Dr. Danny Lange, Senior VP of AI at Unity Technologies – Interview Series
Dr. Danny B. Lange is VP of AI and Machine Learning at Unity Technologies. Formerly, Danny was Head of Machine Learning at Uber where he led an effort to build the world’s most versatile Machine Learning platform to support Uber’s rapid growth.
What initially attracted you to Artificial Intelligence?
I built and programmed computers from a very young age and I was almost immediately fascinated by the idea of making these systems autonomous. What captivates me about autonomy is the challenges you as a developer have to overcome in creating a system made from sequences of rigid code that can safely respond to unpredictable and never-seen-before circumstances. The field of Artificial Intelligence (AI) has over the years provided us with increasing powerful tools from object-oriented programming, rule-based inference, to machine learning and more recently deep learning. It is the increased capabilities of these technologies that fuel the rapid progress in the field of AI.
You have been a leader in the space for many years such as being General Manager for Amazon Machine Learning in AWS, and Head of Machine Learning at Uber. What are some of the lessons that you have learned from these past experiences?
Machine learning is a truly transformative technology, but to realize the potential to its fullest, it is necessary to bring it to every corner of the enterprise. Repeatedly, machine learning has demonstrated its ability to create unimaginable optimizations and lift business operations to levels that cannot be achieved by ordinary human processes alone. However, true disruption only occurs when a critical mass of business processes are operated in this way. What organizations such as Amazon and Uber clearly have demonstrated is that if we make the machine learning systems broadly approachable and available to every team we experience a broad adoption that invariably leads to a virtuous cycle of continued improvements to the overall business as a network effect takes place
You've been the VP of AI at Unity Technologies since 2016. What was it about this company’s vision that excited you?
Unity is a fantastic place for the AI enthusiast. We have a remarkable culture of solving hard problems for our customers – with AI being one of the mightiest challenges that i can think of. Our leadership is committed to power and drive the future of AI. We have the technologies, resources, customers, and partners to do just that. I cannot imagine a better place to work on changing the world.
You’ve spoken before about the importance of synthetic data, could you share with us what this is precisely?
Synthetic data is created by an algorithm as opposed to data captured from the real world. A real-time 3D engine with a realistic physics emulator, is the ideal tool to create realistic yet synthetic training data for a wide variety of applications ranging from object recognition in computer vision systems to path planning for navigational robots.
What makes synthetic data so important when it comes to building machine learning systems?
I have on many occasions called Unity the perfect AI Biodome. And it is true. Working with AI in the real world and using real-world data can be outright scary. Do I have to mention self-driving vehicles on the streets of San Francisco or face recognition systems deployed in public spaces? There are worries about safety, bias is always lurking, and privacy concerns often collide with common use cases. And then there is the scarcity and high cost associated with collecting the necessary amounts of training data. With Unity, we have not only democratized data creation, you also have access to an interactive system for simulating advanced interactions in a virtual setting. Within Unity you can develop the control systems for an autonomous vehicle without the risk of hitting and injuring anyone.
Can you discuss how Unity Simulation can assist companies with the generation of synthetic data?
With Unity Simulation we have taken a real-time 3D engine designed for human consumption whether that is gaming, film, or engineering – and turned into an cloudoptimized instance that not only runs at unimaginable high frame rates, but also allows for scaling to thousands of instances running in parallel. In this way Unity Simulation allows developers to generate experiences for their AI systems orders of magnitude faster than wallclock time. Until recently, this scale of data generation was only available to a few privileged corporations, but with Unity Simulation we have truly levelled the playing field.
Recently, Unity has teamed up with The Institute for Disease Modeling (IDM) to build real-time 3D in-store simulations that model COVID-19 spread. Can you discuss how Unity Simulation can effectively simulate the spread of COVID-19?
Computer simulation has been used for decades by researchers, engineers, problem solvers, and policy makers in many fields, including the study of infectious disease. Unity Simulation enables a special kind of real-time spatial simulation that can be scaled on the cloud to holistically study large, complex, and uncertain systems. We built a simplified demonstration project to simulate coronavirus spread in a fictitious grocery store and explored the impact that store policy has on exposure rates. By running tenth of thousands of simulations, we were able to identify the behaviors and policies that appeared to have the greatest impact on the spread of this terrible infectious disease.
Recently, you’ve been speaking a lot about Artificial General Intelligence (AGI). Can you explain what emergent behavior is and why it’s important for the development of AGI?
In just 100,000 years, the human race went from surviving on picking berries in the wild to putting a person on the moon. We know from archaeogenetics that the human brain has not changed significantly during that period of time. You can say there were no significant hardware upgrades to the processor. So what was it then that was so transformative? The key should be found in our ability to accomplish something together. We use the term emergent behavior of a system that does not depend on its individual parts, but rather on their relationships to one another. Emergent behavior cannot be anticipated by investigating the individual parts of a system. It can only be predicted by understanding the relationships between the parts. Emergent systems are characterized by the observation that the whole is greater than the sum of the parts. While I have repeatedly shown entertaining examples of emergent behavior in relatively simple multi-agent systems, just imagine what will happen when you have a plethora of AI systems collaborating at the speed of light.
Do you believe that there is a possibility that we can achieve AGI within the next decade?
When it comes to AGI we have to remember that it is all about the journey and not the destination. Nobody knows exactly when AGI will happen as it will not be at a specific moment in time, but rather a gradual change over time. It is in the nature of AGI that it will be hard for us humans to pinpoint just how intelligent a system at any given moment. Looking at the progress made over the last decade, I am sure that this decade will bring us plenty of interesting progress towards AGI.
Is there anything else that you would like to share about Unity Technologies?
At Unity, we continue to see ourselves powering the future of AI and playing a significant role in the advancement of AI technologies. As our relationship with DeepMind is a clear demonstration of our technology is the perfect environment for researchers and developers to safely push the boundaries of AI. We are gearing up to support our customers and partners in creating virtual environments that operate at previously unseen scale to solve the challenges of tomorrow whether that is climate change, logistics, or health challenges.
Thank you for the amazing interview, I enjoyed learning about Unity and your views on AGI. Anyone who wishes to learn more should visit Unity Technologies.
- NVIDIA: From Chipmaker to Trillion-Dollar AI Powerhouse
- Laura Petrich, PhD Student in Robotics & Machine Learning – Interview Series
- Liquid Neural Networks: Definition, Applications, & Challenges
- Patrick M. Pilarski, Ph.D. Canada CIFAR AI Chair (Amii) – Interview Series
- AI Leaders Warn of ‘Risk of Extinction’