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Artificial Intelligence and its future for DX

EBS Integrator
Dec 13, 2021,

An exploration into the futurism and wishful optimism for the future of Digital Transformation (DX), especially when it comes to Artificial Intelligence! So, let’s get some cheerful spirit by looking how things improved, are improving and where they might lead us in the future!

With the holidays fast approaching, we’re in an ever-cheerful spirit, despite everything that’s going on in the world. And today we’ll discuss one of the reasons why we think the world is still going in the right direction, at least in the software development domain.

Judging by the title, you already know that we are talking about Artificial Intelligence! Even ten years ago, the simple mention of this word in business circles would quickly downgrade you to “Sci-Fi-nerd”. Now however, with an ever-rising trajectory of innovation and breakthroughs in AI technology, people are not so quick to judge.

Today we’ll try to gather our facts together, decipher some terminology, and explain how it all gels together with DX.

Then we’ll look at some new breakthroughs and give some wild speculation on where they could lead us! Because no matter how many years you have in the industry, how much knowledge or experience you think you possess, predicting the future with any certainty, especially in the kaleidoscopic world of software is a futile endeavour.

Unless you’re a hyper sophisticated prediction algorithm… more on that later.

Disclaimer: We’ll be giving an extremely basic explanation for an exceedingly difficult topic. Out of everything we’ve ever discussed, nothing comes even close to the level of complexity as does Artificial Intelligence (AI) and Machine Learning (ML).

With that said, strap in and let’s roll!

What is AI – Artificial Intelligence?

First thing’s first – let’s explain what Artificial Intelligence is! We’ve briefly addressed this question in our discussion of Chatbots… and our look into malicious types as well.

Oxford dictionary defines AI as:

Artificial Intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence – visual perception, speech and pattern recognition or decision-making.

As you can see, AI or Artificial Intelligence, is not the commonly (and wrongly) perceived terminator type robots, or HAL 9000-esque machines. Besides those kinds of robots are way out of what is currently possible and of what we know about “true intelligence”.

Artificial Intelligence is more of a number of technologies that achieves the task – mimic human intelligence, but only up to a degree. “Intelligence” in its turn, is a very loosely defined word by itself, not every software is strictly speaking “intelligent”.

By the way, what people imagine in the aforementioned terminators (true intelligent robots) is called – “Strong AI”.

So, for our purpose, any sufficiently complex algorithm, be that a basic chatbot, a data/picture classifier, driving car bot etc; are all – AI.

You can already notice that not all AI is created equally, some are more complex than others. What defines the complexion of AI is either their purpose, structure, or size.

With that settled, how do you create this complexion? What is it? Well essentially any “AI” software as stated is a collection of If/then/else statements set up in to: decision trees, regression trees, classification trees, support vectors machines (SVM), etc. Those are just some of computer algorithms that exist today.

A decision tree.

Incidentally, professional AI developers call complex AI structures – Forests.

For simplicity’s sake we’ll be using the more analytical AI, closely used in Big Data for most of our speculation.

Let’s dive in – What is Neural Networks?

To give a better understanding of what AI is, let’s examine how it functions or is built. For that we must understand what a Neural Network is. What’s Machine Learning (the aforementioned forests). And what Deep Learning is.

First, why a Neural Network? Computer scientists have long discovered that the best and most optimal way of building complex AI has already been invented, by mother nature no less.

A picture containing clothing, scarf Description automatically generated

A human brain’s neurons connect, process, and send information to each other via chemical and electrical means. These neurons take one or more signals as input, process it, and then emit their own signal.

These neuron groups, counting into billions of synapses, allows your brain to process complex information – like reading this article for example.

Artificial Neural Networks are built much the same, except comparing to our brains, they still fall short both in size, processing power and overall complexity, for now…

Quite similar, wouldn’t you agree?

Now before we explain how it’s built and functions, let’s settle one question – why it is called “learning” in the first place?

The answer is quite simple – AI software learns, much like a human child, by experiences. Experience in essence is Data. By feeding enough relevant data, you’re essentially “teaching” or “training” your software to predict patterns, with higher accuracy. AI takes many iterations (repeats) of going over that data.

Hence, machine learning; but how does it work?

What is Machine Learning and Deep Learning?

Machine learning in essence is the process of teaching your “AI” to recognize defined and specified patterns, whatever those might be, and it does that with a Neural Network approach.

Most basic explanation is “trial and error” multiplied by a factor of thousands.

Simple Neural networks are built on limited amount of data; hence they provide lower accuracy and generally are relatively basic. Deep Learning algorithms are built upon many hidden layers, that essentially filter out your information on each step. Every single one of those transitions is a mathematical equation that needs solving.

High degree of accuracy = more hidden layers = more data to process = higher demands on hardware = higher cost.

Basic ML usually is done on CPUs alone. Deep Learning algorithms rely almost exclusively on GPU based computation. If that means nothing to you, then imagine it’s a difference of hundreds of equations per second, to tens of thousands.

Depending on the purpose of your AI, it has different requirements to types of data it needs to be exposed to, and for how long. Most if not all algorithms in use today have been built using this method of machine learning.

Remember that computers are good at one thing, and that is computation. Actually, making thousands of computations per second. Hence it is far easier and quicker to let the computer learn to accomplish what you want it to do by essentially “trial and error”; compared to manually writing a set of actions for every possible outcome there is.

Filtering this information is incredibly complex and involves A LOT of math, so we won’t go into that right now.

Right now, with a very basic overview of what AI is, lets figure how it affects Digital Transformation efforts!

AI and DX – Applications

So, if you’re new to our blog, or to the trendy word of Digital Transformation, question – how did you get here? Digital Transformation (aka DX) is all the buzz right now, especially so with stay-at-home era we’re still arguably part of.

DX as many things that are popular right now, has its roots way back in time, in this case the 90s. We’ll assume you understand the various nuances of Digital transformation, and simply move onto how exactly DX is being affected by breakthroughs in AI.

Let’s have a look where AI is already heavily used in the business world.

We cannot stress this enough, when we’re talking about AI in the following cases, we’re not talking about bots, rather algorithms of many kinds.

Social Media and Search Algorithms

It is no secret that everything we see on our wall or on our search history, be that Twitter, Facebook, Instagram, LinkedIn or any other social media platform – is heavily curated by complex AI algorithms.

This is done by carefully analysing user actions, preferences, and circumstances. If you’re creating a brand-new account, from a protected IP on a new device; chances are, Google, won’t exactly know how to curate the best information for you, most appropriate searches, ads etc.

Now imagine the huge umbrella that Google has over the world, after all, it controls 70% of all global search traffic. Now imagine the amounts of BigData that Google has to process, and still provide the user not only with the best results, but hyper customized to each individual.

Informational Security

Around 44% of 835 companies that took part of TCS’s survey, encompassing 13 different industries, already use AI algorithms for their IT security. While Gartner describes how 3 out of 4 software security tools have already implemented deep learning algorithms for their predictive and prescriptive analytics.

We already said it before, but 0-day attacks keep happening and Anti-malware software only work 9 times out of 10, because they can only detect things they have seen before. Well with the introduction of AI things might take a massive turn for the better!

After all, trial and error approach, and carpet bombing your IT security infrastructure, will eventually find a hole. And much better if it’s done by an in-house AI that instantly reports the exploit to InfoSec department.

Customer Service and Support

We’ve had one of our colleagues discuss the merits of AI within the domain of customer service. However, reality stands that customers dislike waiting in line, being put on hold, spending hours trying to get to talk with the “right person”.

Companies have long realised how incredibly important customer service is, and making it available 24/7 is among the top priorities. Though setting up a basic chatbot to make preliminary first contact point analysis is only half the battle.

Many companies have already resorted to creating more complex AI algorithms, based on their own companies’ experiences and circumstances. This allows these companies to essentially customize a virtual assistant tailor made to resolve issues with their product. And unlike basic scripts that chatbot use with predefined responses, Deep Learning Algorithms have the ability to imitate true human interaction, and to continuously improve the quality of their service

HRMS integration

Good Human Resource practices are paramount when it comes to quality retention among your team. However, there is simply too much data to process among new hires’, as well as working staff. For this reason, Artificial Intelligence naturally saw a high rise in demand, to help with the workload and its integration with built HRMS software.

We’re not saying to replace your HR colleagues with beep-boop robots as fast as you can, the “human touch” must always remain with human resources. But many have already integrated AI as an artificial partner that helps with all the tedious paperwork and filtering process.

Where is it all headed?

Of course, we’re not saying that literally everyone and their grandparents have transitioned to adopting AI technology into their enterprise. Far from it, but it still shows a huge growth, so much so that many new and exciting ways are propping up, such as:

AI-OPs

Not so long ago many people we’re getting used to the new word – DevOps. Many still don’t really understand what that means. And whether we really need a new “OPs” word is up for debate, but reality is such that many companies have begun providing new platform solutions for AI algorithms.

Of course, industries benefiting from these solutions are primarily huge enterprises dealing with BigData analytics, which already screams ML solution, or vice versa?

Sometimes however, your operations team cannot act proactively and in a timely matter when it comes to certain aspects of their job – they are only human. AIOps aims to deliver AI algorithms to support DevOps on truly colossal scale.

This involves huge enterprises and a number of “menial” alerts or simple solutions. As well as deep learning AI to manage more or less day-to-day requirements of your Ops team, letting them focus on decision-making and priority problems. Besides, no one likes to wake up at 4 AM due to a fallen server, let the AI handle it!

Word of the day is a helpful hand for better optimization.

Edge AI and IoT

There was a time when AI was largely in the domain of huge datacentres, requiring massive amounts of processing power to even function. Those days are behind us, as AI is making grounds into the Edge Computing, solving a host of distributed data and analytics problems.

Edge AI is the embedding of intelligent capabilities at the point of data origin, whether that’s an IoT endpoint, a smartphone, or a connected car.

Essentially speaking, Edge AI promotes higher degree of optimization to the whole industry. This is as some would an unbeatable combo.

Farewell

But hey, that’s what we have for today. There are still many things we want to talk about in regard to AI and machine learning. Eventually we’ll talk you through planning and creating your first chatbot, perhaps even “deep learn” it as well.

Today’s Article was more about setting up the groundwork that we’ll be building up in 2022! But know this, that with how much cheaper it gets to create AI each year, you can be sure most if not all DX efforts in the following 10 years will be enhanced by AI – and that’s a fact!

With that said – what is your favourite thing about artificial intelligence? Tell us in the comments below!

Stay classy business and tech nerds!