You probably suspect AI can dramatically improve your business, but perhaps you are not sure exactly how -- perhaps you are even worried that a start up or tech company is gunning for your business. Ask yourself two questions: 1. what are the high level business problems that could be transformational if solved and 2. which business problems are feasible to solve with AI to some meaningful degree.
5 Tips for Securing Executive Buy-In on Your AI Project
When securing executive buy-in for your potential AI project, there are a number of considerations to keep in mind. It’s necessary to prepare a strong proposal that not only takes account the motivations of your leaders, but also makes a case for your project’s technical feasibility. In this article we dive into five tips to consider when attempting to secure executive buy-in:
Modern AI Text Generation: An Exploration of GPT-3, Wu Dao 2.0 & other NLP Advances
Within this last year alone, there has been a paradigm shift in model development as research groups are ingesting (nearly) the entire world's worth of information on the internet to train massive deep learning models capable of performing fantastic or frightening feats, depending on your perspective. In this article, we explore an AI compositional technology, known as generative modeling, and demonstrate its ability to simulate human-realistic text.
10 Reasons Why AI Innovation Fails According to an AI Consultant
As an AI Consultant, I get a front row seat to AI and innovation; I repeatedly see when it succeeds, and when it fails. I’ve been building AI and machine learning based systems for over 20 years, in many different companies, cultures, and problem domains. During this time, I’ve noticed a number of patterns that cause the deployment of AI systems and AI powered features to fail.
Explaining a Passenger Survival AI Model Using SHAP for the RMS Titanic
In 1912, the RMS Titanic hit an iceberg in the North Atlantic Ocean about 400 miles south of Newfoundland, Canada and sank. Unfortunately, there were not enough lifeboats onboard to accommodate all passengers and 67% of the passengers died. In this article, we walk through the use of SHAP values to explain, in a detailed manner, why an AI model decides to predict whether a given passenger will or will not survive.