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ai's Introduction

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.

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).

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.

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.

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
  2. AI100 - Samples
  3. AI200 - Reference Architectures
  4. AI300 - Best Practices
  5. 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:

git clone --recurse-submodules https://github.com/microsoft/ai

if you have git older than 2.13 run:

git clone --recursive https://github.com/microsoft/ai.git

To pull a single submodule (e.g. DeployDeepModelKubernetes) run:

git clone https://github.com/microsoft/ai
cd ai
git submodule init submodules/DeployDeepModelKubernetes
git submodule update

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 Python notebooks with ML and deep learning examples with Azure Machine Learning
Azure Cognitive Services Python SDK Learn how to use the Cognitive Services Python SDK with these samples
Azure Intelligent Kiosk Here you will find several demos showcasing workflows and experiences built on top of the Microsoft Cognitive Services.
MML Spark Samples MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions.
Seismic Deep Learning Samples Deep Learning for Seismic Imaging and Interpretation.

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 Python CPU Real-Time Scoring Train LightGBM model locally using Azure ML, deploy on Kubernetes or IoT Edge for real-time scoring Build Status
Deploy Deep Learning Model on Kubernetes Python Keras Real-Time Scoring Deploy image classification model on Kubernetes or IoT Edge for real-time scoring using Azure ML Build Status
Hyperparameter Tuning of Classical ML Models Python CPU Training Train LightGBM model locally and run Hyperparameter tuning using Hyperdrive in Azure ML
Deploy Deep Learning Model on Pipelines Python GPU Batch Scoring Deploy PyTorch style transfer model for batch scoring using Azure ML Pipelines Build Status
Deploy Classic ML Model on Pipelines Python CPU Batch Scoring Deploy one-class SVM for batch scoring anomaly detection using Azure ML Pipelines
Deploy R ML Model on Kubernetes R CPU Real-Time Scoring Deploy ML model for real-time scoring on Kubernetes
Deploy R ML Model on Batch R CPU Scoring Deploy forecasting model for batch scoring using Azure Batch and doAzureParallel
Deploy Spark ML Model on Databricks Python Spark Batch Scoring Deploy a classification model for batch scoring using Databricks
Train Distributed Deep Leaning Model 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 Accelerate the development of computer vision applications with examples and best practice guidelines for building computer vision systems
Natural Language Processing State-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
Recommenders Examples and best practices for building recommendation systems, provided as Jupyter notebooks.
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

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.

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. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

ai's People

Contributors

akarsh135 avatar danielleodean avatar dciborow avatar gramhagen avatar grecoe avatar jreynolds01 avatar md260 avatar microsoft-github-policy-service[bot] avatar microsoftopensource avatar miguelgfierro avatar msalvaris avatar msftgits avatar

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ai's Issues

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Issues with description

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It is said to be requested as an empty project, I hope this is correct, but I completely don't understand what I or any Reviewer should do with this project. Fair said, try to add some wordings, or what is actually helpful, you can add binary to your code, which is a great idea, being binary, we can modify it easily, of course it is easier said than done. python is better at de-bugging, but knowing about the core advantage is a great way to distribute to reviewers and ask them to compile and figure out if there are any issues.


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Error in Creating new Project under VS 2019 Using the Virtual Assistant Template

After installing all prerequisites for the Azure Bot Virtual Assistant on my laptop, I have selected on Virtual Studio 2019 new project. On the windows that new project select opened, I have selected the Virtual Assistant Template which guide with another windows the configuration of the new project with the Virtual Assistant template. Using the default configuration provided, I selected the Create action (versus go back).

I then encounter the following error - Refer here to attached Word document that has the errors windows that are occurring for this issue,

Issue when attempting to create new project with Virtual Assistant Template using Virtual Studio 2019.docx

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[SCENARIO]

importing required libraries.

import pyttsx3
import datetime
import speech_recognition as sr
import wikipedia
import smtplib
import webbrowser as wb
import os
import requests
from pprint import pprint
import pyautogui
import pyjokes

engine = pyttsx3.init()
voices = engine.getProperty('voices') # getting details of current voice
engine.setProperty('voice', voices[1].id) # For Female Voice

def speak(audio):
engine.say(audio)
engine.runAndWait()

def time():
Time = datetime.datetime.now().strftime("%I:%M:%S")
print(Time)
speak("The current Time is")
speak(Time)

def date():
year = int(datetime.datetime.now().year)
month = int(datetime.datetime.now().month)
dates = int(datetime.datetime.now().day)
speak("The current date is")
print(dates)
print(month)
print(year)
speak(dates)
speak(month)
speak(year)

def wishme():
speak("Welcome!")
hour = datetime.datetime.now().hour
if hour >= 6 and hour < 12:
speak("Good Morning Sir")
elif hour >=12 and hour < 18:
speak("Good Afternoon Sir")
elif hour >=18 and hour < 24:
speak("Good Evening Sir")
else:
speak("I hope you are enjoying your Night Sir")
speak("Friday at your service. Please tell me how can i help you ")

def takeCommand():
r = sr.Recognizer()
with sr.Microphone() as source:
print("Listening...")
r.pause_threshold = 1
audio = r.listen(source)
try:
print("Recognizing...")
query = r.recognize_google(audio, language='en-in')
print(f"You Said: {query}\n")

except Exception as e:
    print(e)
    print("Sorry Sir, Say that again")
    speak("Sorry Sir, Say that again")
    return "None"
return query

def sendEmail(to, content):
server = smtplib.SMTP('smtp.gmail.com', 587)
server.ehlo()
server.starttls()
server.login('[email protected]', 'Password')
server.sendmail('[email protected]', to, content)
server.close()

def screenshot():
img = pyautogui.screenshot()
img.save('C:/Users/Amandeep/Desktop/Friday/screenshot.png')

def jokes():
haha = pyjokes.get_joke()
print(haha)
speak(haha)

if name == "main":
wishme()
while True:
query = takeCommand().lower()

    if 'the time' in query:
        time()

    if 'the date' in query:
        date()

    elif 'wikipedia' in query:
        speak("Searching Wikipedia...")
        query = query.replace("wikipedia", "")
        result = wikipedia.summary(query, sentences=2)
        print(result)
        speak(result)

    elif 'send email' in query:
        try:
            speak("What should i say?")
            content = takeCommand()
            to = '[email protected]'
            sendEmail(to, content)
            speak("Email has been sent successfully.")
        except Exception as e:
            print(e)
            speak("Sorry Sir, Unable to send the email")

    elif 'chrome' in query:
        speak("What should i search?")
        chrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'
        try:
            search = takeCommand().lower()
            print('I think you said:\n' +search +'.com')
            wb.get(chrome_path).open_new_tab(search+'.com')
        except Exception as e:
            print(e)

    elif 'logout' in query:
        os.system("shutdown -l")
    elif 'shutdown' in query:
        os.system("shutdown /s /t 1")
    elif 'restart' in query:
        os.system("shutdown /r /t 1")

    elif 'how is the weather' and 'weather' in query:
        url = 'https://api.openweathermap.org/data/2.5/weather?q=<PLACE_NAME>&appid=<YOUR API KEY>'
        res = requests.get(url)
        data = res.json()
        weather = data['weather'] [0] ['main'] 
        temp = data['main']['temp']
        wind_speed = data['wind']['speed']
        latitude = data['coord']['lat']
        longitude = data['coord']['lon']
        description = data['weather'][0]['description']
        speak('Temperature : {} degree celcius'.format(temp))
        print('Wind Speed : {} m/s'.format(wind_speed))
        print('Latitude : {}'.format(latitude))
        print('Longitude : {}'.format(longitude))
        print('Description : {}'.format(description))
        print('weather is: {} '.format(weather))
        speak('weather is : {} '.format(weather))

    elif 'open' in query:
        os.system('explorer C://{}'.format(query.replace('Open','')))

    elif 'play song' in query:
        songs_dir = 'E:/MUSICS/Hindi Fav' 
        songs = os.listdir(songs_dir)
        os.startfile(os.path.join(songs_dir, songs[0]))
        
    elif 'remember' in query:
        speak("what should i remember?")
        data = takeCommand()
        speak("You said me to remember that"+data)
        remember = open('data.txt', 'w')
        remember.write(data)
        remember.close()
    elif 'do you know anything' in query:
        remember = open('data.txt', 'r')
        speak("You said me to remember that"+remember.read())

    elif 'take screenshot'in query:
        screenshot()
        speak("Screenshot Saved")

    elif 'joke' in query:
        jokes()

    elif 'who made you' in query or 'who created you' in query:
        speak("i am created by Amandeep")

    elif "how are you" in query:
        speak("I am Boombastic, How are You?")
    
    elif 'i am fine' in query or 'I am good' in query:
        speak("it is good to hear that you are fine")

    elif "search for me" in query:
        speak("What sir?")
        a = takeCommand().lower()
        print("Searching \n"+a)
        wb.open(f"https://www.google.com/search?q={a}")

    elif 'my location' in query:
        try:
            response = requests.get('https://ipinfo.io?token=<TOKEN>')
            locInfo =  response.json()
            print(30*"-")
            print(locInfo['city'])
            print(locInfo['region'])
            if locInfo['country'] == 'IN':
                locInfo['country'] = 'India'
            print(locInfo['country'])
            speak(f"Sir, you are currently in {locInfo['city']} in {locInfo['region']}.")
            print(30*"-")
        except Exception as e:
            print("Sorry, sir. I am having issues gathering your location")

    

    elif 'offline' in query:
        speak("Shutting Down Sir")
        quit()

Monetise

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Issue Type: Bug

Über die genau vorgehensweise bin ich mit nicht sicher, aber ich kann Ihnen einige ungenau schritte erläutern wie dieser Fehler entstanden ist.
Visual Studio Code version: 14.02

Schritt 1: Al Language installieren
Schritt 2: Taste F1 Drücken und befehl Al: Go nutzen
Schritt 3: Eine tabelle Erstellen und eine Liste (der inhalt der Tabelle ist in diesem Falle egal)
Schritt 4: Launch.json auf Ihren Business central 14 Server einstellen
Schritt 5: Erstmaliges anlegen einer Übersetzungdatei (xlf)
Beschreibung:
Ich habe in App.json mit "features": ["TranslationFile"] eine Übersetzungsdatei erstellt.
Schritt 6: Veröffentlichen und Schließen
Schritt 7: Umbenenen der Übersetztungsdatei in "de-DE.xlf"
Schritt 8: "target-language" auf "target-language="de-DE"" ändern
Schritt 9: Übersetzen
Schritt 10: Erneut veröffentlichen und Schließen
Schritt 11: Neue Felder hinzufügen mit entsprechenden Captions
Schritt 12: Erneut veröffentlichen Schließen
Schritt 13: Alle Daten aus der Unübersetzte datei (Standart) Kopieren und in der Übersetzten alles mit diesen Daten ersetzten
Schritt 14: "target-language" auf "target-language="de-DE"" ändern
Schritt 15: Übersetzen
Schritt 16: Erneut veröffentliche nun sollten nur die Captions zusehen sein und die Übersetzungsdatei wird ignoriert

Ich habe keine ahnung wie ich den Fehler besser Beschreiben soll.

VS Code version: Code 1.40.2 (f359dd6, 2019-11-25T14:54:45.096Z)
OS version: Windows_NT x64 10.0.18362

System Info
Item Value
CPUs Intel(R) Core(TM) i7-4712MQ CPU @ 2.30GHz (8 x 2295)
GPU Status 2d_canvas: enabled
flash_3d: enabled
flash_stage3d: enabled
flash_stage3d_baseline: enabled
gpu_compositing: enabled
metal: disabled_off
multiple_raster_threads: enabled_on
oop_rasterization: disabled_off
protected_video_decode: unavailable_off
rasterization: enabled
skia_renderer: disabled_off
surface_control: disabled_off
surface_synchronization: enabled_on
video_decode: enabled
viz_display_compositor: enabled_on
viz_hit_test_surface_layer: disabled_off
webgl: enabled
webgl2: enabled
Load (avg) undefined
Memory (System) 15.88GB (8.68GB free)
Process Argv
Screen Reader no
VM 0%
Extensions (6)
Extension Author (truncated) Version
al-code-outline and 1.0.21
al Mic 3.0.106655
vscode-language-pack-de MS- 1.40.2
cpptools ms- 0.26.1
al-var-helper ras 2.4.0
crs-al-language-extension wal 1.1.21

Microsoft.Bot.Builder.Solutions Points to this Repo as its Source Repository

The Microsoft.Bot.Builder.Solutions Nuget Package points as this Repo as the Project Url
On Nuget this Project is also listed as the Source Repository

However neither is accurate Likely not an issue with this project as much as with the project that has this project listed but given I cant find that repository to log the issue.

Any guesses who or where Microsoft.Bot.Builder.Solutions actually is?

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GitHub Issue Title: “Bug: App crashes on startup

Issue Description

I encountered a critical issue where the app crashes immediately upon startup. This is a major problem as it prevents users from using the app entirely.

Steps to Reproduce

  1. Open the app.
  2. Observe the crash.

Expected Behavior

The app should start up without any crashes and allow users to use its features.

Actual Behavior

The app crashes as soon as it's launched.

Environment

  • App Version: [Specify the app version]
  • Device: [Specify the device and OS version]
  • Any other relevant information: [Add any additional context or information that might be helpful in diagnosing the issue]

Please let me know if you need any more information to investigate and resolve this issue. Thanks!

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