The Dangerous Dismissal of AI Edge Cases



Original Source Here

The Dangerous Dismissal of AI Edge Cases

FACT: Edge cases are limitless and model performance cannot be predicted by traditional accuracy metrics.

We’ve all done it. Deployed a model only to have it fail on some obscure situation and dismissing it as merely an edge case that was unlikely to occur in the first place and claim success as it is quickly trained out of the model. Problem solved? Not so fast.

AI is reorganizing our world at a dizzying pace. The many innovations are absolutely remarkable but we are doing ourselves a disservice when we dismiss AI limitations as merely edge cases. This dismissal and failure to acknowledge the true nature and magnitude of edge cases puts our pace of AI adoption at risk, and thus the entire industry. Instead of dismissing such limitations as edge cases, we need to acknowledge their true nature so we can work towards actually addressing the problem.

An edge case is something that will rarely occur in practice and we often report that it’s unlikely to happen again once addressed. Sometimes the situation is so obscure that we’ll double down stating that even some humans may have been confused enough to make a wrong decision.

The fallacy we are selling is that there are a very small finite number of edge cases that we need to worry about, and this is absolutely wrong.

The truth is, there are a limitless number of edge cases. It’s a very real case of the probable improbable where across the world, chances are, an AI system will encounter edge cases every single day. These edge cases form a very long tail of situations that must be dealt with by AI models, especially as we begin deploying them to mission critical systems.

In a long-tailed distribution, the most frequently occurring 20% of items represent less than 50% of occurrences; or in other words, the least frequently occurring 80% of items are more important as a proportion of the total population. -Alpheus Bingham and Dwayne Spradlin (2011). The Long Tail of Expertise.

Further, while even some humans may be confused by edge cases, most won’t be and those that are will typically deal with the confusion more gracefully than an AI. Conversely, when an AI is confused by an edge case, ALL of the AI will be confused, as in the case of autonomous vehicles.

Edge Cases are Everywhere
…and Impossible to Predict

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.io/2021/07/31/the-dangerous-dismissal-of-ai-edge-cases/

Popular posts from this blog

I’m Sorry! Evernote Has A New ‘Home’ Now

Image
Original Source Here My Apologies! I was critical about the grandfather of all apps — Evernote a few months back when I wrote Why is Evernote losing its Ground? on Medium. With that said, I also wrote another story on Evernote in November of last year — Has Evernote Made A Comeback After Years Of Stagnation? This was the time when Ian Small and his team were making inroads into the technology and strategically making enhancements and revamping the app across all platforms. Evernote Is At A War-front And Wants To Prove A Point. I must h ave written this in my earlier blogs but just to re-iterate — I have been and am an avid user of Evernote since 2009 and is a part of my life for planning my daily life, projects, content writing, and almost everything. Recently, Evernote has launched its HOME or you may call it a DASHBOARD that provides its users a one-stop place to get access to their important and critical notes and also provides a few AI-enabled capabilities that I wil
Read more

Jensen Huang: Racism is one flywheel we must stop

Image
https://venturebeat.com/wp-content/uploads/2021/07/jensen-huang.jpg?w=1200&strip=all Original Source Here All the sessions from Transform 2021 are available on-demand now. Watch now. Jensen Huang , CEO of Nvidia , received the distinguished lifetime achievement award by the Asian American Engineer of the Year from the Chinese Institute of Engineers (CIE) group. And in his acceptance speech, he made a rare comment beyond Nvidia’s business matters: “Racism is one flywheel we must stop.” The comments came at the end of an acceptance speech where he thanked the group for its award, an annual gathering that recognizes outstanding Asian American scientists, engineers and role models. In a virtual ceremony, Huang was awarded for his contributions as “a visionary and innovator in parallel computing technology that accelerates the realization of AI computing.” He also spoke of his experience as an immigrant and an Asian American. “It is strange to accept a lifetime achievement
Read more

5 Best Machine Learning Books for ML Beginners

Image
Original Source Here Reading books is important because they books you endless knowledge. If you plan to become a Data Scientist or Machine Learning Engineer, reading machine learning books will help you gain the knowledge and skills required in this field. In a simple definition, machine learning is known as the subset of AI that has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. Email spam and malware filtering, product recommendations, image recognition, automatic language translation — these are all examples of real-life machine learning applications. The Futu r e of Jobs Report 2020 predicts that artificial intelligence (of which machine learning is a subset) will create 12 million new jobs across 26 countries by 2025. There are many books available in the market to learn machine learning. In this article, I have compiled the 5 best machine learning books that will help you start building your care
Read more