AI and the internet are transforming our understanding of how the future happens, enabling us to acknowledge the chaotic unknowability of our everyday world.

Back when we humans were the only ones writing programs, data looked like the oil fueling those programs. But now that machines are programming themselves, data looks more like the engine than its fuel. This is changing how we think about the world from which data arises, and that data is now shaping as never before. We’ve accepted that the intelligence of machine intelligence resides in its data, not just its algorithms—particularly in the countless, complex, contingent, and multidimensional interrelationships of data. But where does the intelligence of data come from? It comes from the world that the data reflects. That's why machine learning models can be so complex, we can't always understand them. The world is the ultimate black box. Weinberger looks at the implications of this for people who work with data.

iRobot, the leading global consumer robot company, designs and builds thoughtful robots and intelligent home innovations that make life better for customers across the globe. With over 50 million units sold worldwide, iRobot relies on accurate, highly reliable data to drive decision making and power operations across the business to fuel their explosive growth. Leber discusses why and how the team invested in data observability with Monte Carlo to improve data quality across the business and help its company maximize the potential of their data.

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Are you drowning in data but lacking in insight? Eighty percent of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. Companies are using graph databases to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. Mohr shows real-world use cases that include real-time recommendations, fraud detection, network and IT operations, AI/ML, supply chain management, and more.

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There’s no stopping the introduction of AI-based technologies into the enterprise.

Data science methods provide a means to establish analytic tradecraft, capable of managing a large amount of data, allowing for full characterization of actor behaviors, and providing valuable insights. As data volume increases, AI/ ML plays a significant role in this "high data entropy" space, providing users with the means to combine multi-sourced datasets, with the goal of learning and identifying patterns, and develop actionable insights while assuring they follow the organization’s law and policy boundaries. Rodriquez presents a case study on how the intelligence community (IC) is addressing these challenges by establishing innovative AI/ML governance and data management methodologies, supporting the development of a policy-compliant AI governance ecosystem, predicating strategies to enforce legal and policy considerations, and establishing data controls.

MLOps can streamline ML development, thus increasing operational effectiveness.

Jablonski looks at the journey to defining and implementing an MLOps solution for your organization. Jablonski begins with the metrics necessary for successful model lifecycle measurement, then discusses the technology stack to be deployed and the operational model necessary for success at scale. These three must all be defined and built collectively to ensure alignment between operational needs, technology capabilities, and success metrics. High model performance, elimination of bias, and predictability are all key elements of an MLOps strategy.

Neural networks can be used for many applications in the world of Artificial Intelligence.

ChatGPT, Large Language Models (LLMs), and generative AI have captured the attention of people worldwide. As these tools, many based on neural networks, move from experimental lab projects to widespread usage by the general public, questions arise about their business applications. Can these tools be fine-tuned to enhance competitive intelligence and find insights into customer behavior? How do they aid in answering product marketing questions, monitoring competitor strategies, and influencing decision making? What competitive edge can these AI-based tools provide to you?

Knowledge graph technology expands by employing neural networks.

Probably the most important reason for building knowledge graphs has been to answer this age-old question: “What is going to happen next?” Given the data, relationships, and timelines we know about a customer, patient, product, etc. (“the entity of interest”), how can we confidently predict the most likely next event? Graph neural networks (GNNs) have emerged as a mature AI approach for knowledge graph enrichment. GNNs enhance neural network methods by processing graph data through rounds of message passing. Aasman describes how to use graph embeddings and regular recurrent neural networks to predict events via GNNs and demonstrates creating a GNN in the context of a knowledge graph for building event predictions.

Technology is evolving, and many are understandably fixated on “the next big thing.” As enterprises increasingly recognize that business outcomes are limited less by technological capability and more by the comfort and confidence in adoption and impact, futurist Mike Bechtel suggests that, to arrive at our preferred tomorrows ahead of schedule, organizations need to focus on the element of trust in emerging technologies.

Geospatial datasets have long been difficult to join and make available in a single place. Businesses needed specialized resources, skill sets, and tools in order to prepare, clean, and transform the data before extracting value. Perhaps for this reason, only 26% of data strategy leaders today, according to Forrester, report that their organizations are utilizing location intelligence to its full potential. How can businesses go from resource-intensive geospatial data processes to fast-and-easy data unification, pattern detection, and operationalized AI/ML? Patel unveils a new geospatial technology that will transform how businesses extract value from location intelligence.

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Integrating AI into business operations is accelerating, thanks to cloud computing.

The challenge in embedding AI into businesses involves how to do it in an agile, scalable, and cost-effective way. The organizations that will have a competitive advantage are those that take an end-to-end approach with hybrid cloud AI that includes both AI core infrastructure and AI edge infrastructure for easier management and rapid deployment. In this panel session, Voruganati and Kelleher speak to the latest technological advances and field learnings in scaling AI/ML workloads.

The promise of AI extends beyond business processes, having real impacts in healthcare.

Thalassemia and sickle cell disease are rare blood disorders that can have devastating effects on the patient. Both diseases are inherited abnormalities that impact the ability of hemoglobin to carry oxygen throughout the body. Advances in neural networks, combined with large volumes of rare disease healthcare datasets, now can successfully identify undiagnosed thalassemia patients and predict a sickle cell crisis with a high degree of precision.

Digital innovation is a corporate necessity but with the newest developments in AI-based technologies, how much do C-level executives and board members understand?

Before attempting to initiate digital improvements, your organization must improve its data and technology decision making. Aiken and Cesino provide executive insight on appropriate investments, key talent, and focused implementation. These permit organizations to rapidly realize concrete top- and bottom-line improvements directly attributable to maturing data practices as well as keeping regulators at bay. Balanced people and process investments allow your organization to best use valuable data and new technologies to achieve a sustained competitive advantage.

This panel of experts focuses not only on what has transpired during the conference but what we can expect going forward.

Generative AI, more intelligent machine learning, quantum computing, algorithmic determinations of behavior, and other technological advances are changing how we work and what we work with. This panel considers the implications of technology advances for good and bad.

With all the hype surrounding modern data architectures today, it’s harder than ever to discern what’s authentic from market noise. In order to guide people, Radiant Advisors and Database Trends and Applications designed and conducted a market survey in Q1 2023 that collected data and analyzed what companies are doing. The study focused on the perceptions, planning, and adoption of cloud architecture, the data lake house, data mesh, data fabric, streaming data architectures, and robust multi-modal analytics across industries and company sizes. See how your thoughts from this conference are validated and compared with the insights from this market study.