September 14, 2023
As the wave of Artificial Intelligence (AI) sweeps across industries, organizations are eagerly hopping aboard, hoping to unlock new frontiers of innovation and efficiency. The allure of AI’s transformative potential is undeniable. Yet, the bitter reality many organizations confront is the gap between promise and practice. The bottleneck? It’s not the sophistication of AI algorithms, but the accessibility and organization of the data they’re fed.Many organizations often find themselves ensnared by the limitation of a single data modality approach. Such a constrained perspective narrows the broad landscape of available data, leaving vast chunks either underutilized or entirely out of reach for AI systems. It’s akin to a painter being confined to one color, limiting the full spectrum of artistic expression. In a similar vein, AI, when tethered to limited data, yields an incomplete picture, possibly skewing insights away from a comprehensive understanding. The full potential of AI systems truly unfurls when they can tap into multi-modal data via a decentralized data model or data mesh, amalgamating it into a continuous stream of machine-readable data capable of planetary-scale processing. Moreover, understanding how this multi-modal data changes over Space (location) and Time is critical for understanding predictive and prescriptive outcomes.
While data lakes can be beneficial for AI systems, they are not without pitfalls. Often, they lack a logical ontology and have constrained data structures, compelling data scientists to devote a significant amount of their time sifting through and reshaping data to make it usable and model friendly. This exemplifies the 80/20 paradigm, where analysts or data scientists dedicate 80% of their time to finding, wrangling, and formatting data, leaving only 20% for actual analysis and insight extraction. Such scenarios prompt the recurring question: “Why can’t the data be readily available in the needed format when I need it?” Moreover, these professionals often grapple with a limited perspective on how different data pieces interlink to address complex questions. To surmount these challenges, what’s imperative is a system that methodically arranges data and delineates its interconnectedness. Such a configuration not only unveils data connections but also equips the analyst or data scientist with insights to answer questions they didn’t even know they could ask.
Scaling AI systems beyond traditional data types often presents a monumental challenge for many organizations. As data continues to grow in both volume and diversity, standard systems frequently fall behind, resulting in delayed decision-making or overlooking timely insights. Furthermore, the prevalent practice of Extracting, Transforming, and Loading (ETL) data into data lakes or equivalent systems introduces not only latency but also redundancy and additional expenses. The obligatory ETL process also hampers swift access to various data sources and systems, leading to potential inaccuracies in the ensuing analysis.
SeerAI’s Geodesic platform stands out as more than just a data fusion solution. It is an analytic and fusion engine powering planetary scale AI and Deep Learning against mammoth data sets. This includes the notoriously intricate realm of Spatiotemporal data—a domain where Geodesic shines by treating time as a first-class citizen. To put it simply, if you strip away space and time from spatiotemporal data, you’re only left with conventional data. Recognizing the nuances of both space and time, and enabling your AI to decipher these complexities, is paramount for predictive and prescriptive understanding.
The decentralized data mesh—a groundbreaking solution pioneered by SeerAI with their Geodesic platform. Built on the world’s first and only Spatiotemporal Data Mesh technology, Geodesic dissolves the barriers of complex data transformations. The decentralized nature of this data model forms a cohesive mesh, weaving together disparate data sources and types. With its innovative transform-on-query architecture, the data mesh revolutionizes the way we interact with data. Previously challenging and inaccessible data sets are now readily available, presented in formats that machines can effortlessly process while remaining intuitively comprehensible to humans. This seamless fusion of machine efficiency and human intuition exemplifies the pinnacle of human-machine teaming, ensuring that both entities work in harmony to derive maximum value from data.
Modern AI requires a paradigm shift—a transition from isolated data silos to a Linked Data Environment. This approach truly sets Geodesic apart with its Enterprise Knowledge Graph. Leveraging RDF*, the graph crafts intricate data relationships, capturing nuanced attributes and nesting triples to ensure a deep, interconnected data comprehension. Geodesic encodes multimodal data, piecing together the puzzle to answer complex, multifaceted questions. With a logical data ontology, the Geodesic platform supports a spectrum of AI and Deep Learning models compatible with a vast array of data classes.
SeerAI’s vision goes beyond just integration—it’s about making data-driven decisions at a planetary scale, in real-time. Their emphasis on Planetary-Scale Spatiotemporal fusion empowers organizations to view the bigger picture, understanding how data segments interlink and providing insights even for questions previously deemed unaskable.
As AI continues to evolve, so must our approach to it. It’s not just about sophisticated algorithms but also about the framework and infrastructure that back them. For organizations looking to truly harness AI’s power, transitioning to a decentralized data mesh and linked data environment isn’t just a choice—it’s a necessity. And with trailblazers like SeerAI leading the charge, the future of AI looks not just intelligent but also interconnected.