Image for post
Image for post
source: youtube.com

Unlike the Matt Damon movie Ford vs Ferrari, this post will not focus on racing cars for 24 hours straight through the north of France. Instead, we will focus on comparing automobiles, or more specifically comparing their logos using Artificial Intelligence.

Many years ago, I was walking with my niece, who was three at the time. As a car drove past, she pointed at it and exclaimed in her toddler voice, “BMW.” Sure enough, she was correct. The car was in fact a BMW that she recognized based on their iconic brand and logo.


How does machine learning apply to your business?

Image for post
Image for post

We live in a world where data is being generated all around us and large organizations are pushing through a digital transformation to extract as much value as possible from it. So with large enterprises driving towards the implementation of ML and AI, how can it be that only 51% of SMBs consider it to be important to their business?

I think the answer to the question lies in another question. How many understand how AI and ML could impact their business?

Incorporating machine learning and artificial intelligence into your organization can be time consuming and expensive. Time and money are typically not things that SMBs have to waste, and thus they are not targeted by the large technology providers. Because they are not the target of marketing and advertising, it is presented in a way that insinuates AI is not for them. …


Correlation to causation is paved with human intuition

Image for post
Image for post
Photo by John Lockwood on Unsplash

Correlation vs. Causation

Anyone who has worked with data has been presented with the question of whether the results are due to causation or simply correlation. This is mainly based on the famous statement that “correlation does not imply causation”.

According to Wikipedia:

In statistics, the phrase “correlation does not imply causation” refers to the inability to legitimately deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them

We will seek to explain this through examples but first, let’s give some basic definitions:

Correlation:

a relation existing between phenomena or things or between mathematical or statistical variables which tend to vary, be associated, or occur together in a way not expected on the basis of chance…


Image for post
Image for post
Photo by Glen Carrie on Unsplash

Everybody is talking about machine learning and digital transformation. You know you should be implementing it but like most emerging technologies, it is difficult to know where to start without an advanced degree in Data Science. In fact, statistics show that the shortage of AI talent is the leading barrier to building and deploying predictive models.

In this post, we are looking to knock down that barrier in 3 easy steps with the Elipsa Analytics Platform.

Getting Started with Machine Learning

There are a lot of great articles and blog posts out there to get started with machine learning and predictive analytics. If you have the technical acumen, and more importantly the time, you can learn a lot and be well on your way. …


Image for post
Image for post
Photo by Ibrahim Rifath on Unsplash

The elipsa Analytics Platform provides predictive data solutions driven by approachable AI. Our plug and play predictive tools enable data users to conduct complex data experiments with just a few clicks.

Data Drivers is a diagnostic analytics tool that allows users to quickly and easily identify and extract patterns within their data to go beyond the analysis of what happened and start to explain why it happened.

Elipsa’s intuitive platform allows data users to unlock the answers hidden in their data leading to accurate data-driven decisions.

Customer Churn Analysis

We will examine the use of Data Drivers to help explain the problem of Customer Churn. …


Image for post
Image for post
Photo by Omar Flores on Unsplash

Elipsa’s mission is to create Approachable AI, enabling organizations to scale by empowering their business users to assume the role of the data scientist through a no-code solution. The three pillars of this mission are useability, explainability, and accessibility. This is a three-part series on those pillars and how a focus on each of these will enable broader adoption of predictive analytics and a faster journey from data to insight.

Signup for elipsa’s free beta program for early access to new features.

In our first two posts, we discussed the need to make systems easy to use and easy to understand in order to have Approachable AI. The first and final pillar is Accessibility. On the surface, most would think this is the most obvious but also assume that it is the easiest and that it exists in all software today. After all, how can you use a platform if you can’t access it? However, Approachable AI is less about access to the platform and more about access to the insights. …


Image for post
Image for post
connect the dots of the black box with explainable AI

Elipsa’s mission is to create Approachable AI, enabling organizations to scale by empowering their business users to assume the role of the data scientist through a no-code solution. The three pillars of this mission are useability, explainability, and accessibility. This is a three-part series on those pillars and how a focus on each of these will enable broader adoption of predictive analytics and a faster journey from data to insight.

Signup for elipsa’s free beta program for early access to new features.

Explainability

In the first post, we discussed how useability is a hindrance preventing companies from scaling their AI capabilities. Equally as important, if not more so, than an easy to use system to create models is the ability to interpret and understand the results. Even for organizations that have data scientists, there is often difficulties getting their hard work into production because of an AI language barrier. In other words, the typical business user does not understand the model references or statistical metrics that is common in data science vocabulary. On top of this, many of the models still appear to be black boxes. There are many organizations that are not able to implement a machine learning model if they cannot explain how it got to the results. That requirement is sometimes internal but often is required of regulators and so companies are reluctant to incorporate a black box product into their processes out of concern for regulatory backlash. …


Image for post
Image for post
Complex technology often leads to manual processes

Elipsa’s mission is to create Approachable AI, enabling organizations to scale by empowering their business users to assume the role of the data scientist through a no-code solution. The three pillars of this mission are useability, explainability, and accessibility. This is a three-part series explaining how a focus on each of these will enable broader adoption of predictive analytics and a faster journey from data to insight.

Signup for elipsa’s free beta program for early access to new features.

Useability

According to a Forbes survey, >75% of organizations list limited AI skills as a roadblock for their AI initiatives. Business users are not skilled in machine learning and data science, requiring organizations to scale up teams of Data Scientists or hire expensive consulting firms just to get started. …

About

Jeff Kimmel

Machine Learning enthusiast. CEO @ elipsa.ai

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store