In this talk, we'll navigate the complex terrain of AI data origins—text, audio, images, and beyond—highlighting challenges in attribution and copyright that echo the music industry's evolution from Napster to Spotify. Drawing parallels with how early digital platforms changed the landscape of music consumption and artist compensation, we'll explore potential pathways for establishing fair compensation models in AI. This talk aims to uncover how creating legitimate frameworks for compensating rights holders can foster an ecosystem where innovation exists alongside the rights and recognition of artists.  

In this lecture David McRaney, science journalist, author of How Minds Change, and host/producer of the You Are Not So Smart Podcast, will take us through the latest research revealing how to best incubate the most important aspect of any data-driven organization attempting to navigate the Great Acceleration: Plasticity. 

Minds can and do change, but not without a little resistance (and sometimes not without a lot), and in this era of fully fragmented media, narrowcasted cross-platform content, any-to-any information flow directed into our lives via identity-shaping algorithms, having access to as much data as possible isn’t enough – in fact, without a robust understanding of how brains do and do not update their understanding of the world when overwhelmed by epistemic chaos, it’s a problem. The solution is a culture devoted to plasticity, and the good news is that with a little tweaking of a few social and psychological knobs, that kind of culture is available to any organization committed to continuously re-establishing a first-mover advantage.

Every algorithm in tech reflects hidden priorities of behinds-the-scenes powers, namely tech’s founders and funders. Even small biases introduced in a technology’s founding code base can proliferate into full-blown injustices when brought to scale, making the early priorities of founders and funders all the more significant. To ensure founders and funders prioritize ethical considerations when building tomorrow’s tech, we must first understand why they weren’t prioritized before. This is the purpose of this talk: to explore how early web coder culture and its devout embrace of meritocracy – celebrating programming skills over prestigious degrees; peer collaborations over hierarchies -- effectively banished the idea that the digital era's new “gods” – tech’s founders and funders – might actually be the same as those of our analogue past: a tiny slice of almost exclusively those born into substantial social privilege and intergenerational wealth. From this context, the talk details how tech’s "No Gods” problem continues to quietly fuel algorithmic bias and block meaningful progress on prioritizing ethical algorithms, despite what may very well be the best of intentions to do better among all involved. The talk concludes with concrete ways to course correct for future tech development, starting with diversifying the pool of founders and founders to reflect a broader range of lived experiences. 

Generative AI has triggered a new era of innovation and collaboration: the Age of Artificial Generative Innovation. AI systems now turn all types of content, from written materials to scientific data, into Copilots that help creators and innovators. These systems use specialized knowledge to break through old limits, sparking fresh ideas and partnering directly with engineers, scientists, and creators. From creating new drugs to inventing new materials, they speed up the innovation cycle. Systems of AGI fundamentally transform how we interact with and communicate knowledge, and how we apply this knowledge. In this talk I will illustrate how AI streamlines and accelerates innovation, reshaping how we work together with machines to invent the future.

At Airbnb Payments, integrating AI with blockchain can transform our services by enhancing transaction efficiency, security, and personalization. AI analyzes blockchain data, identifying potential financial anomalies and reconciling transactions immediately. 

It also optimizes blockchain operations, increasing transaction speed and reducing AWS usage.

Perhaps most importantly, AI also supports predictive analysis in for our users, creating more personalized experiences using blockchain's secure data. AI's analysis of individual preferences on blockchain enables personalized payments advice, such as how to price a listing or what payment methods to utilize. 

Overall, this integration leads to more efficient, secure, and tailored financial services, revolutionizing the industry with data-driven decision-making.

“Lego style” data teams, with their modular structure, can be capable of adapting to challenges faster, can facilitate quick feedback cycles, and better utilize individual skill sets to realize a shorter time to value.

Veronika joins us to discuss her strategy for optimizing time to value with a “lego style” team and how to unlock their true potential.

Data quality is the most important attribute of a successful data platform that can accelerate data adoption and empower any organization with data-driven decisions. However, traditional profiling-based data quality and counts-based data quality and business rules-based data quality are outdated and not practical at the scale of petabyte-scaled data platforms where billions of rows get processed every day. In this talk, Sandhya Devineni and Rajesh Gundugollu will present a framework for using machine learning to detect data quality at scale in data products. The two data leaders at Asurion will highlight the lessons learned over years of crafting the advanced state of data quality using machine learning at scale, as well as discuss the pain points and blind spots of traditional data quality processes. After sharing lessons learned, the pair will dive into their implemented framework which can be utilized to improve the accuracy and reliability of data-driven decisions by identifying bad quality data records and revolutionizing how organiations approach data-driven decision making.

A good BI tool should integrate with a larger ecosystem, not be a monolith. At Hashboard we’ve built Hashquery, a new Python SDK which we believe connects the dots. Import models from your semantic layer of choice, express complex queries with ease, and push the result wherever you need it. In this talk we’ll explore how.

2024 is the year of the AI agent. But what are AI agents and how are they different from traditional chatbots we all know? In this talk, we'll dive into how AI agents work and what makes them different from legacy chatbots. Listeners will leave with a good understanding of AI agent architecture and their newly unlocked capabilities.

Using algorithms for marketing is now a common practice—but there are significant concerns about their ethical implications and how they can interact with consumers. In this talk, NYU's Jose Mendoza offers a state-of-the-art review of ethical algorithms in marketing, focusing on the latest research, industry initiatives, and best practices.

Attendees will learn about transparency and accountability in algorithmic decision-making; the need for diversity and inclusion in algorithm development; and the role of marketers in ensuring that algorithms are ethical and responsible. We will also explore the challenges and opportunities of using ethical algorithms in marketing and how businesses can leverage these algorithms to drive positive change.

Come to this session for a understanding of the ethical considerations surrounding algorithmic decision-making in marketing and contemporary practices and developments.

In this executive debate, leading industry analyst Mike Ferguson welcomes leaders from premier software companies to discuss key topics in data management and analytics. Panelists will debate the impact of Generative AI, the implications of key industry trends, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, data and AI governance and sharing, and on-the-horizon issues that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data-driven enterprise from seasoned executives and experienced analysts in a packed, unscripted, candid discussion.

Innovation is much more about people than algorithms and systems. Our panel will explore the criticality of cultural transformation in driving successful technology, data, and AI initiatives.

From fostering curiosity and experimentation to addressing resistance and promoting inclusivity, the session navigates the interplay of cultural dynamics and implementation success.

Our panel will share insights into strategies, challenges, and best practices for cultivating a culture that embraces change, empowers individuals, and drives organizations to reap the benefits that continued technology transformation can deliver.

The majority of AI risk discussion has been about how to safeguard privacy and address algorithmic bias. Less discussed – but equally important – is how to manage AI cybersecurity risk in today’s regulatory environment, where technology far outpaces regulation.

At present, laws and regulations provide for only general security requirements (i.e., they don’t necessarily account for the unique cybersecurity risks posed by AI). Given the slow pace at which new laws and regulations are adopted, it is likely that regulators will stretch existing requirements to cover all things algorithmic. 

Companies, therefore, need to be ready to defend their AI-related security practices now. They can start doing that by adopting an AI risk management approach that encompasses not only physical and cyber security measures but also the procedural and personnel aspects of such measures.

In this session, Proskauer's Michelle Ovanesian and Marc Palmer offer a view from the frontlines of data security, privacy, and the law in the rapidly expanding field of artificial intelligence.

Being a solitary data scientist can be lonely, whether in an embedded role or pioneering data science adoption within an organization. As you collaborate with individuals with varying business contexts, subject-matter expertise, and data backgrounds, there are strategies you can apply to set yourself up for success.

 In this session, Lauren Burke will demonstrate how solitary or siloed data scientists can thrive by gaining support and buy-in from key stakeholders. She'll cover the "road trip" strategy for identifying allies and finding quick wins to demonstrate value. She'll also discuss communicating the difference between traditional analytics and data science, as well as techniques for educating stakeholders and leveling up junior practitioners.

 Attendees will learn how to identify organizational needs and effectively scope projects, ensuring alignment with business objectives and defining clear measures of success while identifying opportunities to deliver incremental value. We'll cover the importance of creating end-user-focused documentation, where it should live, and how to use "SEO" to make your data science presence more visible. 

Many organizations strive toward data-driven, yet most of them struggle to get relevant insights to the right stakeholders in a timely manner, resulting in a reactive rather than proactive approach. Given a world of fast-paced data intake and customer expectations of real-time, drawing insights and making decisions faster are critical paths to building timely and contextual relevance with customers. Join Murli Buluswar, Head of Analytics - US Personal Bank at Citi, to learn how delivering preemptive solutions though leveraging next generation technologies increases operational efficiency. Murli will discuss how to use conversational Generative AI to increase democratization of intelligence and reduce the friction between data, insight, decision, and outcome.

Data is an asset, and the best organizations know what they own. But identifying, defining, and classifying your critical data is only the first step in compliance; knowing what you own doesn't mean it's safe.

In this session, Imperva's Terry Ray looks at data security, and how to incorporate infosec attitudes into compliance and governance. Attendees will learn how to distinguish regulation from security, and leave with practical tools and questions they can use to complete their secure compliance journey.

Join us for a dynamic discussion featuring a Chief Data Officer (CDO), Vice President of Data Science, and Technical Industry Lead as they delve into the real-world challenges of implementing AI and machine learning within Fortune 500 companies and large-scale government entities. This panel will shed light on the common pitfalls, the harsh realities faced, and practical strategies for overcoming these obstacles. Don't miss this opportunity to gain valuable insights from industry experts on navigating the complex landscape of AI/ML application in high-stakes environments.

This presentation covers the critical transition from traditional analytics to the strategic integration of AI into business processes, from the compelling reasons for adoption to the practical implementation steps. The 'What' segment introduces the foundational elements enabling the AI revolution and the business processes ripe for disruption. Addressing the 'Why', we highlight the responsible adoption of AI with accurate, reliable data as the backbone of any AI-driven initiative. This section underscores the need for solid data benchmarks and testing to measure AI's effectiveness, to ensure that AI implementations lead to tangible, positive outcomes, and to alert leaders to issues early. The 'How' section provides actionable insights on implementing AI to scale data-driven decisions effectively. With real-life examples, this section covers best practices for data management, technology integration, and strategies for fostering a culture that embraces data and AI. Attendees will learn about scaling AI-driven analytics, including considerations for data security, privacy, and ethical AI use.

Silicon Valley engineers and engineering challenges have ruled the data world for 20 years. The net result is data infrastructure companies focus on the largest scale and fastest systems to process enormous amounts of data, regardless of usability. We don't all have movie libraries the size of Netflix, search indexes the size of Google, or social graphs the size of Meta.

Instead of focusing on consensus algorithms for large-scale distributed computing, our engineers should focus on making data more accessible and usable and reduce the time between "problem statement" and "answer". In this session, we explore the changes in hardware and mindsets, enabling a new breed of software optimized for the 95% of us who do not have petabytes to process daily.

Yello is currently embarking on a journey to modernize because their existing platform inhibits their ability to provide speed to insights for internal and external clients. Yello needed a solution that not only improved our ability to extract insights, but also enables the team to establish a single source of truth and enhance their level of data stewardship. Yello's new data architecture needed to be nimble, flexible, and agile - developing a solution that not only works for their clients, but also works internally for downstream consumers. Hear from Shawn Crenshaw and Peter Lim as they share insights from this moderinzation journey, and discuss how to develop and implement a data lakehouse as part of the journey. This final data lakehouse architecture will satisfy client needs and accomplish the mission of the Yello Data Services team, which is to improve the health and accessibility of data at Yello.

As Generative AI becomes increasingly relevant in our everyday lives, many businesses are trying to figure out how this technology can be used to leverage their data. But while its power can be applied to any organization with a large amount of customer and industry data, the application of this new technology has been highly uneven across sectors.

In this session, Mr. Chi will explore how GenAI can be used to benefit society’s unsung heroes, including teachers, students, and authors. In a panel discussion with experts in publishing and education, he will examine what these sectors can do to thrive in a world dictated by data, and how to mitigate any unwarranted pitfalls.

Join us for an insightful session on the evolving landscape of Data Quality and Observability practices, transitioning from manual to augmented approaches driven by semantics and GenAI. Discover the framework enabling organisations to build the architecture for conversational data quality, leaving behind the limitations of traditional, resource-heavy methods and legacy technology. Learn why context is paramount in data quality and observability, and leave with actionable insights to propel your organisation into the future of data management.

When data is the most valuable asset of your company, protecting it is a non-negotiable. While Information Security professionals are focused on Bad Actors, we have data operations and data governance professionals focused on Bad Data… Are they one and the same? What’s similar and what’s different between the worlds of data integrity and data security?

Drawing from a wealth of experience and real-world challenges, Gorkem will shed light on the pivotal role of data quality in the forefront of information security. We’ll discuss opportunities for early detection, auto-detection, and the establishment of tiered rules to manage and remediate bad data effectively. Learn how proactive governance and observability can transform data management from a reactive stance to a formidable defense mechanism, ensuring the integrity and security of your data ecosystem. 

The next biggest fight in the industry is about building trustworthy AI models that put human interests, lives, and prosperity first. Open source has a long history of accelerating innovation. Given the importance of human feedback and input, it is more critical than ever to level the playing field and ensure accessible pathways for public participation, feedback, and input into open-source AI projects.

Elena Yunusov is the Founder of the Human Feedback Foundation, a nonprofit on a mission to build a safer and more transparent future for AI. In this session she will discuss the path toward ethical AI. Participants will learn about the diversity of human feedback, including ethical, geographical, value-based and cultural aspects. The session will discuss ways to build safer, more accurate open-source AI models for critical applications in deliberative democracy, AI governance, healthcare, and other critical fields.