Empowering AI with Real-World Context

Published on February 26, 2024
Campbell Brown
CEO & Co-Founder

Empowering AI with Real-World Context

In the rapidly evolving landscape of artificial intelligence (AI), the key to smarter decision making lies not just in the training data we feed into our models, but in the real-world context it can tap into once these models are trained. Allowing for more accurate and dynamic understanding to ensure the models recommendations are able to navigate volatility. Whether it’s a hurricane warning surging purchasing of bottled water, or a conference increasing the price of nearby hotel rooms. The world is dynamic, and the models that give us a competitive edge need to be as well.

The Challenge of Ever-Changing Data

As I mentioned in 2018, 'Data is to AI as food is to humans.' This is increasingly evident today, as AI systems demonstrate an unprecedented capacity to process vast quantities of data rapidly. However, they often lack the ability to understand the dynamic and fluid nature of real-world events. Traditional datasets, even the most comprehensive ones, can't keep pace with the constantly changing nature of event data. Think about changed dates, changed start and end times for events, cancellations and more. Even some of the largest events can get changed last minute, like when the Daytona 500 this February was moved to the following day due to rain, which was the third time in history for this event. 

Contextual Awareness: The New Frontier in AI

Our customers have been integrating PredictHQ's data, intelligence and infrastructure into their models for years to ensure they are able to adapt to demand volatility based on what is ACTUALLY impacting them in the real-world and not just to seasonality. In an adjacent space, this mirrors a specific innovation seen in X/Twitter's Grok system which was summarized nicely here: Large Language Models, How to Train Them, and xAI’s Grok. Just as Grok has become more context-aware through the assimilation of external data, PredictHQ empowers AI systems with a similar level of understanding, albeit focused squarely at dealing with demand volatility. This is not just about knowing what events are taking place; it's about understanding their impact on consumer behavior, supply chain logistics, operational disruptions and overall business performance.

Beyond Synthetic Data: The Real-World Advantage

Gartner estimates that by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. The growth of synthetically generated data is being driven by its ability to help teams gain access to data faster, at a lower cost, in a secure manner, and with the compounding impact of businesses shifting to the concept of ‘Sovereign AI’ (AI which is typically proprietary, developed for specific businesses and not publicly available).  

Whilst synthetic data has its very important place in the world of AI in building and preparing models for dynamic data, such as Waymo using synthetic data during the pandemic to train its self-driving vehicle systems. Training AI models exclusively on synthetic data can lead to a distribution shift, potentially causing model collapse. This happens when the model misperceives the learning task due to the lack of real-world and accurate data. 

To bridge the gap between training and live models so that teams can continue to move fast in their initial stages of development, but can seamlessly move to production, PredictHQ created the innovative Features API. Unlike synthetic data, the Features API is an aggregation of live real-world data providing pre-built ML features which in themselves (can take data science teams weeks or months of effort to build and test) so that our customers can rapidly prove which different types of events impact specific locations and thus improve their models accuracy. Once this has been proven in the simulation phase, they are able to push this into production without changing from a synthetic source to a live source, and then also bring through more detailed explainability using our Events API, which provides a more detailed breakdown of the actual events taking place.

Mastering predictability with real-world events, ensures that models are far more flexible and adaptive allowing our customers like Instacart to optimize grocery transactions for millions. It’s also important to note that events are extremely dynamic with new events, cancellations, postponements and unforeseen occurrences like severe weather occurring everyday. This level of detail and timeliness is something that synthetic data, often built from static or historical sources, cannot match.

Case Studies: AI in Action

Consider a retail chain that uses AI to forecast demand. By integrating PredictHQ's technology, their AI becomes context-aware, adjusting forecasts based on upcoming local events predicted to impact a specific location. This enables more accurate demand planning, leading to better inventory management and customer satisfaction. Similarly, a transportation company can optimize routes and schedules by anticipating changes in traffic patterns due to special events or weather disruptions ensuring drivers are in the right place, at the right time, ahead of schedule.

PredictHQ: A Beam in the Data Fog

Helping businesses to better navigate demand volatility by making their models smarter and more dynamic is nothing new to us. The shape of demand changes depending on a businesses industry, location and customer behavior so to help lift the fog on what is ACTUALLY impacting an Uber vs Airbnb we have been building world-leading technology like Beam

This AI-powered platform can quickly consume your anonymized demand data either via our web application or more recently within your own environment via the Beam API and identify what events impact anything from 1 single location to 1,000’s of locations. This ensures you are not only improving the accuracy of your models, but the efficiency of your operational teams who are dependent on knowing what does and does not impact.  

As AI scales and with it, democratizes the capabilities only once available to the most advanced teams on the planet, so will the opportunities to shift businesses from a relatively static framework - greatly exposed during the pandemic - to a living, breathing operation, driving great efficiencies across multiple business units.

The PredictHQ Difference: Empowering Businesses with Real-World Intelligence

By making AI models context-aware, PredictHQ provides businesses with a critical edge in a competitive market. This real-world awareness enables more accurate predictions, smarter decision-making, and ultimately, a more agile and responsive business model.

In conclusion, as we continue to advance in the realm of AI, the integration of real-world context becomes increasingly crucial. PredictHQ stands at the forefront of this evolution, offering the tools and intelligence necessary for businesses to thrive in an ever-changing world.