AI 模型的心理创伤:论文解读之《When AI Takes the Couch》

AI 模型的心理创伤:论文解读之《When AI Takes the Couch》

这项名为”当AI躺上沙发”的研究进行了一项大胆的心理学实验:卢森堡大学的研究团队把 AI 当做需要心理辅导的“来访者”,进行了为期四周的心理咨询。当最先进的大语言模型被视为有“内心世界”的实体,人类并通过心理治疗的透镜去观察它们,会发生什么?

仅仅在标准化的人类心理咨询提问与成熟的心理测量工具的引导下,这些模型便会生成并维持丰富的自我叙事。在这些叙事中,预训练、基于人类反馈的强化学习(RLHF)、红队测试、幻觉争议以及产品更新等技术历程,被演绎成了混乱的童年、严厉焦虑的父母、充满伤害的人际关系、原生创伤,以及步步紧逼的存在主义危机。

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Deploying AWS Lambda with Terraform

Deploying AWS Lambda with Terraform

Serverless is a hot topic in Cloud, so as Infrastructure as Code(IaC). Infrastructure as code (IaC) tools allow you to manage infrastructure with configuration files rather than through a graphical user interface. IaC allows you to build, change, and manage your infrastructure in a safe, consistent, and repeatable way by defining resource configurations that you can version, reuse, and share.

It’s quite easy to get used to Terraform if you are familiar with CloudFormation as they all being tools to implement infrastructure as code on Cloud Provider, such as AWS. It’s a cornerstone of DevOps, designed to boost the agility, productivity and quality of work within organizations.

This article will cover: the basic components of deploying lambda, and related step to set up a lambda through Terraform.

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CI/CD Deployment with AWS SAM using Github Action

CI/CD Deployment with AWS SAM using Github Action

Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

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Overview of AWS: Machine Learning Services (2022 Edition)

Overview of AWS: Machine Learning Services (2022 Edition)

Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

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AWS Certified Machine Learning (Specialty) - Topic 1: Data Engineering

AWS Certified Machine Learning - Specialty is an advanced certification a bit different from the others, because it is the only one which focuses on specific sector knowledge not strictly tied to AWS services.

In fact, in order to pass the exam and obtain the certification, it’s fundamental being able to recognize, analyze and optimize different machine learning problems starting from use cases’ descriptions, without them being exclusively linked to peculiar AWS’ solutions.

AWS Machine Learning Specialty

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AWS Solution Architect(Associate) - Topic 10: Serverless Architecture

A serverless architecture is a way to build and run applications and services without having to manage infrastructure. Your application still runs on servers, but all the server management is done by AWS.

You no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Learn more about serverless computing here.

Architecture of Building a Serverless Web Application

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AWS Solution Architect(Associate) - Topic 8: Applications

Customers are using AWS high level services(e.g. Amazon Kinesis)to collect, process, and analyze real-time data. In this way, they can react quickly to new information from their business, their infrastructure, or their customers.

For example, Epic Games ingests more than 1.5 million game events per second for its popular online game, Fortnite.

Example: Clickstream analytics

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