<jupyter_start><jupyter_text>June 27th 2018, Microsoft Bits & BitesIntro to Datascience: AI for all... explained<NAME>, PhD, Data Strategist Microsoft / LinkedInStay in touch! Twitter: @Thomas Roca || LinkedIn || Github Folder  Empowering every person and organization to achieve more  "A data scientist is someone who is better at statistics  than any software engineer and better at software engineering than any statistician"         Content   1. What is AI: a short reminder   2. Data flows in the information age   3. AI for all: Microsoft framework      What is Intelligence, what is AI ?    What is human intelligence ?- ability to **learn and understand** (i.e. cognition) and by extension, ability to **make informed decision** What about AI- AI so far has been focusing on **mastering human capabilities** (Turing):    - **vision, text, language, sentiment, voice recognition and generation, classification and categorization** but basically **AI is a brut-force way to sort an awful lot of information**  AI past and future    AI pre-information age  Not so smart:- **Procedural** versus "informational" algorithmic- the **difference between Chess and Go** (when programmer just cannot embed all possible scenarios if / then)  What changed lately with AI, why such enthusiasm ? - **New Algorithms: Deep and reinforcement learning**- **Computing power** and **parallel processing** (+ GPU, FPGA)- (Big) **Data** available... to train those model - An **ubiquitous** & **low latency** (fast) **infrastructure: the cloud****Results**:- significant **breakthrough** (autonmous car, voice and image recognition)- as Data is now a crucial ingredient, **we need a complete ecosystem**  Where are we now? How shiny is shiny?   - **Making smart decision**: Kinda at least automating complexe tasks and be more efiicient than human at it- **understanding the context**: pretty well when it is not ambiguousa cow in the meadow, ordering a hair cut. Tougher with cows at the beach, irony, etc. and.. we now have the knoweledge, physical and digital infrastructure to diffuse AI for all  From AI's algorithms *on your computer* to AI *as a service*    Let's talk a bit about the infrastructure ...it is crucial to understand AI for all  In the 21st century, (big) data does not flow in spreadsheet- Data travel through networks- Data is stored and processed in the cloud- and consumed on many devices (Cell phone, PC, IoT)  How does that work?- Data travels in flat text files (csv, json, XLM,..)- Data structure must be standardized and machine readable- Data are made available from machine to machine through API```javascript{ "authenticationResultCode": "ValidCredentials",  "copyright": "Copyright © 2018 Microsoft and its suppliers.",  "coordinates": [50.8813095092773, 4.47601509094238],  "name": "<NAME>",  "address": {    "locality": "Zaventem",    "adminDistrict2": "Flemish Brabant",    "adminDistrict": "Flemish Region",    "countryRegion": "Belgium" }}```  Data life cycle: from collection to dissemination   Basic example: getting real time pollution dataEnd point of the [API](https://api.data.gov.in/resource/3b01bcb8-0b14-4abf-b6f2-c1bfd384ba69?api-key=579b464db66ec23bdd000001cdd3946e44ce4aad7209ff7b23ac571b&format=json&offset=0&limit=7)<jupyter_code>HTML('''<iframe width="100%" height="500" src="https://jsfiddle.net/ThomasRoca/bq4avah4/embedded/result,js/" allowpaymentrequest allowfullscreen="allowfullscreen" frameborder="0"></iframe>''')<jupyter_output><empty_output><jupyter_text>How? API: the way to provide data and services     What is an API (Application Programming Interface) ?**an intermediate between server & client**  Why using an API- Programmatic access = Automate- allows sending request to a DB: Real time, and always up to date- Provide differentiated and controlled (accreditation) access to data  Then we can build data application or share AI as a service  Machine learning as a service- Nowaday, AI is being made accessible through the cloud- API are used to send data to ML models hosted on a server and retrieve the results     what to do with it ?- **Deploy already trained AI models into Apps, website, software, IoT**- **The example of azure cognitive services: **https://docs.microsoft.com/en-us/azure/pivot=products&panel=ai  Example of the cognitive services[Link](file:///C:/OneDrivePRO/OneDrive%20-%20Microsoft/Desktop/Cognitive%20services%20AI%20Vision%20demo%20JS/analyze.html)  Cloud Computing and AI implementation: Microsoft's framework![](http://blog.binayakinfotech.in/wp-content/uploads/2018/02/FireShot-Pro-Screen-Capture-005-What-is-Azure-%E2%80%94-Microsoft-cloud-service-I-Microsoft-Azure-azure_microsoft_com_en-in_overview_what-is-azure-300x187.png)- [Open Source Library CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/)- [Hybrid solutions (e.g. custom Vision)](https://customvision.ai/)- [Azure Cognitive Services (AI as a Service)](https://docs.microsoft.com/en-us/azure/cognitive-services/)     AI is already fueling applications  A citizen in the 21st century must be digital or data literate.     Microsoft AI school**“Well informed, people are citizens; ill informed, they become subjects”** - Alfred Sauvy  Using AI for text classification, the LinkedIn example[Notebook](http://localhost:8888/notebooks/Machine%20learning/LinkedIn%20profile%20Text%20Classifier%20to%20present.ipynb/slide-0-0)      Thanks! Q&A time<jupyter_code>from IPython.display import HTML
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