Conference:  Black Hat Asia 2023

Authors: Maxine Holt, Marina Krotofil, Tara Seals, Fyodor Yarochkin, Stefano Zanero

2023-05-11

Artificial Intelligence (AI) has the potential to revolutionize cybersecurity by enhancing detection and response capabilities, automating routine tasks, and identifying threats that are invisible to humans. However, AI also poses significant risks, including the potential for attackers to use AI to develop more sophisticated attacks and evade detection. Panelist will explore how AI can be used to improve cybersecurity, the ethical considerations of using AI in security, and how to manage the risks associated with AI-powered security systems. Additionally, the panel will discuss the future of AI and cybersecurity and the role the InfoSec community and policymakers can have in shaping the development and use of AI in security.

Conference:  Transform X 2022

Authors: Emad Mostaque, Alexandr Wang

2022-10-19

The speaker discusses the democratization of AI and the importance of diversity in data sets to ensure aligned artificial intelligence. They argue for the need to build smaller, more democratized models that impact a broader set of people and allow for adaptation to social issues. The speaker also emphasizes the importance of transparency in the development of AI models and the need for human feedback in reinforcement learning.

• AI democratization and diversity in data sets are crucial for aligned artificial intelligence
• Smaller, more democratized models are needed to impact a broader set of people and adapt to social issues
• Transparency in AI model development is necessary
• Human feedback is crucial in reinforcement learning

Conference:  Transform X 2022

Authors: Pieter Abbeel

2022-10-19

Pieter Abbeel wears many hats: Professor at UC Berkeley, Director of the Berkeley Robot Learning Lab, Founder of three companies, podcast host, and investor. The common thread is that Professor Abbeel is passionate about AI and robotics. In this keynote presentation, he explores the possibility of training a large neural network to enable faster learning in robotics. Professor Abbeel discusses his lab’s approach to solving this problem and will cover how video prediction is an excellent proxy for generalizable robots, the relevant models and datasets useful for pre-training, how unsupervised learning can help robots learn from themselves; and the usefulness of a human-in-the-loop. He describes a four-step framework that might be able to lead, ultimately, to generalized robotics. Professor Abbeel is co-director of the Berkeley Artificial Intelligence (BAIR) Lab and founded Gradescope, which provides AI to help instructors with grading homework and exams, and Covariant, which provides AI for robotic automation of warehouses and factories. He is also a founding partner at AIX Ventures, a venture capital firm focused on AI start-ups, and is the host of The Robot Brains podcast, which explores what AI and robotics can do today and where they are headed.

Conference:  Transform X 2021

Authors: Lt Gen Kirk S. Pierce

2021-10-07

Lt. Gen. Kirk S. Pierce is the Commander, Continental U.S. North American Aerospace Defense Command Region - 1st Air Force. He joins Mark Valentine, Head of Scale Federal to discuss the potential of AI to provide direct assistance to warfighters within the contexts of Information Dominance and Decision Superiority. They explore the operational risks that could slow down the adoption of AI to support broader joint All Domain Command and Control. Delivering artificial intelligence to warfighters is a strategic imperative but there are still many questions that remain unanswered. What will the adoption of AI mean for the United States military? How can military organizations take advantage of this emerging technology, as they look towards current and future national security imperatives? Join this session to hear an example-rich discussion of the opportunities for AI to support national defense.

Conference:  Transform X 2021

Authors: Steven Escaravage

2021-10-07

Steve Escaravage, Senior Vice President at Booz Allen Hamilton and Mark Valentine, Head of Federal at Scale AI, discuss how the Artificial Intelligence (AI) and Machine Learning (ML) community can help federal customers build, deploy and operationalize realistic AI/ML capabilities that bring tangible and measurable wins, within the strict compliance and governance limitations of federal IT environments.

Conference:  Transform X 2021

Authors: Dragomir Anguelov

2021-10-07

In this keynote, Drago Anguelov, Head of Research at Waymo, discusses Waymo’s progress towards building a scalable technology stack for autonomous driving vehicles. With more than a decade of experience in solving autonomous driving, Waymo is now operating the world’s first commercial ride-hailing service Waymo One in Phoenix and has recently welcomed its first riders in San Francisco by kicking off the Trusted Tester program. Drago will give an overview of the key autonomous driving challenges and describe how Waymo is leveraging the cutting edge ML systems across the stack to handle them. He will also outline promising avenues to keep expanding the scope of ML in the stack in the future and showcase some of Waymo’s work in the space.

Conference:  Transform X 2021

Authors: Akshat Kaul, Ganapathy (Krish) Krishnan, Dave Glick, Jason Murray, Jim Miller

2021-10-07

Online commerce marketplaces, from groceries to furniture to real-estate and everything in between, are seeking to leverage artificial intelligence and machine learning to enhance buyer and seller experiences. In this panel, learn from executives with experience at Wayfair, Amazon, Flipkart, Flexe, Shipium, and Redfin about how they approach their AI/ML development to improve the customer experiences on their platforms.

Conference:  Transform X 2021

Authors: Dmitri Dolgov

2021-10-07

Waymo's investment in data mining, training cycle, and automation of the feedback loop is key to building a robust and generalizable autonomous driving system.

• Investment in frameworks and infrastructure for closing the loop on data mining training cycle
• Investment in feedback loop as a first-class object in the development life cycle
• Automation and low human engineering cost in the ML infrastructure
• Discovery of interesting long-tail examples through data mining and hard data example mining strategy
• Challenges in optimizing for both long-tail and average case distributions
• Unification and simplification of technology development and team organization to build a robust and generalizable core stack