1. Company Name: Unhedged
2. Website: www.unhedged.com.au
3. Key Staff & Titles: Peter Bakker – Founder & CEO, Mike Cohen – Co-Founder & COO, Glen VanBavinckhove – CTO, Jeremy Beasley – Growth, Jeremy Machet – Growth, and 6 others who are building like crazy
4. Location(s): Melbourne and Sydney
5. In one sentence, what does your fintech do?: Unhedged uses AI to deliver algorithmic returns to the everyday investor
6. How / why did you start your fintech company?: Being an Algotrader and working with rich people I got annoyed that these advanced tools were not available to my friends. When I looked up the returns of robo-investors I got really annoyed and thought: there must be a better way
7. What is the best thing your company has achieved or learnt along the way (this can include awards, capital raising etc)?: Raised 500K in 3 days which was faster then I ever raised before.
8. What’s some advice you’d give to an aspiring start-up?: Watch your cashflow: companies die of lack of cash, not lack of ideas
9. What’s next for your company? And are you looking to expand overseas or stay focussed on Australia?: Lauchinh the fund in April/May, a crowd fund raise in June and launching the retail product in July….
10. What other fintechs or companies do you admire?: Finserv (most stable earnings and growth), Blackrock: amazing money machine. Ellevest: a narrow target markets that works. CacheInvest: fundmanager as a service
11. What’s the most interesting or funniest moment that’s happened in your company’s lifetime?:
An investor transferring 100K without any documentation nor live fund (we returned the cash). We are still wondering how he knew where to transfer to.
Deepfake detectors and datasets exhibit racial and gender bias, USC study shows
Some experts have expressed concern that machine learning tools could be used to create deepfakes, or videos that take a person in an existing video and replace them with someone else’s likeness. The fear is that these fakes might be used to do things like sway opinion during an election or implicate a person in a crime. Already, deepfakes have been abused to generate pornographic material of actors and defraud a major energy producer.
Fortunately, efforts are underway to develop automated methods to detect deepfakes. Facebook — along with Amazon and Microsoft, among others — spearheaded the Deepfake Detection Challenge, which ended last June. The challenge’s launch came after the release of a large corpus of visual deepfakes produced in collaboration with Jigsaw, Google’s internal technology incubator, which was incorporated into a benchmark made freely available to researchers for synthetic video detection system development. More recently, Microsoft launched its own deepfake-combating solution in Video Authenticator, a system that can analyze a still photo or video to provide a score for its level of confidence that the media hasn’t been artificially manipulated.
But according to researchers at the University of Southern California, some of the datasets used to train deepfake detection systems might underrepresent people of a certain gender or with specific skin colors. This bias can be amplified in deepfake detectors, the coauthors say, with some detectors showing up to a 10.7% difference in error rate depending on the racial group.
Biased deepfake detectors
The results, while surprising, are in line with previous research showing that computer vision models are susceptible to harmful, pervasive prejudice. A paper last fall by University of Colorado, Boulder researchers demonstrated that AI from Amazon, Clarifai, Microsoft, and others maintained accuracy rates above 95% for cisgender men and women but misidentified trans men as women 38% of the time. Independent benchmarks of major vendors’ systems by the Gender Shades project and the National Institute of Standards and Technology (NIST) have demonstrated that facial recognition technology exhibits racial and gender bias and have suggested that current facial recognition programs can be wildly inaccurate, misclassifying people upwards of 96% of the time.
The University of Southern California group looked a three deepfake detection models with “proven success in detecting deepfake videos.” All were trained on the FaceForensics++ dataset, which is commonly used for deepfake detectors, as well as corpora including Google’s DeepfakeDetection, CelebDF, and DeeperForensics-1.0.
In a benchmark test, the researchers found that all of the detectors performed worst on videos with darker Black faces, especially male Black faces. Videos with female Asian faces had the highest accuracy, but depending on the dataset, the detectors also performed well on Caucasian (particularly male) and Indian faces. .
According to the researchers, the deepfake detection datasets were “strongly” imbalanced in terms of gender and racial groups, with FaceForensics++ sample videos showing over 58% (mostly white) women compared with 41.7% men. Less than 5% of the real videos showed Black or Indian people, and the datasets contained “irregular swaps,” where a person’s face was swapped onto another person of a different race or gender.
These irregular swaps, while intended to mitigate bias, are in fact to blame for at least a portion of the bias in the detectors, the coauthors hypothesize. Trained on the datasets, the detectors learned correlations between fakeness and, for example, Asian facial features. One corpus used Asian faces as foreground faces swapped onto female Caucasian faces and female Hispanic faces.
“In a real-world scenario, facial profiles of female Asian or female African are 1.5 to 3 times more likely to be mistakenly labeled as fake than profiles of the male Caucasian … The proportion of real subjects mistakenly identified as fake can be much larger for female subjects than male subjects,” the researchers wrote.
The findings are a stark reminder that even the “best” AI systems aren’t necessarily flawless. As the coauthors note, at least one deepfake detector in the study achieved 90.1% accuracy on a test dataset, a metric that conceals the biases within.
“[U]sing a single performance metrics such as … detection accuracy over the entire dataset is not enough to justify massive commercial rollouts of deepfake detectors,” the researchers wrote. “As deepfakes become more pervasive, there is a growing reliance on automated systems to combat deepfakes. We argue that practitioners should investigate all societal aspects and consequences of these high impact systems.”
The research is especially timely in light of growth in the commercial deepfake video detection market. Amsterdam-based Deeptrace Labs offers a suite of monitoring products that purport to classify deepfakes uploaded on social media, video hosting platforms, and disinformation networks. Dessa has proposed techniques for improving deepfake detectors trained on data sets of manipulated videos. And Truepic raised an $8 million funding round in July 2018 for its video and photo deepfake detection services. In December 2018, the company acquired another deepfake “detection-as-a-service” startup — Fourandsix — whose fake image detector was licensed by DARPA.
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AI is ready to take on a massive healthcare challenge
Which disease results in the highest total economic burden per annum? If you guessed diabetes, cancer, heart disease or even obesity, you guessed wrong. Reaching a mammoth financial burden of $966 billion in 2019, the cost of rare diseases far outpaced diabetes ($327 billion), cancer ($174 billion), heart disease ($214 billion) and other chronic diseases.
Cognitive intelligence, or cognitive computing solutions, blend artificial intelligence technologies like neural networks, machine learning, and natural language processing, and are able to mimic human intelligence.
It’s not surprising that rare diseases didn’t come to mind. By definition, a rare disease affects fewer than 200,000 people. However, collectively, there are thousands of rare diseases and those affect around 400 million people worldwide. About half of rare disease patients are children, and the typical patient, young or old, weather a diagnostic odyssey lasting five years or more during which they undergo countless tests and see numerous specialists before ultimately receiving a diagnosis.
No longer a moonshot challenge
Shortening that diagnostic odyssey and reducing the associated costs was, until recently, a moonshot challenge, but is now within reach. About 80% of rare diseases are genetic, and technology and AI advances are combining to make genetic testing widely accessible.
Whole-genome sequencing, an advanced genetic test that allows us to examine the entire human DNA, now costs under $1,000, and market leader Illumina is targeting a $100 genome in the near future.
The remaining challenge is interpreting that data in the context of human health, which is not a trivial challenge. The typical human contains 5 million unique genetic variants and of those we need to identify a single disease-causing variant. Recent advances in cognitive AI allow us to interrogate a person’s whole genome sequence and identify disease-causing mechanisms automatically, augmenting human capacity.
A shift from narrow to cognitive AI
The path to a broadly usable AI solution required a paradigm shift from narrow to broader machine learning models. Scientists interpreting genomic data review thousands of data points, collected from different sources, in different formats.
An analysis of a human genome can take as long as eight hours, and there are only a few thousand qualified scientists worldwide. When we reach the $100 genome, analysts are expecting 50 million-60 million people will have their DNA sequenced every year. How will we analyze the data generated in the context of their health? That’s where cognitive intelligence comes in.
Create a serverless pipeline to translate large documents with Amazon Translate
In our previous post, we described how to translate documents using the real-time translation API from Amazon Translate and AWS Lambda. However, this method may not work for files that are too large. They may take time too much time, triggering the 15-minute timeout limit of Lambda functions. One can use batch API, but this is available only in seven AWS Regions (as of this blog’s publication). To enable translation of large files in regions where Batch Translation is not supported, we created the following solution.
In this post, we walk you through performing translation of large documents.
Compared to the architecture featured in the post Translating documents with Amazon Translate, AWS Lambda, and the new Batch Translate API, our architecture has one key difference: the presence of AWS Step Functions, a serverless function orchestrator that makes it easy to sequence Lambda functions and multiple services into business-critical applications. Step Functions allows us to keep track of running the translation, managing retrials in case of errors or timeouts, and orchestrating event-driven workflows.
The following diagram illustrates our solution architecture.
This event-driven architecture shows the flow of actions when a new document lands in the input Amazon Simple Storage Service (Amazon S3) bucket. This event triggers the first Lambda function, which acts as the starting point of the Step Functions workflow.
The following diagram illustrates the state machine and the flow of actions.
The Process Document Lambda function is triggered when the state machine starts; this function performs all the activities required to translate the documents. It accesses the file from the S3 bucket, downloads it locally in the environment in which the function is run, reads the file contents, extracts short segments from the document that can be passed through the real-time translation API, and uses the API’s output to create the translated document.
Other mechanisms are implemented within the code to avoid failures, such as handling an Amazon Translate throttling error and Lambda function timeout by taking action and storing the progress that was made in a /temp folder 30 seconds before the function times out. These mechanisms are critical for handling large text documents.
When the function has successfully finished processing, it uploads the translated text document in the output S3 bucket inside a folder for the target language code, such as en for English. The Step Functions workflow ends when the Lambda function moves the input file from the /drop folder to the /processed folder within the input S3 bucket.
We now have all the pieces in place to try this in action.
Deploy the solution using AWS CloudFormation
You can deploy this solution in your AWS account by launching the provided AWS CloudFormation stack. The CloudFormation template provisions the necessary resources needed for the solution. The template creates the stack the us-east-1 Region, but you can use the template to create your stack in any Region where Amazon Translate is available. As of this writing, Amazon Translate is available in 16 commercial Regions and AWS GovCloud (US-West). For the latest list of Regions, see the AWS Regional Services List.
To deploy the application, complete the following steps:
- Launch the CloudFormation template by choosing Launch Stack:
- Choose Next.
Alternatively, on the AWS CloudFormation console, choose Create stack with new resources (standard), choose Amazon S3 URL as the template source, enter
https://s3.amazonaws.com/aws-ml-blog/artifacts/create-a-serverless-pipeline-to-translate-large-docs-amazon-translate/translate.yml, and choose Next.
- For Stack name, enter a unique stack name for this account; for example, serverless-document-translation.
- For InputBucketName, enter a unique name for the S3 bucket the stack creates; for example, serverless-translation-input-bucket.
The documents are uploaded to this bucket before they are translated. Use only lower-case characters and no spaces when you provide the name of the input S3 bucket. This operation creates a new bucket, so don’t use the name of an existing bucket. For more information, see Bucket naming rules.
- For OutputBucketName, enter a unique name for your output S3 bucket; for example, serverless-translation-output-bucket.
This bucket stores the documents after they are translated. Follow the same naming rules as your input bucket.
- For SourceLanguageCode, enter the language code that your input documents are in; for this post we enter auto to detect the dominant language.
- For TargetLanguageCode, enter the language code that you want your translated documents in; for example, en for English.
For more information about supported language codes, see Supported Languages and Language Codes.
- Choose Next.
- On the Configure stack options page, set any additional parameters for the stack, including tags.
- Choose Next.
- Select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
- Choose Create stack.
Stack creation takes about a minute to complete.
Translate your documents
You can now upload a text document that you want to translate into the input S3 bucket, under the drop/ folder.
The following screenshot shows our sample document, which contains a sentence in Greek.
This action starts the workflow, and the translated document automatically shows up in the output S3 bucket, in the folder for the target language (for this example, en). The length of time for the file to appear depends on the size of the input document.
Our translated file looks like the following screenshot.
You can also track the state machine’s progress on the Step Functions console, or with the relevant API calls.
Let’s try the solution with a larger file. The test_large.txt file contains content from multiple AWS blog posts and other content written in German (for example, we use all the text from the post AWS DeepLens (Version 2019) kommt nach Deutschland und in weitere Länder).
This file is much bigger than the file in previous test. We upload the file in the drop/ folder of the input bucket.
On the Step Functions console, you can confirm that the pipeline is running by checking the status of the state machine.
On the Graph inspector page, you can get more insights on the status of the state machine at any given point. When you choose a step, the Step output tab shows the completion percentage.
When the state machine is complete, you can retrieve the translated file from the output bucket.
The following screenshot shows that our file is translated in English.
If you don’t see the translated document in the output S3 bucket, check Amazon CloudWatch Logs for the corresponding Lambda function and look for potential errors. For cost-optimization, by default, the solution uses 256 MB of memory for the Process Document Lambda function. While processing a large document, if you see Runtime.ExitError for the function in the CloudWatch Logs, increase the function memory.
It’s worth highlighting the power of the automatic language detection feature of Amazon Translate, captured as auto in the SourceLanguageCode field that we specified when deploying the CloudFormation stack. In the previous examples, we submitted a file containing text in Greek and another file in German, and they were both successfully translated into English. With our solution, you don’t have to redeploy the stack (or manually change the source language code in the Lambda function) every time you upload a source file with a different language. Amazon Translate detects the source language and starts the translation process. Post deployment, if you need to change the target language code, you can either deploy a new CloudFormation stack or update the existing stack.
This solution uses the Amazon Translate synchronous real-time API. It handles the maximum document size limit (5,000 bytes) by splitting the document into paragraphs (ending with a newline character). If needed, it further splits each paragraph into sentences (ending with a period). You can modify these delimiters based on your source text. This solution can support a maximum of 5,000 bytes for a single sentence and it only handles UTF-8 formatted text documents with .txt or .text file extensions. You can modify the Python code in the Process Document Lambda function to handle different file formats.
In addition to Amazon S3 costs, the solution incurs usage costs from Amazon Translate, Lambda, and Step Functions. For more information, see Amazon Translate pricing, Amazon S3 pricing, AWS Lambda pricing, and AWS Step Functions pricing.
In this post, we showed the implementation of a serverless pipeline that can translate documents in real time using the real-time translation feature of Amazon Translate and the power of Step Functions as orchestrators of individual Lambda functions. This solution allows for more control and for adding sophisticated functionality to your applications. Come build your advanced document translation pipeline with Amazon Translate!
For more information, see the Amazon Translate Developer Guide and Amazon Translate resources. If you’re new to Amazon Translate, try it out using our Free Tier, which offers 2 million characters per month for free for the first 12 months, starting from your first translation request.
About the Authors
Jay Rao is a Senior Solutions Architect at AWS. He enjoys providing technical guidance to customers and helping them design and implement solutions on AWS.
Seb Kasprzak is a Solutions Architect at AWS. He spends his days at Amazon helping customers solve their complex business problems through use of Amazon technologies.
Nikiforos Botis is a Solutions Architect at AWS. He enjoys helping his customers succeed in their cloud journey, and is particularly interested in AI/ML technologies.
Bobbie Couhbor is a Senior Solutions Architect for Digital Innovation at AWS, helping customers solve challenging problems with emerging technology, such as machine learning, robotics, and IoT.
How Genworth built a serverless ML pipeline on AWS using Amazon SageMaker and AWS Glue
This post is co-written with Liam Pearson, a Data Scientist at Genworth Mortgage Insurance Australia Limited.
Genworth Mortgage Insurance Australia Limited is a leading provider of lenders mortgage insurance (LMI) in Australia; their shares are traded on Australian Stock Exchange as ASX: GMA.
Genworth Mortgage Insurance Australia Limited is a lenders mortgage insurer with over 50 years of experience and volumes of data collected, including data on dependencies between mortgage repayment patterns and insurance claims. Genworth wanted to use this historical information to train Predictive Analytics for Loss Mitigation (PALM) machine learning (ML) models. With the ML models, Genworth could analyze recent repayment patterns for each of the insurance policies to prioritize them in descending order of likelihood (chance of a claim) and impact (amount insured). Genworth wanted to run batch inference on ML models in parallel and on schedule while keeping the amount of effort to build and operate the solution to the minimum. Therefore, Genworth and AWS chose Amazon SageMaker batch transform jobs and serverless building blocks to ingest and transform data, perform ML inference, and process and publish the results of the analysis.
Genworth’s Advanced Analytics team engaged in an AWS Data Lab program led by Data Lab engineers and solutions architects. In a pre-lab phase, they created a solution architecture to fit specific requirements Genworth had, especially around security controls, given the nature of the financial services industry. After the architecture was approved and all AWS building blocks identified, training needs were determined. AWS Solutions Architects conducted a series of hands-on workshops to provide the builders at Genworth with the skills required to build the new solution. In a 4-day intensive collaboration, called a build phase, the Genworth Advanced Analytics team used the architecture and learnings to build an ML pipeline that fits their functional requirements. The pipeline is fully automated and is serverless, meaning that there is no maintenance, scaling issues, or downtime. Post-lab activities were focused on productizing the pipeline and adopting it as a blueprint for other ML use cases.
In this post, we (the joint team of Genworth and AWS Architects) explain how we approached the design and implementation of the solution, the best practices we followed, the AWS services we used, and the key components of the solution architecture.
We followed the modern ML pipeline pattern to implement a PALM solution for Genworth. The pattern allows ingestion of data from various sources, followed by transformation, enrichment, and cleaning of the data, then ML prediction steps, finishing up with the results made available for consumption with or without data wrangling of the output.
In short, the solution implemented has three components:
- Data ingestion and preparation
- ML batch inference using three custom developed ML models
- Data post processing and publishing for consumption
The following is the architecture diagram of the implemented solution.
Let’s discuss the three components in more detail.
Component 1: Data ingestion and preparation
Genworth source data is published weekly into a staging table in their Oracle on-premises database. The ML pipeline starts with an AWS Glue job (Step 1, Data Ingestion, in the diagram) connecting to the Oracle database over an AWS Direct Connect connection secured with VPN to ingest raw data and store it in an encrypted Amazon Simple Storage Service (Amazon S3) bucket. Then a Python shell job runs using AWS Glue (Step 2, Data Preparation) to select, clean, and transform the features used later in the ML inference steps. The results are stored in another encrypted S3 bucket used for curated datasets that are ready for ML consumption.
Component 2: ML batch inference
Genworth’s Advanced Analytics team has already been using ML on premises. They wanted to reuse pretrained model artifacts to implement a fully automated ML inference pipeline on AWS. Furthermore, the team wanted to establish an architectural pattern for future ML experiments and implementations, allowing them to iterate and test ideas quickly in a controlled environment.
The three existing ML artifacts forming the PALM model were implemented as a hierarchical TensorFlow neural network model using Keras. The models seek to predict the probability of an insurance policy submitting a claim, the estimated probability of a claim being paid, and the magnitude of that possible claim.
Because each ML model is trained on different data, the input data needs to be standardized accordingly. Individual AWS Glue Python shell jobs perform this data standardization specific to each model. Three ML models are invoked in parallel using SageMaker batch transform jobs (Step 3, ML Batch Prediction) to perform the ML inference and store the prediction results in the model outputs S3 bucket. SageMaker batch transform manages the compute resources, installs the ML model, handles data transfer between Amazon S3 and the ML model, and easily scales out to perform inference on the entire dataset.
Component 3: Data postprocessing and publishing
Before the prediction results from the three ML models are ready for use, they require a series of postprocessing steps, which were performed using AWS Glue Python shell jobs. The results are aggregated and scored (Step 4, PALM Scoring), business rules applied (Step 5, Business Rules), the files generated (Step 6, User Files Generation), and data in the files validated (Step 7, Validation) before publishing the output of these steps back to a table in the on-premises Oracle database (Step 8, Delivering the Results). The solution uses Amazon Simple Notification Service (Amazon SNS) and Amazon CloudWatch Events to notify users via email when the new data becomes available or any issues occur (Step 10, Alerts & Notifications).
All of the steps in the ML pipeline are decoupled and orchestrated using AWS Step Functions, giving Genworth the ease of implementation, the ability to focus on the business logic instead of the scaffolding, and the flexibility they need for future experiments and other ML use cases. The following diagram shows the ML pipeline orchestration using a Step Functions state machine.
Business benefit and what’s next
By building a modern ML platform, Genworth was able to automate an end-to-end ML inference process, which ingests data from an Oracle database on premises, performs ML operations, and helps the business make data-driven decisions. Machine learning helps Genworth simplify high-value manual work performed by the Loss Mitigation team.
This Data Lab engagement has demonstrated the importance of making modern ML and analytics tools available to teams within an organization. It has been a remarkable experience witnessing how quickly an idea can be piloted and, if successful, productionized.
In this post, we showed you how easy it is to build a serverless ML pipeline at scale with AWS Data Analytics and ML services. As we discussed, you can use AWS Glue for a serverless, managed ETL processing job and SageMaker for all your ML needs. All the best on your build!
Genworth, Genworth Financial, and the Genworth logo are registered service marks of Genworth Financial, Inc. and used pursuant to license.
About the Authors
Liam Pearson is a Data Scientist at Genworth Mortgage Insurance Australia Limited who builds and deploys ML models for various teams within the business. In his spare time, Liam enjoys seeing live music, swimming and—like a true millennial—enjoying some smashed avocado.
Maria Sokolova is a Solutions Architect at Amazon Web Services. She helps enterprise customers modernize legacy systems and accelerates critical projects by providing technical expertise and transformations guidance where they’re needed most.
Vamshi Krishna Enabothala is a Data Lab Solutions Architect at AWS. Vamshi works with customers on their use cases, architects a solution to solve their business problems, and helps them build a scalable prototype. Outside of work, Vamshi is an RC enthusiast, building and playing with RC equipment (cars, boats, and drones), and also enjoys gardening.
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