How to Create an AI Solution?

Wondering how to create an AI solution?

AI is set to become one of the leading software development growth markets. According to Statista, the global market for AI is expected to reach $126 billion by 2025.

Developing an AI system requires a deep understanding of supervised and unsupervised machine learning algorithms, Python programming language, ML development frameworks, etc.

If you don’t have a professional team with relevant expertise to take on the task of AI development, then submit a request for a complimentary discovery call, and one of our tech account managers who managed similar projects will contact you shortly.

Before we get into what‘s involved in building AI systems that leverage artificial intelligence and data science, let‘s start with a definition of what the term “artificial intelligence” means:

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal.

Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human brain functions such as “learning” and “problem-solving”.

If you‘re thinking of exploring artificial intelligence or machine learning, you need to ask yourself the question:

Do I really need to implement artificial intelligence in my software solution?

This raises another question:

How do you know if you need to create an AI solution!?

Here are some guidelines:

Do you deal with vast quantities of data?
Is the data in various formats?
Are you dealing with frequently changing parameters?
Is the data arriving at ever-increasing velocity?

If you have answered yes to most of these points, then you might want to consider integrating AI and machine learning into your tech stack.

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In order to have a better insight into this topic, check out the pros and cons of artificial intelligence.

An Example: Managing Email Spam

Consider an email spam problem and consider the problems you‘d encounter by implementing the application using traditional programming techniques. Well, most of this can be alleviated by implementing an AI/ML approach.

The problem

Email spam detection is ultimately a text classification problem.

Fortunately, this is a well-studied area, and several techniques can be employed by the machine to determine to which Category the new incoming email belongs.

Algorithm and approach

Before we delve into the technicalities of which API to use when you create AI software, it can be helpful to further understand the background of how such a solution can be approached by using Supervised Learning.

If you‘re unfamiliar with this, Supervised Learning is a technique that involves the construction of a “Classifier”. The Classifier neural networks are responsible for categorizing text into a Category (sometimes known as a Label).

Some classification techniques include:

Naïve Bayes;
Maximum Entropy;
Support Vector Machines (SVM).

Naïve Bayes is a relatively simple and accurate way for the machine to identify which category an email belongs to.

Bayesian Theorem is a probability theory used to arrive at predictions considering recent evidence. The theorem was discovered by an English Presbyterian and mathematician called Thomas Bayes and published posthumously in 1763.

The rule is written like this:

p(A|B) = p(B|A) p(A) / p(B)

To deconstruct the rule, here are descriptions of each component that form it:

p(A|B)

‘The probability of A given B’. This basically means the probability of finding observation A, given that some part of evidence B is there. This is what we want to find out.

p(B|A)

This is the probability of the evidence turning up, given the outcome obtained.

p(A)

This is the probability of the outcome occurring, without the knowledge of the new evidence.

p(B)

This is the probability of the evidence arising, without regard to the outcome.

We can apply this rule to our email spam problem and plug in the parameters:

P(spam |words) = P(words/spam)P(spam) / P(words)

This is all fine and well but it may be hard to visualize without data and values. So, taking the above into consideration, imagine we had a database of 100 emails.

We also believe that emails that contain the word “buy” are spam emails. We can now apply the Bayes Rule:

Training Data

100 emails in total
60 of those 100 emails are spam
48 of those 60 emails that are spam have the word “buy”
12 of those 60 emails that are spam don’t have the word “buy”
40 of those 100 emails aren’t spam
4 of those 40 emails that aren’t spam have the word “buy”
36 of those 40 emails that aren’t spam don’t have the word “buy”

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What is the probability that an email is a spam if it has the word “buy” in the content?

The answer to the above is as follows: There are 48 emails that are spam and have the word “buy” and there are 52 emails that have the word “buy”: 48 that are spam plus 4 that aren’t spam.

So the probability that an email is spam if it has the word “buy” is 48/52 = 0.92

As mentioned previously, the rule and notation are based on probabilities, so we can redefine the problem to use probabilities rather than quantities. Using the same database of emails.

60% of those emails are spam.
80% of those emails that are spam have the word “buy”.
20% of those emails that are spam don’t have the word “buy”.
40% of those emails aren’t spam.
10% of those emails that aren’t spam have the word “buy”.
90% of those emails that aren’t spam don’t have the word “buy”.

What is the probability that an email is spam if it has the word “buy”? The notation to arrive at the answer looks like this:

P(spam) = the probability that an email is a spam.

P(not spam) = the probability that an email isn’t spam.

P(“buy”|spam) = the probability that an email that is spam has the word “buy”.

P(“buy”|not spam) = the probability that an email that isn’t spam has the word “buy”.

P(spam|”buy”) = the probability that an email that has the word “buy” is spam.

So P(spam|”buy”) is the answer we are looking for

P(“buy”|spam) * P(spam) counts all the emails that are spam and have the word “buy”.

P(“buy”|not spam) * P(not spam) counts all the emails that aren’t spam and have the word “buy”.

Summing the previous two P(“buy”|spam) * P(spam) + P(“buy”|not spam) * P(not spam) we count all the emails that have the word “buy”.

Meaning the resulting equation looks like this: (This is Bayesian Theorem)

P(spam|”buy”) = P(“buy”|spam) * P(spam) / (P(“buy”|spam) * P(spam) + P(“buy”|not spam) * P(not spam))

Or , to inject the numbers: 0.8 * 0.6 / (0.80.6 + 0.10.4) = 0.48 / 0.52

The result of the simulation was: 0.922248596074798

In Plain English

After running incoming emails through this classification model, we can safely assume that with a probability of 92%, if emails contain the word “buy”, they should be placed in the spam folder!

Training Data

The neural network classifier must be “trained” with sample datasets prior to determining future text classifications. In our email example, we had 100 emails, some contained the word “buy” and others didn‘t.

Without an accurate training process, the machine cannot accurately make reliable predictions about future events using data.

APIs

With the theory and example out of the way, you now have an appreciation for how probabilities can be determined by the machine.

Rather than code up numerous combinations of string patterns and complex business rules, spam identification can be determined using maths. This is a use case in which the machine will excel in!

The great news is that you don‘t need to code up theorems such as the Bayes Rule when you create AI software, the heavy lifting is now being done for you by the likes of Microsoft and IBM!