Imitation of Intelligence : Exploring Artificial Intelligence!

What is the difference between “calculate” and “compute”?

Light & flexible

I assure you, we are not going to discuss such quintessential terms related to computing world, which might bore some of us, as it might have given the impression 😀

But this is something out of curiosity about the crux of what we are going to go through.



So, the calculation involves an arithmetic process. Computation is involved in the implementation of non-arithmetic steps of the algorithm which actually brings things up to the calculation.

You got the idea where I am going with this right? We can try to visualize every aspect of data processing stages from data collection, cleansing, processing and then transforming it through mathematical operations to map data into something which makes more sense i.e. “Insight“. But the intelligence used for such meaningful transformation used to be the human intervention which now can be “Artificial” as per the new digital trend.

Getting to know …

Artificial Intelligence in the industry will change everything about the way we produce, manufacture and deliver. Cognitive computing, machine learning, natural language processing – different aspects have emerged as the development of the technology has progressed in recent years. But they all encapsulated the idea that machines could one day be taught to learn how to adapt by themselves, rather than having to be spoon-fed every instruction for every eventuality. There are certain important emerging digital trends we can track considering the technology & future that are together converging very fast. Years ago the industrial revolution immutably remolded society and another revolution is underway with potentially even further reaching consequences. These digital trends are all potentially disruptive unless we plan ahead for the impact and change that is coming. Likely things benefited will be more agility, smarter business processes, and better productivity by converging focus and efforts on right things.

Goals of Artificial Intelligence

Artificial intelligence (AI) has become ubiquitous in business in every industry where decision making is being fundamentally transformed by Machines brains. The need for faster and smarter decisions and the management of big data that can make the difference is what is driving this trend. The convergence of big data with AI is inevitable as the automation of smarter decision-making is the next evolution of big data. while adapting to this change some will inevitably prosper and some will fail. Those that manage to succeed are likely to be those which can manage to see beyond the hype and understand how this technology can add real value and drive positive change.

The best way to look at AI is automating things which have been worked on and implemented logically to solve the problem already. This will help to apply the existing problem-solving logic effectively, smartly using artificial brains and using human brain, efforts to focus on problems which still need more attention. The world where machines and devices all communicate with each other to get the work done (IoT), leaving us free to relax and enjoy life can be imagined through effective use of AI.

Let’s wonder around this digital trend and explore how can it be integrated for a better life 🙂

#bigdata is becoming the norm for many organizations, using it to profile people, analyze their behavioral patterns and inform their decision-making processes, whether that’s to determine a basic day-to-day life process or business strategic decisions.

#ArtificialIntelligence or #AI is stepping out of the world of science-fiction and into real life, providing the ‘thinking’ ability behind virtual personal assistants, automated robots, and smart cars.

#MachineLearning algorithms are providing intelligence for discovering patterns in the huge amount of data that traditional data analysis couldn’t hope to find, helping to detect fraud and diagnose diseases.

I have tried to interact with Google Home, AI application developed by Google and asked some questions to know information related to this trend, following is the conversation.

Me: What is Data Analytics?
Google Home: Data Analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

“Ok Google”

Me: What is Machine Learning?

Google Home: Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. … Both systems search through data to look for patterns.

Me: What is Artificial Intelligence?
Google Home: the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Well, that’s cool.

Artificial Intelligence – often classified into one of two fundamental groups – applied and general.
Applied AI is more common – systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.
Generalized AI – systems or devices which can, in theory, handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.

The relation between Artificial Intelligence and Machine Learning:

Artificial Intelligence, Human Intelligence exhibited by Machines, is the broader concept of machines being able to perform tasks which imitate human intelligence i.e artificial.
Machine Learning,  out of many other goals/approaches of AI, an approach to achieve Artificial Intelligence, is an application of AI revolving around the idea that let machines learn for themselves given access to information.

Deep Learning has enabled many practical applications of Machine Learning and in turn the overall field of AI. It breaks down tasks in ways that make all kinds of machine obliges seem possible, even likely.

Concept evolution!

As technology and understanding of how human minds work has progressed, our concept of what constitutes AI has changed. Rather than progressively complex calculations, work in the field of AI concentrated on imitating human decision-making processes and carrying out tasks in even more hominid ways. Being innovations have been in place, engineers realized that rather than training computers and machines, it would be far more efficient to code them to think and learn human brain and provide the internet as a learning platform to give them access to all of the information in the world.

To make computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy, and lack of bias, development of neural networks played the key role.

Going a step ahead to avoid this complexity of learning concepts of AI and algorithmic journey of ML, to provide with a platform to develop an AI application with simple logistic and freeing developer to focus on AI problem statement to solve is the next advancement.

Happy to see some leaders in the industry are taking interest in it and making complex technologies such as AI and ML available as a simple platform to create such voice/text assistant to address this perspective of data science.

Amazon Alexa

And many in the market. Such initiatives will be always appreciated.

About Google API.AI – Understand Google and build AI Assistant

Looking at the other side of this …

There are concerns that this technology will lead to widespread unemployment which is beyond the scope of this discussion, but it does touch on the point we should consider. Employees are often a business’s biggest expense, but does that mean it’s sensible to think of AI as primarily a means of cutting HR costs?

I don’t think so.

Think about it!

The fully autonomous, AI-powered, human-free industrial operation seem to be away from becoming reality and human employees working alongside AI machines is likely to be the way of things. How can an intelligence developed by humans REPLACE a human? Surely it can replace repetitive mechanizable efforts of a human at some places where artificial intelligence can work. so if you’re looking to generate value in the near future, then thinking about ways to empower humans with technology, rather than replace them, is likely to be more productive.In doing these things we can free people to put all of our creativity, passion, and imagination into thinking about the bigger opportunities ahead of us.

Trends are only disruptive if we are unprepared to factor them into our strategy. How trends impact our workforce, customers, market, services and in turn our lives should be carefully pondered. And perhaps most importantly, a business needs a clear use case and a genuine perception of how, and why, they can gain value from it. With anything new and exuberant in business, there’s often a race to be involved, driven primarily by a fear of being left behind. Scrambling into automating and smartening an enterprise without having a clear outlook of what you hope to achieve is a misdirection to intelligence.

As said by Mark Zukerberg, “A frustration I have is that a lot of people increasingly seem to equate an advertising business model with somehow being out of alignment with your customers, … I think it’s the most ridiculous concept. What, you think because you’re paying Apple that you’re somehow in alignment with them? If you were in alignment with them, then they’d make their products a lot cheaper!”

Another frustration we should feel is … we increasingly seem to diverge efforts put in various technology trends being out of alignment with their use and impact on our life, I think it’s even more ridiculous concept. To be productive, efforts need to be meticulous and put in the proper direction and AI can help find this direction quick and easy. If we were in alignment with the constructive use and right influence of technology trends, then it’d make our lives easier and happier!

Let’s embrace the change and explore integrity!

Image credits: Google

Recommending to watch.


– Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
Difference between Artificial Intelligence and Machine Learning

Understand Google and build Artificial Intelligent Assistant

Getting things done and keeping in touch with friends and family has never been easier.

Well promoted by Google, what can be more beautiful than imitating our intelligence to better our lives? Good Work!

Artificial Intelligence is trending and the world of artificially intelligent assistants is growing — Siri, Cortana, Alexa, Ok Google, Facebook M, Bixby – are some available by known tech leaders. Voice-enabled applications are in a lot of action recently with voice-activated speaker devices likely to get more user acceptance. It is significant to explore this ecosystem early enough to help create the optimum voice experiences as the field matures.

In this post, we will be getting to know specifically about Google Home and API.AI, a platform to build conversational assistant powered by NLP and Machine learning.i

Google Home is voice-activated speaker device powered by the Google Assistant.
Ask it questions.
Tell it to do things.
And with support for multiple users, it can distinguish our voice from others in your home so we get a more personalized experience. However, it is fascinating to realize that it’s quite easy to build our own AI assistant too, customize it to our own needs, our own IoT connected devices, our own custom APIs. The sky’s the limit.

Google opened up the Google Assistant platform for developers in December and currently, the platform supports building out Conversation Actions for the Google Home device. It is widely expected that the same Actions will eventually be available across Google’s other devices and applications.

Screen Shot 2017-05-23 at 11.41.47 AM
Image credits: Google home (formerly Speaktoit) is a developer of human–computer interaction technologies based on natural language conversations. It provides conversational user experience platform enabling brand-unique, natural language interactions for devices, applications, and services. Developers can use API.AI services for speech recognition, natural language processing (intent recognition and context awareness), and conversation management to quickly and easily differentiate their business, increase customer satisfaction and improve business processes.
It is acquired by Google in September 2016, it provides tools to developers building apps (“Actions”) for the Google Assistant virtual assistant.

How do they do it

Image credits: Google

To build chatbots or conversation assistant, one of the first things to consider is conversation workflow management.  It’s the layer in your bot stack that handles all your natural language processing needs. Whenever a user types or talks something to bot, you need a good conversation workflow management tool to help you deal with the messiness of human verbal communication. provides us with such a platform which is easy to learn and comprehensive to develop conversation actions. It is a good example of the simplistic approach to solving complex man to machine communication problem using natural language processing in proximity to machine learning.

Some key concepts:

Agents, NLU (Natural Language Understanding) modules for applications. Their purpose is to transform natural user language into actionable data and can be designed to manage a conversation flow in a specific way. Agents are platform agnostic. You only have to design an agent once and then can integrate it with a variety of platforms using our SDKs and Integrations, or download files compatible with Alexa or Cortana apps.

Machine Learning, allows an agent to understand user inputs in natural language and convert them into structured data, extracting relevant parameters. In the API.AI terminology, the agent uses machine learning algorithms to match user requests to specific intents and uses entities to extract relevant data from them. The agent learns from the data you provide in it (annotated examples in intents and entries in entities) as well as from the language models developed by API.AI. Based on this data, it builds a model (algorithm) for making decisions on which intent should be triggered by a user input and what data needs to be extracted. The model is unique to the agent. The model adjusts dynamically according to the changes made in agent and in the API.AI platform. To make sure that the model is improving, the agent needs to constantly be trained on real conversation logs.

Intent represents a mapping between what a user says and what action should be taken by software.

Entity represents concepts and serves as a powerful tool for extracting parameter values from natural language inputs. The entities that are used in a particular agent will depend on the parameter values that are expected to be returned as a result of agent functioning.

More concepts …

API.AI relation to other components & process flow

Image credit

Getting to know and getting started …

Easy to Learn:

“Ok Google. Let’s get started with API.AI”
These videos overview and tutorials help to get acquainted with this platform. It is useful to get the understanding of the platform and its intent not just in development perspective but how it all impact daily life and can be used to drive the development in a good direction.

It will be helpful to think about the conversational flow that we are expecting to happen, maybe get pen and paper,  draw it, then include the components accordingly to build that flow using API.AI.

Conversation workflow with Google home, Google assistant, and

Image credits: Google

Easy to Develop:

“Ok Google. How to develop conversation app quickly?”

This five-step development guide help to get hands-on experience with simple conversation assistant, like from buiding concept, development, and its integration.

Screen Shot 2017-05-23 at 12.52.54 PM
Developer Console:  Design, test, tune – all at one place

Easy to Test:

“Ok Google. How to preview and test actions?”

Google provides a web simulator which lets us preview actions that built in API.AI or the Actions SDK in an easy-to-use interface with debugging and voice input. It helps we make sure that Conversation Actions actually sound conversational and let us use the device without having the hardware.
In addition to the simulator, we can also test on an actual device by launching a preview version of actions built.

Easy to Deploy:

“Ok Google. Let’s deploy the conversation action.”

After setting up Actions on Google integration, we can deploy agent so that it is live and available for use by other users. We just need to first set up a Google App Engine project and register Conversation Action with Google.

So what are you waiting for? imitate some intelligence and “Hey machines, let’s talk! ;)”

Here at Clairvoyant, we’ve had a lot of fun learning these new conversational interactions and looking forward to building some cool intelligence. This post should be enough to get your interest towards something newly intelligent, get you up and running on creating your own cool new services. If you have any questions, feel free to leave a comment below. While believing in improving lives through design and technology, would also like to hear you if you have something interesting to share on this.

Wish you a happy time imitating your intelligence into first Google Action and getting introduced to API.AI.

Build you AI assistant with API.AI
Get started with Google Actions