The Story of Deep Pan Pizza :AI Explained for Dummies

Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning….

Most probably, the words on the top are the widely used and widely discussed buzz words today. Even the big companies use them to make their products appear more futuristic and “market candy” (Like a ‘tech giant’ recently introduced something called a ‘neural engine’)!

Though AI and related buzz words are so much popular, still there are some misconceptions with people on their definitions. One thing that clearly you should know is; AI, machine learning & deep learning is having a huge deviation from the field called “Big Data”. It’s true that some ML & DL experiments are using big data for training… but keep in mind that handling big data and doing operations with big data is a separate discipline.

So, what is Artificial Intelligence?

“Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.” – Wikipedia

Simple as that. If a system has been developed to perform the tasks that need human intelligence such as visual perception, speech recognition, decision making… these systems can be defined as a intelligent system or an so called AI!

The most famous “Turing Test” developed by Alan Turing (Yes. The Enigma guy in the Imitation Game movie!) proposed a way to evaluate the intelligent behavior of an AI system.


Turing Test

There are two closed rooms… let’s say A & B. in the room A… we have a human while in the room B we have a system. The interrogator; person C is given the task to identify in which room the human is. C is limited to use written questions to make the determination. If C fails to do it- the computer in room A can be defined as an AI! Though this test is not so valid for the intelligent systems we have today, it gives a basic idea on what AI is.

Then Machine Learning?

Machine learning is a sub component of AI, that consists of methods and algorithms allows the computer systems to statistically learn the patterns of data. Isn’t that statistics? No. Machine learning doesn’t rely on rule based programming (It means that a If-Else ladder is not ML 😀 ) where statistical modeling is mostly about formulation of relationships between data in the form of mathematical equations.

There are many machine learning algorithms out there. SVMs, decision trees, unsupervised methods like K-mean clustering and so-called neural networks.

That’s ma boy! Artificial Neural Networks?

Inspired by the neural networks we all have inside our body; artificial neural network systems “learn” to perform tasks by considering many examples. Simply, we show a thousand images of cute cats to a ANN and next time.. when the ANN sees a cat he is gonna yell.. “Hey it seems like a cat!”.

If you wanna know all the math and magic behind that… just Google! Tons of resources there.

Alright… then Deep Learning?

Yes! That’s deep! Imagine the typical vanilla neural networks as thin crust pizza… It’s having the input layer (the crust), one or two hidden layers (the thinly soft part in the middle) and the output layer (the topping). When it comes to Deep Learning or the deep neural networks, that’s DEEP PAN PIZZA!


DNNs are just like Deep Pan Pizzas

Deep Neural Networks consist of many hidden layers between the input layer and the output layer. Not only typical propagation operations, but also some add-ins (like pineapple) in the middle. Pooling layers, activation functions…. MANY!

So, the CNNs… RNNs…

You can have many flavors in Deep Pan Pizzas! Some are good for spicy lovers… some are good for meat lovers. Same with Deep Neural Networks. Many good researchers have found interesting ways of connecting the hidden layers (or baking the yummy middle) of DNNs. Some of them are very good in image interpretation while others are good in predicting values that involves time or the state. Convolutional Neural Networks, Recurrent Neural Networks are most famous flavors of this deep pan pizzas!

These deep pan pizzas have proven that they are able to perform some tasks with close-to-human accuracy and even sometimes with a higher accuracy than humans!deep-learning

Don’t panic! Robots would not invade the world soon…


Image Courtesy : DataScienceCentral | Wikipedia

Chatbots : What & Why?

robot-customer-serviceThe word ‘chatbots’ has become one of the most whispered words in the tech world today. Each and every tech company is putting a lot of effort on researching and developing bot related technologies.

The very first thing that you should keep in your mind is “Bot is not an acronym neither a magic app”. Bot is an application that operates as an agent for a user or another program or simulates a human activity.

I would say, there’s no Artificial intelligence or natural language processing attached with most of the chatbots you see out there. But AI and machine learning have become prominent factors of giving bots more human side.

The evolution of chatting paradigms and the rapid adaptation of millennials for chatting platforms like Facebook messenger, WhatsApp and Viber increased the need of chatbots that can handle business processes.


Evolution of user interaction

The same way a website is interacting with a user, bot acts as the interface into the service. Simplicity, increasing productivity, personalized service lines are some of the major benefits that we can achieve with getting chatbots into the play.

Super bots Vs domain specific bots

Probably the very first thought that comes to your mind when it says ‘bots’ might be “Siri, Cortana or Google assistant”. Dominating our pockets with their ability of interacting as a personal assistant, these software utilities can be defined as super bots. They are equipped with speech recognition as well as natural language understanding. Normally there’s a persona specifically designed for these super bots. The backend of these intelligent applications is backed with machine learning and deep learning based technological interventions.


AI powered personal assistants in your pocket and home

Domain specific bots are easy to find and easy to build (comparatively to the super bots). They are specifically designed aligning to a particular business process.  Ordering a pizza from nearest pizza shop, customer service call centers or booking a flight ticket are some example business processes that can be easily adopted to a conversational bot interface. These bots may use machine learning techniques for natural language understanding.

Business bots Vs Consumer bots

Bots are not only mend to be to involve in business process. Fun is mandatory! The consumer bots are specifically designed to maintain human like conversations with the users. Sometimes even for flirting 😉 Mitsuku is known as one of the prominent consumer bots that have built for today.

Text or the voice?

Interacting with a chatbot can be done in several ways. Textual communication is just one thing. Speech recognition enables the user to interact with the chatbots with speech. Some chatbots provide interactive clickable cards for user interaction. Amazon Alexa even has a hardware component that interacts with the user with voice commands.

Building bots

There are plenty of programming paradigms prevailing today that helps you to build conversational bots. Microsoft Bot Framework is a programmer friendly framework that supports C# or node.js for deploying bots. Integrating chat channels like Skype and messenger can be too done through the framework.

Natural Language understanding provides more human like nature for bot’s conversations. For that, LUIS service by Microsoft, API.AI, are some prominent services used today by the programmers. No need to go from the scratch of machine learning algorithms. Just an API call will do the magic for you.

Bots can be given more human like abilities with machine learning based intelligent API services and SDKs. Microsoft Cognitive services is a valuable toolset that you can use to give your chatbot the ability to see, hear and even think!

What’s next?

I guess, codeless bot building services (some are already there in the market, but not so matured) and natural language generation would be the next big things in the conversational bot building industry. Deep learning will come to scene with language generation for sure.

Time to market is a prominent factor in the world of business. Then why not going with the trend and adopt a chatbot for your own business or start building bots as your business? 😉

Nepal Earthquake; from the Eye of Social Networks

APTOPIX Nepal Earthquake Baby Rescue

4-month-old baby rescued from Nepal earthquake rubble : Source –

   It was 25th April noon. I was heading back home by train and got my phone out to check the notifications.

first seen

The shocked post hit my timeline

A post on my Facebook timeline made me unconscious and even forgot what to do for like a minute. It brought me the news about the tragic earthquake happened in Nepal. For the next couple of hours I was busy with trying to contact my friends from Nepal and checking whether they are OK…The phone lines in Nepal were not properly working due to network failures. It was very hard to find out whether out friends and colleagues are secured.

During this disaster situation, the social media did a tremendous job by establishing communication channels as well as getting out the news from the affected areas an spreading them out worldwide.

Here I’ve listed out some social media and technological interventions came out as a relief for the victims of the disaster.

Tweeting birds carried the news as fast as they could!

As I noticed Twitter was the fastest channel of carrying out the news about the situation in affected areas .Tweets from the verified news sources even became news sources for local news channels.

With the help of hashtags it was easy even for the public to grab the info about the disaster. Here the graphs shows how vital #NepalEarthquake was during the situation.

Recognizing its importance as a tool for coordinating relief efforts, and as a backup for official channels of communication, Twitter says it will be concentrating its efforts in that direction.

“We aren’t a relief organization or an NGO,” the company says in an official blog post, “but we have mobilized a team to do three immediate things:

  1. Disseminate information and news about the disaster globally
  2. Help key nonprofits raise funds and material donations for victim relief
  3. Help local agencies coordinate relief efforts”

I would say Twitter is a powerful social media tool that can be occupied in various applications, even we couldn’t think of. So far Twitter is not just a useless bird now! 😀

Facebook; the giant of social media in the mission!


Facebook Safety check

In my case, My Facebook timeline was the first news source about the disaster for me. As many people were sharing news and posts about the earthquake, my Facebook timeline gradually became a news line.

Facebook Safety Check feature which was implemented after the 2011 Tsunami in Japan, was activated aftermath of the 7.9 magnitude earthquake that struck Nepal. Facebook’s Safety Check invites users to “mark themselves safe” if they are in the earthquake affected areas. Users can also check whether their friends are currently in the affected areas and if they have marked themselves safe.

It is quite good to see that people using social media channels for the social goodness.


It was good to see all all my friends from Nepal marked Safe

If it is about computers, Google is there!

Google too has activated their Person Finder tool that was launched in the aftermath of the 2011 Haiti earthquake. It is an open source API that lets people create a ‘record’ of themselves.

It allows to track the whereabouts of their friends and family and subscribe to updates about people’s locations. The tool also lets you embed it on your website to enable people to search or track people’s locations.

I tried it with several entries, but for me it seems not so effective. Google should have develop further.

person finder

Google Person Finder

Make free calls with Viber & Skype

As many mobile networks were down for several hours, the voice calling services like Viber & Skype came out with free call facilities to and from Nepal.

“In light of the devastating events that have taken place in Nepal – and the subsequent impact to the local communications infrastructure – we are making all Skype calls to landlines and mobiles in and out of Nepal free of charge,* with immediate effect.” The skype team expressed their idea in their blog making skype calls free.

In this post disaster situation, many fund raising initiatives and help hand activities are going on with getting the help of social network reach. Those campaigns seems effective as millions of people engaged with social networks are clicking the like buttons and share the posts about a disaster even without having good sense.

Technology should always for the social goodness. Though social networks are pointed out as social beasts, here in a disaster situation they can act a vital role helping the victims using its tremendous reach and availability.

Will the social networks be the next disaster alert system?