Conference:  Transform X 2022

Authors: Daphne Koller

2022-10-19

Daphne Koller discusses how Insitro is using machine learning models to predict the outcome of drug development experiments and design novel, safe, and effective therapies.

• Drug development is becoming more challenging and expensive due to the high failure rate of experiments.
• Insitro is using high-quality data and machine learning models to predict the outcome of experiments and design novel therapies.
• The focus is on learning meaningful representations of clinical state using self-supervised machine learning models.
• Insitro has partnered with pharmaceutical companies to access data on liver disease and used machine learning to predict patient progression.
• The ultimate goal is to develop a new approach to drug development that helps more people faster and at a lower cost.

Conference:  Transform X 2022

Authors: Mostafa Rohaninejad, Ariana Eisenstein, Louis Tremblay, Jack Guo, Russell Kaplan

2022-10-19

Machine learning leaders from robotics (Covariant), home automation (Resideo), autonomous delivery (Nuro), and warehouse automation (Pickle Robot) sit down with Russell Kaplan, Scale’s Director of Engineering, to share their approaches to dataset management. Pickle Robot CTO Ariana Eisenstein will share how she thinks about modulating quantities from different data sources like synthetic and public open datasets with real-world data for training datasets. Mostafa Rohaninejad, Founding Research Scientist at Covariant, will describe how the object “picking” problem requires synthetic data for unsafe scenarios and how he also incorporates structured and time-series data—supervised and unsupervised learning should go hand-in-hand. Jack Guo, Head of Perception at Nuro, will explain how it’s essential to have tools and mechanisms to automatically highlight recorded data that deviates from the norm, especially if it was captured in a new location. Like Rohaninejad, he will stress the importance of simulation as a component of successful reinforcement learning. Louis Tremblay, AI/ML Engineering Leader at Resideo, will explain how security cameras in the home represent an even more unbounded environment than do warehouses. The group will also discuss why maintaining separate datasets and training pipelines for different customers is both costly and incurs additional technical debt over time. Testing on fault-tolerant customers first before deploying to the wider fleet is also important. Scale’s Kaplan will share how, in his experience, when metrics and anecdotes seem at odds, it makes sense to re-think the metrics and establish new ones.

Conference:  Transform X 2022

Authors: Jason Matheny, Alexandr Wang

2022-10-19

Learn how Dr. Jason Matheny, CEO of the RAND Coporation, and his team of researchers seek to make the world safer and more secure, healthier and more prosperous providing insights on advanced technology to policymakers. Dr. Matheny will sit down with Scale CEO and Co-Founder Alexandr Wang to discuss many of the urgent challenges facing AI, healthcare, and public policy today. They discuss advances in synthetic biology and AI, including DeepMind's AlphaFold, have an enormous upside potential for medicine, but also pose a threat because it makes this technology more available for bad actors. Dr. Methany will also cover large language models and code generation tools, and how they will make developers and governments more efficient and more capable. He will also talk about whether AI’s offensive or defensive capabilities are more advantageous, and why public sector adoption of machine learning capabilities is so important. Other topics he will cover include how to ensure the US is a desirable destination for STEM talent including AI researchers, and how private sector technologists can provide value to policymakers to better understand technology and make more informed policy decisions. Dr. Matheny has served as Deputy Director of National Security, in other senior roles in the security field, and in various capacities in the healthcare industry.

Conference:  Transform X 2022

Authors: Emily Harding, Stephanie Halcrow, Paul Lekas, John Dulin, Lee Hudson

2022-10-19

The Department of Defense needs to become an attractive market for commercial companies and startups to apply innovative products and services in a meaningful way. Many potential technology providers believe the barriers to working with the DoD are too high. Congress and the department have created several initiatives and organizational structures to lower those barriers, but the DoD still struggles with transitioning innovation into production programs.With so few examples of new technology companies gaining footholds in the DoD, many perceive that the department is engaged in “innovation theater” by pursuing the appearance of innovation while maintaining institutional resistance to the concept. Without a real culture of innovation, the DoD risks losing ground to other entities willing to leverage commercial technology.This panel will discuss how maintaining political reputations and the fear of failure holds back innovation. They also will cover practical approaches for how the DoD can adopt pioneering technology more quickly, including revising the consortium model, providing better education about how AI and other advanced technologies work, and simplifying the procurement process to attract more private-sector technology companies.

Conference:  Transform X 2022

Authors: Thomas Kurian, Alexandr Wang

2022-10-19

Thomas Kurian, the CEO of Google Cloud, will join Alexandr Wang, CEO and Founder of Scale, to discuss how AI helps businesses across various industries and use cases. Google Cloud is well-known for developer adoption, helping machine learning teams to create production-grade machine learning models. With platforms like Vertex AI and TensorFlow, Google boasts the most popular machine learning platforms adopted by over 3 million developers globally. Google also has succeeded with the widespread adoption of machine learning capabilities in its consumer and business products, including Gmail smart replies and predictive search.Kurian will advise how to best roll out machine learning capabilities to many customers and ensure they are widely adopted. He will also discuss that, with the advent of foundation models, now is the time for all industries to more broadly adopt AI or risk falling behind the competition. He will detail practical use cases for retail, logistics, manufacturing, and healthcare. Kurian and Wang will also discuss the future of machine learning and what it will take to get there.Before Google, Kurian spent 22 years at Oracle; his nearly 30 years of experience have given him a deep knowledge of engineering, enterprise relationships, and leadership of large organizations. Throughout his career, he has demonstrated a unique capability to align the latest technological developments, including machine learning, with real business problems to provide practical solutions to customers.

Conference:  Transform X 2022

Authors: Varun Mohan

2022-10-19

Graphics Processing Units (GPUs) are used for training artificial intelligence and deep learning models, particularly those related to ML inference use cases. However, using GPUs to deploy models at scale can create several challenges for ML practitioners. In this session, Varun Mohan, CEO and Co-Founder of Exafunction, shared the best practices he’s learned to build an architecture that optimizes GPUs for deep learning workloads. Mohan explained the advantages for using GPUs for ML deployment, as well as where they might not have as many benefits. Mohan also discussed cost, memory, and other factors in the GPU-vs-CPU equation. He discussed inefficiencies that may arise in different scenarios and some of the issues related to network bandwidth and egress. Mohan offered techniques, including the importance of batching workloads and optimizing your models, to solve these problems. Finally, he discussed how some companies are using GPUs to run their recommendation and serving systems. Before Exafunction, Mohan was a technical lead and senior manager at Nuro, where he saw the power of deep learning and the large challenges of productionizing it at scale.

Conference:  Transform X 2022

Authors: Nick Beighton, Julia de Boinville

2022-10-19

During his 13-year tenure, Nick Beighton, former CEO of ASOS, grew the online apparel company from 200 million to 4 billion GBP in revenue by investing in data. Beighton is joined by Jules de Boinville, Head of Scale’s UK deployments, to discuss how his team improved catalog data at ASOS and the key measurements they used to understand the business impact of their digital initiatives. Beighton also covers the core business values he helped create and best practices to effectively use customer and product data in eCommerce to deliver higher conversion rates. Beighton describes how ASOS started by working on optimizing discovery tools, delivering product recommendations, and correctly tagging product attributes, four years before they invested in digital marketing. Underlying it all was a spirit of experimentation, which Beighton explains in more depth. Before joining ASOS, Beighton worked for KPMG, specializing in business transformation, and held finance-related positions in other online retailers.

Conference:  Transform X 2022

Authors: Dan Shiebler

2022-10-19

Abnormal Security builds ML products that help protect systems against cyber attacks. Dan Schiebler, Head of Machine Learning at Abnormal Security, discusses best practices for building cybercrime detection algorithms. In this session, Schiebler covesr how to design, monitor, and launch resilient ML systems and how to train ML models on production issues. He talks about the different types of problems that production ML systems can encounter, including features that become unavailable because of upstream data issues, distribution changes, or features that become stale. Schiebler addresses the different types of iteration loops in most companies—online vs offline—and how that plays into testing and training, as well as the company’s ablity to tolerate risk. Historical logs and data also play a key role. Before joining Abnormal, Schiebler worked at Twitter: first as an ML Researcher working on recommendation systems, and then as the Head of Web Ads Machine Learning. Before Twitter, he built smartphone sensor algorithms at TrueMotion.

Conference:  Transform X 2022

Authors: Jordan Fisher

2022-10-19

Jordan Fisher, CEO of Standard AI, is passionate about changing the real-world retail experience by unlocking better shopping experiences with computer vision. Fisher discusses applying computer vision models to the physical world to create human-centric applications; augmenting retail staff with better inventory tools and store layout analytics; and creating better shopper experiences with autonomous checkout. Even given the challenges in today’s retail environment—the labor shortage, inflation, the supply chain crunch, and difficulties competing against tech giants—shoppers expect more options and a better experience. Fisher’s vision is to allow customers to come in, shop, skip the checkout line, and get a receipt within minutes after they leave. At a shop at San Jose State University, traffic increased by 20%, average total receipts increased by almost 23%, and there was a decrease in wait time by over 50%. Further, store employees get analytics about which items are out of stock or misplaced, as well as traffic patterns about individual shoppers that do not collect PII. Fisher has spent his career focusing on both fundamental research and product development. He has worked in computational fluid dynamics, securities regulations, video games, machine learning, and retail, and seeks out areas where innovative products can be forged by tackling difficult research initiatives.

Conference:  Transform X 2022

Authors: Dr. Will Roper

2022-10-19

Digital engineering is a transformative capability for hardware that enables fully digital design, testing, and certification of systems without physically building them.

• Digital engineering is doing engineering digitally with the help of computer models and similar technology.
• It is possible to fully digitally design, test, and certify systems without physically building them.
• Computer models are capable of creating physics and structures to a high degree of reliability.
• Digital engineering solves the problems of expensive, slow, and environmentally impactful physical prototyping.
• The McLaren racing team's approach to digital engineering is an inspiring level of digital agility.
• Digital engineering is a transformative capability for hardware that enables an agile approach to hardware development.