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IBM and startup Grillo seek to bring low-cost, early-warning earthquake detection devices to Puerto Rico



With the backing of the Clinton Global Initiative, the partners are calling on the open-source community to help the Caribbean island implement economically friendly EEW systems.


Image: iStock/petrovich9

In January 2020, Puerto Rico was throttled by earthquakes over a stretch of several weeks, wreaking havoc on homes, infrastructure and causing displacement of a mass of its citizens. According to some estimates, the economic toll from earthquakes in the Greater Antilles area that year resulted in $3.1 billion in damages. The Caribbean, in general, is a highly seismic region because of its location —sitting within an intersection of juggernaut tectonic plates. 

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And, what’s more, traditional earthquake early warning (EEW) systems, which are designed to provide people with time to protect themselves from seismic events, are extremely expensive, resulting in most countries and territories not having full-spectrum ones that cover their entire regions. Enter Grillo, which calls itself a “Seismology-as-a-Service” startup, and one that wants to change the affordability and stretch of such systems. In collaboration with IBM and the Clinton Global Initiative and others, it is set to provide Puerto Rico with an open-sourced, EEW alternative. 

Dubbed OpenEEW (open-source earthquake early warning), Grillo has developed sensors (with assistance from also the Linux Foundation) that are relatively cheap, open-source hardware designs — ones that can quickly detect if the ground moves, access cloud-based algorithms to verify an earthquake is happening (or about to), and then provide alerts to people via a mobile app or wearable.

SEE: The CIO’s guide to quantum computing (free PDF) (TechRepublic)

Dubbed OpenEEW (open-source earthquake early warning), Grillo has developed sensors (with assistance from also the Linux Foundation) that are relatively cheap, open-source hardware designs — ones that can quickly detect if the ground moves, access cloud-based algorithms to verify an earthquake is happening (or about to), and then provide alerts to people via a mobile app or wearable.

More specifically, Grillo is cutting EEW overhead by using IoT, cloud computing and AI, as well as Node-RED analysis tools and a Docker-centered container solution. As Grillo has it, OpenEEW is a “promising low-cost, accessible option using off-the-shelf technology instead of the million-dollar systems often used today.”  

Last month, the former U.S. president, Bill Clinton, announced $25,000 in credits and open-source contributions from IBM, in conjunction with Grillo’s proposed plans. In addition, the Puerto Rico Science Trust is promising funding for the project as well. 

Since 2017, Grillo has launched its kits in Mexico, Chile and Costa Rica. And the company now wants to make its technology deployable in other seismically active regions, such as Nepal and New Zealand. Puerto Rico will be the first location in the Caribbean to land the open-source devices (for now, around 90 of them are set to be placed around the island).   

And why do so few countries have nationwide earthquake early warning systems in the first place? The asking price for the implementation, Andres Meira, the Grillo co-founder told TechRepublic. “The Japanese EEW is said to have cost around $1 billion,” Meira says. Adding: “Others such as ShakeAlert and the Mexican Seismic Alert System (SASMEX) regularly require 10s of millions of USD.” 

The early-detection technology ShakeAlert, which was created by the United States Geological Survey (USGS), is the EEW platform the U.S. currently uses for the West Coast (that is, in California, Oregon and Washington). SASMEX initiated operations in 1993 and has come with a hefty price as well.    

Where do open-source volunteers factor into the project? According to Pedro Cruz, a developer advocate at IBM, “anyone from the open source community can get involved with and help OpenEEW; not just in Puerto Rico, but all over the world.” 

Cruz says “different communities across the world can help by advancing the different components (sensors, algorithms, alert devices) and by deploying sensor networks in different countries.” 

As TechRepublic previously reported, “[u]nlike a national seismic platform, the team’s open-source EEW project is designed to create a global partnership rather than a nationalized network, allowing people around the globe to deploy these systems in their communities as part of a larger humanitarian patchwork of sensors.”

OpenEEW came about from the Call for Code, an initiative to give solutions, via technology, that can be deployed in the communities with the greatest needs and make change. 

“Since 2018 this movement,” Cruz said, “has grown to over 400,000 participants across 179 nations, and developers have already created more than 15,000 applications using IBM technologies.”

Call for Code was started by the global leader David Clark Cause, with IBM serving as its founding partner. 

In a press release, Grillo and IBM say the detection code for their devices can use help by programmer volunteers and is being developed in Python and pushed out in Kubernetes. Moreover, Grillo says it is currently working on a “Carbon/React dashboard,” which the public will be able to see and interact with the OpenEEW, as well as see recent earthquake occurrences. 

Meira says “there are also individual citizen scientists who are installing their own OpenEEW sensors and connecting to our global system in the cloud.” He adds that they are hoping that “eventually sufficient density of these stations” come about so “that a global EEW emerges.” 

On OpenEEW’s website, it lays out how one can go about deploying sensors, implement detectors for earthquakes and send out alerts about one that may occur.

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Digital Onboarding: BNY Mellon and Saphyre to Leverage AI to Enhance Customer Experience



BNY Mellon (NYSE: BK), an American investment banking services holding company headquartered in New York City with over $380 billion in assets, and Saphyre recently revealed that they’ll utilize AI tech to enhance the customer experience while also automating and expediting client onboarding.

This partnership with Saphyre supports the bank’s OMNISM strategy to work cooperatively with Fintechs to better support customers’ investment goals.

Saphyre’s platform has been developed to provide seamless communication between customers and priority stakeholders by enhancing traditional communication methods, like email, fax, and phone calls.

This latest integration between the two firms will allow for improved communication while lowering time to market, and also enabling more efficient international trading.

Caroline Butler, Global Head of Custody at BNY Mellon, stated:

“Time is a finite and precious commodity. BNY Mellon’s work with Saphyre aims to create true savings for our custody clients and truly expedite the client onboarding process. What once took days or weeks, is now near real time. This is yet another example of the digitization efforts BNY Mellon has undertaken in the past two years with a direct client benefit.”

Gabino M. Roche, Jr., CEO and Founder at Saphyre, remarked:

“Having BNY Mellon join the Saphyre endeavor is a great honor. By applying our patented technology to their leading asset servicing operations we’ve demonstrated the ability to intelligently pre-fill client custody packs, allow for digital signatures, auto-setup SWIFT Reporting, Trade Message Routing, and Corporate Action standing instruction – while intelligently and dynamically tracking market requirements and their respective document statuses. In a post-COVID world where AI and digital is paramount, BNY Mellon is fully seizing the innovation mandate.”

Earlier this year, BNY Mellon released a report in which it noted that the bank thinks there’s now real demand for Bitcoin and other cryptocurrencies. In its report, the bank clarified that it’s not attempting to derive a price target or formalize “a valuation mode” for these new forms of assets. However, they intend to look into the different “analogies” and “dissimilarities” that may be applied to Bitcoin and “potentially other areas of cryptos.”

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Passengers Learn To Adapt As Airlines Adopt Dynamic Pricing



With airlines keener than ever to maximize revenues, dynamic pricing is making inroads into the industry. Not everyone is welcoming the trend, but industry insiders say dynamic pricing can benefit both passengers and airlines.

Like it or not, dynamic pricing is here to stay in the airline industry. Photo: Don Wilson / Sea-Tac Airport

Airlines know a lot about their passengers, dynamic pricing harnesses that knowledge

Dynamic pricing is a process whereby an airline will pitch a fare at you based on what they know about you or think they know about you. Airlines are masters at gathering data. They harvest your frequent flyer data and track your searches and interests online via cookies. Hand over your Amex details to buy a drink inflight or a case of wine from the airline’s wine store and that airline gets an insight into your drinking preferences. Hundreds or thousands of these tracked behaviors all add up.

Log on to the British Airways website (or any airline’s website) after cleaning up your browser, and a message like this pop up.

“By continuing to use, you will be agreeing to the website terms and conditions and the use of cookies while using the website and our services. Please also read our privacy policy under which, to the extent stated, you consent to the processing of your personal data.”

Airlines already know a lot about their passengers – we’ve largely lost or surrendered that privacy battle. Now, many airlines are harnessing that data and learning to use it to boost revenues. On an individual passenger level, dynamic pricing tries to determine what a passenger is willing to pay to fly from Madrid to Heathrow next Sunday.

Your favorite airline already knows a lot about you. Photo: Don Wilson / Sea-Tac Airport

Just how much will a passenger pay to fly at a certain time on a certain day?

Justin Jander, Director of Product Management at digital commerce platform PROS  says airlines are trying harder than ever to create a sticky end-to-end passenger journey. One way they can do that is to use artificial intelligence (AI) to learn from the past behavior of a passenger. The airline can then attempt to predict what they will do next – including what they are willing to pay for an airline ticket.

“Dynamic pricing is extremely relevant to the airline industry as it allows airlines to break away from the barriers of fare classes with fixed price points,” says Jander. “Imagine a scenario where there are two filled fares, one at $100 and the other at $200. If a passenger is willing to pay $150, the airline either offers that passenger the $100 fare and loses $50 in incremental income. Or the airline can offer the $200 fare and lose the entire $150. Having this flexibility to identify an optimal price point allows airlines to be more effective in capturing revenue.”

We know airlines adjust fares according to broad seasonal factors. We also know an airline will adjust fares to a particular destination at a certain time if a big event is on in that city, say a football final. Equally, airlines will drop fares at off-peak times to stimulate travel demand. Dynamic pricing is about taking this to a more granular, individual passenger level.

Passengers can stand to benefit from dynamic pricing if they learn how it works. Photo: Ontario International Airport

Dynamic pricing can work for passengers

Justin Jander says dynamic pricing can work for passengers as well as airlines. At a basic level, interested passengers can learn how dynamic pricing works and is applied. It’s like learning how frequent flyer, hotel loyalty, or shopping programs work. Once you understand the nuts and bolts of dynamic pricing, passengers can potentially work dynamic pricing to their advantage.

“It is more expensive to acquire a new customer than to retain one,” says the PROS Director. “It makes sense for airlines to prioritize getting to know their existing customers.

“For passengers that are brand loyal customers of a particular airline, they will benefit from receiving personalized flight packages based on the AI that the airline has been able to leverage to understand them and their preferences.”

Most airline insiders agree dynamic pricing is here to stay. As the AI behind it gets smarter, so to will dynamic pricing. It will become more subtle and less driven by sometimes clunky algorithms. There always has been and always will be some tension between buyer and seller. Dynamic pricing in the airline industry won’t take that away> But over time, dynamic pricing may become better at fixing the median price that satisfies both airline and passengers.

Do you agree with dynamic pricing in the airline industry? Is it here to stay? Post a comment and let us know.

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Artificial Intelligence

Waabi’s Raquel Urtasun explains why it was the right time to launch an AV technology startup



Raquel Urtasun, the former chief scientist at Uber ATG, is the founder and CEO of Waabi, an autonomous vehicle startup that came out of stealth mode last week. The Toronto-based company, which will focus on trucking, raised an impressive $83.5 million in a Series A round led by Khosla Ventures. 

Urtasun joined Mobility 2021 to talk about her new venture, the challenges facing the self-driving vehicle industry and how her approach to AI can be used to advance the commercialization of AVs.

Why did Urtasun decide to found her own company?

Urtasun, who is considered a pioneer in AI, led the R&D efforts as a chief scientist at Uber ATG, which was acquired by Aurora in December. Six months later, we have Waabi. The company’s mission is to take an AI-first approach to solving self-driving technology. 

I left Uber a little bit over three months ago to start this new company, Waabi, with the idea of having a different way of solving self-driving. This is a combination of my 20-year career in AI as well as more than 10 years in self-driving. Thinking about a new company was something that was always in my head. And the more that I was in the industry, the more that I started thinking about going away from the traditional approach and trying to have a diverse view of how to solve self-driving was actually the way to go. So that’s why I decided to do this company. (Time stamp: 1:21)

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Event-based fraud detection with direct customer calls using Amazon Connect



Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that in some countries when fraud is suspected the credit card is blocked immediately, which leaves the cardholder without a reason as to why, how, or when. Depending on the situation, it can take anywhere from a few hours to days until the customer is notified and even longer to resolve.

With Amazon Connect, a cardholder can be notified immediately of a suspected card fraud and interactively verify if the suspected transactions were indeed fraudulent over the phone. Amazon Connect enables an event-based reaction, which allows you to contact the cardholder directly via phone without the need for an agent-in-the-loop. This makes for a cost-effective and frictionless cardholder experience.

This post shows you how to build, train, and deploy a fraud detection model and rules using Amazon Fraud Detector and integrate predictions with Amazon Connect in order to connect with customers in real time. Amazon Fraud Detector is a fully managed service that uses ML and more than 20 years of fraud detection expertise from Amazon to identify potentially fraudulent activity so you can catch online fraud faster and more frequently. Amazon Connect is an omnichannel cloud contact center that helps you provide superior customer service at a lower cost.

Solution overview

In this post, we use the following services:

  • AWS CloudFormation sets up the resources for our contact flow.
  • Amazon Connect is your contact center in the cloud. It allows you to set up contact centers and contact flows. We use a template that you can upload that helps you create your first contact flow.
  • Amazon DynamoDB is the NoSQL data storage used to store your customer data. You can exchange it in your existing cloud infrastructure for instance with Amazon Aurora
  • Amazon Fraud Detector is the AI/ML service that lets you build, train, and deploy your ML models. We provide all the data and code for you in this artifact.
  • AWS Lambda handles the event-driven request coming from a transaction and the connection between Amazon Connect and your DynamoDB table.

The following diagram illustrates our serverless architecture.

Whenever a transaction is made, you can send the metadata of that transaction to a Lambda function (Step 1). This function invokes your Amazon Fraud Detector model and predicts whether this transaction is fraudulent. If it is, the function looks up customer data from the DynamoDB table (2), sends these attributes to Amazon Connect, and calls the customer (3) whose credit card is affected. When the customer picks up the phone (4), they can interact with Amazon Connect and learn more about what happened. The customer can then decide whether it was fraud. If it was, another Lambda function is invoked through Amazon Connect (5) and blocks the corresponding credit card by setting a flag in the DynamoDB table (6).


For this walkthrough, you should have the following prerequisites:

Set up Amazon Connect and the associated contact flow

In this section, we walk through setting up Amazon Connect and our contact flow. We also discuss the contact flow in more detail.

Create an Amazon Connect instance

The first step is to create an Amazon Connect instance. For the rest of the setup, we use the default values, but don’t forget to create an administrator login.

Instance creation can take 1-2 minutes, after which we log in to the Amazon Connect instance using the admin account created previously. We’re now ready to create our flow and claim a number to attach to the flow.

Set up the contact flow

For this post, we have a predefined contact flow template that we can import from our GitHub repo.

  1. In the repo, select the file contact-flow/credit-card-fraud and choose Import.

For detailed import instructions, see Import/export contact flows.

  1. Under the name of the contact flow, choose Show additional flow information.

Here you can find the ARN of the contact flow.

  1. Note the following information from the ARN to use in a later step: the contact flow ID and contact center instance ID.

Claim your phone number

For instructions on claiming a number, see Step 3: Claim a phone number. Make sure to choose the previously imported contact flow while claiming the number. If no numbers are available in the country of your choice, raise a support ticket.

Understanding the contact flow

The following diagram shows our contact flow.

The contact flow does the following:

  • Enables logging
  • Sets the output voice to Amazon Kendra
  • Gets customer input using DTMF (only keys 1 and 2 are valid)
  • Based on the user’s input, the flow takes the following action:
    • Prompts a goodbye message stating no action will be taken and exits
    • Prompts a goodbye message stating an action will be taken, invokes a Lambda function to log the credit card in your DynamoDB table, and exits
    • Fails with a fallback block stating that the machine will be shut down and exits

You can also enhance your system with an Amazon Lex bot.

Create the CloudFormation stack

We use a CloudFormation stack to set up our resources.

  1. Choose Launch Stack:

  1. In the Parameters section, provide the following information:
    1. The fraud detector entity name
    2. The event name of this detector
    3. The name of your fraud detector
    4. A name for your DynamoDB table, such as customer-table-fraud-detection
    5. The IDs of your Amazon Connect instance and your contact flow (which you noted when you set up the contact flow)
    6. The S3 bucket name you want to use (the word “sagemaker” needs to be present)
    7. The number you claimed in Amazon Connect
  2. Choose Next.

  1. Acknowledge the creation of new AWS Identity and Access Management (IAM) resources and choose Create stack.

After the stack is deployed, you can find your AWS Lambda function, Amazon DynamoDB table, and an up-and-running Amazon SageMaker notebook called event-based-fraud-detection.

Build, train, and deploy the Amazon Fraud Detector model

In this section, we build, train, and deploy the Amazon Fraud Detector model using an example Jupyter notebook. You can build your model on the Amazon SageMaker console. For programmatic deployment, access the Amazon SageMaker notebook instance you created by choosing Open Juypter.

The instance contains a clone of the GitHub repository. It contains the example Jupyter notebook and example dataset. A config JSON was also created for you. To view these items, navigate into event-based-amazon-fraud-detector/fraud-detector-example.

Open the Fraud_Detector_End_to_End_Blog_Post.ipynb notebook to get started. For more examples on Amazon Fraud Detector, see the GitHub repository.

The notebook walks you through building and training the model. It also contains a lot of explanations and additional code that helps you deploy your first fraud detector.

Model metrics

This example also contains functionality to see and analyze the training metrics of your model. You can view these metrics interactively on the Amazon Fraud Detector console. On the Models page of the Amazon Fraud Detector console, choose the model that was trained. From there you can see several versions, if applicable, that were trained. Choose a version (for this post, version 1.0). The Model performance section contains an interactive graph similar to the following screenshot.

You can change thresholds to see how the confusion matrix changes. Furthermore, you can look at the ROC curve in the Advanced metrics section.

You’re model is now ready to be called on demand, for example from a website.

Add a customer record to your DynamoDB table

To add a record to your DynamoDB table, complete the following steps after the ML model is deployed:

  1. On the Amazon DynamoDB console, choose the table you created with the AWS CloudFormation template (for this post, thefraudtable).

  1. Choose Create item.

A new window appears that displays an example JSON string.

  1. Enter the JSON file example-customer.json provided in the examples folder of the downloaded GitHub repository.
    1. Change the fields last_name, first_name, phone_number, and salutation).
    2. Leave the customer­_id field as is. We use this ID when we test our whole architecture.
  2. Choose Create item.

Your first customer record is saved in the Amazon DynamoDB table.

Test your event-based architecture

To test the solution, complete the following steps:

  1. On the AWS Lambda console, choose the function fraud-detection.

We add a test event to this function, which is also part of the cloned GitHub repository and can be found in examples/lambda-test-event.json. In this test event, you find four different keys:

  • customer – The customer ID you gave your contact in the Amazon DynamoDB table. If you followed our guidance and didn’t change the ID when creating a customer in Amazon DynamoDB, you can leave this number as is.
  • card_number – Any number (this number is passed into Amazon Connect and the last four digits are read to the customer).
  • amount – A sample dollar amount that the customer hears when they are called.
  • payload – The metadata generated during the transaction. We use this to predict whether the transaction was fraud (for this test, it’s a fraudulent case to demonstrate that the fraud detector is working).

After you test the original payload, you can change numbers and see what prediction Amazon Fraud Detector makes based on new input.

  1. Choose Test.
  2. For Event name, enter a name.
  3. Enter the JSON example code.
  4. Choose Create.

  1. Choose Test

The Lambda function starts running, and you should get a phone call to the phone number you have placed in your Amazon DynamoDB table. If you choose to block your credit card during the call, your Amazon DynamoDB customer entry changes the field is_blocked from false to true.

Clean up

To avoid incurring future charges, delete the resources you created:

  • Amazon Fraud Detector model, events, entities, and detector
  • AWS CloudFormation stack
  • Amazon S3 bucket created by the script


Congratulations! You just developed your first event-driven fraud detection architecture. We deployed a complete serverless architecture in which we integrated Amazon Fraud Detector with Amazon Connect to connect with customers in real time if credit card fraud is detected. You can putting the AWS Lambda function fraud-detection behind an Amazon API Gateway and call it directly from your website. Or integrate the architecture into your existing transaction chain and call the AWS Lambda function any time a new transaction is made.

The advantage of this event-driven architecture is that the building blocks are completely exchangeable. For example, instead of predicting fraud, you might want to classify images on your shop floor. To do so, you could use Amazon Lookout for Vision as your backend ML model. An example for this can be found here. The opportunities are almost limitless. So, get started today!

About the Author

Michael Wallner is a Global Data Scientist with AWS Professional Services and is passionate about enabling customers on their AI/ML journey in the cloud to become AWSome. Besides having a deep interest in Amazon Connect, he likes sports and enjoys cooking.

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