From 7fbe0d03660e523046c202b5d461af10838f44a5 Mon Sep 17 00:00:00 2001 From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com> Date: Sun, 27 Aug 2023 21:19:10 +0530 Subject: [PATCH 1/4] Refactor README --- README.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e215572..c488033 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,16 @@ # AI Introduction -Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to intelligence of humans and other animals. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. +The ability for a machine to mimic the functions of the human brain is termed as Artificial Intelligence(AI). Example tasks in which it is performed include speech recognition, computer vision, translation between (natural) languages, and the list goes on. -AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), automated decision-making, and competing at the highest level in strategic game systems (such as chess and Go). +Artificial Intelligence has several applications, some of which include: +- Advanced Web Search Engines, like Google Search +- Recommendation Systems, like the ones used by YouTube and Amazon +- Natural Language Processing(NLP) or Understanding Human Speech, as used by voice assistants like Cortana, Siri and Alexa +- Generative tools, like ChatGPT, DALL-E and Midjourney +- High-level decision making, such as Stockfish in the game of chess -As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. +Since its inception as an academic study in around 1956, artificial intelligence has gone through multiple phases of optimism, disappointment, and funding loss (dubbed AI winter"), followed by new approaches, success, and renewed investment.Numerous approaches to AI research have been tried and rejected, including replicating the brain, modelling human problem solving, formal logic, massive databases of information, and mimicking animal behaviour. In the first decades of the twenty-first century, highly mathematical and statistical machine learning dominated the subject, and this technique proved extremely successful, assisting in the resolution of many difficult problems in industry and academia. -Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. - -The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields. +The numerous subfields of AI study are centred on specific aims and the usage of certain methods. Traditional AI research aims include reasoning, knowledge representation, planning, learning, natural language processing, perception, and object movement and manipulation. One of the field's long-term goals is general intelligence (the capacity to solve any problem). AI researchers have adapted and incorporated a wide range of problem-solving techniques, such as search and mathematical optimisation, formal logic, artificial neural networks, and methodologies based on statistics, probability, and economics, to solve these difficulties. AI also makes use of computer science, psychology, linguistics, philosophy, and a variety of other disciplines. @@ -40,7 +43,7 @@ git clone --recursive https://github.com/microsoft/ai.git ``` To pull a single submodule (e.g. DeployDeepModelKubernetes) run: -``` +```bash git clone https://github.com/microsoft/ai cd ai git submodule init submodules/DeployDeepModelKubernetes From 674f036407e9af41477f144c575d88111ac6e22a Mon Sep 17 00:00:00 2001 From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com> Date: Sun, 27 Aug 2023 21:19:51 +0530 Subject: [PATCH 2/4] Refactor README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c488033..ebbe067 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Artificial Intelligence has several applications, some of which include: - Natural Language Processing(NLP) or Understanding Human Speech, as used by voice assistants like Cortana, Siri and Alexa - Generative tools, like ChatGPT, DALL-E and Midjourney - High-level decision making, such as Stockfish in the game of chess - + Since its inception as an academic study in around 1956, artificial intelligence has gone through multiple phases of optimism, disappointment, and funding loss (dubbed AI winter"), followed by new approaches, success, and renewed investment.Numerous approaches to AI research have been tried and rejected, including replicating the brain, modelling human problem solving, formal logic, massive databases of information, and mimicking animal behaviour. In the first decades of the twenty-first century, highly mathematical and statistical machine learning dominated the subject, and this technique proved extremely successful, assisting in the resolution of many difficult problems in industry and academia. The numerous subfields of AI study are centred on specific aims and the usage of certain methods. Traditional AI research aims include reasoning, knowledge representation, planning, learning, natural language processing, perception, and object movement and manipulation. One of the field's long-term goals is general intelligence (the capacity to solve any problem). AI researchers have adapted and incorporated a wide range of problem-solving techniques, such as search and mathematical optimisation, formal logic, artificial neural networks, and methodologies based on statistics, probability, and economics, to solve these difficulties. AI also makes use of computer science, psychology, linguistics, philosophy, and a variety of other disciplines. From 6be225ec8035b4576e5e3f696cf13a124ce76402 Mon Sep 17 00:00:00 2001 From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com> Date: Tue, 29 Aug 2023 14:43:37 +0530 Subject: [PATCH 3/4] Update README.md --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index ebbe067..7ceba7b 100644 --- a/README.md +++ b/README.md @@ -10,10 +10,6 @@ Artificial Intelligence has several applications, some of which include: Since its inception as an academic study in around 1956, artificial intelligence has gone through multiple phases of optimism, disappointment, and funding loss (dubbed AI winter"), followed by new approaches, success, and renewed investment.Numerous approaches to AI research have been tried and rejected, including replicating the brain, modelling human problem solving, formal logic, massive databases of information, and mimicking animal behaviour. In the first decades of the twenty-first century, highly mathematical and statistical machine learning dominated the subject, and this technique proved extremely successful, assisting in the resolution of many difficult problems in industry and academia. -The numerous subfields of AI study are centred on specific aims and the usage of certain methods. Traditional AI research aims include reasoning, knowledge representation, planning, learning, natural language processing, perception, and object movement and manipulation. One of the field's long-term goals is general intelligence (the capacity to solve any problem). AI researchers have adapted and incorporated a wide range of problem-solving techniques, such as search and mathematical optimisation, formal logic, artificial neural networks, and methodologies based on statistics, probability, and economics, to solve these difficulties. AI also makes use of computer science, psychology, linguistics, philosophy, and a variety of other disciplines. - - - # Samples, Reference Architectures & Best Practices This repository is meant to organize Microsoft's Open Source AI based repositories. From 79ab7d67f6c5d0815ff7258e7d20813d46bb595d Mon Sep 17 00:00:00 2001 From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com> Date: Tue, 29 Aug 2023 15:23:27 +0530 Subject: [PATCH 4/4] Update README.md --- README.md | 42 ++---------------------------------------- 1 file changed, 2 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 7ceba7b..6c92899 100644 --- a/README.md +++ b/README.md @@ -1,43 +1,25 @@ # AI Introduction -The ability for a machine to mimic the functions of the human brain is termed as Artificial Intelligence(AI). Example tasks in which it is performed include speech recognition, computer vision, translation between (natural) languages, and the list goes on. - -Artificial Intelligence has several applications, some of which include: -- Advanced Web Search Engines, like Google Search -- Recommendation Systems, like the ones used by YouTube and Amazon -- Natural Language Processing(NLP) or Understanding Human Speech, as used by voice assistants like Cortana, Siri and Alexa -- Generative tools, like ChatGPT, DALL-E and Midjourney -- High-level decision making, such as Stockfish in the game of chess - -Since its inception as an academic study in around 1956, artificial intelligence has gone through multiple phases of optimism, disappointment, and funding loss (dubbed AI winter"), followed by new approaches, success, and renewed investment.Numerous approaches to AI research have been tried and rejected, including replicating the brain, modelling human problem solving, formal logic, massive databases of information, and mimicking animal behaviour. In the first decades of the twenty-first century, highly mathematical and statistical machine learning dominated the subject, and this technique proved extremely successful, assisting in the resolution of many difficult problems in industry and academia. - +The ability for a machine to mimic the functions of the human brain is termed as Artificial Intelligence(AI). Example tasks in which it is performed include speech recognition, computer vision, translation between (natural) languages, and the list goes on. Artificial Intelligence has several applications, some of which include advanced web search engines, like Google Search, recommendation systems, like the ones used by YouTube and Amazon, Natural Language Processing(NLP) or Understanding Human Speech, as used by voice assistants like Cortana, Siri and Alexa, generative tools, like ChatGPT, DALL-E and Midjourney, high-level decision making, such as Stockfish in the game of chess. # Samples, Reference Architectures & Best Practices - This repository is meant to organize Microsoft's Open Source AI based repositories. - # Keywords batch scoring, realtime scoring, model training, MLOps, Azure Machine Learning, computer vision, natural language processing, recommenders - ## Table of contents 1. [Getting Started](#Getting-Started) 2. [AI100 - Samples](#ai100) 3. [AI200 - Reference Architectures](#ai200) 4. [AI300 - Best Practices](#ai300) 6. [Contributing](#Contributing) - # Getting Started This repository is arranged as submodules so you can either pull all the tutorials or simply the ones you want. To pull all the tutorials run: - ```bash git clone --recurse-submodules https://github.com/microsoft/ai ``` - if you have git older than 2.13 run: - ```bash git clone --recursive https://github.com/microsoft/ai.git ``` - To pull a single submodule (e.g. DeployDeepModelKubernetes) run: ```bash git clone https://github.com/microsoft/ai @@ -47,7 +29,6 @@ git submodule update ``` # [AI100 - Samples](https://azure.microsoft.com/en-us/overview/ai-platform/) Samples are a collection of open source Python repositories created by the Microsoft product teams, which focus on AI services. - | Title | Description | |-------|-------------| | [Azure ML Python SDK](https://github.com/Azure/MachineLearningNotebooks)|Python notebooks with ML and deep learning examples with Azure Machine Learning| @@ -55,10 +36,8 @@ Samples are a collection of open source Python repositories created by the Micro | [Azure Intelligent Kiosk](https://github.com/microsoft/Cognitive-Samples-IntelligentKiosk)|Here you will find several demos showcasing workflows and experiences built on top of the Microsoft Cognitive Services.| | [MML Spark Samples](https://github.com/Azure/mmlspark/tree/master/notebooks/samples)|MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions.| | [Seismic Deep Learning Samples](https://github.com/microsoft/seismic-deeplearning/)|Deep Learning for Seismic Imaging and Interpretation.| - # [AI200 - Reference Architectures](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/machine-learning-at-scale) Our reference architectures are arranged by scenario. Each architecture includes open source practices, along with considerations for scalability, availability, manageability, and security. - | Title | Language | Environment | Design | Description | Status | |----------------------------------------------|-------------|-------------|-------------|-----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Deploy Classic ML Model on Kubernetes](https://github.com/microsoft/MLAKSDeployAML) | Python | CPU | Real-Time Scoring| Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for _real-time_ scoring | [![Build Status](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_apis/build/status/AI%20CAT/Python-ML-RealTimeServing?branchName=master)](https://dev.azure.com/AZGlobal/Azure%20Global%20CAT%20Engineering/_build/latest?definitionId=21&branchName=master) @@ -70,34 +49,17 @@ Our reference architectures are arranged by scenario. Each architecture includes | [Deploy R ML Model on Batch](https://github.com/Azure/RBatchScoring) | R | CPU | Scoring | Deploy forecasting model for _batch_ scoring using Azure Batch and doAzureParallel | | | [Deploy Spark ML Model on Databricks](https://github.com/Azure/BatchSparkScoringPredictiveMaintenance) | Python | Spark | Batch Scoring | Deploy a classification model for _batch_ scoring using Databricks | | | [Train Distributed Deep Leaning Model](https://github.com/Azure/DistributedDeepLearning/) | Python | GPU | Training | Distributed training of ResNet50 model using Batch AI | | - # AI300 - Best Practices Our best practices are arranged by topic. Each best pratice repository includes open source methods, along with considerations for scalability, availability, manageability, and security. - | Title | Description | |-------|-------------| |[Computer Vision](https://github.com/microsoft/computervision)| Accelerate the development of computer vision applications with examples and best practice guidelines for building computer vision systems |[Natural Language Processing](https://github.com/microsoft/nlp)|State-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.| |[Recommenders](https://github.com/microsoft/recommenders)| Examples and best practices for building recommendation systems, provided as Jupyter notebooks.| |[MLOps](https://github.com/microsoft/MLOps)| MLOps empowers data scientists and app developers to help bring ML models to production. | - - ## Recommend a Scenario If there is a particular scenario you are interested in seeing a tutorial for please fill in a [scenario suggestion](https://github.com/Microsoft/AIReferenceArchitectures/issues/new?assignees=&labels=&template=scenario_request.md&title=%5BSCENARIO%5D) - ## Ongoing Work We are constantly developing interesting AI reference architectures using Microsoft AI Platform. Some of the ongoing projects include IoT Edge scenarios, model scoring on mobile devices, add more... To follow the progress and any new reference architectures, please go to the AI section of this [link](https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/). - # Contributing - -This project welcomes contributions and suggestions. Most contributions require you to agree to a -Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us -the rights to use your contribution. For details, visit https://cla.microsoft.com. - -When you submit a pull request, a CLA-bot will automatically determine whether you need to provide -a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions -provided by the bot. You will only need to do this once across all repos using our CLA. - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or -contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. +Contributions are always welcome!! If you feel like contributing, kindly read the [Contribution Guidelines](https://github.com/microsoft/AI/blob/master/CONTRIBUTING.md).