AI for Everyone

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Pravin Kamble

What is AI?

A probable oxymoron that talks about machines being intelligent ‘Artificially.’ But no! It is not an Alienware science tech where hardware tools make the decisions themselves, but it is about how we teach them to do. Remember, Siri? We all have, at some point, either conversed with Siri or Google Voice, asking her questions that matter or questions that are fun to follow! The technology that runs behind is all artificially comprehended with super-intelligent programming.

Elon Musk?

With CyberTrucks and self-driving cars, he is probably all over the place displaying his skills with the automatic driving program that is all well-fed and taught. Since the invention of computers in 1936, we have been training them with a set standard of programs to perform in a certain way. We evolved in our language and yet fed computers with a binary understanding to grasp and work accordingly. Now, with so many technologies and data, we needed our machines to be smarter to handle the trillions of data generated by us every day, and that is where we need AI!

AI is an amalgamation of science and engineering, where we train our machines with smart programs or paradigms to take over our manual tasks. In a nutshell, the whole concept of AI lies behind the feel that:

  • We need to unload ourselves from the ambiguous tasks
  • We need to handle the volume of data which in current phases is difficult
  • Be prompt with dangers and errors to handle them with care

AI is also divided into two parts: Artificial Narrow Intelligence(ANI) and Artificial General Intelligence(AGI).

Understanding historical data to suggest the users based on it is ANI. It is when users are looking for a house, and the algorithms predict their previous behavior to recommend them a house accordingly. Such personalizations help the users connect, and you follow up well.

Let’s start with the basics of Neural Networks!

Neural Networks:

The term is quite familiar to us with the structure of our working brain, and on computational cycles, it is no different. Neural Network is a series of algorithms that mimics the human mind to track and identify relationships in the set of data.

Machine Learning:

The background star that works behind making AI make it a superhero is Machine Learning. It is the art of learning that learns to train a machine statistically just by mining through data and not by programming.

Let’s take an example of an inventory. You want certain products that share the same characteristic as dresses. Like long, short, and flare, while the ones with collars, buttons, polo marked as shirts. Now, you did this for a hundred items and then allowed machine learning to take over. After scrutinizing the data, the algorithm in itself starts segregating products as what it learns.

And machine learning just made a job of a thousand years turned into a minute job! With machine learning, the data in itself teaches the algorithm best to train and perform similarly.

Deep Learning:

Though we take it as a subset of AI and machine learning, it works a lot like machine learning. Deep Learning also understands the user’s data to predict their behavior. The difference lies between the algorithms they use. Where Data Science uses technologically advanced algorithms using neural networks, Machine Learning still uses a lot of first measures.

Data Science:

Data science is an interdisciplinary field to extract knowledge or insights from data, which counts the data insights and works on business terms. Let’s look at the previous example, where we segregated products using ML algorithms for an eCommerce site.

When they do so, looking at how many products were bought by the shoppers, data science helps to know which products are raining revenues for you, which products are not, and helps to boost the products accordingly!

What can AI realistically help in our lives?

No doubts how wonderfully AI has affected our lives, and it is everywhere. We presumably slaughter the people working behind this technology by saying that they are sneaking on our data. But on the other hand, when we get personalized recommendations for our fashion taste or our music library, we love it!

The hypocrisy also lies in the fact that whatever we do, we love AI and its applications.

For instance, recently, Spotify was launched in India. A million users downloaded it even though we already had some excellent music apps like Wynk, Saavn, and Apple music. Why?

Because, based on your behavior of listening to a few artists, you are recommended to similar kinds of music and artists every week.



For another instance, once when you search for Nike shoes on your chrome tab, wherever you go, be it Facebook or Instagram, Nike recommendations follow you. Just in case you forget to buy it.

And a massive application was included with Gmail. Now, when you type your emails, looking at the type of your mail, you are recommended with the probable text that you might want to type. And Voila! It works 8 out of 10 times.

Another application that is also going to take a crucial turn with 2020 is Chatbots. Chatbots understand your intent, throw you with suggestions, and get to you in minimal steps with a human touch.

And then, we have product recommendations on an eCommerce site where you get product recommendations based on your historical behavior. Who wouldn’t like that?

Do you know we can also involve AI techniques in our daily lives to make our organizational problems easier?

When we look at so many applications, we realize if AI is becoming so prominent and many companies have adopted the same, then why not us?

It collaborates efficiency by providing succinct results with minimal supervision. Even now, all we can see is big companies making it bigger by contributing to user intentions and user capabilities. The reason only 8% of the organizations have incorporated AI techniques in their work scheduling is because of the formidable cultural and organizational changes that come in!

No organization is ready to revamp its structure and automate its processes. But it is needed and with complete responsibility to make employees fit enough to adapt machine learning tech, to provide easier access to learning, and to be an open arm to opportunities and ideas.

For organizations:

  1. Work cross-teams: Automate your task lists, worksheets, and timelines. Work across various teams with a seamless experience that doesn’t hamper your deadlines and saves on resources.
  2. Integrate bots: Include bots wherever possible, and that can make tasks more manageable. For instance, if we integrate Slack, a plethora of in-built bots come into the picture like Attendance bot, Chatbot, and email bot. All you need to input your leaves is to go to #AttendanceBot and put down your blades.
  3. Simplify test cases: Every product company has loads of product updates and product features that roll out now and then. So, to ensure your talented bunch of folks is not entirely busy manually testing each feature, automate it. Use tools like LambdaTest that can automate your web testing across multiple browsers. Save time and resources!
  4. Code to train the machine: Train your computer to take care of support requests automatically by machine learning algorithms. Avoid wasting time and automatically generate a ticket for the same.


If you follow the steps, you are making your organizational tasks easier to follow and process. We learned a lot through this article, ranging from the basics of confusing AI terms to how beautifully you can, and you should leverage automation in your organization.

My next blog will talk about: how can non-technical people take advantage of AI and still keep in touch with innovation!