Post Syndicated from ajibho original https://aws.amazon.com/blogs/messaging-and-targeting/how-to-verify-an-email-address-in-ses-which-does-not-have-an-inbox/

Overview of solution

Amazon Simple Email Service (Amazon SES) is an email platform that provides a straightforward and cost-effective solution for sending and receiving emails using your own email addresses and domains.

One of the most common use cases for using separate verified from email address is in online retails/e-commerce platforms. Online/e-commerce platform need to send emails to their customers where the from address should look like “[email protected]. In these cases, the From addresses like [email protected] does not have inbox setup for receiving emails. Using the following solution, you can avoid setting up an inbox for the email identity while still verifying the email address for sending and receiving.

In order to send emails from SES using email/domain identity, we need to have the From email identity or domain verified in Amazon SES in a supported region. When verifying a domain,you have the option to use Easy DKIM or Bring Your Own DKIM(BYOD). For verifying an email address, you need to create an identity in Amazon SES for the respective region. Once the required email address identity is created, you will receive a verification link in your inbox. To successfully verify the email address, simply open the link in your browser. In this case, you would need to have inbox setup for email address to receive the verification link from [email protected].

Verifying a domain in Amazon SES allows you to send emails from any identity associated with that domain. For example, if you create and verify a domain identity called example.com, you don’t need to create separate subdomain identities for a.example.com, a.b.example.com, nor separate email address identities for [email protected], [email protected], and so on. Therefore, the settings for the domain remain the same for all From addresses and you cannot separate you sending activity. You can use this solution to verify the From address without setting up an inbox and differentiate sending activity and tracking based on settings. The benefits of having different email settings from the domain are mentioned below.

Benefits of verifying the email separately for the same domain:

1) When you verify the email along with your domain, you can keep the settings different for the two Identities. You can setup different Configuration sets, notifications and dedicated IP pools for the verified email. This separation enables you to manage domain and email settings independently.
2) You can have two separate emails for sending transaction ([email protected]) and Marketing emails ([email protected]). After assigning different configuration sets, you can monitor the bounces and complaints separately for the sender. A best practice here would be separating the Transactional and Marketing in sub domains. Having both types in the same domain can adversely affect the reputation for your domain, and reduce deliverability of your transactional emails.
3) Using different dedicated IP pools, you can separate the sending IPs for Marketing and transaction or any other emails. Thus, your IP reputation for one use case is not affected by any other emails.

Prerequisite

1) An active AWS account.
2) Administrative Access to the Amazon SES Console and Amazon Simple Storage Service(S3) console.
3) A verified identity (Domain) with an MX record for the domain pointing to a Receiving Endpoint in one of the following region in Amazon SES.

Region Name Region Receiving Endpoint
US East (N. Virginia) us-east-1 inbound-smtp.us-east-1.amazonaws.com
US West (Oregon) us-west-2 inbound-smtp.us-west-2.amazonaws.com
Europe (Ireland) eu-west-1 inbound-smtp.eu-west-1.amazonaws.com

Solution walkthrough

In order to verify the email in SES, we need to verify the link send from Amazon SES in the email inbox. We will setup receiving rule set and add S3 bucket with required permissions to store emails from Amazon SES in S3 bucket. After receiving the email in S3 bucket, download the email to get the verification link. Open the verification link in a browser to complete the process.

Step 1 : How to setup SES Email Receiving Ruleset for S3 bucket

1) Open the Amazon SES console.
2) In the navigation pane, under Configuration, choose Email Receiving.
Email Receiving Rule set

3) To create a new rule set, choose Create a Rule Set, enter a rule set name, and then choose Create a Rule Set.
Note: If you create a new rule set, select the rule set, and then choose Set as Active Rule Set. Only one of your receipt rule sets can be the active rule set at any given time.


4) Choose Active Rule Set and Choose Create Rule.

Active Ruleset

5) Enter a unique rule name. If your use case requires TLS or spam and virus scanning, then choose Require TLS or Enable spam and virus scanning. To make this an active rule, select the Enabled checkbox. Choose Next.
Receiving Rule Setting

6) To receive emails for specific verified domain, click Add new recipient condition and enter the domain/email address. You can leave it blank and it will store for all the verified domain addresses with receiving setup.
Add recipient condition

7) Choose Add new action, and then choose Deliver to S3 bucket
Action Deliver to S3 bucket

8) Click on Create S3 bucket
Create S3 bucket

9) Enter a unique S3 bucket name and click on ‘Create Bucket’
Note: S3 Bucket policy will be added automatically.
Provide Unique S3 bucket name

(Optional) Choose Message encryption for Amazon SES to use an Amazon Key Management Server (Amazon KMS) key to encrypt your emails.
(Optional) For SNS topic, select an Amazon Simple Notification Service (Amazon SNS) topic to notify you when Amazon SES delivers an email to the S3 bucket.
Add Action in Receiving rule set

10) Click Next and Create Rule.
Review and Create Ruleset

Step 2: Verifying email address in Amazon SES using S3

The following procedure shows you how to verify Email address in Amazon SES.
1) Open the Amazon SES console.
2) In the navigation pane, under Configuration, choose Verified identities.
3) Choose Create identity.
Create Verified Identity

4) Under Identity details, choose Email address as the identity type you want to create.
5) For Email address, enter the email address that you want to use. The email address must be an address that’s able to receive mail and that you have access to.
(Optional) If you want to Assign a default configuration set, select the check box.
6) To create your email address identity, choose Create identity. After it’s created, you should receive a verification email within five minutes from [email protected].

Create Verified identity and Enter
7) Open the Amazon S3 console.
Go to S3 bucket

8) Open the S3 Bucket that you configured to store the Amazon SES emails. Verify that the bucket contains the test email that you sent. It can take a few minutes for the test email to appear.
Select the Received Email in S3 bucket

9) Select the email/object received in S3 bucket. Click Download.
Download the received email/object

10) Open the Downloaded file in Notepad and copy the verification link under the Subject. Paste the link in your Browser and confirm it.
Open the Downloaded email in Notepad

11) Once the link is confirmed, you can check in SES console and confirm under verified identities that your email address is in verified Status.
Browser link after pasting the verification link

Verified Identity confirmation in SES console

Cleaning up:

You should have successfully verified email address in Amazon SES using S3 bucket. To avoid incurring any extra charges, remember to delete any resources created manually if you no longer need them for monitoring.

Steps for removing the resources:

1) Delete all the created/verified Identities.
2) Delete data regarding Amazon SES receiving Rules.
3) Delete data regarding Amazon S3 bucket.

Conclusion:

In this blog post, we explained the benefits of verifying a separate email address for the verified domain without setting up an inbox. Having separate identities for different use cases helps in efficient management of bounces, complaints, and delivery. You can setup different IP pools using configuration set for different use cases.

Follow-up:

https://aws.amazon.com/blogs/messaging-and-targeting/manage-incoming-emails-with-ses/
https://docs.aws.amazon.com/ses/latest/dg/receiving-email.html
https://repost.aws/knowledge-center/ses-receive-inbound-emails

About the Author

Ajinkya bhoite_1Ajinkya Bhoite is Cloud Support Engineer II in AWS and Service Matter Expert in Amazon Simple Email Service(SES). Along with Amazon SES, he is an Amazon S3 enthusiast. He loves helping customers in solving issues related to SES and S3 in their environment. He loves reading, writing and running but not in the same order. He has a fictional novel published on Amazon Kindle by the name Shiva Stone: Hampi’s Hidden treasure.

Stalkerware Vendor Hacked

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/stalkerware-vendor-hacked.html

The stalkerware company LetMeSpy has been hacked:

TechCrunch reviewed the leaked data, which included years of victims’ call logs and text messages dating back to 2013.

The database we reviewed contained current records on at least 13,000 compromised devices, though some of the devices shared little to no data with LetMeSpy. (LetMeSpy claims to delete data after two months of account inactivity.)

[…]

The database also contained over 13,400 location data points for several thousand victims. Most of the location data points are centered over population hotspots, suggesting the majority of victims are located in the United States, India and Western Africa.

The data also contained the spyware’s master database, including information about 26,000 customers who used the spyware for free and the email addresses of customers who bought paying subscriptions.

The leaked data contains no identifying information, which means people whose data was leaked can’t be notified. (This is actually much more complicated than it might seem, because alerting the victims often means alerting the stalker—which can put the victims into unsafe situations.)

Typing Incriminating Evidence in the Memo Field

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/typing-incriminating-evidence-in-the-memo-field.html

Don’t do it:

Recently, the manager of the Harvard Med School morgue was accused of stealing and selling human body parts. Cedric Lodge and his wife Denise were among a half-dozen people arrested for some pretty grotesque crimes. This part is also at least a little bit funny though:

Over a three-year period, Taylor appeared to pay Denise Lodge more than $37,000 for human remains. One payment, for $1,000 included the memo “head number 7.” Another, for $200, read “braiiiiiins.”

It’s so easy to think that you won’t get caught.

How to Manage SMS Opt-Outs with Amazon Pinpoint

Post Syndicated from Tyler Holmes original https://aws.amazon.com/blogs/messaging-and-targeting/how-to-manage-sms-opt-outs-with-amazon-pinpoint/

Managing SMS can be challenging due to the continued increase in global regulation. One of the common functions that needs to exist for you to be in compliance is to be able to opt out your customers from receiving your communication when they request. Amazon Pinpoint has the ability to automatically opt your customers out of SMS communications using predefined keywords such as, “stop” or “unsubscribe” when responding back to the number from which they do not want to receive communications. This is great in many cases because it does not require any extra configuration for you to remain in compliance with SMS regulations but if you are sending communications across multiple use cases this is not ideal. In this blog post we will explain how to manage SMS sending for multiple use cases while still remaining in compliance with SMS regulations.

The default setup for Pinpoint SMS includes an Opt-Out List whose standard functionality is to block opted-out numbers from all SMS communications from the entire account. With the standard configuration, your only option to remove an endpoint from the default Opt-Out List is to use the DeleteOptedOutNumber action via the API. This can only be done once every 30 days to curtail abusive sending so it’s not the ideal method for managing opt-outs. Note: If you do want to opt-out endpoints completely from your account you can always add Endpoints manually to the Opt-Out List via the PutOptedOutNumber Action and use “default” as the “OptOutListName.” Given the previous this means that the common scenario for many customers, needing to send transactional messages such as One Time Password (OTP) as well as marketing related materials, requires configuring new Opt-Out Lists.

The first step in allowing your customers to receive critical communications like OTPs but opt out of other communications like marketing materials is to make sure that each use case has its own Origination Identity (OID). This means that if you have two use cases for SMS, such as OTP and marketing related messaging, you need to procure an OID for each of them. This needs to be done for each country you plan on having support for sending SMS. While this increases your cost, it is the only way to accomplish differentiated sending across multiple use cases.

Prerequisites

  • In order to manage Opt-Out Lists you need to use the V2 API for SMS and voice
    • You can do the setup using the V2 APIs and still use SendMessage if you would like, but setup of Opt-Out Lists are only available in the V2 API
  • You must Purchase/Register an OID for each use-case in each country you plan to support
    • Example: If you are sending marketing materials and OTP messages in the US and are using a short code then you will need to purchase at least two short codes and register each use case.

How to Create Opt-Out Lists and Send SMS
In order to manage opt-outs per origination identity(OID) you need to create an Opt-Out List for each use case. There are two ways to accomplish this, below you will find the process for each The basic process for this is below:

  • Option 1
    • The easiest way to add an Opt-Out List to an OID is when you procure the number.
      • First create an Opt-Out List using CreateOptOutList
      • Then, when procuring a new number either by opening a case or using RequestPhoneNumber, specify the name of that Opt-Out List
        • Note: If you already have a number you can add an Opt-Out List with the UpdatePhoneNumber action
      • Use the PutKeyword action to add keywords
        • Create at least one Opt-In Action Keyword
          • This is the keyword that recipients can use to opt in to receiving your messages for the first time or they can also opt back into previously unsubscribed communications
      • You can now send messages using this phone number and opted-out recipients will be put in your Opt-Out List, not the default Global Account Opt-Out List

While this is a simple way of creating Opt-Out Lists it does not scale well if you are sending globally or are operating as an Independent Software Vendor (ISV) and have multiple locations, use cases, and configurations that you need to assign to lots of numbers.

  • Option 2
    • Create a Pool
      • A Pool is a container for OIDs that share the same use case and thus, the same Opt-Out List. See the image below
      • Set an Opt-Out List for the Pool by using the UpdatePool Action and specifying the “OptOutListName”
    • Associate an OID to that Pool
      • You can have multiple OIDs per Pool if necessary
      • Note: Opt-Out Lists of OIDs and Pools must match, so if you previously associated an Opt-Out List to any OIDs you may need to update those OIDs to match that of the Pool prior to associating it with the Pool.
    • Send your SMS using the Pool Id or Pool Arn for the “OriginationIdentity” using the SendTextMessage Action

Conclusion
In this post you have learned how to create Opt-Out Lists that give you better control over how your customers opt out of your communications. Using custom Opt-Out Lists allows you to have multiple SMS use cases and gives your customers the ability to receive critical communications such as OTP, but not receive communications like marketing that they are not interested in. The V2 API for SMS and Voice has many more useful actions not possible with the V1 API so we encourage you to explore how it can further help you simplify and automate your applications.

Review the documentation for the V2 SMS and Voice API here
Confirm the origination IDs you will need here
Check out the support tiers comparison here

Resources
https://docs.aws.amazon.com/pinpoint/latest/apireference_smsvoicev2/Welcome.html
https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-sms-originating-identities-choosing.html
https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-sms-limitations-opt-out.html

Excel Data Forensics

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/excel-data-forensics.html

In this detailed article about academic plagiarism are some interesting details about how to do data forensics on Excel files. It really needs the graphics to understand, so see the description at the link.

(And, yes, an author of a paper on dishonesty is being accused of dishonesty. There’s more evidence.)

AI as Sensemaking for Public Comments

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/ai-as-sensemaking-for-public-comments.html

It’s become fashionable to think of artificial intelligence as an inherently dehumanizing technology, a ruthless force of automation that has unleashed legions of virtual skilled laborers in faceless form. But what if AI turns out to be the one tool able to identify what makes your ideas special, recognizing your unique perspective and potential on the issues where it matters most?

You’d be forgiven if you’re distraught about society’s ability to grapple with this new technology. So far, there’s no lack of prognostications about the democratic doom that AI may wreak on the US system of government. There are legitimate reasons to be concerned that AI could spread misinformation, break public comment processes on regulations, inundate legislators with artificial constituent outreach, help to automate corporate lobbying, or even generate laws in a way tailored to benefit narrow interests.

But there are reasons to feel more sanguine as well. Many groups have started demonstrating the potential beneficial uses of AI for governance. A key constructive-use case for AI in democratic processes is to serve as discussion moderator and consensus builder.

To help democracy scale better in the face of growing, increasingly interconnected populations—as well as the wide availability of AI language tools that can generate reams of text at the click of a button—the US will need to leverage AI’s capability to rapidly digest, interpret and summarize this content.

There are two different ways to approach the use of generative AI to improve civic participation and governance. Each is likely to lead to drastically different experience for public policy advocates and other people trying to have their voice heard in a future system where AI chatbots are both the dominant readers and writers of public comment.

For example, consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes. To be sure, digital technology has helped.

In 2021, the Council of Federal Chief Data Officers recommended modernizing the comment review process by implementing natural language processing tools for removing duplicates and clustering similar comments in processes governmentwide. These tools are simplistic by the standards of 2023 AI. They work by assessing the semantic similarity of comments based on metrics like word frequency (How often did you say “personhood”?) and clustering similar comments and giving reviewers a sense of what topic they relate to.

Think of this approach as collapsing public opinion. They take a big, hairy mass of comments from thousands of people and condense them into a tidy set of essential reading that generally suffices to represent the broad themes of community feedback. This is far easier for a small agency staff or legislative office to handle than it would be for staffers to actually read through that many individual perspectives.

But what’s lost in this collapsing is individuality, personality, and relationships. The reviewer of the condensed comments may miss the personal circumstances that led so many commenters to write in with a common point of view, and may overlook the arguments and anecdotes that might be the most persuasive content of the testimony.

Most importantly, the reviewers may miss out on the opportunity to recognize committed and knowledgeable advocates, whether interest groups or individuals, who could have long-term, productive relationships with the agency.

These drawbacks have real ramifications for the potential efficacy of those thousands of individual messages, undermining what all those people were doing it for. Still, practicality tips the balance toward of some kind of summarization approach. A passionate letter of advocacy doesn’t hold any value if regulators or legislators simply don’t have time to read it.

There is another approach. In addition to collapsing testimony through summarization, government staff can use modern AI techniques to explode it. They can automatically recover and recognize a distinctive argument from one piece of testimony that does not exist in the thousands of other testimonies received. They can discover the kinds of constituent stories and experiences that legislators love to repeat at hearings, town halls and campaign events. This approach can sustain the potential impact of individual public comment to shape legislation even as the volumes of testimony may rise exponentially.

In computing, there is a rich history of that type of automation task in what is called outlier detection. Traditional methods generally involve finding a simple model that explains most of the data in question, like a set of topics that well describe the vast majority of submitted comments. But then they go a step further by isolating those data points that fall outside the mold—comments that don’t use arguments that fit into the neat little clusters.

State-of-the-art AI language models aren’t necessary for identifying outliers in text document data sets, but using them could bring a greater degree of sophistication and flexibility to this procedure. AI language models can be tasked to identify novel perspectives within a large body of text through prompting alone. You simply need to tell the AI to find them.

In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, “who donated the most to our campaign” or “which company employs the most of my former staffers” become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals. Right now, as much as 90% of constituent communications are mass emails organized by advocacy groups, and they go largely ignored by staffers. People are channeling their passions into a vast digital warehouses where algorithms box up their expressions so they don’t have to be read. As a result, the incentive for citizens and advocacy groups is to fill that box up to the brim, so someone will notice it’s overflowing.

A talented, knowledgeable, engaged citizen should be able to articulate their ideas and share their personal experiences and distinctive points of view in a way that they can be both included with everyone else’s comments where they contribute to summarization and recognized individually among the other comments. An effective comment summarization process would extricate those unique points of view from the pile and put them into lawmakers’ hands.

This essay was written with Nathan Sanders, and previously appeared in the Conversation.