Seattle, Dec. 4, 2019: Amazon Web Services, Inc. (AWS), an Amazon.com company announced at AWS re:Invent event as many as five artificial intelligence (AI) services designed to put machine learning (ML) in the hands of more application developers and end users – with no machine learning experience required.
AWS said in a press release that it had introduced new services that use AI to allow more developers to apply ML to “create better end user experiences”, including new machine learning-powered enterprise search, code reviews and profiling, fraud detection, medical transcription, and human review of AI predictions. To learn more about AWS’s AI Services, visit https://aws.amazon.com/machine-learning/ai-services/.
In the past year, AWS has introduced several new fully managed AI Services like Amazon Personalize and Amazon Forecast that allow customers to benefit from the same personalisation and forecasting machine learning technology used by Amazon’s consumer business to power its award-winning customer experiences.
Amazon Kendra reinvents enterprise search with machine learning
Despite many attempts over many years, internal search remains a vexing problem for today’s enterprises, and most employees still frequently struggle to find the information they need, said AWS. Even with common Web-based search tools widely available, organisations still find internal search difficult because none of the available tools do a good job indexing across existing data silos, don’t provide natural language queries, and can’t deliver accurate results.
Amazon Kendra, said AWS, reinvents enterprise search by allowing employees to search across multiple silos of data using real questions (not just keywords) and deploys AI technology behind the scenes to deliver the precise answers they seek (instead of a random list of links). Employees can run their searches using natural language (keywords still work, but most users prefer natural language searches). As an example, an employee can ask a specific question like ‘when does the IT help desk open?’, and Amazon Kendra will give them a specific answer like ‘the IT help desk opens at 9:30 AM’, along with links back to the IT ticketing portal and other relevant Sites. Customers can use Amazon Kendra across their applications, portals, and wikis. To learn more about Amazon Kendra, visit http://aws.amazon.com/kendra.
Amazon CodeGuru for software development by using machine learning
Amazon CodeGuru is a new ML service that automates code reviews and finds an application’s most expensive lines of code. There are two components of Amazon CodeGuru – code reviews and application profiling, said AWS. For code reviews, developers commit their code as usual (support for GitHub and CodeCommit exist today, with more repositories coming over time) and add Amazon CodeGuru as one of the code reviewers, with no other changes to the normal process or software to install. Amazon CodeGuru receives a pull request and automatically starts evaluating the code using pre-trained models that have been trained on several decades of code reviews at Amazon and the top ten thousand open-source projects on GitHub.
Amazon CodeGuru also contains a machine-learning powered application profiler that helps customers find their most expensive lines of code. To get started, customer install a small, low-profile agent in their application, so Amazon CodeGuru can observe the application run time and profile the application code every five minutes. This code profile includes details on the latency and CPU utilization, linking directly back to specific lines of code. Amazon CodeGuru can help operators find the most expensive line of code in an application, and it produces a flame graph that helps visually identify other lines of code that are creating performance bottlenecks. To learn more about Amazon CodeGuru, visit http://aws.amazon.com/codeguru.
Amazon Fraud Detector
As we all know, tens of billions of dollars are lost to fraud every year by organisations around the world.
Amazon Fraud Detector provides a fully managed service for detecting potential Online identity and payment fraud in real time, based on the same technology used by Amazon’s consumer business – with no machine learning experience required. Amazon Fraud Detector uses historical data of both fraudulent and legitimate transactions to build, train, and deploy machine learning models that provide real-time, low-latency fraud risk predictions. To get started, customers upload transaction data to Amazon Simple Storage Service (S3) to customise the model’s training. Customers only need to provide the email address and IP address associated with a transaction, and can optionally add other data (e.g. billing address, or phone number). Based upon the type of fraud customers want to predict (new account or online payment fraud), Amazon Fraud Detector will pre-process the data, select an algorithm, and train a model – using the decades of experience running fraud detection risk analysis at scale at Amazon. Amazon Fraud Detector also uses machine learning-based data detectors that were trained on data from Amazon. These data detectors help identify patterns commonly associated with fraudulent activity at Amazon (e.g. anomalous email naming conventions) to help boost the accuracy of the trained model even if the number of fraudulent examples provided by a customer to Amazon Fraud Detector are low. To learn more about Amazon Fraud Detector, visit http://aws.amazon.com/fraud-detector.
Amazon Transcribe Medical
Today, physicians are required to conduct detailed data entry into electronic health record (EHR) systems as part of their everyday duties. However, the solutions that help them accurately record and document patient encounters are sub-optimal. Another option is to leverage existing front-end dictation software, but existing tools are limited and physicians still end up spending several hours on clinical documentation every day. A third option is for healthcare providers to hire human scribes to assist physicians with notetaking during patient encounters, but human scribes can be unsettling to patients, physicians often mention that their output is imperfect, and medical organizations struggle to schedule and coordinate scribes at scale. Existing solutions fall short, both in terms of enhancing clinical documentation efficiency and enabling better patient care.
Amazon Transcribe Medical solves these problems by using machine learning technology to automatically transcribe natural medical speech. Clinical documentation applications built on top of Amazon Transcribe Medical’s speech-to-text capabilities produce accurate and affordable transcripts. Amazon Transcribe Medical consists of multiple machine learning models that have been trained on tens of thousands of hours of medical speech to deliver accurate, machine learning-powered medical transcription. Transcripts are generated in real time, eliminating the multi-day turnarounds. To get started with Amazon Transcribe Medical, visit http://aws.amazon.com/transcribe/medical.
Amazon Augmented Artificial Intelligence (A2I)
Machine learning can provide highly accurate predictions for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language. In each case, machine learning models provide a prediction and also a confidence score that expresses how certain the model is in its prediction. The higher the confidence number, the more the result can be trusted. For many use cases, when developers receive a high confidence result, they can trust that the results are likely to be accurate, and they can process them automatically (e.g. automatically moderating user-generated content on a social network, or adding subtitles to a video). However, in situations where confidence is lower than desired, due to some ambiguity in the prediction result, results may require a human review to resolve this ambiguity. This interplay between machine learning and human reviewer is critical to the success of machine learning systems, but human reviews are challenging and expensive to build and operate at scale, often involving multiple workflow steps, custom software to manage human review tasks and results, and recruiting and managing large groups of reviewers. As a result, developers sometimes spend more time managing the human review process than building their intended application, or they have to forego having a human review, which leads to less confidence and utility in many predictions.
Amazon Augmented Artificial Intelligence (A2I) is a new service that makes it easier to build and manage human reviews for machine learning applications. Amazon A2I provides pre-built human review workflows for common machine learning tasks (e.g. object detection in images, transcription of speech, and content moderation) that allow machine learning predictions from Amazon Rekognition and Amazon Textract to be human-reviewed more easily. To get started with Amazon A2I, visit aws.amazon.com/augmented-ai.