AI for Everyone

You might have heard of a lot about AI, Machine learning, Data science, Deep learning, etc,... But, what exactly these terms mean and how is the connection between those. Here is my understanding:

There are two ideas of AI:

- ANI (Artificial Narrow Intelligence): E.g., smart speaker, self-driving car and web search.

- AGN (Artificial General Intelligence): do anything a human can do.

ANI is realistic and incredibly valuable. Though AGN is still too far away, and there is no need to unduly worry about it.

When talking about data in term of AI that means talking about dataset. There are several methods to get a dataset:

- Manual labeling

- Observing user behaviors

- Observing behaviors of other things such as machine

- Downloading dataset from a website or acquiring it from a partner.

Machine learning (ML) is a tool in AI. Supervised learning is a type of ML that learns A to B, or input to output mappings. Deep learning/Neural networks is a type of supervised learning which can maximize the performance of AI when dataset is big today.

Is AI a subset of data science?

No! Data science uses many tools from AI machine learning, but has some other separate tools as well that solves a very set of important problems in driving business insights.

- ML project output: a running AI system

- Data science project output: a set of insights

Reference

https://www.coursera.org/learn/ai-for-everyone/

Comments

Google I/O 2017 Notes

WOW! How meaningful this below video explains about the name of "I/O". Sundar Pichai talked a lot of Machine Learning Machine Learning is a very hot trend these days. Google uses it for their products. Google Assistant: Easily booking an online meal by talking with Google Assistant like a staff of partners, for example. Google Home: Hands-free calling. Google Photos: sharing suggestion, shared library, photo books and google lens. Youtube: 360 degree video, live stream. Kotlin became an official programming language for Android https://kotlinlang.org I'm on the way to Kotlin! ^^ Reference: [1]. https://www.youtube.com/watch?v=Y2VF8tmLFHw

Junit - Test fails on French or German string assertion

In my previous post about building a regex to check a text without special characters but allow German and French . I met a problem that the unit test works fine on my machine using Eclipse, but it was fail when running on Jenkins' build job. Here is my test: @Test public void shouldAllowFrenchAndGermanCharacters(){ String source = "ÄäÖöÜüß áÁàÀâÂéÉèÈêÊîÎçÇ"; assertFalse(SpecialCharactersUtils.isExistSpecialCharater(source)); } Production code: public static boolean isExistNotAllowedCharacters(String source){ Pattern regex = Pattern.compile("^[a-zA-Z_0-9_ÄäÖöÜüß áÁàÀâÂéÉèÈêÊîÎçÇ]*$"); Matcher matcher = regex.matcher(source); return !matcher.matches(); } The result likes the following: Failed tests: SpecialCharactersUtilsTest.shouldAllowFrenchAndGermanCharacters:32 null A guy from stackoverflow.com says: "This is probably due to the default encoding used for your Java source files. The ö in the string literal in the J...

JSF, Primefaces - Invoking Application Code Even When Validation Failed

A use case I have a form which has requirements as follow: - There are some mandatory fields. - Validation is triggered when changing value on each field. - A button "Next" is enable only when all fields are entered. It turns to disabled if any field is empty. My first approach I defined a variable "isDisableNext" at a backend bean "Controller" for dynamically disabling/enabling the "Next" button by performing event "onValueChange", but, it had a problem: <h:form id="personForm"> <p:outputLabel value="First Name" for="firstName"/> <p:inputText id="firstName" value="#{person.firstName}" required="true"> <p:ajax event="change" listener="#{controller.onValueChange}" update="nextButton"/> </p:inputText> <p:outputLabel value="Last Name" for="lastName"/> <p:i...

Only allow input number value with autoNumeric.js

autoNumeric is a jQuery plugin that automatically formats currency and numbers as you type on form inputs. I used autoNumeric 1.9.21 for demo code. 1. Dowload autoNumeric.js file from https://github.com/BobKnothe/autoNumeric 2. Import to project <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.11.0/jquery.min.js"></script> <script type="text/javascript" src="js/autoNumeric.js"></script> 3. Define a function to use it <script type="text/javascript"> /* only number is accepted */ function txtNumberOnly_Mask() { var inputOrgNumber = $("#numberTxt"); inputOrgNumber.each(function() { $(this).autoNumeric({ aSep : '', aDec: '.', vMin : '0.00' }); }); } </script> 4. Call the function by event <form> <input type="text" value="" id="numberTxt"/>(only number) </form> <script ty...

How I did customize "rasa-nlu-trainer" as my own tool

Check out my implementation here Background I wanted to have a tool for human beings to classify intents and extract entities of texts which were obtained from a raw dataset such as Rocket.chat's conversation, Maluuba Frames or here . Then, the output (labeled texts) could be consumed by an NLU tool such as Rasa NLU. rasa-nlu-trainer was a potential one which I didn't need to build an app from scratch. However, I needed to add more of my own features to fulfill my needs. They were: 1. Loading/displaying raw texts stored by a database such as MongoDB 2. Manually labeling intents and entities for the loaded texts 3. Persisting labeled texts into the database I firstly did look up what rasa-nlu-trainer 's technologies were used in order to see how to implement my mentioned features. At first glance rasa-nlu-trainer was bootstrapped with Create React App. Create React App is a tool to create a React app with no build configuration, as it said. This too...

vhandit

Search This Blog