On July 26, a packed Javits Center gathered to hear keynotes and sessions billed as “The cloud event for everyone.” I attended the AWS Summit and I’ll share my key takeaways – all focused on AI in some form.
“AI used to be the domain of a small group of researchers and data scientists,” he wrote. “Today, you can’t open a newsfeed without some reference to AI, specifically generative AI. It may come as a surprise, but the concepts of AI have been around since the 1950s.”
Swami then wondered why this technology – percolating for decades – is seeing so much interest now. “Simply put, AI has reached a tipping point thanks to the convergence of technological progress and an increased understanding of what it can accomplish,” he continued. “Couple that with the massive proliferation of data, the availability of highly scalable compute capacity, and the advancement of ML technologies over time, and the focus on generative AI is finally taking shape.”
With that in mind, here are my top five takeaways from the AWS Summit.
1) Generative BI Capabilities in Amazon QuickSight
That’s right—it’s not a typo: it’s generative BI. These capabilities will help companies quickly find all the insights and “aha” moments in their data. Without the help of this AWS tool, it would take a very long time to build this capability in-house.
A few of the key capabilities, according to Amazon, include:
- Creation of visuals in seconds with QuickSight Q-powered visual authoring.
- The ability to fine-tune and format visuals using natural language.
- Calculations using natural language with no need for specific syntax.
You can learn more about generative BI with QuickSight.
2) Introducing Agents for Amazon Bedrock
Throw around the word “Bedrock” and you might think of Fred Flintstone’s hometown. But this Bedrock is a bit different.
Billed as “The easiest way to build and scale generative AI applications with foundation models (FMs)” by AWS, Bedrock’s updated generative AI services will enable companies to ingest complex data and output complex text.
The FMs enable companies to apply different algorithms to curated data sets. The agents enable connection to other data sets. At the event, I asked Atul Deo, GM of Amazon Bedrock, about some of the early use cases.
“Some of the common use cases currently revolve around improving Q&A for enterprise employees,” he said. “If you think about the traditional way Q&A is typically done using a bot, so there is invariably a lot of back and forth because there is rarely an exact ready-made answer for the inquiry, as a lot depends on the individual. With the agents for Bedrock, relevant contextual information can be passed on the fly and the correct answer crafted.”
Also see: Top Generative AI Apps and Tools
3) HealthScribe to Create Clinical Documentation Automatically
AWS’s new service, HealthScribe, uses speech recognition and generative AI to create preliminary clinical documentation from patient-clinician interactions automatically. Early customers and partners include 3M Health Information Systems, Babylon Health, and ScribeEMR.
The goal is for clinicians to spend less time building, maintaining, and operating foundational health data. Instead, Bratin Saha, VP of Machine Learning and Artificial Intelligence Services at AWS, says, “Clinicians can spend more time with the patients during the face-to-face or telehealth visits. Documentation is a particularly time-consuming effort for healthcare professionals, which is why we are excited to leverage the power of generative AI in AWS HealthScribe and reduce that burden.”
4) AWS Glue Studio Notebook powered by Amazon CodeWhisperer
AWS Glue, introduced in 2017, helps integrate data from multiple sources. Amazon CodeWhisperer is a companion for AI coding, with underlying foundational models that help with development productivity. At the summit, AWS announced that AWS Glue Studio notebooks now support Amazon CodeWhisperer for AWS Glue users.
AWS says this will “improve your experience and help boost development productivity. Now, in your Glue Studio notebook, you can write a comment in natural language (in English) that outlines a specific task, such as ‘Create a Spark DataFrame from a json file.'”
Anything that can enable faster development through natural language will be a winner.
Also see: Generative AI Companies: Top 12 Leaders
5) A Preview of Vector Engine for Amazon OpenSearch Serverless
AWS introduced a preview of the vector engine for Amazon OpenSearch Serverless. This new tool promises to improve search and make it more accurate.
In a blog post, AWS said, “The vector engine provides a simple, scalable, and high-performing similarity search capability in Amazon OpenSearch Serverless that makes it easy for you to build modern machine learning (ML) augmented search experiences and generative artificial intelligence (AI) applications without having to manage the underlying vector database infrastructure.”
OpenSearch Serverless should be a simpler and more scalable way to store and query vectors quickly. AWS says response times will be in milliseconds.
A Commitment to Responsible Generative AI
The headlines about AI generally focus on the dangers. The AWS Summit underscored the company’s ongoing commitment to responsible AI, which it has extended to generative AI. AWS noted that it will work across six areas: accuracy, fairness, intellectual property and copyright considerations, appropriate usage, toxicity and privacy.
For example, AWS noted that it built Titan FMs “to detect and remove harmful content in the data that customers provide for customization, reject inappropriate content in the user input, and filter the model’s outputs containing inappropriate content (such as hate speech, profanity, and violence).”
This is a tremendous ongoing commitment to responsible AI and generative AI.
It was an action-packed summit with lots of intriguing developments. I look forward to seeing how these key points develop over time. AWS re:Invent is only a few months away and I’m sure there will be more on generative AI.