Pinterest began as a small startup website in 2010 with just a few thousand users. Since then, the visual bookmarking application has grown into a tech powerhouse with 400+ million monthly users, serving over 200 billion pins scattered across 4 billion boards and counting. No small feat for a company
Duolingo came on the scene in 2011 operating as a teach and translate language learning app. It has since transformed into a $700+ million business, and become the first EdTech app to top $140 Million in annual revenue. Pretty good numbers, for a language learning app that still doesn’t charge
We can make predictions with machine learning by generalizing our data’s pertinent characteristics. Summarizing diverse datasets provides insight that can help produce more relevant generalizations.
Data predictions provide probabilities of future outcomes by mining and analyzing existing data, also called training data. Effective prediction is a mix of engineering, statistics, and intuition. Summarization can help by shaping this intuition. In the generalization phase, we test our training data against new data, called test data, to calculate if our model is good enough to be used in real life. These two processes simplify large multidimensional datasets, so machine learning predictions can be applied to them. This article describes how summarization leads to generalization and then prediction through a real estate example.
Healthcare AI explainability might be the most important dilemma of this century. This article explains why it will define medical outcomes for future generations.
Today’s AI algorithms provide medical recommendations by analyzing big data, but they can’t always give a reason for their conclusions other than the patterns they detect. Even though these AI-recommended solutions can’t be explained in terms of human understanding, many such treatments might improve the quality of patients’ lives and even save lives. This article discusses the controversial topic of medical explainability from a viewpoint that supports applying technological advancements to healthcare.
It’s no secret that the AI revolution has begun. I’m not the only one who believes that AI is making significant changes to our world. These quotes from some of the best-known leaders in science and technology point in the same direction
How to be prepared for the change that will transform the business landscape forever.
Worldwide access to vast amounts of data has changed the business landscape. Competitive marketing depends on knowing how to manage, process, and analyze that data. This article describes the path organizations need to take from collecting data to maximizing its use.
Today’s organizations are undergoing a challenging transformation process around their technical systems. The static software platforms that might have stored and processed a business’ data are no longer sustainable in the current web environment. Enterprises need cutting-edge technology to collect big data in real-time, analyze that data, and then get the information they need to stay competitive in today’s marketplace.
A bird-eye view of the machine learning landscape.
The main goal of this article is to cover the most important concepts of machine learning, and lay-out the landscape. The reader will have the vision to understand what kind of solution matches a specific kind of problem, and should be able to find more specific knowledge after diving into a real-life project.
I’ll start with a 60 years old definition, but still valid today:
The name is pretty self-explanatory, and the definition reinforces the same concept.
