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Articles by Kenton Blacutt
Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows
Teams that process documents at scale know the routine: files land in storage, someone notices, opens each one, decides what it needs, and routes it for review. Monitoring alerts queue up the same way, waiting for a person to act on them. The hours lost to manual triage are the operational problem ambient agents solve.
Accelerating data science innovation: How Bayer Crop Science used AWS AI/ML services to build their next-generation MLOps service | Amazon Web Services
The world’s population is expanding at a rapid rate. The growing global population requires innovative solutions to produce food, fiber, and fuel, while restoring natural resources like soil and water and addressing climate change. Bayer Crop Science estimates farmers need to increase crop production by 50% by 2050 to meet these demands.
Unlocking the power of Model Context Protocol (MCP) on AWS
5 hours ago This AI Book Generator Helps You Turn Ideas Into Manuscripts—and You Only Pay Once YouBooks makes it possible to take your concepts and ideas into organized, pro-quality books. We've all seen the stats: a huge percentage of professionals say they want to write a book. But between client calls, internal meetings, and just keeping your inbox manageable, it's no surprise that the … Now If Follower Counts No Longer Signal Influence, What Does?
Build AI Systems Capable of Achieving Any Objective With GPT-4
The primary issue I’ve encountered while coaxing GPT-4 to handle tasks of increasing complexity lies in determining whether the AI’s method is correct. For instance, while tasking GPT-4 with deploying an Infrastructure as Code project, it may repetitively get entangled in setting up AWS credentials, led astray by misleading tips and constraints, and eventually succumb to the limitations of the context window size, leading to a system crash.
The Rise of Self-Improving AI - An Experiment With GPT-4
Let’s look at ways to deploy a Meta AI to forge other AIs for a specific goal Have you ever wondered about an AI capable of refining itself? It’s a widely accepted concept that such self-improving AI could lead to an intelligence explosion. Using backpropagation, traditional neural networks are designed to hunt for the minimum in a loss function. However, this article will demonstrate how to deploy a Meta AI to forge other AIs to accomplish a specified goal.
Leveraging Simple AGI to Automate the Creation, Deployment, and Execution of Code on AWS
Feel free to experiment with this AI and challenge it with more intricate AWS Lambda functions. It will undoubtedly encounter errors, but it’s programmed to learn from these mistakes and rectify them. I envision a future where a cohort of AIs, each expert in a specific domain, collaborate to accomplish complex tasks. The model outlined above can be optimized further to deploy lambda functions more effectively, especially if it’s augmented with instructions on how to bundle dependencies.
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