If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
An AI can defeat expert human players in the board game Stratego, which has more possible game scenarios than chess, Go or poker. The AI developed by the UK-based company DeepMind became one of the top-ranked online players of the Napoleonic-themed board game Stratego by learning to bluff with weaker pieces and sacrifice important pieces for the sake of victory. "To us the most surprising behaviour was [the AI's] ability to sacrifice valuable pieces to gain information about the opponent's set-up and strategy," says Julien Perolat at DeepMind. The game of Stratego involves two players trying to capture the opponent's flag hidden among an array of 40 game pieces. Most pieces consist of soldiers numbered from one to 10, with the higher-ranked soldiers defeating lower-ranked soldiers during encounters on the board. But players cannot see the identities of opponent game pieces unless two pieces from opposing armies encounter one another – unlike games such as chess or Go where both players can see everything.
Amazon SageMaker now allows you to compare the performance of a new version of a model serving stack with the currently deployed version prior to a full production rollout using a deployment safety practice known as shadow testing. Shadow testing can help you identify potential configuration errors and performance issues before they impact end-users. With SageMaker, you don't need to invest in building your shadow testing infrastructure, allowing you to focus on model development. SageMaker takes care of deploying the new version alongside the current version serving production requests, routing a portion of requests to the shadow version. You can then compare the performance of the two versions using metrics such as latency and error rate.
As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when they are not. In this post, we explore how companies can improve visibility into their models with centralized dashboards and detailed documentation of their models using two new features: SageMaker Model Cards and the SageMaker Model Dashboard. Both these features are available at no additional charge to SageMaker customers. Model governance is a framework that gives systematic visibility into model development, validation, and usage.
Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console. Role Manager offers predefined personas and ML activities combined with a wizard to streamline your permission generation process, allowing your ML practitioners to perform their responsibilities with the minimal necessary permissions. If you require additional customization, SageMaker Role Manager allows you to specify networking and encryption permissions for Amazon Virtual Private Cloud (Amazon VPC) resources and AWS Key Management Service (AWS KMS) encryption keys, and attach your custom policies.
The world is at increasing risk of global food shortage as a consequence of geopolitical conflict, supply chain disruptions, and climate change. Simultaneously, there's an increase in overall demand from population growth and shifting diets that focus on nutrient- and protein-rich food. To meet the excess demand, farmers need to maximize crop yield and effectively manage operations at scale, using precision farming technology to stay ahead. Historically, farmers have relied on inherited knowledge, trial and error, and non-prescriptive agronomic advice to make decisions. Key decisions include what crops to plant, how much fertilizer to apply, how to control pests, and when to harvest.
The slew of social clips did not appear to change Nintendo and developer Game Freak's patch schedule; "Arceus" received its first substantial patch at approximately the same time. But while previous game entries' official patch notes contain specific changes made within each update, "Scarlet" and "Violet's" patch notes simply state that "other select bug fixes have been made," suggesting a higher-than-usual volume of updates.
Forecasting rates of sea level change in polar ice shelves: Polar scientists, along with atmospheric and ocean scientists, face an urgent need to understand sea level rise around the globe. Ice-shelf environments represent extreme environments for sampling and sensing. Current efforts to collect sensed data are limited and use tethered robots with traditional sampling frequency and collection limitations. The ability to collect extensive data about conditions at or near the ice shelves will inform our understanding about changes in ocean circulation patterns, as well as feedbacks with wind circulation. New research on intelligent sensors would support selective data collection, onboard data analysis, and adaptive sensor steering.
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices to automate their deployment infrastructure. Previously, when data scientists wanted to take the code they built interactively on notebooks and run them as batch jobs, they were faced with a steep learning curve using Amazon SageMaker Pipelines, AWS Lambda, Amazon EventBridge, or other solutions that are difficult to set up, use, and manage. With SageMaker notebook jobs, you can now run your notebooks as is or in a parameterized fashion with just a few simple clicks from the SageMaker Studio or SageMaker Studio Lab interface. You can run these notebooks on a schedule or immediately.
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"Heuristics" may sound like a fancy word, but as Holly Emblem explains, it's in fact a clear, streamlined approach to problem-solving. Holly's post provides a clear definition and practical data science use cases for you to consider. A comprehensive look at the latest in object detection. There are deep dives, and then there's Chris Hughes and Bernat Puig Camps' overview of the YOLOv7 model. Don't let its hefty 50-minute reading time scare you--it's engaging and easy to follow, and offers a smooth blend of theory and practice.