E-commerce teams are increasingly turning to artificial intelligence tools to save time, save money, and increase revenue. But entering into a conversation about e-commerce AI can be intimidating if you don’t know the basics of AI.
This is a growing list of articles that explain AI, it’s history, limitations, applications and more.
Chances are, if you’re reading this post, you will want to know how machine learning can impact your e-commerce business. This article will go over the top and most effective ways machine learning is being used for e-commerce.
“The thing is e-commerce companies have a lot of data at their fingertips. But making use of that data is a challenge.”
This article is a good intro to understand how AI, ML, and DL are related and different. What’s nice about this article is that it goes into the roots of AI research to explain how we got to the point we are now.
“AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at the Dartmouth Conferences in 1956 and birthed the field of AI.”
Read this article to learn the basic terms and concepts of AI, ML, supervised learning and unsupervised learning. From there, it gives you a rundown on how deep learning works.
“Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs.”
Deep learning is not the same as machine learning. By reading this article, you’ll get a sense of how these buzzwords can misrepresent important details. A problem that is exacerbated the more trendy the topic becomes.
“DL has over the past few years given rise to a massive collection of ideas and techniques that were previously either unknown or known to be untenable.”
MIT’s Technology Review discusses how Google’s DeepMind is addressing one of the biggest challenges in AI: speed. AI has a ways to go, Google is hoping it can make the route faster.
“Intelligent machines still lag behind humans in one crucial area of performance: the speed at which they learn. ...The basic idea behind DeepMind’s approach is to copy the way humans and animals learn quickly.”
Instacart found a creative way to explain deep learning: emojis! With a line-up of popular food emojis, the Instacart team explains how they were able to use deep learning to optimize shopping routes. Overall this is a great example to wrap your head around deep learning concepts.
“This approach has reduced our shopping times by minutes per trip. At scale, every minute saved will translate into 618 years of shopping time per year.”
This series of articles is an accessible, yet detailed explanation of deep learning.
“Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic.”
This article discusses the “information bottleneck” theory which explains how deep learning neural networks are able to filter to a correct answer. Read this if you want to understand the level of deep learning research that is going on simply to understand how existing technology works.
“No underlying principle has guided the design of these learning systems, other than vague inspiration drawn from the architecture of the brain (and no one really understands how that operates either).”
Before we get carried away with AI, it’s best to read this article to appropriately manage your expectations. AI is still a work in progress, it’s imperfect, and it’s relatively simple compared to Hollywood versions.
“We are surrounded by hysteria about the future of artificial intelligence and robotics — hysteria about how powerful they will become, how quickly, and what they will do to jobs.”
AlphaGo is an AI program that plays the board game Go. It made headlines for beating world champions. While it was an incredible display of AI technology, the press around the event tended to overstate the real impact of this achievement. This article sets the record straight. It’s a good place to learn about the limits of the most advanced AI.
“While AlphaGo does not introduce fundamental breakthroughs in AI algorithmically, and while it is still an example of narrow AI, AlphaGo does symbolize Alphabet’s AI power.”