AI - bobbae/gcp GitHub Wiki

Artificial Intelligence systems demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

The narrow AI is what we see all around us in computers today: intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so.

General AI is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or reasoning about a wide variety of topics based on its accumulated experience.

https://cloud.google.com/blog/topics/developers-practitioners/ai-all-humans-course-delight-and-inspire

https://github.com/amusi/awesome-ai-awesomeness

https://i.am.ai/roadmap/

Vertex AI

Vertex AI brings AutoML and AI Platform together into a unified API, client library, and user interface. With Vertex AI, both AutoML training and custom training are available options.

https://cloud.google.com/vertex-ai/docs

AutoML vs. custom training

AutoML lets you create and train a Machine Learning model with minimal technical effort. Even if you want the flexibility of a custom training application, you can use AutoML to quickly prototype models and explore new datasets before investing in development. For example, you can use it to learn which are good features in a dataset.

Custom training lets you create a training application optimized for your targeted outcome. You have complete control over training application functionality; you can target any objective, use any algorithm, develop your own loss functions or metrics, or do any other customization.

https://cloud.google.com/vertex-ai/docs/start/training-methods

Migrating to Vertex AI

Vertex AI supports all features and models available in AutoML and AI Platform. However, the client libraries do not support client integration backward compatibility. In other words, you must plan to migrate your resources to benefit from Vertex AI features.

https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai

Migrating Applications to Vertex AI

API changes you need to make when you migrate your applications from AutoML or AI Platform to Vertex AI.

https://cloud.google.com/vertex-ai/docs/start/migrating-applications

Vertex AI for AutoML users

List comparisons between the AutoML products and Vertex AI to help AutoML users understand how to use Vertex AI.

https://cloud.google.com/vertex-ai/docs/start/automl-users

Vertex AI for AI Platform users

From AI Platform users' point of view.

https://cloud.google.com/vertex-ai/docs/start/ai-platform-users

Vertex AI Tutorials

https://cloud.google.com/vertex-ai/docs/tutorials

Introductory Tutorial

https://blog.doit-intl.com/google-vertex-ai-the-easiest-way-to-run-ml-pipelines-3a41c5ed153

Debug Training jobs using shell

https://cloud.google.com/blog/topics/developers-practitioners/debugging-vertex-ai-training-jobs-interactive-shell

Vertex AI Examples

https://github.com/GoogleCloudPlatform/vertex-ai-samples

Vertex Pipelines

MLOps is the practice of applying DevOps strategies to Machine Learning systems. DevOps strategies let you efficiently build and release code changes, and monitor systems to ensure you meet your reliability goals. MLOps extends this practice to help you reduce the amount of time that it takes to reliably go from data ingestion to deploying your model in production, in a way that lets you monitor and understand your ML system.

Vertex Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, and storing your workflow's artifacts using Vertex ML Metadata. By storing the artifacts of your ML workflow in Vertex ML Metadata, you can analyze the lineage of your workflow's artifacts — for example, an ML model's lineage may include the training data, hyperparameters, and code that were used to create the model.

https://cloud.google.com/vertex-ai/docs/pipelines/introduction

Use Vertex Pipelines to build AutoML Classification Workflow

The example workflow trains a custom model using AutoML; evaluates the accuracy of the trained model; and if the model is sufficiently accurate, deploys it to Vertex AI for serving.

https://cloud.google.com/blog/topics/developers-practitioners/use-vertex-pipelines-build-automl-classification-end-end-workflow

Vertex Explainable AI

Vertex Explainable AI integrates feature attributions into Vertex AI. This page provides a brief conceptual overview of the feature attribution methods available with Vertex AI.

https://cloud.google.com/vertex-ai/docs/explainable-ai

AI Explanations

Systems built around AI will affect and, in many cases, redefine medical interventions, autonomous transportation, criminal justice, financial risk management and many other areas of society.

https://storage.googleapis.com/cloud-ai-whitepapers/AI%20Explainability%20Whitepaper.pdf

XAI

Explainable AI relates to the ways to explain or to present in understandable terms to a human.

https://pair.withgoogle.com/

AutoML

Vertex AI AutoML beginners guide: https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide

AI Hub

AI Hub offers a collection of components for developers and data scientists building AI systems.

Learning with AI Hub

https://cloud.google.com/ai-hub/docs/learn

AI Hub vs. TensorFlow Hub

TensorFlow Hub provides a library of TensorFlow modules that you can use to speed up the process of training your model. On the AI Hub, you can explore and use a variety of AI asset categories.

AI Hub Quickstarts

https://cloud.google.com/ai-hub/docs/quickstarts

AI Products and Solutions

https://cloud.google.com/products/ai

https://cloud.google.com/solutions/ai

Contact Center

https://cloud.google.com/solutions/contact-center

Document AI

https://cloud.google.com/document-ai

AI Infrastructure

https://cloud.google.com/ai-infrastructure

DialogFlow

https://cloud.google.com/dialogflow

Recommendations AI

https://cloud.google.com/blog/topics/developers-practitioners/recommendations-ai-modeling

Vertex AI Vizier

Vertex AI Vizier is a black-box optimization service that helps you tune hyperparameters.

Optimization AI

https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-optimization-ai-cloud-fleet-routing-api

Risks

Limits of AI

https://www.researchgate.net/publication/321326439_The_Emperor_of_Strong_AI_Has_No_Clothes_Limits_to_Artificial_Intelligence

Limits of Data Science

https://towardsdatascience.com/the-limits-of-data-science-b4e5faad20f4

Limits of Machine Learning

https://towardsdatascience.com/the-limitations-of-machine-learning-a00e0c3040c6

Computational limits

https://www.discovermagazine.com/technology/the-computational-limits-of-deep-learning-are-closer-than-you-think

https://arxiv.org/pdf/2007.05558.pdf

Bias

https://venturebeat.com/2021/08/08/ai-bias-is-prevalent-but-preventable-heres-how-to-root-it-out

AI Ethics

The ways in which artificial intelligence is built and deployed will significantly affect society.

We are living in times when it is paramount that the possibility of harm in AI systems has to be recognized and addressed quickly. Thus, identifying the potential risks, bias, privacy and security issues caused by AI systems means a plan of measures to counteract them has to be adopted as soon as possible.