Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Bryce Murray
Artificial Intelligence
Algorithms too often don't reflect the complexity each stakeholder brings to the table. A branch of mathematics that has been around since the 1960s can help.
Designing XAI for People Data Original
People-data is sensitive, and our algorithms should do justice to the humans involved in the dataset. In this post, I dive into a practical example while highlighting three must-haves to make your insights valuable and effective. Photo by Jacek Dylag on Unsplash With advances in AI, society calls for more transparency in how algorithms process data. One specific type of data stands above the rest when scrutinized — People Data.
The Most Important Types of XAI
This post discusses three critical types of XAI: interpretable AI, transparent AI, and interactable AI. During my time in AI, I’ve found more practicality out of these three than any others. In this post, I give an overview and example of each. Photo by Jr Korpa on Unsplash Artificial Intelligence (AI) surrounds many aspects of ourlives. We interact with many applications that AI has influenced in our day-to-day lives. There’s a chance an AI recommended this medium article for you to read about AI.
Fuzzy Integral: A deep dive into data fusion
Photo by Jeswin Thomas on Unsplash The Fuzzy Integral (aka Choquet Integral) is arguably a pretty simple concept once we cut through the jargon. It took me longer than I’d like to admit to realize this algorithm is three simple operations — addition, subtraction, and multiplication. Of course, the devil is in the details, and it becomes increasingly complex in its capabilities the further we go. However, as long as we come back to the fundamentals, we can add more complexity as we go.
Fuzzy Systems: Life between the 1’s and 0's
Fuzzy Systems is a branch of Computational Intelligence hoping to represent the uncertainty of a fuzzy, uncertain world. Fuzzy systems find their inspiration in the imprecision of human language. Fuzzy systems are widely known by Fuzzy Logic, and in this post, we dive into Fuzzy Rule Bases, Fuzzy Control, and the Fuzzy Integral. Photo by Gradienta on Unsplash Computational Intelligence brings together three branches of topics — Evolutionary Computation, Neural Networks, and Fuzzy Systems.
Neural Networks : More than Deep Learning
Photo by eberhard 🖐 grossgasteiger on Unsplash The amount of research produced each yearby the Computational Intelligence (CI) community is astounding. One specific branch of CI is the ever-popular branch of Neural Networks. Neural Networks hit the mainstream because of their performance and the fascination to see how deep we could make the architectures. Deep learning seems to be synonymous with “Artificial Intelligence” these days, which has driven their acceptance.
Tired of AI? Let's talk about CI.
Full disclosure -I'm biased towards Fuzzy Systems, so I'll try to stick to the facts. There are many ways to explain this topic, but I like to start with Fuzzy Sets. Traditional set theory forces elements to belong to one set or another. For example, a horse belongs to the set of mammals, and a frog belongs to the set amphibians. This framework (aka crisp set theory) works for situations with precise segmentation; however, the world isn't this precise. Consider representing the color gray.
Evolutionary Computation: A hidden gem of CI
Photo by Jr Korpa on Unsplash Evolutionary Computation (EC) is the branch of CI inspired by biological systems. In my last post, I highlighted each branch of CI, but herein, we solely focus on EC. Within the IEEE CIS, the EC community has a strong presence. Their journal has an impact factor of 11.554, which isn’t too shabby. While this set of algorithms share inspiration, there are different categories within EC.
Are We Thinking about eXplAInability Backwards?
Photo by Fakurian Design on Unsplash The bulk of my doctoral researchrevolved around eXplainable Data Fusion. As a result, I spent a fair portion of my time researching eXplainable AI. Explainability intrigues me because it involves the algorithm translating something about itself to its users. Philosophical debates surround what constitutes an explanation, but algorithmic explanations need to show something about themselves to a user. We develop XAI methods in different ways.
Tired of AI? Let’s talk about CI.
Artificial Intelligence (AI) is everywhere. It has slowly crept away from its original definition and has become a buzzword for most automated algorithms. In this post, I don’t argue what AI is or isn’t — that’s a highly subjective argument at this point. However, I’d like to highlight Computational Intelligence — a well-defined topic. Photo by Michael Dziedzic on Unsplash What is Artificial Intelligence? Who knows. It’s an ever-moving target to define what is or isn’t AI.
Fuzzy Logic Makes AI More Explainable
Despite its growth and popularity, AI is not as widely adopted as it could be—particularly by small- and mid-size businesses that lack the enormous data sets of business and tech giants. One reason is that AI designers are not doing enough to consider the end user. Consider the many stages an AI solution must pass through in order to be successfully integrated into a company’s workflow. An algorithm begins with an AI expert who must incorporate knowledge from a subject matter expert.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Bryce
Contact Bryce, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack