Patrick Schwerdtfeger is a business futurist specializing in technology trends including deep reinforcement learning and neural networks, both of which form the foundation of machine learning and artificial intelligence. He’s the author of “Anarchy, Inc.: Profiting in a Decentralized World with Artificial Intelligence and Blockchain” (2018, Authority Publishing) and has lectured at numerous academic institutions including Stanford and Purdue Universities. The two videos below are lectures from MIT. Although a lot of hype has spread about evolution of artificial intelligence, all of the case histories so far have resulted from just one innovation, and that innovation is deep reinforcement learning. It’s based on the development of neural networks that function similar to the human brain, allowing the computer to stack observations on a series of layers, optimizing each layer to create deeper understanding of the subject mater. The net result is machine learning, and Patrick is passionate about the impact it is having on virtually every industry across our economy.
Past speaking clients include:
Recent speaking destinations include:
Conference Keynote Speech on Deep Reinforcement Learning
The most important ingredient to deep reinforcement learning is data. The more data you have, the better the neural network can learn, and the smarter the artificial intelligence can become. As of 2019, Google’s Waymo had about seven million miles of data accumulated from it’s 6,000 cars. Meanwhile, Tesla had over one billion miles of data. That differential gives Tesla an enormous advantage over Waymo in the development of autonomous driving vehicles. Uber is accumulating an enormous about of data too, as is Didi in China. Patrick’s keynote presentation on machine learning is based on these types of case histories, allowing attendees to learn where the technology is being deployed and how companies are establishing competitive advantage within their respective markets.
The Future of Neural Networks
One of the most fascinating aspects of these technologies is “fleet learning” and how it allows providers to learn from the individual experience of all their customers, solve problems within the primary algorithms, and deploy improvements network wide. Cloud computing is an enabling technology in this respect because it allows for this centralized processing architecture. The true intelligence resides in the cloud, so the individual data is uploaded there first, and problems are then solved in this centralized location. All the venture capital companies and private equity investors are looking for opportunities that involve fleet learning and network effect because that drives the accelerating nature of competitive advantage within the marketplace. This is projected to continue into the future. Technology developers are in a race to accumulate as much data as they possibly can, and then leverage that data to enhance their artificial intelligence capabilities.
Some of the market leaders in the field of neural networks and deep reinforcement learning include the following:
- Microsoft
- Nvidia
- Amazon AWS
- Apple
- IBM
- Intel
- Sensory
- Qualcomm
Machine Learning Keynote Speech
Deep reinforcement learning is impacting virtually every industry across the economy. Some of the leading use cases involve predictive analytics (stemming from recommendation engines), natural language processing (voice recognition), autonomous driving, and object and image recognition (including medical imaging, cancer diagnosis, defect detection in manufacturing, agricultural product processing, and autonomous vehicles). Patrick focuses on these leading use cases and introduces models (or thought experiments) to allow attendees to think more strategically when discussing investment opportunities with their executive peers.
Patrick Schwerdtfeger is a keynote speaker who has spoken at business conferences in North America, South America, Europe, Africa, the Middle East and Asia.
