Like our brains, a new computer program created by Parham Aarabi of the University of Toronto can store and retrieve information strategically. An experimental tool that uses the novel algorithm to aid those with memory loss has also been developed by the associate professor in the Faculty of Applied Science & Engineering's Edward S. Rogers Sr. department of electrical and computer engineering. In the minds of most people, AI is more robotic than humans, according to Aarabi, whose approach is examined in a paper presented at the IEEE Engineering in Medicine and Biology Society Conference in Glasgow. Aarabi believes it should change. Computers have traditionally needed explicit instructions from their users on what data to save.

Hartley first heard the terms "Fuzzy" and "Techie" while studying political science at Stanford University. At Stanford, if you majored in the humanities or social sciences, you were a Fuzzy. If you majored in the computer sciences, you were a Techie. According to Hartley, this informal division has mistakenly created a business mindset and believes Techies are the real drivers of innovation. Hartley believes that the Fuzzies, not the Techies, are the key talent responsible for creating the most successful new business ideas.

A common application of Fuzzy.ai is in powering custom recommendation engines. For many companies, generic solutions don't offer enough flexibility (or require too much work manually setting up links between all of the different products in the catalog), and building a custom recommendation engine from scratch requires way too much time and effort. To show how easily it can be done, we've put together an open source Product Recommendation plugin for Drupal Commerce stores that lets anyone spin up their own product recommendation engine with Fuzzy.ai. When a user is looking at a product page on an online store, the goal of this recommendation agent is to identify the other products in the catalog that might be relevant. The Fuzzy.ai API uses those rules to provide recommendations.

I'm Evan, co-founder and CTO of Fuzzy.ai. Our team is very excited to be opening our doors for new registration today. These rules are optimized over time due to feedback from production use. We've applied this technique to a number of interesting business cases -- fraud detection, recommendations, content optimization, and dynamic pricing. Our Web-based developer environment makes it easy to design an agent quickly, and our SDKs for different programming languages make integrating with our REST API pretty painless.

This column is by Matt Fogel, Co-Founder, Fuzzy.io Data science and machine learning have long been interests of mine, but now that I'm working on Fuzzy.ai I need to keep on top of all the news in both fields. My preferred way to do this is through listening to podcasts. I've listened to a bunch of machine learning and data science podcasts in the last few months, so I thought I'd share my favorites: Every other week, they release a 10–15 minute episode where hosts, Kyle and Linda Polich give a short primer on topics like k-means clustering, natural language processing and decision tree learning, often using analogies related to their pet parrot, Yoshi.

Brake forming is widely applied in the high variety and small batch part manufacturing of sheet metal components, for the bending of straight bending lines. Currently, the planning of the bending sequences is a task that has to be performed manually, involving many heuristic criteria. However, set-up and bend sequencing procedures and knowledge have to be formally formalized and modeled, for the development of computer-aided process planning systems for sheet metal forming. This paper describes the application of fuzzy set theory for the normalization and modeling of the set-up and bend sequencing process for sheet metal bending. A fuzzy-set based methodology is used to determine the optimal bending sequences for the brake forming of sheet metal components, taking into account the relative importance of handling and accuracy.