In the pre-cloud era, computer systems were operated by the organizations which depended upon them. This on-premises approach gave organizations great power over their systems, however, “with great power comes great responsibility” and organizations were left with the ongoing burden of deploying and managing their own infrastructure. Today, Cloud computing has removed much of the responsibility of deploying systems, however, it has also removed much of the power that organizations once had. Organizations must place their trust in the cloud to secure the confidentiality and integrity of their data.

In this talk, I'll consider whether it is possible to regain control over data in the cloud (great power with none of the responsibility) and even enable multiple untrusted parties to compute together on untrusted infrastructure. I’ll introduce the Confidential Consortium Framework (aka CCF), an open-source framework for building a new category of secure multiparty applications with confidentiality, integrity protection, and high availability. CCF utilizes hardware-based trusted execution environments for remotely verifiable confidentiality and code integrity, backed by an auditable and immutable distributed ledger for data integrity and high availability. CCF even enables application developers to bring both their own application logic and a custom multi-party governance model, in the form of a programmable constitution. By the conclusion of this talk, I hope to have convinced you that distributing systems does not necessarily mean distributing trust in the era of confidential computing in the cloud. You can learn more about CCF today at: https://ccf.dev/ 

Database management systems (DBMSs) are complex software that requires sophisticated tuning to work efficiently for a given workload and operating environment. Such tuning requires considerable effort from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS configurations for any application, including automatic physical database design, knob configuration, and query tuning. Despite the many academic papers that tout the benefits of using ML to optimize databases, there have been only a few major success stories in industry in the last decade.

In this talk, I discuss the challenges of using ML-enhanced tuning methods to optimize databases. I will address specific assumptions that researchers make about production database environments that are incorrect and identify why ML is not always the best solution to solving real-world database problems. As part of this, I will discuss state-of-the-art academic research and real-world tuning implementations.

Connections are a part of us, of the world. From the connections between people, between cultures, within language, and more. In these days when more data is collected daily than we could ever hope to explore, the variety in connections being gathered is opening up the possibility to visualize these (often complex) networks. During this talk, Nadieh will take you through the design process of several of her (interactive) data visualization works, from personal projects to client work. The common thread they all share, is that they all reveal connections, but all differently. From a family tree of 3000 people connected to the European Royal houses, to those existing between our Intangible Cultural Heritage created for UNESCO, to connections we have drawn in the night skies, something with cats and dogs, and more. Revealing that all types of connections are unique and revealing the intricacies that lie within them requires a creative, iterative and custom approach.

Is there a better way to communicate with a robot than using natural language? In this talk, you'll learn how we integrated Boston Dynamic's Spot robot with ChatGPT. You'll learn how we use computer vision to solve impossible problems and how ChatGPT 10x'd Spot's capabilities overnight.