Mosaic Data Science has deployed Practical AI to bridge the gap between AI research and practical implementation by creating AI-driven tools, systems, and applications that address specific challenges and tasks. Our solutions are designed to enhance efficiency, automate processes, improve decision-making, and deliver measurable benefits to our customers.
Natural language models have come a long way in the past couple of years. With the advent of the deep learning Transformer architecture, it became possible to generate text that could, plausibly, be passed off as written by a human.
To understand GPT-3, it’s helpful to understand a little bit about the history of language models. The language of computers is numbers. The input to all machine learning algorithms is ultimately numbers as well.
Mosaic built an automated cooking prediction & optimizer using deep reinforcement learning to improve short term cooking operations.
Algorithms can be trained to mimic human behavior, but what happens when the human developing the algorithm inadvertently allows bias into the training process?
Weather has a high impact on operations in many industries, and therefore is of great value to integrate into strategic decision making. Mosaic has roots in aviation research & development, giving us deep expertise in combining weather data streams with planning applications to facilitate efficient resource allocation.
Designing and deploying computer vision is a powerful technology that humans can employ to improve their decision making. The only limits to these technologies lie within our ability to think of problems for them to solve.
Gameplay data are a trove of information about how athletes are acting and reacting in real situations, and there are real benefits to be gained by mining this information at every level, from the athlete to the entire team. In the modern age, the team that can measure and understand itself through its own data will have the competitive edge.
We examine how professional teams can deploy predictive ticket pricing to capture increased revenue and decrease empty seats.
In post 2 of 4 on biometric modeling, we discuss how sporting teams and goods manufacturers can segment their consumer base using biometric insights.
This is post 1 of a 4-part series focusing how data science can incorporate biometrics for sporting good manufacturers and professional sports teams.