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://github.com/amusi/awesome-ai-awesomeness
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
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.
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
Vertex AI Tutorials
https://cloud.google.com/vertex-ai/docs/tutorials
Image Recognition
https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl
Video Classification
https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl
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.
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
Risks
Limits of AI
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://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.
SR 11-07
https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm
Responsible AI
https://cloud.google.com/responsible-ai
https://ai.google/responsibilities/responsible-ai-practices/
Ethical AI tasks
Facial recognition
https://pages.gseis.ucla.edu/faculty/agre/bar-code.html
War
https://theatlantic.com/amp/article/620013/
Causation & correlation
https://chrislovejoy.me/correlation-causation/
https://www.tylervigen.com/spurious-correlations
https://www.statisticsdonewrong.com/
Counterfactual
https://www.inference.vc/causal-inference-3-counterfactuals/
Causality
https://www.youtube.com/watch?v=78EmmdfOcI8
Causal Inference
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system.
https://wikipedia.org/wiki/Causal_inference
Examples
The Brexit vote: A case study in causal inference using machine learning
Fishing activities
Battlesnake
Natural Language discovery and classification
Image processing
https://medium.com/google-cloud/process-images-with-google-cloud-ai-c8a9ff159d99
Teachable Machine
https://teachablemachine.withgoogle.com/
Public sector examples
Googles Open source AI contributions
Tutorials
- https://developers.google.com/machine-learning/crash-course
- https://cloud.google.com/vertex-ai/docs/tutorials
- https://codelabs.developers.google.com/ml-for-developers
- https://github.com/GoogleCloudPlatform/ml-on-gcp/tree/master/tutorials
- https://towardsdatascience.com/training-a-model-on-google-ai-platform-84ceff87b5f3
- https://www.w3schools.com/ai/
- https://course.fast.ai/
- https://www.guru99.com/artificial-intelligence-tutorial.html
- https://www.tutorialspoint.com/artificial_intelligence/index.htm
- https://cloud.google.com/ai-platform/docs/getting-started-keras
- https://cloud.google.com/ai-platform/docs/technical-overview
- https://towardsdatascience.com/training-a-model-on-google-ai-platform-84ceff87b5f3
- https://developers.google.com/learn/topics/datascience
- https://github.com/GoogleCloudPlatform/ai-platform-samples
- http://neuralnetworksanddeeplearning.com/
- https://sebastianraschka.com/blog/2021/dl-course.html
- https://d2l.ai/index.html
- https://www.coursera.org/learn/ai-for-everyone
- https://github.com/owainlewis/awesome-artificial-intelligence
Qwiklabs
Baseline: Data, ML, AI
https://www.qwiklabs.com/quests/34
Kubeflow Pipelines with AI Platform
https://www.qwiklabs.com/focuses/10948?parent=catalog
Predict Housing Prices with Tensorflow and AI Platform
https://www.qwiklabs.com/focuses/3644?parent=catalog