Introducing AI-Powered Portfolio
By: Louis Owen
Tags: Artificial Intelligence, Portfolio, Personal Branding, Data Science, Machine Learning

How?

There are 6 steps you have to do to build your own personal AI assistant and I’ll guide you for each of the steps!

1. Install ‘Semantic Reactor’ Add-On from the G Suite Marketplace

You can simply install the add-on from here. Make sure you are using the same Google account with your Google Sheets account to install the add-on.

Semantic Reactor Page. [Screen-shotted by Author]

2. List all possible responses in Google Sheets

After you installed the Semantic Reactor add-on, you can open your google sheets and then list all of the possible responses.

Example of my possible responses. [Image by Author]

One hacky trick I can suggest you is to generate several lists of responses based on the topic.

In my case, I generated six different kinds of responses list, where one of them is a general responses list, and each of the rest of the five is specific for responses related to my professional working experiences (Traveloka, Tokopedia, Do-It, Qlue Smart City, The World Bank).

3. Start ‘Semantic Reactor’ Add-On

Once you have listed all of the possible responses, you can start the Semantic Reactor add-on.

Start the Semantic Reactor Add-On. [Image by Author]

4. Choose the ‘Local’ model and the ‘Input/Response’ rank method

As explained before, there are several pre-trained models provided by the Add-on. Here, we will choose the ‘Local’ model since it’s the same model with the one we will use in the deployment phase.

We have to choose the ‘Input/Response’ rank method because we will use this add-on for a QnA task.

Model and Rank Method Selection. [Image by Author]

5. Refine your responses based on the query given to the ‘Semantic Reactor’

Most likely, your first set of possible responses is not able to support the pre-trained model to return a suitable answer for a given query. We actually can try to do the transfer learning from the pre-trained model in our dataset.

But for this case, I think it’s wiser to refine our possible responses rather than spending our time and hardware resource to do the transfer learning. Just like as Albert Einstein said:

“Make everything as simple as possible, but not simpler. “

So, you can keep refining all of the possible responses until you see the result is good enough.

Visual when the query is given. [Image by Author]

6. Embed TensorFlow-JS USE Lite model into your HTML file

Once you are confident with your list of possible responses, you can use that list in the deployment. The ‘local’ model in the Semantic Reactor is a Universal Sentence Encoder-Lite model, which is the engine we will use in the deployment.

The first thing to do is to load the TensorFlow-JS and USE module from CDN.

Then you can define functions to select the best response from the responses list, given a query.

Finally, we have to choose which function we want to use or which list of responses we want to use based on the given query. I used the keyword matching method here to choose which lists of responses we should use.

All of the main codes I used to build this personal AI assistant is based on these 3 sources:

Final Words

Photo by Recal Media on Unsplash

Congratulations for keeping up to this point! Hopefully, I can see many new AI-powered portfolios developed after this article is published.

If you love my content, please kindly follow my Medium account to get the notifications for my other future posts. In the next 3 months, I’ll also make an article series about Reinforcement Learning. You can check the first article here.