For today’s edition of #futurefridays let’s talk about Virtual Reality, and how it will change the Future of how Businesses operate today.

Have you ever been in a situation where you were trying to choose a resort to go to for your vacation, and you made your decision based on the pictures you saw?

What if there was a way to “try before you buy” by way of virtually immersing you into the resort, so you can experience what it’s like being there first-hand. Would that make your decision-making faster?

That’s what Virtual Reality can do for you. And it has many more use cases other than that.

Join me as we explore what Virtual Reality is and how it can be helpful in changing the way we do things within and around your business.

Let’s start with a video that defines what Immersive Technology like Virtual Reality is, and some examples of how it applies in the world today.

Video © TEDx Talks

I hope you learned a lot from this video.

Now let’s talk about some use cases where Virtual Reality can apply in businesses today.

Travel Industry

This is pretty much the example I gave you at the beginning of this article.

It’s how you can virtually be immersed into the location you want to visit without actually going there. That way, you’ll get a glimpse of what it’s like, which will help you make a better decision.

Gaming and Entertainment Industry

Well, this is pretty obvious, as this is really where VR has been adapted a lot. And people will continue to take things to the next level in this space.

Aviation and Drones

Particularly unmanned flight. The Air Force does this all the time, where they fly unmanned aircraft and drones remotely.

Even drone companies like DJI have come up with a way for you to pilot their drones while wearing a VR headset, giving you a unique experience as if you were flying like a bird in the sky.

Improving Safety for Hazardous Jobs

There are several use cases for this. For example, imagine being a crane operator at a shipping dock. How many storeys do you have to climb up and down just to get to and from your actual “work desk,” and what if you had to do that several times a day?

What if you can instead control the crane from the ground, safe inside an office cubicle, through Virtual Reality. How much safer and easier do you think your job will be?

Training

The way by which you train employees can be taken to the next level with the help of Virtual Reality, especially if it involves having to travel to certain locations just to learn a skill.

For example, if you need to train employees at a call center in the Philippines what it’s like being inside the main store of your business in London, so that when they talk to customers about it, they can relate to them as if they have already been there, then it would make better sense to let them experience it in Virtual Reality, rather than pay for their travel and accommodation just for them to experience it in real life.

Vehicle and Property Sales

For anything short of smelling the leather, you can probably use Virtual Reality.

For example, if you wanted to buy a new car, but wanted to see what the various color options of the Alcantara leather would look best, why not experience it via Virtual Reality?

Looking to buy a house, but couldn’t make a decision without seeing what it would look like fully furnished with all the beds and furniture? Well what if you could see it in Virtual Reality?

Ending Note

The use cases really span far and wide. I have given you just some of the examples.

Nevertheless, I hope you found this to be of value, and gave you a sense of what Virtual Reality is and where it can be utilized in real-world scenarios.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get updates on the next article, subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.

The post Virtual Reality and its Impact to the Future of Businesses appeared first on Robbie Agustin.

For #transformationtuesdays I’m sharing one of the most common use cases for Machine Learning that a lot of people who are young or young at heart can relate to – computer games.

For today’s example, it’s how AI Bots can be better than humans at playing Starcraft.

Why Starcraft? Because its complexity and the multitude of factors to consider that affect gameplay is closer to the number of factors a human brain considers in real life when making decisions, as compared to other games like Chess for example, where players take turns.

Why is this important? Because implementing Machine Learning Algorithms on games like Starcraft will allow us to learn more about how we can build better Bots that will be applicable to the real world.

Read on for more details and to see the actual use case.

Before I show you the use case, allow me to give you an idea just how complex it is to build one of these Bots that can beat human players in Starcraft.

To give you some perspective, Google’s computer processors that crunch through Machine Learning Algorithms like Google Translate are called TPUs (Tensor Processing Units).

To beat world-class chess players, the AlphaZero algorithm needs 4 TPUs.

On the other hand, AlphaStar, the algorithm designed to beat professional Starcraft players, uses a whopping 32 TPUs! Thats a lot of processing power.

I’m sure by now you’re interested to find out more about the use case, so I won’t delay it any further. Check it out now.

Why Starcraft is the perfect battleground for testing AI

How it worked is they trained the Machine Learning algorithm by having it watch 44 days worth of human game replays for each of the 3 Starcraft races – Protoss, Zergs, and Terrans, for it to identify patterns and develop its own gameplay strategies.

It got so good, that what they’re looking at now is how to reduce the required processing power to accomplish the same tasks.

Taking on challenges like this teaches us humans on how we can be better at creating new AI tools that work in a multitude of environments and with a lot of variables, which are closer to the number of complexities and factors humans actually experience and deal with in the real world.

Ending Note

I hope you found this to be of value.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get more, then subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.

The post How AI Bots can beat Humans at Starcraft using Machine Learning (Use Case) appeared first on Robbie Agustin.

It’s #futurefridays and today we’ll be talking about the basics of Artificial Intelligence.

Previously, I talked about Natural Language Processing, and before that, Computer Vision.

But today we’ll tackle another field of Artificial Intelligence called Machine Learning.

Wikipedia defines Machine Learning as:

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Let me show you some examples of how Machine Learning is being used in real-world scenarios.

But first, I would recommend you watch this video from Accenture that gives a really simple explanation of what Machine Learning is, in 1 just minute.

AI 101: What is Machine Learning?

Video © Accenture

Now that you have a basic understanding of what Machine Learning is, allow me to give some examples of it in action today.

Waze

Have you ever wondered how Waze is able to tell if traffic is heavy heading towards your destination, and it is able to forecast arrival times and recommended routes?

Well, that’s Machine Learning in action. It learns the patterns based on how much time users are spending on each road on the map and at specific times of the day, week, month, etc.

Chat Bots and Virtual Assistants

I’ve talked about this in another post talking about Natural Language Processing as this is actually a combination of Machine Language and NLP.

Check out the detailed article here: Chat Bots Making Phone Calls on Your Behalf using NLP AI (Use Case).

Email Spam Filtering

Did you know that your email software uses Machine Learning to determine which messages to filter which messages are Spam compared to those which are not?

And when you find something in the Spam folder that isn’t supposed to be there, when you flag it as “Not Spam” you’re essentially teaching the Machine Learning algorithm what to do next time.

Again this technology is a combination of Machine Learning and Natural Language Processing working in tandem with each other.

Face Recognition like in Social Media

Have you ever wondered how Facebook is able to auto-detect who the people are in a photo that you upload even if you haven’t tagged them yet?

Here’s an example of how Facebook uses this technology.

This is actually Machine Learning working in tandem with Computer Vision.

This can also be used to identify emotions in faces, like Anger, Sadness, Happiness, and so forth.

Object and Character Recognition using Computer Vision

This is pretty similar to face recognition but can also be utilized for other objects, like telling apart humans from animals, or telling apart cats from dogs, or squares from circles.

This can also be used to identify characters like numbers and letters. And when combined with Natural Language Processing, can be used to identify context and meaning.

Here’s an example of a Lego Brick Sorter using Computer Vision and Machine Learning.

Forecasting and Prediction Models like Product Recommendations

Have you ever wondered how social media sites are able to post ads that are relevant to stuff that you have been researching about?

That’s machine learning in action, where the algorithm is able to recognize your buying intent based on previous purchases and recent searches.

Machine Learning can also be used to identify patterns that can be used for forecasting, such as when NASCAR race cars should pit for fuel and tires, and when crops will grow in certain areas, given the weather and other factors.

Here’s an example of using AI to predict Heart Attacks before it happens.

Ending Note

I hope you found this to be of value, and gave you a sense of what Machine Learning is and where it is being utilized in real-world scenarios.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get updates on the next article, subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.

The post Artificial Intelligence Basics: What is Machine Learning? appeared first on Robbie Agustin.

For #wisdomwendesdays I’ll be helping shed some light on the fears that have been brought about by the coming of Artificial Intelligence.

Though I’ve mentioned in one of my previous articles that:

“Though we all know that technology is continuously improving. But we shouldn’t put all our trust in it just yet. Yes, Google’s AI has nearly twice the IQ of Siri, but a 6-year-old child is still smarter than both. And even if it does improve further, 75% if customers, like you, still say that they would want to interact with a real person more as technology improves. child.”

I’d still like to talk about this a bit more, especially on why AI is still considered “Primitive.”

There’s another article from the IEM Community that talks about this in detail, which I think is worth a read, because I completely agree and share the same sentiments.

As for my view, I believe that Artificial Intelligence tools are like the technological equivalent of Savants – they’re good at one thing, but suck at everything else.

Let me give you a couple of specific examples.

The Lego Sorting AI Machine that I’ve shared previously has been trained to sort Lego Bricks, but if you give it other types of tasks such as sorting different types of recyclable materials, or tools in a workshop, it won’t really be able to understand what to do.

Another example is the AI used to predict Heart Attacks a year before it happens. While we all know that health is wealth, you can’t expect the same tool to predict stock market trends to make you financially wealthy.

So in a nutshell, Artificial Intelligence tools are like the Savants of the tech world. They are better than humans at certain tasks, but they are far from becoming super intelligent beings capable of replacing humanity.

If anything, Artificial Intelligence will be tools that humans use to enhance our own abilities, to allow us to be more productive, and help us achieve greater things.

Ending note

I hope you learned something new today.

If you want to learn how to do these things, and you think a career in Artificial Intelligence is for you, even if you’re just a beginner, I would recommend you check out the IBM Applied AI Professional course, which you can check further for details.

The post Why AI (Artificial Intelligence) is still considered “Primitive” appeared first on Robbie Agustin.

For today’s #transformationtuesdays we’ll talk about an innovation in AI (Artificial Intelligence), particularly Natural Language Processing.

You know when there are big corporate bosses or entrepreneurs who get to delegate tasks to their Assistants such as booking dinner reservations, hair stylist appointments, and other tasks that would require the Assistant to contact other people to complete the task?

What if you had the power to do all that, without having the need to hire a living breathing Assistant, and all you have to do is command your mobile phone to do those kinds if tasks for you. Wouldn’t that be awesome?

Well that’s exactly what I’ll be showing you today.

You see, Google has been busy developing the next generation Google Assistant, where they have injected it with Google Duplex technology to give it the power to make Phone Calls on your behalf.

Some of you may already know this, but for those who aren’t aware of the existence this technology yet, I’ll share with you (1) The Demo when it was launched, so you know how it works, and (2) Where it is being applied now, and where it will be used next.

And for those who want to know what goes on inside these kinds of Chat Bots, and where Natural Language Processing and AI comes into play, then I’ll break down the bits and pieces for you so you’ll get a better understanding.

Let’s go!

If you haven’t seen the Google Assistant + Google Duplex Launch and Demo yet, now is the time to see it. Otherwise, you’re missing half of your life.

Isn’t it amazing that it’s as if you have this little creature living inside your mobile phone whom you can command to make dinner reservations on your behalf?

That launch video was in May of 2018. Fast forward to today, as per this article by Android Authority in October 2019 (which also talks about how you can activate it so give it a read), Google Duplex is now available in 48 States in the US, and has recently been rolled-out in New Zealand.

Now if you’ve read thus far, you’re probably curious about how Chat Bots like these work, and where Natural Language Processing comes into play.

It’s actually a combination of the following:

1. Voice Recognition – How this works is that when you speak into your phone’s microphone, it converts your spoken voice into waveforms, which are basically images of sound. That’s how software algorithms are better able to tell the difference between words like “where,” “were,” and “we’re.” And then it turns that information into Text.
2. Natural Language Processing – I’ve written a separate article that defines NLP, along with various examples, that could demystify this for you. But in essence, it takes the text information from the voice recognition piece, identifies the context, then provides an appropriate response, in text.
3. Text to Speech – This is pretty much like Voice Recognition but in reverse. So it takes the text information from the Natural Language Processing piece and converts it to a waveform that when played, sounds like a voice conversation.

So in its basic form, a Chat Bot can start out as a very elaborate script, where you take the most common things that a human would say, and the Chat Bot would process that information and provide a pre-programmed response.

But if you add a Machine Learning algorithm to it, you can give it more information in the form of training data, it will learn how to adapt to new conversations it hasn’t encountered before, and adapt accordingly based on what it has experienced in the past i.e. based on the training data.

Training data can be composed of information that’s already been labeled by humans.

For example, if the input is a question like “Do you think I should go to the gym today, or sleep in?” The training data can have information on sentiment (e.g. anger, disgust, fear, happy, sad, surprise, neutral) and what the common human responses are to a question like this.

And then you add Voice Recognition to the input side, and Text to Speech on the output side, make all of them work simultaneously, and you’ve got yourself a Chat Bot that has the ability to make Phone Calls on your behalf.

Ending Note

I hope you found this to be of value, and gave you a sense of how Natural Language Processing is being applied in real-world scenarios.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get updates on the next article, subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.

The post Chat Bots Making Phone Calls on Your Behalf using NLP AI (Use Case) appeared first on Robbie Agustin.

For #futurefridays let’s talk about RPA (Robotic Process Automation) and what will happen to it in 2020.

With all the rage about AI (Artificial Intelligence) and the Future of Work, people have been saying that RPA seems to have taken a back seat from all the action.

To the uninitiated, I’ll talk about the difference between RPA and AI.

So back to what will happen to RPA, in the recent past, people have been so afraid about RPA and that people would lose jobs because of it.

That was only for RPA. What more for AI? I guess people are, and were, just afraid of what they don’t know.

But now that people have become accustomed to having a “Digital Workforce” or software robots working alongside them, people’s thoughts have changed from fearing about job security, to scrutinizing where RPA falls short!

In my opinion, that’s a good thing, because we can now start doing innovations!

Here are some of the things I’ve heard people say about RPA lately:

• It’s becoming a band aid solution
• It can only do so much
• With AI coming in, let’s just set our sights on that

With that, I’d like to talk about each of those bullet points in detail.

I will share my thoughts based on my own experience, as well as where I think RPA will be going to next.

RPA is becoming a Band Aid Solution

Based on my experience, my short answer is Yes, and No. Let me explain.

RPA is considered a band aid solution because ideally the long term solution is for you to fix the systems and applications that RPA interacts with.

That is true IF the development of those tools and applications is within your control, meaning you have access to the source code, you have tech developers available, and you can customize it to suit your needs.

Usually if it’s a third-party application, if it’s not open source, there’s Intellectual Property involved so they will not give access to the source code for you to make changes to it.

In such scenarios, they can provide an API for you connect other systems and applications to their proprietary software.

If you can’t customize the source code, and there is no API available, you have no choice but to go through the User Interface, which is how human users do it.

And if everything you do on the User Interface is rules-based, with no human judgment required, that’s where RPA will shine, especially if the process involves touching more than one system or application for you to complete each task.

If you really want to do away with having BOTs, you might want to look at replacing your existing systems and applications with something that will suit your needs (and costs) better than having several applications and BOTs running at the same time.

RPA can only do so much

Ah, this is where I would have to agree, to a certain extent.

RPA, in its simplest form, is like an independent human user. Meaning it has its own computer (a server) and it can work independently on its own. So to be efficient at it, BOTs have typically been designed to operate in a “batch processing” fashion. Which means it cannot cater to the individual needs of every other human in your workforce.

So in 2020, it’s about time for people to embrace the innovations being done in RPA. And it’s not just AI.

Enter “Farm BOTs” and “Side BOTs.”

These things aren’t necessarily new innovations, but I don’t think companies have been taking advantage of them as much.

Farm BOTs: You know when there are seasonalities in your business, like volumes go up during Christmas season, or during summer, and you tend to hire temporary employees to help you manage the volume?

That’s the same concept behind Farm BOTs, but instead of for humans, it’s for your digital workforce.

That way, you save on costs, as you don’t need to have idle servers sitting around when volumes are low.

Side BOTs: You know how Iron Man talks to Jarvis and asks him to do certain computations to help Iron Man decide action to take do next?

That’s how Side BOTs operate, only they aren’t AI. They are still Robotic Process Automations that work on simple rules-based and calculations, but instead of sitting on a server, where you have to wait for the “batch processing,” they sit in your desktop, and you can call on them real time.

That way, you can simultaneously do rules-based tasks via the BOT, whole you do some thinking and/or work on human judgment based tasks.

With AI coming in, let’s just set our sights on that

If you’ve read this far, I guess you pretty much have an idea how RPA works, and how it will be improved further.

If you want my personal thoughts on this, I believe that RPA will not die. If anything, RPA and AI will actually compliment each other,where RPA will be foundational in automating routinary rules-based tasks, while AI will be used to manage some of the more complex work that are still being done by humans today, or even help humans achieve greater things that haven’t been done before.

Ending note

This post has been inspired by this article from Forbes.

If you want to read other articles I’ve shared about Automation, RPA, and AI, click here.

If you want to learn more about how to find automation opportunities, download a copy of my book for FREE (for a limited time only) – The Business Optimization Blueprint, and learn how you can improve your business process and take it to the next level.

The post What will happen to RPA (Robotic Process Automation) in 2020? appeared first on Robbie Agustin.

For #transformationtuesdays I’m sharing how AI is Transforming the way you can determine if you will encounter any health issues in the near future – specifically Heart Attacks.

Read on to see how they made it work.

What they did was to take a lot of ECG scans, where the researchers already knew the outcome for each i.e. which ones ended up getting a heart attack and by when, and which ones did not get a heart attack.

The researchers then fed the data into a Machine Learning algorithm for it to train on that data.

The results are astounding, as the AI was then able to predict upcoming heart attacks and other heart-related diseases better than real-life Cardiologists e.g. those who said the ECG scans were clear, but the patients still ended up getting heart attacks.

Source: https://www.rt.com/news/473282-ai-predicts-heart-attacks/

Ending note

I hope you learned something new today.

If you want to learn how to do this, and you think a career in Artificial Intelligence is for you, even if you’re just a beginner, I would recommend you check out the IBM Applied AI Professional course, which you can check further for details.

The post Use AI to predict Heart Attacks a year before it happens (Use Case) appeared first on Robbie Agustin.

For today’s #futurefridays I’m going to answer a question that confuses a lot of people trying to learn Data Science and Machine Learning.

The question is “What’s the difference between Classification and Regression?”

Let me give a shot at this with a simple explanation and example.

Think about the output that you want to achieve.

If you have a dataset, and the output you want to get are labels or categories, then it’s classification.

However if the output you want to get is a numerical value based on computations done on the dataset, then it’s regression.

Let me give you a specific example.

Facebook uses both classification and regression in their algorithms.

The difference is their where they are used.

Classification

When you post on Facebook and upload an image, and it has faces of people who are Facebook users, the algorithm is able to determine who these people are, and tag their names accordingly. What’s happening there is called Classification.

Regression

Now when people like, comment, and share the post, the algorithm uses that to determine how “viral” the post is, and if it should be shown to other people’s feeds or not. This is usually based on count data and a corresponding weight for behavior or action. What’s happening there is called Regression.

Ending note

If course this is an oversimplification of Classification and Regression. There’s more to it than that such as contextual analytics, quality of content, determining if it’s a human or a bot, etc.

But my objective here was to demystify things and help shed some light on the topic.

I hope you learned something new today.

If you want to learn how to become a Data Scientist, some of the best learning tracks I’ve seen are the Data Science learning paths from DataCamp.

You can start learning today even for free. Check it out! Click the link below.

Data Science at DataCamp

The post DEMYSTIFIED: What’s the difference between Classification and Regression? appeared first on Robbie Agustin.

It’s #futurefridays and today we’ll be talking about the basics of Artificial Intelligence.

Previously, I talked about Computer Vision. But today we’ll tackle another field of Artificial Intelligence called Natural Language Processing.

Wikipedia defines Natural Language Processing as:

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Let me show you some examples of how Natural Language Processing is being used in real-world scenarios.

But first, I would recommend you watch this video from Accenture that gives a really simple explanation of what Natural Language Processing is, in 1 just minute.

AI 101: What is Natural Language Processing?

Video © Accenture

Now that you have a basic understanding of what Natural Language Processing or NLP is (not to be confused with Neuro-Linguistic Programming), allow me to give some examples of NLP in action today.

Predictive Text

Some of the most basic forms of NLP would have to be predictive text, including auto-correct and auto-complete.

Nowadays you can even teach predictive text new words, and based on your usage pattern, it adds it into the algorithm using machine learning.

Speech to Text

Isn’t it wonderful to have the technology nowadays where you can dictate stuff to your mobile phone, and it will type everything down for you?

What’s brilliant is that it works just as well, even if you have an accent!

Translation

Natural Language Processing has paved the way towards bridging language barriers.

A family friend recently took a trip to Japan. And they did not know how to speak a word of Japanese!

How did they get to enjoy the trip in a foreign country where you don’t understand the language? It’s all thanks to Natural Language Processing.

I’ve posted another article that talks about this in detail. I recommend you give it a read – Google Translate: Travel without Fear of Language Barriers (and How to Build your own Translator!)

OCR with Automatic Indexing and Classification

This works well with businesses that deal with documents that contain unstructured or free-format data.

You use Optical Character Recognition to convert the document into machine-readable format, then use Natural Language Processing to understand the context, and then extract the necessary information and/or categorize the documents accordingly.

I’ve also posted another article that talks about this in detail. Check it out – How can OCR (Optical Character Recognition) become AI (Artificial Intelligence)? Here’s how.

Sentiment Analysis

As with the example above, you can use Natural Language Processing to understand the context behind what customers are saying.

This can be used to understand customer sentiments in stuff like survey responses, feedback or complaint channels, social media, voice calls, and so forth.

Chat BOTs and Virtual Assistants

Chat BOTs and Virtual Assistants like Siri, Alexa, and Google Assistant are probably some of the most complex use cases of Natural Language Processing.

This is probably a whole topic in itself. If you’d want to find out more, let me know by replying with a comment.

Ending Note

I hope you found this to be of value, and gave you a sense of what Natural Language Processing is and where it is being utilized in real-world scenarios.

If you have any questions, reply with a comment. I’d love to hear from you.

And if you think this is helpful and you’d want to get updates on the next article, subscribe for updates, and get a free copy of my book – The Business Optimization Blueprint.

The post Artificial Intelligence Basics: What is Natural Language Processing? appeared first on Robbie Agustin.

For today’s #wisdomwednesdays I’ll talk about the difference between Robotic Process Automation (RPA) and Artificial Intelligence (AI).

In a sense, they can both be categorized as forms of software automation. But the major difference is their purpose.

The short explanation is that RPA is used to automate repetitive rules-based tasks, such as repetitive clicks and keystrokes, copying and pasting, if-then types of processes.

On the other hand, AI is used to automate tasks that require judgment, such as reading unstructured documents, identifying patterns, making decisions based on what you see in photos, or getting context based on what you hear in conversations.

Read on to find out the long explanation as well as some examples. This will also touch on Machine Learning.

Check out the video below for the detailed explanation.

RPA vs AI

Video © Mind Data AI ML

Now let me give you a specific example. Let’s say, you’re a business owner. One of the common tasks that your business would usually be doing regularly is responding to customer inquiries.

Let’s say you have a contact form on your site with the following fields:

• Customer name
• Email address
• Message (free-format field)

When a customer contacts you, your typical task is as follows:

1. Open message from contact form
2. Copy-paste contents into spreadsheet (timestamp, customer name, email address, and message from the free-format field)
3. Read the customer’s free-format message, understand the context
4. Decide if you will either (4.1) respond asking for more information, or if (4.2) you will fill-out a template ticket for the processing team to take action.

If you think about it, in this simple process, steps 1, 2, and 4.2 are rules-based tasks that do not require human judgment.

These are the types of tasks that you can automate using Robotic Process Automation or RPA.

Whereas steps 3 and 4.1 are tasks that require human judgment, as in step 3 you need to read and understand the context of the customer’s message, and in step 4.1 you need to provide a customized response based on your understanding and judgment of what the customer has said in the message.

These are tasks that can potentially be addressed by Artificial Intelligence or AI.

It is in this portion that Machine Learning comes into play. For example, in step 3, you need to teach the program how to understand the context and make a decision.

For that to happen, you need to feed it with a lot of historical data – past messages from customers, and the decisions humans made based on those messages.

i.e. If customers say this, do this. If they say that, do that.

And you should always establish a means to audit the decisions made by the algorithm, as well as the exceptions, so that you can constantly tune it and it will learn from its mistakes, or scenarios that it hasn’t faced before. That way, the Artificial Intelligence will know what to do next time around, hence Machine Learning.

I hope you learned something new today. What’s important is that you understand where RPA and AI can be applicable in a process, and where they do not.

For example, I would prefer pure human intervention on step 4.1, as it would still be best to maintain that human to human connection with your customers.

That’s all for today! Reply with a comment if you have any questions or clarifications. I would love to hear from you.

Are you interested in learning Artificial Intelligence, even if you’re just a beginner? Click here!

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